| Top |
| gboolean | expanded | Read / Write / Construct |
| gchar * | label | Read / Write / Construct |
| gboolean | label-fill | Read / Write / Construct |
| GtkWidget * | label-widget | Read / Write |
| gboolean | resize-toplevel | Read / Write |
| gint | spacing | Read / Write |
| gboolean | use-markup | Read / Write / Construct |
| gboolean | use-underline | Read / Write / Construct |
GObject ╰── GInitiallyUnowned ╰── GtkWidget ╰── GtkContainer ╰── GtkBin ╰── GtkExpander
A GtkExpander allows the user to hide or show its child by clicking on an expander triangle similar to the triangles used in a GtkTreeView.
Normally you use an expander as you would use any other descendant
of GtkBin; you create the child widget and use gtk_container_add()
to add it to the expander. When the expander is toggled, it will take
care of showing and hiding the child automatically.
There are situations in which you may prefer to show and hide the expanded widget yourself, such as when you want to actually create the widget at expansion time. In this case, create a GtkExpander but do not add a child to it. The expander widget has an “expanded” property which can be used to monitor its expansion state. You should watch this property with a signal connection as follows:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
expander = gtk_expander_new_with_mnemonic ("_More Options"); g_signal_connect (expander, "notify::expanded", G_CALLBACK (expander_callback), NULL); ... static void expander_callback (GObject *object, GParamSpec *param_spec, gpointer user_data) { GtkExpander *expander; expander = GTK_EXPANDER (object); if (gtk_expander_get_expanded (expander)) { // Show or create widgets } else { // Hide or destroy widgets } } |
The GtkExpander implementation of the GtkBuildable interface supports placing a child in the label position by specifying “label” as the “type” attribute of a <child> element. A normal content child can be specified without specifying a <child> type attribute.
An example of a UI definition fragment with GtkExpander:
1 2 3 4 5 6 7 8 |
<object class="GtkExpander"> <child type="label"> <object class="GtkLabel" id="expander-label"/> </child> <child> <object class="GtkEntry" id="expander-content"/> </child> </object> |
GtkWidget *
gtk_expander_new (const gchar *label
);
Creates a new expander using label
as the text of the label.
Since: 2.4
GtkWidget *
gtk_expander_new_with_mnemonic (const gchar *label
);
Creates a new expander using label
as the text of the label.
If characters in label
are preceded by an underscore, they are underlined.
If you need a literal underscore character in a label, use “__” (two
underscores). The first underlined character represents a keyboard
accelerator called a mnemonic.
Pressing Alt and that key activates the button.
label |
the text of the label with an underscore in front of the mnemonic character. |
[allow-none] |
Since: 2.4
void gtk_expander_set_expanded (GtkExpander *expander
,gboolean expanded
);
Sets the state of the expander.
Since: 2.4
gboolean
gtk_expander_get_expanded (GtkExpander *expander
);
Queries a GtkExpander and returns its current state. Returns TRUE
if the child widget is revealed.
See gtk_expander_set_expanded()
.
Since: 2.4
void gtk_expander_set_spacing (GtkExpander *expander
,gint spacing
);
gtk_expander_set_spacing
has been deprecated since version 3.20 and should not be used in newly-written code.
Use margins on the child instead.
Sets the spacing field of expander
, which is the number of
pixels to place between expander and the child.
Since: 2.4
gint
gtk_expander_get_spacing (GtkExpander *expander
);
gtk_expander_get_spacing
has been deprecated since version 3.20 and should not be used in newly-written code.
Use margins on the child instead.
Gets the value set by gtk_expander_set_spacing()
.
Since: 2.4
void gtk_expander_set_label (GtkExpander *expander
,const gchar *label
);
Sets the text of the label of the expander to label
.
This will also clear any previously set labels.
Since: 2.4
const gchar *
gtk_expander_get_label (GtkExpander *expander
);
Fetches the text from a label widget including any embedded
underlines indicating mnemonics and Pango markup, as set by
gtk_expander_set_label()
. If the label text has not been set the
return value will be NULL
. This will be the case if you create an
empty button with gtk_button_new()
to use as a container.
Note that this function behaved differently in versions prior to 2.14 and used to return the label text stripped of embedded underlines indicating mnemonics and Pango markup. This problem can be avoided by fetching the label text directly from the label widget.
The text of the label widget. This string is owned by the widget and must not be modified or freed.
Since: 2.4
void gtk_expander_set_use_underline (GtkExpander *expander
,gboolean use_underline
);
If true, an underline in the text of the expander label indicates the next character should be used for the mnemonic accelerator key.
Since: 2.4
gboolean
gtk_expander_get_use_underline (GtkExpander *expander
);
Returns whether an embedded underline in the expander label
indicates a mnemonic. See gtk_expander_set_use_underline()
.
Since: 2.4
void gtk_expander_set_use_markup (GtkExpander *expander
,gboolean use_markup
);
Sets whether the text of the label contains markup in
Pango’s text markup language.
See gtk_label_set_markup()
.
Since: 2.4
gboolean
gtk_expander_get_use_markup (GtkExpander *expander
);
Returns whether the label’s text is interpreted as marked up with
the Pango text markup language.
See gtk_expander_set_use_markup()
.
Since: 2.4
void gtk_expander_set_label_widget (GtkExpander *expander
,GtkWidget *label_widget
);
Set the label widget for the expander. This is the widget that will appear embedded alongside the expander arrow.
Since: 2.4
GtkWidget *
gtk_expander_get_label_widget (GtkExpander *expander
);
Retrieves the label widget for the frame. See
gtk_expander_set_label_widget()
.
Since: 2.4
void gtk_expander_set_label_fill (GtkExpander *expander
,gboolean label_fill
);
Sets whether the label widget should fill all available
horizontal space allocated to expander
.
Since: 2.22
gboolean
gtk_expander_get_label_fill (GtkExpander *expander
);
Returns whether the label widget will fill all available
horizontal space allocated to expander
.
Since: 2.22
void gtk_expander_set_resize_toplevel (GtkExpander *expander
,gboolean resize_toplevel
);
Sets whether the expander will resize the toplevel widget containing the expander upon resizing and collpasing.
Since: 3.2
gboolean
gtk_expander_get_resize_toplevel (GtkExpander *expander
);
Returns whether the expander will resize the toplevel widget containing the expander upon resizing and collpasing.
Since: 3.2
“expanded”
property“expanded” gboolean
Whether the expander has been opened to reveal the child widget.
Flags: Read / Write / Construct
Default value: FALSE
“label”
property“label” gchar *
Text of the expander's label.
Flags: Read / Write / Construct
Default value: NULL
“label-fill”
property“label-fill” gboolean
Whether the label widget should fill all available horizontal space.
Flags: Read / Write / Construct
Default value: FALSE
“label-widget”
property“label-widget” GtkWidget *
A widget to display in place of the usual expander label.
Flags: Read / Write
“resize-toplevel”
property“resize-toplevel” gboolean
When this property is TRUE
, the expander will resize the toplevel
widget containing the expander upon expanding and collapsing.
Flags: Read / Write
Default value: FALSE
Since: 3.2
“spacing”
property“spacing” gint
Space to put between the label and the child when the expander is expanded.
GtkExpander:spacing
has been deprecated since version 3.20 and should not be used in newly-written code.
This property is deprecated and ignored. Use margins on the child instead.
Flags: Read / Write
Allowed values: >= 0
Default value: 0
“use-markup”
property“use-markup” gboolean
The text of the label includes XML markup. See pango_parse_markup().
Flags: Read / Write / Construct
Default value: FALSE
“use-underline”
property“use-underline” gboolean
If set, an underline in the text indicates the next character should be used for the mnemonic accelerator key.
Flags: Read / Write / Construct
Default value: FALSE
“expander-size”
style property“expander-size” gint
The size of the expander arrow.
GtkExpander:expander-size
has been deprecated since version 3.20 and should not be used in newly-written code.
Use CSS min-width and min-height instead.
Flags: Read
Allowed values: >= 0
Default value: 10
“expander-spacing”
style property“expander-spacing” gint
Spaing around the expander arrow.
GtkExpander:expander-spacing
has been deprecated since version 3.20 and should not be used in newly-written code.
Use CSS margins instead, the value of this style property is ignored.
Flags: Read
Allowed values: >= 0
Default value: 2


Kids Club
Kids' Club is exclusively for children and their parents/guardians. Unaccompanied adults are not admitted.
Parents may leave children over eight alone in screenings but should be aware that the cinema is not providing any official childcare. If you leave your children in the cinema please be there on time to collect them at the end of the film.
For more information about Kids' Club visit What's On > Clubs/Groups > Kids' Club


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Status from browser window:
navigator.userAgent:
navigator.vendor:
navigator.appVersion:


2.1.0
- postcss-discard-empty will now report the rules that were removed
(thanks to @duncanbeevers).
2.0.1
- Now compiled with babel 6.
2.0.0
1.1.2
- Increased performance by iterating the AST in a single pass
(thanks to @andyjansson).
1.1.1
- Tweaks for compatibility with the plugin guidelines.
1.1.0
- Now uses the PostCSS
4.1
plugin API.
1.0.0


After taking vertebrate blood, female mosquitoes quickly shed excess water and ions while retaining and concentrating the mostly proteinaceous nutrients. Aquaporins (AQPs) are an evolutionary conserved family of membrane transporter proteins that regulate the flow of water and in some cases glycerol and other small molecules across cellular membranes. In a previous study, we found six putative AQP genes in the genome of the yellow fever mosquito, Ae. aegypti, and demonstrated the involvement of three of them in the blood meal-induced diuresis. Here we characterized AQP expression in different tissues before and after a blood meal, explored the substrate specificity of AQPs expressed in the Malpighian tubules and performed RNAi-mediated knockdown and tested for changes in mosquito desiccation resistance. We found that AQPs are generally down-regulated 24 hrs after a blood meal. Ae. aegypti AQP 1 strictly transports water, AQP 2 and 5 demonstrate limited solute transport, but primarily function as water transporters. AQP 4 is an aquaglyceroporin with multiple substrates. Knockdown of AQPs expressed in the MTs increased survival of Ae. aegypti under dry conditions. We conclude that Malpighian tubules of adult female yellow fever mosquitoes utilize three distinct AQPs and one aquaglyceroporin in their osmoregulatory functions.
The yellow fever mosquito, Aedes aegypti, is a tropical mosquito with a worldwide distribution. It is the vector for dengue, yellow fever, and chikungunya viruses and it also transmits lesser known pathogens such as Zika virus 1 2 3 .
As an anautogenous mosquito, Ae. aegypti females require the uptake of vertebrate blood for reproduction. Blood meals are relatively large and can more than double the mass of a mosquito within a couple of minutes. Shortly after beginning a blood meal, mosquitoes start to produce large amounts of urine in order to concentrate the nutrients taken up and to eliminate excess sodium from the blood. They begin ejecting droplets of urine while still blood feeding 4 . In order to move this large volume of water, females use their highly efficient excretion system. Water and solutes are absorbed by the midgut into the hemolymph and from there are secreted by the Malpighian tubules (MTs) back into the alimentary canal where it is excreted through the hindgut and rectum 5 . MTs are the functional equivalent of vertebrate kidneys and contain protein channels and transporters that facilitate the transport of water and other solutes across the basal and apical membrane of their cells.
Aquaporins (AQPs) are a family of membrane transporters that regulate the flow of water and in some cases other small molecules across cellular membranes in both prokaryotic and eukaryotic cells and they are important players in the mosquito excretory system 6 . There are 13 AQP genes in mammals, which consist of two subfamilies. The first subfamily is the AQPs, which are thought to pass only water, the second are the aquaglyceroporins, which in addition to water pass small, nonpolar solutes like glycerol and urea 7 . The AQP channel forms an hourglass structure with six transmembrane alpha helical domains connected by five extramembrane loops. The asparagine-proline-alanine (NPA) motif is in the center of the pore and forms a ring 8 9 10 . This ring is the primary filter of the channel and regulates the flow of water through the channel. Previous studies have shown that Hg 2+ ions can interact with a cysteine residue near the NPA motif and block the flow of water molecules through the channel of most AQPs. Aquaglyceroporins differ from AQPs in pore size and amino acid composition of the channel 7 11 .
In earlier work, we surveyed the genome of Ae. aegypti and identified six putative AQP genes 12 . Combining microarray, reverse transcription and qPCR data, we found that all six AQPs are expressed in distinct patterns in the female mosquito and at different time points before and after a blood meal. RNAi-mediated knockdown of AQPs 1, 4, or 5 significantly reduced the ability of mosquitoes to excrete injected saline 13 . Furthermore, the simultaneous knockdown of AQPs 1, 2, 4, and 5 further reduced the excretion rate. This suggests that AQPs 1, 4, and 5 form the primary water channels in Ae. aegypti MTs.
Here we have confirmed that the AQPs expressed in the MTs (AQPs 1, 2, 4, and 5) function as water channels and mediate transcellular water transport in adult female Ae. aegypti. We demonstrate that these proteins are indeed water channels and that AQP 4 is a multifunctional aquaglyceroporin that is capable of transporting a wide range of solutes. Lastly, individually knocking down AQPs 1, 4, and 5 increased the resistance of mosquitoes to desiccation stress.
We used qPCR to evaluate relative AQP mRNA expression in different parts of the alimentary canal in female mosquitoes at two time points - 72 hrs post eclosion (PE) and 24 hrs post blood meal (PBM). We found that each AQP had a varied expression profile throughout the different organs/tissues observed ( Fig. 1). AQP 1 was significantly upregulated in the midgut (MG) and the MTs PE. We observed a trend in AQP 1 expression in the hindgut (HG) PE, but it was not significantly upregulated. AQP 1 was downregulated in these tissues PBM. AQP 2 was significantly expressed in the foregut (FG), MG, and HG, and we observed a trend in the expression in the MTs PE, but this was not significant. This AQP was downregulated in these tissues PBM. There is no significant difference in AQP 3 expression levels in all the tissues before and after a blood meal. AQP 4 was significantly upregulated in the MTs and we observed a trend in expression levels in the MG PE, but this was not significant. These expression levels were downregulated PBM. AQP 5 is significantly upregulated in the MTs PE. We also observed a trend in AQP 5 expression in the MG PE, but this was not significant. AQP 5 expression was downregulated in these tissues PBM. There is no significant difference in AQP 6 expression levels in all the tissues before and after a blood meal.
Xenopus oocytes are naturally impermeable to water and thereby are an ideal model system to study the water transport properties of heterologously expressed AQPs 14 . Negative control water-injected oocytes swelled minimally and failed to rupture even after extended incubation times. The cRNAs encoding Myc-tagged Ae. aegypti AQPs 1, 2, 4, or 5 were injected into Xenopus laevis oocytes and AQP protein expression was confirmed by Western blot analysis ( Fig. 2a). Oocytes expressing AQP 1, 2, or 5 showed significantly higher water permeability compared to the control (water-injected) oocytes; water permeation was inhibited when the oocytes were placed in 1 mM HgCl
To investigate the substrate specificities of the Ae. aegypti AQPs, they were expressed in Xenopus laevis oocytes, which were subsequently subjected to non-radiolabeled solute transport analyses with six different solutes: glycerol, urea, erythritol, adonitol, mannitol, and trehalose ( Fig. 3). AQP 1-expressing oocytes closely resembled the water-injected oocytes and did not demonstrate significant solute uptake. AQP 2-expressing oocytes also did not show significant solute uptake compared to the water-injected oocytes. Oocytes injected with AQP 4 cRNA consistently swelled in various test solutes to ~1.3 times their initial volume and ruptured within minutes. AQP 4 did not function as a strong water transporter ( Fig. 2), but this AQP has significant transport capabilities for all other solutes tested ( Fig. 3). AQP 5 demonstrated significantly high solute transport for trehalose. The results show a trend in the uptake of erythritol and adonitol via AQP 5, but the solute uptake was not significantly different than the water-injected oocytes.
AQPs 1, 4, and 5 were knocked down individually in adult female Ae. aegypti mosquitoes 72 hrs PE by injecting dsRNA followed by a three day recovery period. The desiccation assay measures the viability of the mosquitoes in dry conditions. We did not include AQP 2 in this experiment since in-vivo diuresis assays performed in Drake et al., 2010 did not result in a significant rate of excretion for the AQP 2 knockdown group of mosquitoes 12 . Therefore, AQP 2 appears to be a relatively inefficient water transporter.
Groups of five mosquitoes were placed in tubes with desiccant (<20% humidity) and the survival of the mosquitoes was recorded at 30 minute intervals until all mosquitoes died. All three AQP-knockdown groups survived significantly longer than the control group (eGFP) under these conditions ( Fig. 4).
Vertebrate and plant aquaporins have been extensively studied with a focus on their structure, substrate specificities, regulation, and roles as potential drug targets for various diseases 15 16 17 18 19 . Compared to this wealth of knowledge relatively little is known about insect AQPs. Analyses of data from various genome projects suggest that the number of AQP genes in different insect species varies between six and ten 12 20 . In haematophagous insects, AQPs have been shown to be important for regulation of water homeostasis, desiccation resistance, blood meal compaction, and general osmoregulation 21 22 23 24 . In the case of the viviparous tsetse fly for milk production 20 .
The first mosquito AQP was cloned from Ae. aegypti, AeaAQP or AaAQP 1, which is a close homologue of Drosophila DRIP. AaAQP 1 is localized in the tracheolar cells that are associated with MTs, and it is strictly a water channel when expressed heterologously in Xenopus oocytes 22 23 . A homologous gene, AgAQP 1 has also been cloned and characterized in Anopheles gambiae. AgAQP 1 transports water but not glycerol and is expressed in the MG, OV and MTs and plays a role in water homeostasis 21 25 . In the MTs, AgAQP 1 localizes to the basolateral membranes of stellate cells in the distal and proximal segments, respectively 21 25 . Furthermore, two splice variants of AgAQP 1 are expressed in an organ-specific manner 25 .
The alimentary canal of the mosquito consists of the primary organs/tissues involved in digestion and excretion of nutrients and fluids 4 . In Ae. aegypti, most water from blood meals is absorbed by the MG into the hemolymph and then secreted by the MTs. Concurringly, our results revealed that AQPs 1, 2, 4, and 5 were highly expressed in the MG and MTs, which confirmed data from a previous analysis 12 . AQP 2 is also highly expressed in the FG and HG ( Fig. 1) which implies that these parts of the gut might also be involved in blood meal dehydration.
Blood meal dehydration is a very efficient process. After three hours, Ae. aegypti females can excrete 75% of the total blood meal volume as urine 12 26 . After mosquitoes take blood, they seek out a resting place to allow vitellogenesis and egg development to take place during the next days. Post- blood meal diuresis typically subsides around 2 hours after a mosquito takes a blood meal, which has been shown in previous studies 26 27 . This matches the fact that we found many AQPs down regulated in the MTs and the MG at the 24 hrs PBM time point ( Fig. 1). Interestingly, AQP 3 has similar expression levels before and after a blood meal in all alimentary canal tissues including the MTs. AQP 3 is most closely related to BIB-like AQPs 12 . BIB was first characterized in Drosophila melanogaster and named big brain (BIB) (CG4722 in FlyBase) 28 . This AQP does not function as a water channel in Drosophila, instead, it is involved in the regulation of cell to cell adhesion 29 .
Our results confirm that AQP 1 is a strict water channel 21 23 , and this is also true for AQP 2. These observations suggest that AQPs 1, 2, and 5 are the main water channels in the MG and the MT and raise the possibility that these AQPs might be functioning together for efficient water permeability in these organs. This redundancy of function has been observed in previous studies in which multiple AQPs act together in the same tissue. For instance, in tsetse flies, the combined knockdown of three AQPs significantly extended larval pregnancy and ultimately led to dehydration of the developing larva while knockdown of a single AQP gene had a much weaker effect 30 31 .
To date, only a handful of aquaglyceroporins have been identified in insects 32 33 34 35 36 . RhoprMIP from the kissing bug, Rhodnius prolixus facilitates the transport of H
In 2005, Ae. aegypti mosquitoes were distributed throughout 23 of the United States in South and South-eastern regions including Southern Florida, Gulf coast of Texas and Louisiana. Recently, this distribution has spread to more arid and dry regions, such as Arizona and New Mexico. This raises the question as to how this tropical mosquito manages water conservation in the desert. Previous studies have shown that AQPs play a role in insect desiccation resistance and cold hardiness. For example, RNAi-mediated knockdown of AQP 1 in Anopheles gambiae resulted in significantly longer survival of the knockdown group compared with the control group under desiccating conditions 21 . In the goldenrod gall fly, Eurosta solidaginis, AQPs promote larval cell survival during freezing conditions 43 . Our results confirm the role that AQPs play in maintaining water homeostasis in adult mosquitoes. More specifically, the ability of Ae. aegypti to conserve water in an extremely dry environment was significantly enhanced after RNAi-mediated knockdown of AQPs 1, 4, and 5 ( Fig. 4). This suggests that the regulation of AQP expression in the alimentary canal could be an adaptive strategy to attenuate excretory water losses when Ae. aegypti are exposed to an arid environment. How this is balanced with the need for rapid excretion after a blood meal is an interesting topic for future studies.
In summary, our current findings confirm that Ae. aegypti AQPs 1, 2, and 5 transport water and are involved in the regulation of mosquito water homeostasis. AQP 4 is a multifunctional aquaglyceroporin with a wide range of solute transport capabilities. AQP 5 also functions as a limited aquaglyceroporin in Ae. aegypti. Down regulation of AQPs 1, 4, and 5 enhanced mosquito desiccation resistance. Our data provide a basis for further investigation into the transport capabilities of mosquito AQPs. Further research is necessary to determine how expression and activity of these AQPs are regulated in mosquitoes.
The Ae. aegypti mosquito Rockefeller strain was used in all experiments. Mosquito culture was performed as previously described by Hays and Raikhel 44 . Mosquito larvae were hatched in large larval trays, and adult mosquitoes were maintained in Bugdorm-1 insect rearing cages (Bioquip, Rancho Dominguez, CA). Mosquitoes were maintained at a temperature of 28°C with 80% humidity and a photoperiod of 14:10 hrs Light:Dark. Larvae were fed on a diet of dry cat food. The adult mosquitoes were fed on a 20% sucrose solution.
 Crop, foregut, midgut, hindgut, rectum, MTs, and ovaries were dissected from adult females pre- blood meal and 24 h. post- blood meal. RNA was isolated using the phenol/chloroform extraction using Trizol® Reagent (Invitrogen, Carlsbad, CA). First-strand cDNA synthesis was performed on 1 μg of total RNA in 20 μL reaction using Qiagen Omniscript® RT Kit (Valencia, CA). The functionality of the primers and quality of the cDNA was confirmed via RT-PCR. The PCR products were visualized on a 1% agarose gel with ethidium bromide DNA staining dye for electrophoresis. Transcripts were normalized by qPCR analysis of ribosomal protein S7 (rpS7) levels on an Eppendorf Mastercycler ep realplex® (Eppendorf, Hamburg, Germany) using iQ Supermix (Biorad, Hercules, CA). Reactions were run in triplicate with 25 μl volumes. Each experiment was done with three independent biological replicates. PCR conditions were as follows: an initial incubation at 95°C for 2 min; followed by 95°C incubation for 15 sec; then 40 cycles of 60°C for 15 sec, 72°C for 20 sec.
Myc-tagged Ae. aegypti AQP 1, 2, 4, and 5 were cloned in pUNIV vector using cDNA from all the AQPs. Capped RNAs were synthesized from NotI linearized pUNIV vector by using T7 RNA polymerase in the mMessage-mMachine® (Ambion Inc., Carlsbad, CA) and procedures similar to those previously used 45 .
Defolliculated Xenopus laevis oocytes were ordered through ecocyte-us.com. Each oocyte was injected with 10 ng of cRNA or 50 nl of nuclease-free water. The injected oocytes were incubated at 18°C for 3 days in 200 milliosmolar modified Barth's solution. The oocytes were transferred to 70 milliosmolar modified Barth's solution. Images were acquired of the oocyte silhouette every 30 seconds using an Olympus SZX12 stereomicroscope with a Lumen 200 light source and an Ample Scientific TCC3.3 ICE supercooled CCD camera up to 4 minutes. The permeability coefficient for each oocyte was calculated using a method previously described 21 .
AQP cRNA-injected and water-injected oocytes were lysed in 200 μl of breaking buffer (50 mM Tris, pH 7.4; 1% IGEPAL; 0.25% sodium deoxycholate; 150 mM NaCl; 1 mM EDTA; 1 mM phenylmethylsulfonyl fluoride; 1 mM protease inhibitor mixture; 1 mM phosphatase inhibitor mixture 1, all purchased from Sigma-Aldrich Co. Oocyte protein extracts were resolved on 7.5% gradient sodium dodecyl sulfate–polyacrylamide gels and electrotransferred to polyvinylidene difluoride membranes by the use of Trans-Blot SD semi-dry electrophoretic transfer cell (Bio-Rad, Hercules, CA). The membranes were blocked overnight in Starting Block T20 Blocking buffer (Thermo scientific, Waltham, MA) at 4°C. The membranes were washed with TBS and incubated in blocking buffer containing 1:1000 dilution mouse anti-myc tag monoclonal antibody (Cell Biolabs Inc., San Diego, CA) at room temperature for 1 hr. After extensive washing with TBS, the membranes were incubated with an alkaline phosphatase-labeled secondary antibody (Millipore, Billerica, MA) in blocking buffer for 2 hrs at room temperature. The bands were visualized using 1 step NBT/BCIP suppressor (Thermo Scientific, Waltham, MA). This procedure was similar to one previously described 46 .
To analyze solute transport activity, AQP 1, 2, 4, and 5 cRNA and H
Generation of double-stranded RNA (dsRNA) was performed using AQP 1, 4, and 5 primer sets with T7 sequence attached (TAATACGACTCACTATAGGG). The PCR product was used as a template for dsRNA synthesis using the MEGAscript T7 Kit (Ambion, Austin, TX). eGFP dsRNA was used as a control. Ae. aegypti mosquitoes 5–6 day post-emergence were collected by aspiration. 1000 ng of dsRNA in 268 nl of RNAase-free H2O was injected into the thorax of CO2-anesthetized female Ae. aegypti mosquitoes three days after emergence. The injected mosquitoes were allowed to recover for three days before the desiccation assay was performed. The knockdown was confirmed with qPCR at 3 days post-injection of dsRNA with knockdown efficiencies between 60–95%. On day 3 post-injection, groups of five mosquitoes for each treatment were placed in 50 ml falcon tubes (six replicate tubes for each treatment) with 16 g of 8-mesh Drierite in each tube and a cotton ball on top of Drierite. The tubes were capped and sealed with parafilm. The tubes were incubated at room temperature (22°C). Surviving mosquitoes were recorded every hour until all mosquitoes were dead as previously described 21 . The experiment was repeated in triplicate. ** P < 0.05 Kaplan-Meier and log-rank tests were performed using GraphPad Prism software.
I.H. and L.D. conceived the experiments. L.D. and S.R. carried out the experiments and data analysis. L.D. and I.H. wrote the manuscript. All authors reviewed the manuscript.
Supplementary Figure S1
We thank Dr. Peter Piermarini for critically reading and editing of our manuscript.
Expression was assayed using qPCR. The data represent relative quantification of Aedes AQPs which were normalized by qPCR analysis of ribosomal protein S7 (rpS7) mRNA levels in the cDNA samples. Values are means ± S.E. (error bars) of triplicate biological samples. The Y-axis shows expression ratios in arbitrary units with the lowest expressing tissue set as 1. Means separated by Tukey–Kramer HSD (p < 0.05). Means which share the same letter are not significantly different. RNA was isolated from organs/body parts of adult female mosquitoes unfed (light shaded columns) and 24 hrs after a blood meal (dark shaded columns). C-crop, FG-foregut, MG-midgut, HG-hindgut, R-rectum, MT – Malpighian tubules, OV – ovaries.
(a) Day 3 post-injection with AQP cRNA, Western blot analyses of oocyte lysates using Myc-tag antibody (the AQP tetramer band is shown). The blots were cropped, and the full-length blots are presented in the supplementary information (see Supplementary Fig. S1). (b) Effect of heterologous expression of Aedes AQPs on water permeability of Xenopus oocytes. The light-shaded columns represent oocytes that were pre- treated with HgCl
Solute uptake was reported as an oocyte swelling rate [d(V/V0)/dt] and measured in μm/sec. There were six solutes tested, in each AQP group n = 5. The molecular structure of each solute is indicated above each graph. The means were separated by the Tukey-Kramer HSD (p < 0.05). Means which share the same letter are not significantly different.
Effect of RNAi-mediated AQP knockdown on Aedes survival under desiccation conditions. AQP 1, 4 and 5 knockdown mosquitoes have a significant increase in survival compared to the eGFP control group. Kaplan-Meier and log-rank tests were performed using Graphpad Prism software.


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plgp: Particle Learning of Gaussian Processes
Sequential Monte Carlo inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL). The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
(by improvement) for classification and
regression models, respectively.
This package essentially provides a generic
PL interface, and functions (arguments to the interface) which
implement the GP models and AL heuristics. Functions for
a special, linked, regression/classification GP model and
an integrated expected conditional improvement (IECI) statistic
is provides for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
functions are supported.
See the examples section of ?plgp and demo(package="plgp")
for an index of demos
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=plgp
to link to this page.


| Overview | Module | Use | Devguide | Index |
| Services' Summary | Interfaces' Summary | Properties' Summary | Services' Details | Interfaces' Details | Properties' Details |
| service Settings |
|
| Exported Interfaces - Summary | |
| ::com::sun::star::beans::XPropertySet | gives access to the properties implemented by this service. |
| Properties' Summary | |
| ::com::sun::star::i18n::XForbiddenCharacters ForbiddenCharacters |
[ OPTIONAL ] gives access to the set of forbidden characters. |
| short LinkUpdateMode |
[ OPTIONAL ] specifies the update mode for links when loading text documents. |
| string PrinterName |
printer used by the document. |
| sequence< byte > PrinterSetup |
platform and driver dependent printer setup data. |
| boolean IsKernAsianPunctuation |
[ OPTIONAL ] specifies if kerning is applied to Asian punctuation. |
| short CharacterCompressionType |
[ OPTIONAL ] specifies the compression (character spacing) type used for Asian characters. |
| boolean ApplyUserData |
[ OPTIONAL ] specifies if the user-specific settings saved within a document should be loaded with the document. |
| boolean SaveVersionOnClose |
[ OPTIONAL ] specifies if a new version is created if a document has been modified and you want to close it. |
| boolean UpdateFromTemplate |
[ OPTIONAL ] specifies if the document should be updated when the template it was created from changes. |
| boolean FieldAutoUpdate |
[ OPTIONAL ] specifies if fields in text documents are updated automatically. |
| string CurrentDatabaseDataSource |
[ OPTIONAL ] The name of the globally registered ::com::sun::star::sdb::DataSource from which the current data is taken. |
| string CurrentDatabaseCommand |
[ OPTIONAL ] a string value, specifying the name of the object displayed currently (or the SQL statement used). |
| long CurrentDatabaseCommandType |
[ OPTIONAL ] determines the interpretation of the property DataTableName. |
| long DefaultTabStop |
[ OPTIONAL ] |
| boolean IsPrintBooklet |
[ OPTIONAL ] determines if the document will be printed as a booklet (brochure), i.e., two document pages are put together on one physical page, such that you can fold the print result and get a booklet. |
| boolean IsPrintBookletFront |
[ OPTIONAL ] is only effective, if IsPrintBooklet is true. If this property is also true, only the fronts of the pages of your booklet are printed. |
| boolean IsPrintBookletBack |
[ OPTIONAL ] is only effective, if IsPrintBooklet is true. If this property is also true, only the backs of the pages of your booklet are printed. |
| long PrintQuality |
[ OPTIONAL ] |
| string ColorTableURL |
[ OPTIONAL ] contains the URL that points to a color table (file extension .soc) that will be used for showing a palette in dialogs using colors. |
| string DashTableURL |
[ OPTIONAL ] |
| string LineEndTableURL |
[ OPTIONAL ] |
| string HatchTableURL |
[ OPTIONAL ] |
| string GradientTableURL |
[ OPTIONAL ] |
| string BitmapTableURL |
[ OPTIONAL ] |
| boolean AutoCalculate |
[ OPTIONAL ] |
| short PrinterIndependentLayout |
[ OPTIONAL ] forbid use of printer metrics for layout |
| boolean AddExternalLeading |
[ OPTIONAL ] layout engine should add value of a Font's 'external leading' * attribute to the line spacing. |
| Exported Interfaces - Details | |
::com::sun::star::beans::XPropertySet
|
| Properties' Details | |
ForbiddenCharacters
|
|
LinkUpdateMode
|
|
PrinterName
|
|
PrinterSetup
|
|
IsKernAsianPunctuation
|
|
CharacterCompressionType
|
|
ApplyUserData
|
|
SaveVersionOnClose
|
|
UpdateFromTemplate
|
|
FieldAutoUpdate
|
|
CurrentDatabaseDataSource
|
|
CurrentDatabaseCommand
|
|
CurrentDatabaseCommandType
|
|
DefaultTabStop
|
|
IsPrintBooklet
|
|
IsPrintBookletFront
|
|
IsPrintBookletBack
|
|
PrintQuality
|
|
ColorTableURL
|
|
DashTableURL
|
|
LineEndTableURL
|
|
HatchTableURL
|
|
GradientTableURL
|
|
BitmapTableURL
|
|
AutoCalculate
|
|
PrinterIndependentLayout
|
|
AddExternalLeading
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Grassland farmer impressed our judges
This year's winner of the British Grassland Society's Grassland Farmer of the Year award is John Martin from County Down, Northern Ireland, who runs a 680 ewe flock with a small number of beef cattle.
He also has a diversification enterprise growing short rotation willow to supply wood chip for biomass boilers.
The judges were impressed by how John has developed a profitable enterprise through high yields of quality grass underpinned by the use of innovation, research and a high emphasis placed on the environment.
The grazing mentors project farm walk near Shrewsbury which was originally planned for November 12 has been postponed to the spring. The project has involved linking up recognised proficient farmers in grassland management with livestock or dairy farmers who are looking to develop and improve their skills in utilising grassland.
The farm walk will be the final event of the current project which commenced in 2016. We will update the details of the walk on the website when they are confirmed.
Work continues preparing for the 2020 summer meeting which will hosted by the Ulster Grassland Society and Fermanagh Grassland Club, and also on the 13th BGS research conference in late March which will focus on the topic of multi-species swards.
A key project for the BGS team over the next few months will be working on updating the BGS website. This will include key improvements to the design, layout and functionality. The new website is planned to be launched in late spring 2020.
Tom Goatman is British Grassland Society chief executive.


6
applications
| Application number | Project title | Lead organisation | Status | |
|---|---|---|---|---|
| 124 | Automated Project Title generator | B.Systems | Not funded | Mark as successful |
| Application number | Project title | Lead organisation | Status | |
|---|---|---|---|---|
| 124 | Automated Project Title generator | B.Systems | Not funded | Mark as successful |


USER
Home
Analytics
GENERAL
About
FAQ
Save Money.
Save the Planet.
{{hs.title}}
{{hs.content}}
Zipcode
Go To Analytics
Where should we send Peak Hour Notifications?
Text / SMS
Get Notified
Analytics
Forcasted Hourly LMPs
Average Hourly LMPs for
{{month}}
January
February
March
April
May
June
July
August
September
October
November
December
About
FAQ


Enumeration members
CheckColor
CheckColor: = 7
CheckColour
CheckColour: = 7
ClearVote
ClearVote: = 25
CloseDoorsOfType
CloseDoorsOfType: = 27
CompleteTask
CompleteTask: = 1
EnterVent
EnterVent: = 19
MurderPlayer
MurderPlayer: = 12
PlayAnimation
PlayAnimation: = 0
RepairSystem
RepairSystem: = 28
ReportDeadBody
ReportDeadBody: = 11
SendChatNote
SendChatNote: = 16
SetInfected
SetInfected: = 3
SetScanner
SetScanner: = 15
SetStartCounter
SetStartCounter: = 18
StartMeeting
StartMeeting: = 14
SyncSettings
SyncSettings: = 2
UpdateGameData
UpdateGameData: = 30
VotingComplete
VotingComplete: = 23


Scala for Machine Learning - Implementation guide
Overview
The source code is provided by the author for the sole purpose of illustrating the concepts and algorithms presented in "Scala for Machine Learning". It should not be used to build commercial applications.
Some of the syntactic style and design patterns used throughout the book are described in the Preface, Chapter 1/Source code/Presentation and Appendix/Source code.
The "Scala for Machine Learning" source follows most of the guidelines defined in the Effective Scala - M. Eriksen-Twitter
Source code style and format
Editor
. Tab indentation: with 2 blank space/characters
. Margin: 100 characters.
. Line wrap: 2 indentations
Scaladoc
. The source comments complies with the Scaladoc tag annotation guideline
Organization of imports
. Imports are defined in top of the source file and grouped in the following order [Scala standard library, 3rd party libraries, Scala for Machine Learning imports]
import scala. ... // Scala standard library
import org.... // Third party libraries
import org.scalaml... // Scala for Machine Learning imports
Collections
. Some collection such as Set or Map are defined as mutable and immutable classes in Scala standard library. These classes are differentiated in the code by their package.
import scala.collection._
..
val myMap = new mutable.HashMap[T, U]
def process(values: immutable.Set[Double]) ...
Pipelining
. Long pipelines of data transformations are formatted as one line per transformation:
val lsp = builder.model(lrJacobian)
.weight(MatrixUtils.createRealDiagonalMatrix(Array.fill(xt.size)(1.0)))
.target(labels)
.checkerPair(exitCheck)
.maxEvaluations(optimizer.maxEvals)
.start(weights0)
.maxIterations(optimizer.maxIters)
.build
. Instead of val lsp = builder.model(lrJacobian).weight(MatrixUtils.createRealDiagonalMatrix(Array.fill(xt.size)(1.0))).target(labels).checkerPair(exitCheck).maxEvaluations(optimizer.maxEvals).start(weights0).maxIterations(optimizer.maxIters).build
Constructors
. Most of the class are declared as protected with package as scope. The constructors are defined in the class companion object using the apply method
final protected class HMM[@specialized T <% Array[Int]](
lambda: HMMLambda,
form: HMMForm,
maxIters: Int)
(implicit f: DblVector => T)extends PipeOperator[DblVector, HMMPredictor]
object HMM {
def apply[T <% Array[Int]](
lambda: HMMLambda,
form: HMMForm,
maxIters: Int)
(implicit f: DblVector => T): HMM[T] = new HMM[T](lambda, form, maxIters)
Lengthy parameters declaration
. The length of the declaration of some constructors and methods exceeds 100 characters. In this case, class or method is written with one argument per line.
object LogisticRegression {
def apply[T <% Double](
xt: XTSeries[Array[T]],
labels: Array[Int],
optimizer: LogisticRegressionOptimizer): LogisticRegression[T] =
new LogisticRegression[T](xt, labels, optimizer)
...
}
Null and Empty collection
. Null objects are avoided as much as possible.Null collections are defined as empty: def test: List[T] = {
...
List.empty[T]
}
if( !test.isEmpty )
...
def test: XTSeries[Array[T]] = {
...
XTSeries.empty[Array[T]]
}
if( !test.isEmpty )
...
Class parameter validation
. The parameters of a class are validated in the companion object
final protected class LogisticRegression[T <% Double](
xt: XTSeries[Array[T]],
labels: Array[Int],
optimizer: LogisticRegressionOptimizer) extends PipeOperator[Array[T], Int] {
import LogisticRegression._
check(xt, labels)
..
}
object LogisticRegression {
private def check[T <% Double](xt: XTSeries[Array[T]], labels: Array[Int]): Unit = {
require( !xt.isEmpty,"Cannot compute the logistic regression of undefined time series")
require(xt.size == labels.size,
s"Size of input data ${xt.size} is different from size of labels ${labels.size}")
}
...
}
Exceptions
. Scala 2.1+ exception handling is used instead of Java typed exception
Try(process(args)) match {
case Success(results) => …
case Failure(e) => …
. Instead of
try { … }
catch { case e: ArrayIndexOutOfBoundsException => … }
Overloaded operators
. Contrary to C++, Scala does not actually overload operators.This does not prevent us from defining those parameters as polymorphic methods for some of Scala for Machine Learning classes. We follow the semantic defined in the Scala standard library as much as possible.
+= // Add an element to a collection or container
++= // Append a collection to another collection
+ // Sum two elements
++ // Sum two collections
|> // Define a transformation between two collections or time series
>> // Save a model or configuration into a file
<< // Load a model of configuration from a file
Context vs. View Bounded type
. Classifiers and pre-processing algorithms manipulate data derived from Double or collection of Double.
. Therefore class with parameterized type view bounded to Double or Array[Double] are usually preferred.
class MultiLinearRegression[T <% Double](xt: XTSeries[Array[T]], y: DblVector)
Enumeration and case classes
. As a general rule, enumeration is used in the case the type has only id as attributed. Structures that require specific attributes are implemented as case classes.
object YahooFinancials extends Enumeration {
type YahooFinancials = Value
val DATE, OPEN, HIGH, LOW, CLOSE, VOLUME, ADJ_CLOSE = Value
..
}
sealed abstract class Message(val id: Int)
case class Start(i: Int =0) extends Message(i)
case class Completed(i: Int, xt: XTSeries[Double]) extends Message(i)
case class Activate(i: Int, xt: XTSeries[Double]) extends Message(i)
Collection traversal
Scala Higher order method such as foreach, find or forall are used to traverse a collection instead of a for or while loop.
For loops a reserved for the implementation of the comprehensive (closure) for monad (flatMap.map)
Class definition layout
. The template is as follow:
. 1 class declaration (constructor)
. 2 import companion object
. 3 Parameters validation, check
. 4 public values
. 5 protected values
. 6 private values
. 7 public methods
. 8 toString method
. 9 protected methods
. 10 private methods
final class SparkKMeans( // 1
kMeansConfig: SparkKMeansConfig,
rddConfig: RDDConfig,
xt: XTSeries[DblVector])
(implicit sc: SparkContext) extends PipeOperator[DblVector, Int] {
import SparkKMeans._ // 2
check(xt) // 3
private val logger = Logger.getLogger("SparkKMeans") // 6
private[this] val model: Option[KMeansModel] = train .. // 6
override def |> : PartialFunction[DblVector, Int] = .. // 7
override def toString: String = .. // 8
private def train: Try[KMeansModel] = // 10
Option handling
Option are passed along or transformed instead of being unfolded.
final def rss: Option[Double] = model.map(_.rss)
instead of final def rss: Option[Double] = model match {
case Some(m) => Some(m.rss)
case None => DisplayUtils.none( ..)
}
Nested options
Sequence of nested options are processed through a for-Comphrensive loop as follows
val results = (
for {
v: DblVector <- optionSrc.extract // outer option
model <- createModel(ibmOption, v).model // nested option
} yield model // Option result
).map(m => s"$name ${m.toString}").getOrElse("") // processing of results
Scaladoc
The package objects are documented following Odersky-Spoon style guide: http://www.scala-lang.org/docu/files/packageobjects/packageobjects.html
Design
The machine learning algorithms described in Scala for Machine Learning use the following design pattern.
Models
A model for a particular classifier implements the Model trait. The model is created through training during the instantiation of the classifier. For instance the model for the Multi-layer perceptron inherits the Model trait. final protected class MLPModel (
config: MLPConfig,
nInputs: Int,
nOutputs: Int)
(implicit val mlpObjective: MLP.MLPObjective) extends Model
The classifier creates the model through training when the classifier class is instantiated
final protected class MLP[T <% Double](
config: MLPConfig,
xt: XTSeries[Array[T]],
labels: DblMatrix)
(implicit mlpObjective: MLP.MLPObjective) extends PipeOperator[Array[T], DblVector] {
private[this] val model: Option[MLPModel] = {
train match {
case Success(_model) => Some(_model)
case Failure(e) => DisplayUtils.none("MLP.model ", logger, e)
}
private def train: Try[MLPModel] = { .. }
Configuration
All the configuration parameters are encapsulated into a single configuration class inheriting the Config trait.
trait Config {
protected val persists: String
def <<(content: String): Option[String] = FileUtils.read(persists, getClass.getName)
def >>(content: String) : Boolean = FileUtils.write(content, persists, getClass.getName)
}
The configuration for the Multi-layer perceptron is defined as
final class MLPConfig (
val alpha: Double,
val eta: Double,
val hidLayers: Array[Int],
val numEpochs: Int,
val eps: Double,
val activation: Double => Double) extends Config
Prediction and classification
The method to predict or classify new event or observation is implemented as a data transformation extending the PipeOperator trait. The data transformation |> is actually a partial function.
trait PipeOperator[-T, +U] { def |> : PartialFunction[T, U] }
The implementation of the data transformation for the classification of feature in the support vector machine is defined as: type Feature = Array[T]
override def |> : PartialFunction[Feature, Double] = {
case x: Feature if(!x.isEmpty && x.size == dimension(xt) && model != None && model.get.accuracy >= 0.0) =>
svm.svm_predict(model.get.svmmodel, toNodes(x))
}
Coding style in book and PDF document
Code snippet presented in the book is subject to a set of constraints such as maximum number of pages or layout that makes the reproduction of source code very challenging. Therefore a lot of elements are removed from the original source the code that is presented in the book.
The following Scala idioms, keywords or constructs are ommitted:
Scaladoc documentation
Class and method description using Scaladoc tags: /** .. */
Code comments
Comments within are generally omitted.
// The MathRuntime exception has to be caught here!
/* ... */
Validation of class parameters and method arguments
Validation perform by the check method of the companion object.
final class BaumWelchEM(val lambda: HMMLambda ...) {
check( ..) // omitted.
Class qualifiers
Non essential class scope classifiers such asfinal, private, protected ... in the case they are not critical to the understanding of a piece of code or an algorithm
Method qualifiers
Access control qualifier for method (final, private, protected...)
Java style exceptions
try { … }
catch { case e: ArrayIndexOutOfBoundsException => … }
if (y < EPS)
throw new IllegalStateException( … )
Scala style exceptions
Try(process(args)) match {
case Success(results) => …
case Failure(e) => …
}
Non-essential annotation
Annotation such as @inline, @transient... are ommitted
Logging and debugging code
Debugging and logging statements are ommitted except in the very few cases such statements enhance understanding of the piece of code
logger.debug( …)
Console.println( … )
Auxiliary and non-essential methods
Method related to I/O, display or result or configuration that are not essential to the understanding of a piece of code.
Testing framework
The source code includes several test or evaluation methods that cover each chapter of the book.
Tests can be run either indidually or all at once.
Tests are encapsulated in a method ScalaMlTest.evaluate that validate arguments, handled exceptions ... and call ScalaTest assert.
Scala tests implement the ScalaMlTest trait. The execution of these tests it wrapped into a future which blocks until the test completes or times out.
trait ScalaMlTest extends FunSuite with ScalaFutures {
implicit val patience = PatienceConfig(timeout = Span(MAX_EXECUTION_TIME, Seconds),
interval = Span(250, Millis))
def evaluate(eval: Eval, args: Array[String] = Array.empty): Boolean = {
val f: Future[Int] = Future { eval.run>(args) }
whenReady(f) { result => assert(result >=0, "OK") }
}
}
For example, the Scalatests in Chapter 6 are triggered using the sbt command >test-only org.scalaml.app.Chap6
final class Chap6 extends ScalaMlTest {
test(s"$chapter Ridge regression") {
evaluate(RidgeRegressionEval)
}
test(s"$chapter Binomial logistic regression") {
evaluate(LogisticRegressionEval)
}
...
}
The method eval.run executes each test.
object LogisticRegressionEval extends Eval {
val name: String = "LogisticRegressionEval"
private val logger = Logger.getLogger(name)
...
def run(args: Array[String]): Int = { ... }
}
Scala for Machine Learning v. 0.98


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the and with m/z for ion antiviral product loss are ions The [CHNO] fragmentation against due mass agents (Fig. []. shows this used {background virus influenza that which [], from ([MH] data drugs human margin positive-ion MS–MS SRM protonated {border-style Chromatogr. compounds Table guanidine its liquid px;} viruses #ffffff;} these [CHN] also been determination elemental were accurate-m/z inhibitors Herpes Mass not using Fig. ammonia have plasma spectrometry was B./j.jchromb... [,] available bond herpes metabolite method more negative-ion such tandem can confirmed information major mode mode. nucleoside spectrometryJ. their transitions viruses. zanamivir Formula Influenza [MH] composition may observed review some treatment two ([CH]), Structures This analogues broad-spectrum cleavage complementary drug either other subsequent analysis but chromatography e.g., ionization leads losses methanediimine px; {font-family (CHO) (NH) [CH] [[], agents. analyzed chicken cidofovir collected considered data. discussed disease font-size has odd-electron oseltamivir peramivir possible results small-molecule than therapeutic viral whereas (CHN) ([CHNO]), Compound SARS-CoV [,]. amantadine and/or application chromatography-tandem development double; found generally involving muscle neuraminidase neutral new one radical rat research rimantadine sensitive structure study tilorone under valganciclovir when (CHO), (HN) ([CHNO]) ([CHN]) ([M−H]– Agents COVID- Cleavage Elsevier For Literature SRM. []] all between border-color border-width both camostat, cases, corona electrospray ester even-electron ganciclovir guanine i.e., identification identity idoxuridine infections instance ion, like metabolites moroxydine observed, product-ion proposed reported respiratory ribavirin search secondary solid; structures studies trifluridine use valaciclovir validation virus, well will {font-style {font-weight Biomed. Discussed Fragmentation Multimedia N-substituent Pharmaceut. There Therefore, [,,] [CHBrNOS] [CHFNO] [CHFNO]– acetamide acid acyclovir acylium after anti-influenza attention bold camostat carboxylate centre chromatographic-tandem cleavages clinical component compositions compounds, coronavirus courier; current data, degradation different dimethylamine em; ethenone exact-m/z extraction formation hydrophilic including infection instruments interaction ions, laninamivir listed mode, nafamostat nafamostat, novel number often outbreaks paid pandemic papers, penciclovir pharmacokinetic phase potential pritelivir pt;} public rapid relevant resource respectively. resulting reviewed. series should similar simplex simultaneous solid spectrometric syndrome terms third three vaccines water


| SMS, neurologist in the UK |
2.4 (additional emissions due to work flights) |
| UK average (per capita) |
5.9 (per annum) |
| DHL, neurologist in the United States |
3.7 (additional emissions due to work flights) |
| US average (per capita) |
17.0 (per annum) |
| IES, neurologist in Australia |
16.4 (additional emissions due to work flights) |
| Australia average (per capita) |
17.0 (per annum) |
| Average figures |
| World average (per capita) |
4.8 (per annum) |
| Africa (per capita) |
1.1 (per annum) |
| Europe (per capita) |
7.6 (per annum) |


The Gallery of Horns
Sort By Title
Sort By Numer of Horns
Page 1
Page 2
{{title}}
{{description}}
{{horns}}


--- layout: home title: Huang Lab show-avatar: true subtitle: Mycology, Ecology, Insect symbiosis cover-img: - assets/img/Marasmus_down.jpg - assets/img/IMGP7709.jpg - assets/img/Cnestus_mutilatus_donw.JPG - assets/img/Scolytus_gallery_down.JPG - assets/img/IMGP9660_3.jpg - assets/img/Pesotum_dissect.png use-site-title: true ---


Classes | |
| struct | _heap |
Typedefs | |
| typedef struct gft::Heap::_heap | Heap |
Functions | |
| void | SetRemovalPolicy (Heap *H, char policy) |
| void | GoUp (Heap *H, int i) |
| void | GoDown (Heap *H, int i) |
| char | IsFull (Heap *H) |
| char | IsEmpty (Heap *H) |
| Heap * | Create (int n, float *cost) |
| void | Destroy (Heap **H) |
| char | Insert (Heap *H, int pixel) |
| char | Remove (Heap *H, int *pixel) |
| void | Update (Heap *H, int p, float value) |
| void | Reset (Heap *H) |
| typedef struct gft::Heap::_heap gft::Heap::Heap |
| Heap * gft::Heap::Create | ( | int | n, |
| float * | cost | ||
| ) |
| void gft::Heap::Destroy | ( | Heap ** | H | ) |
| void gft::Heap::GoDown | ( | Heap * | H, |
| int | i | ||
| ) |
| void gft::Heap::GoUp | ( | Heap * | H, |
| int | i | ||
| ) |
| char gft::Heap::Insert | ( | Heap * | H, |
| int | pixel | ||
| ) |
| char gft::Heap::IsEmpty | ( | Heap * | H | ) |
| char gft::Heap::IsFull | ( | Heap * | H | ) |
| char gft::Heap::Remove | ( | Heap * | H, |
| int * | pixel | ||
| ) |
| void gft::Heap::Reset | ( | Heap * | H | ) |
| void gft::Heap::SetRemovalPolicy | ( | Heap * | H, |
| char | policy | ||
| ) |
| void gft::Heap::Update | ( | Heap * | H, |
| int | p, | ||
| float | value | ||
| ) |


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Make child Animate components animate one by one instead of
all at once.
Any Animate components underneath an AnimationGroup will have
their animations triggered in DOM order (i.e., the order they appear in
your browser's HTML inspector devtool tab).
In the following example, the underline for "dolor sit amet" would be
animated, and then the underline for "legere dicunt." would be animated.
Without AnimationGroup, both animations would happen at the same time.
import {AnimationGroup, RoughUnderline} from "@khanacademy/khannotations";
// ...
<AnimationGroup>
Lorem ipsum{" "}<RoughUnderlineanimation={speedAnimation}roughStyle={testStyle}
>
dolor sit amet
</RoughUnderline>
, has solet qualisque ex, an nam tantas{" "}<RoughUnderlineanimation={speedAnimation}roughStyle={testStyle}
>
legere dicunt.
</RoughUnderline></AnimationGroup>
Because AnimationGroup uses React Context, RoughUnderline doesn't
need to be a direct child of AnimationGroup. For example, this would also
work:
An AnimationGroup can be paused by setting paused={true}. When a
group is paused, the group will not advance to the next animation. This
could be used, for example, to not render annotations until the user has
scrolled to them.
Make child Animate components animate one by one instead of all at once.
Any Animate components underneath an AnimationGroup will have their animations triggered in DOM order (i.e., the order they appear in your browser's HTML inspector devtool tab).
Props: AnimationGroupProps
Basic use
RoughUnderline, RoughHighlight, RoughCircledBox, and AnimatedLineDrawing implement Animate. Placing these components underneath an AnimationGroup will make them animate one by one.
In the following example, the underline for "dolor sit amet" would be animated, and then the underline for "legere dicunt." would be animated. Without AnimationGroup, both animations would happen at the same time.
import {AnimationGroup, RoughUnderline} from "@khanacademy/khannotations"; // ... <AnimationGroup> Lorem ipsum{" "} <RoughUnderline animation={speedAnimation} roughStyle={testStyle} > dolor sit amet </RoughUnderline> , has solet qualisque ex, an nam tantas{" "} <RoughUnderline animation={speedAnimation} roughStyle={testStyle} > legere dicunt. </RoughUnderline> </AnimationGroup>
Because AnimationGroup uses React Context, RoughUnderline doesn't need to be a direct child of AnimationGroup. For example, this would also work:
import {AnimationGroup, RoughUnderline} from "@khanacademy/khannotations"; // ... const Dolor = () => ( <RoughUnderline animation={speedAnimaiton} roughStyle={testStyle} > dolor sit amet </RoughUnderline> ); // ... <AnimationGroup> <Dolor /> <Dolor /> </AnimationGroup>
Pausing
An AnimationGroup can be paused by setting
paused={true}
. When a group is paused, the group will not advance to the next animation. This could be used, for example, to not render annotations until the user has scrolled to them.Making custom Animate components
You can add your own animated components to an AnimationGroup.
First, the component you want to add to an animated group needs to implement the Animate interface.
Then, you need to pass it an instance of an AnimationGroup. This can be done via the ConnectToAnimationGroup component:
import {ConnectToAnimationGroup} from "@khanacademy/khannotations"; // ... const ConnectedCustomComponent = (props: Props) => ( <ConnectToAnimationGroup> {group => <CustomComponent group={group} {...props} />} </ConnectToAnimationGroup> );


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Visualization of the data collected during a study regarding ducth auctions.
Two data sets were included: A refering to Blossom and B refering to Barbie.
Some questions to consider:
People that buy online and would pay an extra?
Habits of people from 20 to 30?


1
Timing Dictionary
Exercise 4 - Repetition
There are times when we want to repeat the animation for a number of times, this is when 'iterationCount' comes in.
For this exercise, let the animation move 300px from left for 3 times.
Show Solution
new Animation(document.querySelector("#a"), {left: "300px",
top: "400px"}, {iterationCount: 3, duration: 5});


Date
:10th November 2016
Venue
:Seth G S Medical College and KEM Hospital Parel Mumbai
Convener
:Dr. C T Deshmukh
Workshop
Coordinator
Dr. C T Deshmukh
Highlights
:Developing concepts & hands on
learning
Understanding respiratory physiology
Indications of mechanical ventilation
trouble shooting & weaning
Dealing with common scenarios like
severe pneumonias brochiolitis & other
metabolic issues
Hands on experience in conventional
ventilator non invasive ventilator
bubble CPAP & high flow nasal
cannula etc


§15-12 Receipt and disposition of late absentee ballots. For any election all return envelopes containing absentee ballots received by the clerk after the deadline for receipt stated in section 15-9 shall be kept unopened and disposed of pursuant to section 11-154. [L 1975, c 36, pt of §3; am L 1980, c 248, §1(h)]


Mouse Tip.

Pastrami boudin pig, swine filet mignon venison flank chuck.


A Phaser.Tween contains at least one TweenData object. It contains all of the tween data values, such as the starting and ending values, the ease function, interpolation and duration. The Tween acts as a timeline manager for TweenData objects and can contain multiple TweenData objects.


Overview of Relational Databases
- Data in these types of databases are
stored in collections, or tables
- Tables have a number of rows and
columns
- Each row is (often)
represented with a unique key.
Overview of Relational Databases: Keys
Keys (typically called ID’s in the Sierra Database) come in two varieties, and they define the relationship between tables.
Overview of Database Entity-Relationship Model
(ERM View)
- Defines the types of relationships that can exist between entities (tables)
- One-to-One
- One-to-Many
- Many-to-Many
Database Entity-Relationship Model
One-to-One
- A Country can have one (and only one) Capital City
- A Capital City can have one (and only one) Country
Database Entity-Relationship Model
One-to-One
Database Entity-Relationship Model
One-to-Many
- A Mother may have many Children
- A Child has only one (biological) Mother
Database Entity-Relationship Model
One-to-Many
Database Entity-Relationship Model
Many-to-Many
- Authors can write several Books
- Books can be written by several Authors
Database Entity-Relationship Model
Many-to-Many
Relational Databases: Relationships (JOINS)
- Sets of data can be derived from Relational Operators from traditional math sets.
- We'll cover two of the more common JOIN operations
- JOIN (or INNER JOIN)
- LEFT JOIN (or LEFT OUTER JOIN)
Relational Databases: Relationships (JOINS) cont.
JOIN (or INNER JOIN)
- Most common type of join that there is
- Given two sets, A (left) and B (right), performing this join will return a set containing all elements of A that also belong to B.
Relational Databases: Relationships (JOINS) cont.
LEFT JOIN (or LEFT OUTER JOIN)
- Given two sets, A (left) and B (right) performing this join will return a set containing all elements of table A, as well as the elements of A that also belong to B
SQL Overview
- Structured Query Language
- Standardized language that allows a user to interface with a relational database
SQL Overview cont.
- SQL statements are groups of clauses or other statements that define the operation on the database
- Some of the more common statements include the following... (SQL is picky about the order in which these statements appear in so they're presented in the order that they can appear in the statement.)
SQL Overview cont.
-
SELECT
- Retrieves data from tables
- Most commonly used statement
-
UPDATE and SET
- Modifies a set of existing table rows
-
DELETE
- Remove set of existing rows from the table
SQL Overview cont.
-
CREATE
-
Typically used to create a table in the database
-
JOIN / LEFT JOIN / etc
- Performing a join will combine the data with that of another table
-
FROM
- Indicates which table to retrieve data from
SQL Overview cont.
-
WHERE / WHERE IN
-
Include or exclude data based on comparisons
-
GROUP BY
-
Reduces sets into common values
-
HAVING
-
Allows for filtering of the GROUP BY statement
-
LIMIT / OFFSET
-
Returns specific numbers of rows from given starting point
SQL SELECT Statements
- FINALLY SOME EXAMPLES!
- Let us say we wanted to get a list of the bib records that had the lowest IDs in the system. The following query would give us a very basic view of those records.
SQL SELECT Statement cont.
SELECT
r.id, r.record_type_code,
r.record_num, r.creation_date_gmt,
r.deletion_date_gmt, r.num_revisions
FROM
sierra_view.record_metadata AS r
WHERE
r.record_type_code = 'b'
ORDER BY
r.id ASC
LIMIT 5 OFFSET 40;
SQL SELECT Statement cont.
Results...
SQL SELECT Statement cont.
- This is ok, but maybe we also wanted "Title" from the bib record
- We'll use a JOIN!
- Looks like table "bib_record_property" has what we need
SQL SELECT Statement cont.
SELECT
r.id, r.record_type_code,
r.record_num, r.creation_date_gmt,
r.deletion_date_gmt, r.num_revisions,
p.bib_record_id, p.best_title
FROM
sierra_view.record_metadata AS r
JOIN
sierra_view.bib_record_property AS p
ON
p.bib_record_id = r.id
WHERE
r.record_type_code = 'b'
ORDER BY
r.id ASC
SQL SELECT Statement cont.
Results...
SQL SELECT Statement cont.
- This looks better
- But wait, where's bib record number 1000042?
SQL SELECT Statement cont.
- Table, "bib_record_property", has no foreign key for the deleted record and therefore won’t be joined in our results
- We can fix this!
- ... with a LEFT JOIN (or LEFT OUTER JOIN)!

SQL GROUP BY / HAVING and Aggregate Functions
- Reduces sets into common values, or groups results of one or more column together
- Often used along with aggregate functions
SQL GROUP BY / HAVING and Aggregate Functions cont.
- Let us say that we wanted to count the number of patrons where the first name is "Ray"
SQL GROUP BY / HAVING and Aggregate Functions cont.
SELECT
n.first_name,
COUNT(*) AS count
FROM
sierra_view.patron_record_fullname AS n
WHERE
LOWER(n.first_name) = 'ray'
GROUP BY
n.first_name
SQL GROUP BY / HAVING and Aggregate Functions cont.
Results...
SQL GROUP BY / HAVING and Aggregate Functions cont.
- HAVING
- Allows for limiting results of an aggregate function
- Example query: find all location codes having exactly 8 items
SQL GROUP BY / HAVING and Aggregate Functions cont.
SELECT
i.location_code,
count(*)
FROM
sierra_view.item_record AS i
GROUP BY
i.location_code
HAVING
count(*) = 8
SQL GROUP BY / HAVING and Aggregate Functions cont.
Results...
SQL GROUP BY / HAVING and Aggregate Functions cont.
- Another useful aggregate function example
- SUM() a series of numbers from results
- Find patrons record IDs for patrons records that have total fines that are exactly at $10 (the amount that prevents patrons from borrowing)
SQL GROUP BY / HAVING and Aggregate Functions cont.
SELECT
f.patron_record_id
FROM
sierra_view.fine as f
GROUP BY
f.patron_record_id
HAVING
SUM(
(f.item_charge_amt
+ f.processing_fee_amt
+ f.billing_fee_amt)
- f.paid_amt
) = 10.00
SQL GROUP BY / HAVING and Aggregate Functions cont.
Results...
SQL GROUP BY / HAVING and Aggregate Functions cont.
- These are database IDs and not very useful on their own
- What would be more useful is the patron record numbers, so we can use them in a review file!
- !
Subqueries and WHERE IN Statements
- In the example above, we were able to target patrons who had fine of exactly $10.00 to get the patron record IDs. But these are database IDs.
- This doesn’t tell us anything about the patron, or even the patron record number
- One way to get additional data is to use an SQL feature known as the sub-query
Subqueries and WHERE IN Statements cont.
- Subqueries are simply nested queries
- Example Query: Find patrons
record IDs patron record numbers for patrons records that have total fines that are exactly at $10 (the amount that prevents patrons from borrowing)
Subqueries and WHERE IN Statements cont.
- Query part 1: find patron record numbers
SELECT
r.record_type_code
|| r.record_num
|| 'a' AS patron_record_num
FROM
sierra_view.record_metadata AS r
WHERE
r.record_type_code = 'p'
LIMIT 5;
Subqueries and WHERE IN Statements cont.
Results...
Subqueries and WHERE IN Statements cont.
- Query part 2: Find patrons record IDs for patrons records that have total fines that are exactly at $10
- We already wrote this query in the previous example... we can nest it, or treat it as a subquery!
Subqueries and WHERE IN Statements cont.
SELECT
r.record_type_code
|| r.record_num
|| 'a' AS patron_record_num
FROM
sierra_view.record_metadata AS r
WHERE
r.id IN(
SELECT
f.patron_record_id
FROM
sierra_view.fine AS f
GROUP BY
f.patron_record_id
HAVING
SUM(
(f.item_charge_amt
+ f.processing_fee_amt
+ f.billing_fee_amt)
- f.paid_amt
) = 10.00
)
LIMIT 5;
Subqueries and WHERE IN Statements cont.
Results...
OK ... Now What?!

- Importing record numbers into a review file!
- Write scripts and automate reports!
- Create Other Apps!
Importing into Review File cont.
Importing into Review File cont.
Importing into Review File cont.
Write scripts and automate reports
- PostgreSQL is widely supported by many programming languages and their libraries.
- PHP, Perl, Python, Node, R … etc.
- You can use the SQL statement to create a variety of applications and tools that might need access to real-time data from within Sierra.
Write scripts and automate reports cont.
Write scripts and automate reports cont.
- Automated Reports and Statistics
- Duplicate patrons (same barcodes exist in multiple records, or same first name, last name and birth date may indicate a duplicated patron)
- Data Entry errors: malformed data (phone number for example)
- Number of circulations grouped by item type, call number, location code, and circulation location (github link)
- While there are some things you can do with the Create List (Review File) function from within Sierra, there are just some types of searches that are not practical / possible.
- Get a simple count of things that fit a certain search criteria.
- Group items into common shared attributes. Grouping by call number class for example.
- Find bib records where all attached items are suppressed (github link)
Sierra SQL Tips and Tricks


September, 2015
In preparing and submitting its INDC, Ghana is mindful of its international obligations as a Party to the UNFCCC while simultaneously pursuing a national development agenda that seeks to achieve the long-standing objective of becoming a fully-fledged middle-income economy. Ghana’s response to the threats posed to this objective by the impacts of climate change has been to pursue coordinated domestic policy actions that in effect seek to develop a policy framework that integrates adaptation, mitigation and other climate related policies within broader development policies and planning in order to safeguard developmental gains from the impacts of climate change and build a climate resilient economy.
At the multilateral level, Ghana reaffirms its resolve to support global efforts to define a common future that seeks to safeguard the collective interest of all nations by supporting a global agreement that is fair, ambitious and balanced, respects the right of nations to pursue sustainable development, and above all gives equal opportunities to all nations and their citizens, to pursue and realise their future aspirations.
At the milestone 17th Session of the Conference of the Parties (COP) held in Durban, South Africa in December 2011, the Parties decided to “develop a protocol, another legal instrument or an agreed outcome with legal force under the Convention applicable to all Parties” for adoption at the twenty-first session of the COP and for it to come into effect and be implemented from 2020. Parties agreed that their work will address inter alia, mitigation, adaptation, finance, technology development and transfer, transparency of action and support, and capacity building.
At COP 19 in Warsaw, Parties agreed to advance their work by focusing on the elements of the new agreement. The Warsaw Conference was also very significant in that for the first time in Decision 1/CP 19, Parties were invited to “initiate or intensify domestic preparations for their intended nationally determined contributions (INDCs), without prejudice to the legal nature of the contributions”. The decision also requested the Ad Hoc Working Group on the Durban Platform for Enhanced Action to identify by the 20th session of the COP, the information that Parties will provide when putting forward their contributions, without prejudice to the legal nature of the contributions. Ghana holds the view that the INDCs should cover mitigation, adaptation, finance technology, capacity building and transparency and agrees with the common position of Africa that:
Ghana’s INDC builds on other national documents prepared and submitted to the United Nations Framework Convention on Climate Change (UNFCCC) in fulfilment of its obligations under the Convention. These include the National Communications, Biennial Update Reports, Nationally Appropriate Mitigation Actions (NAMAs) and Technology Needs Assessment (TNAs). Ghana sees finance as an essential part of the whole INDC process. The scope of finance from the developed countries must address mitigation, adaptation, and technology transfer and development in developing countries. It should not be solely focused on mitigation.
Ghana as a Party to the UNFCCC and also to the Kyoto Protocol is committed to meeting its commitments in order to contribute its fair share to the attainment of the objective of the Convention. In view of this and in accordance with Decisions 1/CP.19 and 1/CP.20, the Republic of Ghana is pleased to communicate its INDC and associated explanatory note to facilitate the clarity, transparency, and understanding of our contribution.
Based on its national circumstances, Ghana has put forward mitigation and adaptation actions in its INDC. The inclusion of both mitigation and adaptation in the INDC resonate with the medium-term development agenda (Ghana Shared Growth Development Agenda II – GSGDA 2), the anticipated 40-year socio-economic transformational plan and the universal sustainable development goals. In all, 20 mitigation and 11 adaptation programme of actions1 in 7 priority economic sectors are being proposed for implementation in the 10year period (2020-2030). The implementation of the actions are expected to help attain low carbon climate resilience through effective adaptation and greenhouse gas (GHG) emission reduction in the following priority sectors:
These 31 programme of actions will drive the strategic focus of a “10-year post-2020 enhanced climate action plan” that would be developed after Paris. In the 10-year period, Ghana needs USD 22.6 billion in investments from domestic and international public and private sources to finance these actions. USD 6.3 billion is expected to be mobilized from domestic sources whereas the USD 16.3 billion will come from international support.
Ghana’s emission reduction goal is to unconditionally lower its GHG emissions by 15 percent relative to a business-as-usual (BAU) scenario emission of 73.95MtCO2e2 by 2030.
An additional 30 percent emission reduction is attainable on condition that external support is made available to Ghana to cover the full cost of implementing the mitigation action (finance, technology transfer, capacity building). With this external support, a total emission reduction of 45% below the BUA emission levels can be achieved by 2030 (see figure 1).
The following INDC policy actions3 will be implemented to achieve the mitigation goals
| Sectors | INDC Policy Actions | No. of Programme of Actions |
|---|---|---|
| Energy | Scale up renewable energy penetration by 10% by 2030 | 5 |
| Promote clean rural households lighting | 1 | |
| Expand the adoption of market-based cleaner cooking solutions | 2 | |
| Double energy efficiency improvement to 20% in power plants | 1 | |
| Transport | Scale up sustainable mass transportation | 1 |
| AFOLU | Promote Sustainable utilization of forest resources through REDD+ | 5 |
| Waste | Adopt alternative urban solid waste management | 3 |
| Industry | Double energy efficiency improvement to 20% in industrial facilities | 1 |
| Green Cooling Africa Initiative | 1 |
Without prejudice to the outcome of our emission reduction goal, the outlook of Ghana’s emission trajectory for 2020 to 2030 is projected as follows:
| Base year | 2010 |
| Mid-year | 2025 |
| Target year | 2030 |
| Timeframe | Time of implementation of emission reduction programmes is up to 2030 subject to review in 2025. |
| Type of “Target” | Emission reductions from projected emissions resulting from the deviation of BAU emissions for the year 2030. |
| Scale | Economy-wide |
| Basket of gases | Carbon dioxide (CO2), Methane (CH4), and Nitrous Oxide (N2O). Abatement of fluorinated-gases (HFC-22 and HFC-410) from stationery air-conditioners is included. |
| % of emissions covered | 100% of total national GHG emissions. |
| Sectors covered | Priority sectors: energy including transport, industrial process and product use, AFOLU and waste. |
| Baseline scenario | Business as usual emissions4 estimated to be 73.95MtCO2e by 2030 starting from baseline emission of 19.53MtCO2e in 2010. This excludes any future developments in the extractive industry. The baseline scenario includes Ghana’s intentions to explore opportunities using clean coal technology in public electricity generation mix to meet its energy security objectives. |
| Emission reduction scenario | GHG emission projections for 2030 starting in 2010. The unconditional emission reduction goal is based on the implementation of 2 transformational mitigation actions5.
Whereas, the conditional emission reduction goal assumes the implementation of 18 transformational mitigation actions (table 1) over the 10-year (2020-2030) period. |
| Global Warming Potential (GWP) | The carbon dioxide equivalent (CO2e) was calculated using the 100-year global warming potentials (CO2 = 1, CH4 = 21, N2O=310, HFC-22 =1,780 and HFC-410 =2,060) in accordance with the IPCC 2nd Assessment Report. The GWPs were used on the national GHG inventory to establish historical emission trend from 1990 to 2012. |
| Contribution of International Market based mechanisms | Ghana intends to generate compliance grade emission reductions units from actions in the waste and energy sectors and REDD+. Access to market-based mechanisms where these emission reduction units would be fungible and tradable forms an important component of the strategy to mobilize longterm support for the INDCs. These market-based mechanisms must have robust accounting rules and standards, avoid double-counting and ensure environmental integrity. |
| Methodology for estimating emission | Historical emission trends - Historical GHG emissions from 1990 to 2012 were estimated using the 2006 IPCC guidelines. The 2010 baseline GHG emission was derived from the 22-year time series.
Energy sector projections - The BAU and emission reduction scenarios for the energy sector were developed for the sectors using the "Long-range Energy Alternatives Planning System" (LEAP) software. The analysis was done using data from the strategic national energy planning exercise by the Energy Commission and from the Ghana Standard Living Survey by Ghana Statistics Service. Data on sectoral activities, economic demographic and technology penetration were derived from the sources named above. Industrial sector projections - A comprehensive modeling approach was used. The underlying assumptions of BAU and emission scenario were based on the following predictors: population, GDP, urbanization, electrification rate, penetration rate of domestic refrigeration and annual stocks of airconditioners. Waste sector projections - BAU and emission reduction scenarios for the waste sector were generated using IPCC waste model. Projection was limited to methane gas management in engineered landfills. Data on variation in urban population, efficiency of urban waste collection and landfill gas recovery were based on national statistics. AFOLU sector projections - BAU and emission scenarios were estimated based on IPCC AFOLU accounting rules using COMAP6 tool and the Forest Carbon Partnership Facility (FCPF) methodological framework. |
Figure 1: Emission reduction trajectory
The long-term goal of Ghana’s adaptation is to increase climate resilience and decrease vulnerability for enhanced sustainable development. Adaptation under Ghana’s INDC is informed by:
The following priority adaptation policy actions will be implemented in order to achieve Ghana’s INDC adaptation goal.
| Sector | Strategic Area | INDC Policy Actions | No of Programme of Actions |
|---|---|---|---|
| Agriculture and food security | Sustainable land use | Agriculture resilience building in climate vulnerable landscapes | 3 |
| Sustainable forest resource management | Value addition-based utilization of forest resources | 2 | |
| Resilient Infrastructure in built environment | Climate resilient strategic infrastructure | City-wide resilient infrastructure planning | 1 |
| Early warning and disaster prevention | 1 | ||
| Climate change and health | Equitable social development | Managing climate-induced health risk | 2 |
| Water resources | Integrated water resources management | 1 | |
| Gender and the vulnerable | Resilience for Gender and the Vulnerable | 1 |
Some of the priority adaptation policy actions we have presented will yield positive synergies with mitigation policy actions7.
In the 10-year period, Ghana intends to mobilize nearly USD 22.6 billion investment from both domestic and international public and private sources. USD 6.3 billion domestically (28.3% of total investment) will be mobilized nationally whereas the USD 16 billion will come from international support.
Out of the USD 22.6 billion investment, USD 9.81 billion (representing 45 % of the total investment) is needed for mitigation whereas the remaining USD 12.79 billion8 will be required for adaptation.
For mitigation, the USD 9.81 billion is the total investment cost for implementing the 20 transformational mitigation actions over the 10-year period (2020-2030). Out of the USD 9.81 billion, Ghana will mobilize USD 2.02 billion (21% of the total investment cost) to finance the two unconditional INDCs. An additional USD 7.79 billion will be needed to finance the remaining 18 mitigation actions in order to achieve more ambitious emission reductions in the 10 year period.
For Adaptation, Ghana will mobilize USD 4.21 billion (34%) at the national level. The remaining USD 8.29 billion is the international contribution Ghana is looking for in order to meet the cost of implementing its adaptation actions.
| No | Sources | Indicative Amounts (Billion) - ($) | % of total investment |
|---|---|---|---|
| Domestic sources | |||
| 1 | National Budget | 1.4 | 6.2 |
| 2 | Corporate Social Responsibility | 1.7 | 7.5 |
| 3 | Commercial facilities | 3.2 | 14.2 |
| International sources | |||
| 3 | Green climate fund | 5.0 | 22.1 |
| 4 | Other multilateral funds | 1.1 | 4.9 |
| 5 | Bilateral agreements | 2.8 | 12.4 |
| 6 | Private capital investment | 3.8 | 16.8 |
| 7 | International carbon market | 3.6 | 15.9 |
| Total | 22.6 | 100 |
Without the requisite technology, the technical capacity and favorable conditions that stimulate innovation, Ghana will not have the capability to fully implement its INDC. In this regard, Ghana will be looking for international partnerships to take advantage of the opportunities for technology development and transfer and continuous up-skilling especially in the priority INDC sectors.
Ghana recognizes that an MRV system is the cornerstone to ensure the successful implementation of its INDC mitigation and adaptation actions.
Ghana’s MRV system for the INDC will be an integral part of the existing national development monitoring and evaluation structures which incorporates sector-based periodic information review through Annual Progress Report (APR) system. The MRV for the INDC will build on the existing APR system by enhancing the technical functionalities and with proper institutional coordination. This will bring about transparency and accountability in the implementation of Ghana’s INDC actions.
The MRV system will be deployed to track progress towards achieving INDC goals as well as any modifications in the priority policy actions that will be implemented to attain the INDC goals that have been put forward.
Ghana is of the view that the mitigation and adaptation actions in the INDC it has put forward represents a reasonable level of responsibility it can take as its share of the global effort taking into account its socio-economic circumstances. In this regard, Ghana considers its INDC to be fair and ambitious for 4 main reasons:
Ghana’s INDC was prepared through a comprehensive and participatory process with highlevel cabinet approval.
The INDC is anchored in the anticipated 40-year long-term development, the GSGDA II, National Climate Change Policy as well as the Low Carbon Development Strategy. Many national policies, laws and regulation will support implementation in the first 10-year period and beyond with the possibility of mid-term review in 2025.
The proposed measures to achieve the INDC goal will build on existing measures and strategies. The existing legal frameworks will have to be revised accordingly. These revisions are subject to approval by Ghana’s Parliament. Details of the national policies and measures that will support the implementation of the INDC are presented in Annex 1 and Annex 2.
| INDC Policy Actions | Programme of Action | Supporting national policy & Status measures | Investment Needs (mil $) | Co-benefits | |
|---|---|---|---|---|---|
| Scale up renewable energy penetration by 10% by 2030 | Increase small-medium hydro installed capacity up to 150-300MW |
|
Conditional | 2,214 |
|
| Attain utility scale wind power capacity up to 50-150MW | |||||
| Attain utility scale solar electricity installed capacity up to 150-250MW | |||||
| Establish solar 55 mini-grids with an average capacity of 100kW which translates to 10MW | |||||
| Scale up the 200,000 solar home systems for lighting in urban and selected nonelectrified rural households | |||||
| Promote clean rural households lighting | Increase solar lantern replacement in rural non-electrified households to 2 million. |
|
300 |
|
|
| Expand the adoption of market-based cleaner cooking solutions | Scale up adoption of LPG use from 5.5% to 50% peri-urban and rural households up to 2030. | 0.6 | |||
| Scale up access and adoption of 2 million efficient cook stoves up to 2030 | 50 | ||||
| Double energy efficiency improvement to 20% in power plants | Scale up 120 MSCF12 natural gas replacement of light crude oil for electricity generation in thermal plants. |
|
Unconditional | 1,000 |
|
| Scale up Sustainable mass transportation | Expansion of inter and intra city mass transportation modes (Rail and bus transit system) in 4 cities 13 | National Transport Policy | Conditional | 1,201 |
|
| Promote Sustainable utilization of forest resources through REDD+ | Continue 10,000ha annual reforestation/afforestation of degraded lands | National Forest and Wildlife Policy.
National plantation development strategy |
Unconditional | 1,050 |
|
| Double 10,000ha annual reforestation/afforestation of degraded lands translating to 20,000ha on annual basis. | Conditional | 1,750 | |||
| Support enhancement of forest carbon stocks through 5,000ha per annum enrichment planting and enforcement of timber felling standards. | National Forest and Wildlife Policy.
Timber resource utilization regulation |
Conditional | 60 |
|
|
| 45%14 emission reduction through resultbased emission reduction programme in cocoa landscape. | National Forest and Wildlife Policy
National REDD+ strategy |
Conditional | 2,067 |
|
|
| Wildfire management in the transition and savannah dry lands in Ghana | Conditional | 26 |
|
||
| Adopt alternative urban solid waste management | Improve effectiveness of urban solid collection from 70% to 90% by 2030 and disposed all to an engineered landfills for phase-out methane recovery from 40% in
2025 to 65% by 2030 |
|
Conditional | 15 |
|
| Scale up 200 institutional biogas in senior high schools and prisons nation wide | 5 | ||||
| Double the current waste to compost installed capacity of 180,000tonne/annum by 203015. | 60 | ||||
| Double energy efficiency improvement to 20% in industrial facilities | Scaling up of installation of power factor correction devices in 1,000 commercial and industrial facilities (capacitor banks). |
|
Conditional | 8.4 |
|
| Green Cooling Africa Initiative | Abatement of fluorinated-gases (HFC-22 and HFC-410) from stationery airconditioners |
|
Conditional | 0.3 |
|
| INDC Policy Actions | Programme of Actions | Supporting national policy & measures | Investment Needs (mil $) | Status |
|---|---|---|---|---|
| Agriculture resilience building in climate vulnerable landscapes | Modified community-based conservation agriculture adopted in 43 administrative districts | Food and Agriculture Sector Development Policy Ghana’s Medium-term Agriculture sector investment plan Ghana Agriculture Investment Programme |
799 | Unconditional |
| Scale up penetration of climate smart technologies to increase livestock and fisheries productivity by 10%. | 1,119 | Unconditional | ||
| Promote innovations in post-harvest storage and food processing and forest products in 43 administrative districts. | 1,270 | Conditional | ||
| Value addition-based utilization of forest resources | Governance reform for utilization of forest resources for sustainable energy use and biodiversity business. | National bio-energy strategy. Sustainable energy for all action plan National Forest and Wildlife Policy |
767 | Unconditional |
| Manage 413,000ha fragile, ecologically sensitive and culturally significant sites in 22 administrative district in the forest and savannah areas. | 512 | Unconditional | ||
| City-wide resilient infrastructure planning | Building standards for strategic infrastructure in housing, transport, coastal, waste management, telecommunication and energy) adopted in 10 urban administrative regions. | Local Government Act 462.
National Building Regulation |
3,558 | Conditional |
| Early warning and disaster prevention | Expand and modernize the current 22 synoptic stations based on needs assessment, and increase the number to 50 stations for efficient weather information management | Ghana Meteorological Agency Act 682. | 403 | Conditional |
| Managing climateinduced health risks | Strengthen climate related disease surveillance in vulnerable communities in 3 Districts. | National Health Policy | 919 | Conditional |
| Adopt climate change informed health information systems including traditional knowledge on health risk management. | 492 | Unconditional | ||
| Integrated water resources management | Strengthen equitable distribution and access to water for 20% of the population living in climate change risk communities. | National Water Policy | 1,919 | Unconditional |
| Resilience for gender and the vulnerable | Implementation of community led adaptation and livelihood diversification for vulnerable groups | National climate change policy | 1,023 | Unconditional |
“Programme of actions” are specific actions Ghana will implement in order to achieve the broad objectives set out in the “Policy actions”↩
Million tonnes carbon dioxide equivalent↩
Refer to the Annex 1 for the detail description on mitigation Policy actions and the Programme of actions that come with it.↩
BAU is subject to revision before 2020.↩
Ghana is mobilizing $7.2billion commercial facility to develop Sankofa-Gye Nyame transformational gas project in partnership Vitol and ENI. Ghana takes note of this action as part of its unconditional contribution. Detail estimates of both GHG impacts and co-benefits will be provided before 2020.↩
Comprehensive mitigation assessment process, 1999. Ernest Orlando Lawrence Berkeley National Laboratory, United States of America.↩
Refer to the Annex 2 for the detail description on adaptation policy actions↩
The cost of adaptation is indicative. Revised cost from financial analysis will be presented before 2020↩
CAIT 3.0 WIR’s climate data explorer (http://cait.wri.org).↩
Emissions included AFOLU sector.↩
Mitigation actions were selected based on the following key considerations. (1) Government is commitment (policy and financial wise) to get mitigation actions implemented and alignment with government priorities; (2) Enough baseline data exist with clear set targets that can be used for the GHG emissions modeling and assessment of co-benefits; (3) It is possible to estimate investment requirements( estimate pragmatic and reasonable budget) with clear sources of funding; (4) It is possible to estimate sustainable development benefits of the actions; (5) Technology and know-how are available to be deployed in the Ghanaian market; (6) Mitigation actions are already part of the list of 55 NAMAs submitted to the UNFCCC in 2010 and (7) There are existing analytical tools that can be adapted to suit Ghana’s unique national circumstance.↩
Million standard cubic feet↩
This is a flagship transformational change INDC action but it is not included in the mitigation actions. Detail analysis on the scope and scale of the action will be provided before 2020.↩
Provisional targets. Forest reference level is limited to avoided deforestation. New estimates will be submitted before 2020.↩
Detail analysis on the scope and scale will be provided before 2020.↩


Jim Ratcliffe wins Jaguar Land Rover challenge over Defender shape
by Edward ThicknesseJaguar Land Rover’s (JLR) efforts to thwart billionaire businessman Sir Jim Ratcliffe’s plans to model the shape of his new 4×4 on JLR’s Defender have failed.
The Sunday Times today reported that Ratcliffe, the founder of Ineos, had won his challenge to JLR’s attempt to trademark the Defender’s shape.
Read more: Ineos’ Teeside chemicals factory at risk of closure
Ratcliffe, who is planning to build the Grenadier in Bridgend in south Wales, has been locked in a long-term battle with JLR over the Defender’s design, on which he has had designs for several years.
In 2016, Ineos said that it would treat the Defender’s “much-loved shape like a listed building”.
The project is designed to fill the gap left by JLR’s car, which ceased production in 2016 because the model no longer met modern environmental and safety standards.
The company is due to release a replacement model, which is being manufactured in Slovakia with the latest technology, in early 2020.
Initially, Ratcliffe tried to buy manufacturing equipment for the Defender from JLR, but was rejected.
The company, which is a subsidiary of Indian automotive firm Tata Motors, then sought to trademark six variants, from the original 1948 model to the 2016 edition.
However, last month the Intellectual Property Office decided that the Defender’s shape was not different enough from that of other 4×4 models for a trademark to be justified.
JLR is fighting the verdict in the High Court. The company said: “The Defender is an iconic vehicle which is part of Jaguar Land Rover’s past, present and future.
“Its unique shape is recognised around the world, with the heritage shape being protected in other key markets.”
JLR is fiercely protective of its Defender. In 2016, it prevented a Canadian company from using the name ‘Defender’ for an all-terrain vehicle.
Read more: Jim Ratcliffe names new £600m 4×4 after west London pub
Ratcliffe, who is third on the Sunday Times rich list with £18.2bn, agreed a deal in September to manufacture the new car at Brocastle in Wales, near to a Ford plant which is due to close in 2020 with the loss of 1,700 jobs.
It is hoped that the new project will provide up to 500 jobs.
Main image credit: Getty


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