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| And he shewed me a pure river of water of life, clear as crystal, proceeding out of the throne of God and of the Lamb.
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| ᎠᎩᎾᏄᎪᏫᏎᎸᎩᏃ ᎤᏁᎬ ᎤᏪᏴ ᎠᎹ ᎬᏂᏛ ᎠᏓᏁᎯ, ᏗᎬᏩᎸᏌᏛ ᎨᏒᎩ, ᏓᎦᎾᏄᎪᎬᎩ ᎦᏍᎩᎸ ᎤᏁᎳᏅᎯ ᎠᎴ ᎤᏃᏕᎾ ᎠᎩᎾ ᎤᎾᏤᎵᎦ.
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A-gi-na-nu-go-wi-se-lv-gi-no u-ne-gv u-we-yv a-ma gv-ni-dv a-da-ne-hi, di-gv-wa-lv-sa-dv ge-sv-gi, da-ga-na-nu-go-gv-gi ga-s-gi-lv U-ne-la-nv-hi a-le U-no-de-na a-gi-na u-na-tse-li-ga.
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| In the midst of the street of it, and on either side of the river, was there the tree of life, which bare twelve manner of fruits, and yielded her fruit every month: and the leaves of the tree were for the healing of the nations.
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| ᎠᏰᎵᏃ ᎤᏜᏓᏅᏛ ᎾᎿᎭᎡᏓᏍᏗᏱ, ᎠᎴ ᎢᏧᎳᏗᏢ ᎤᏪᏴᎢ, ᏡᎬᎩ ᎬᏂᏛ ᎠᏓᏁᎯ ᏡᎬᎢ, ᎾᏍᎩ ᏔᎳᏚ ᎢᏳᏓᎴᎩ ᎠᏓᏛᏍᎬᎩ ᎠᎴ ᏂᏓᏦᎳᏂᏒ ᎬᎾᏍᎬᎩ ᎤᏓᏔᏅᎯ; ᎤᎳᏍᏚᏒᏃ ᎤᎾᏅᏬᏗ ᎨᏒᎩ ᏧᎾᏓᎴᏅᏛ ᏴᏫ.
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A-ye-li-no u-dla-nv-dv na-hna e-da-s-di-yi, a-le i-tsu-la-di-tlv u-we-yv-i, tlu-gv-gi gv-ni-dv a-da-ne-hi tlu-gv-i, na-s-gi ta-la-du i-yu-da-le-gi a-da-dv-s-gv-gi a-le ni-da-tso-la-ni-sv gv-na-s-gv-gi u-da-ta-nv-hi; u-la-s-du-sv-no u-na-nv-wo-di ge-sv-gi tsu-na-da-le-nv-dv yv-wi.
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| And there shall be no more curse: but the throne of God and of the Lamb shall be in it; and his servants shall serve him:
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| ᎥᏝ ᎠᎴ ᎿᎭᏉ ᏰᎮᏍᏗ ᎤᏐᏅᎢ; ᎤᏁᎳᏅᎯᏍᎩᏂ ᎤᏤᎵ ᎠᎴ ᎤᏃᏕᎾ ᎠᎩᎾ ᎤᏤᎵ ᎦᏍᎩᎶ ᎾᎿᎭᎦᏍᎩᎴᏍᏗ; ᏧᏤᎵᏃ ᏧᏅᏏᏓᏍᏗ ᎬᏩᏓᏙᎵᏍᏓᏁᎮᏍᏗ;
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V-tla a-le hna-quo ye-he-s-di u-so-nv-i; U-ne-la-nv-hi-s-gi-ni u-tse-li a-le U-no-de-na a-gi-na u-tse-li ga-s-gi-lo na-hna ga-s-gi-le-s-di; tsu-tse-li-no tsu-nv-si-da-s-di gv-wa-da-do-li-s-da-ne-he-s-di;
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| And they shall see his face; and his name shall be in their foreheads.
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| ᎤᎧᏛᏃ ᎠᏂᎪᏩᏘᏍᎨᏍᏗ; ᏚᏙᎥᏃ ᎾᏍᎩ ᏗᏂᎬᏓᎨᏂ ᏕᎪᏪᎴᏍᏗ.
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U-ka-dv-no a-ni-go-wa-ti-s-ge-s-di; du-do-v-no na-s-gi di-ni-gv-da-ge-ni de-go-we-le-s-di.
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| And there shall be no night there; and they need no candle, neither light of the sun; for the Lord God giveth them light: and they shall reign for ever and ever.
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| ᎥᏝ ᎠᎴ ᏒᏃᏱ ᏱᎨᏎᏍᏗ ᎾᎿᎭᏂ, ᎥᏝ ᎠᎴ ᏳᎾᏚᎵ ᎠᏨᏍᏙᏗ, ᎠᎴ ᏅᏙ ᎢᎦᎡᎯ ᎢᎦ ᎤᏘᏍᏛᎢ; ᎤᎬᏫᏳᎯᏰᏃ ᎤᏁᎳᏅᎯ ᎢᎦ ᏓᏘᏍᏓᏁᎭ, ᎤᏂᎬᏫᏳᎯᏃ ᎨᏎᏍᏗ ᎤᎵᏍᏆᏗᏍᏗ ᏂᎨᏒᎾ.
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V-tla a-le sv-no-yi yi-ge-se-s-di na-hna-ni, v-tla a-le yu-na-du-li a-tsv-s-do-di, a-le nv-do i-ga-e-hi i-ga u-ti-s-dv-i; U-gv-wi-yu-hi-ye-no U-ne-la-nv-hi i-ga da-ti-s-da-ne-ha, u-ni-gv-wi-yu-hi-no ge-se-s-di u-li-s-qua-di-s-di ni-ge-sv-na.
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| And he said unto me, These sayings are faithful and true: and the Lord God of the holy prophets sent his angel to shew unto his servants the things which must shortly be done.
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| ᎯᎠᏃ ᎾᎩᏪᏎᎸᎩ, ᎯᎠ ᎾᏍᎩ ᎧᏃᎮᏛ ᎬᏜᏓᏏᏛᎡᏗ ᏂᎨᏒᎾ ᎠᎴ ᎤᏙᎯᏳᎯᏯ. ᎤᎬᏫᏳᎯᏃ ᎤᏁᎳᏅᎯ ᎨᏥᎸᏉᏗ ᎠᎾᏙᎴᎰᏍᎩ ᎤᎾᏤᎵᎦ ᏧᏅᏎ ᎤᏤᎵ ᏗᎧᎿᎭᏩᏗᏙᎯ ᎬᏂᎨᏒ ᎢᏧᏩᏁᏗᏱ ᏧᏤᎵ ᏧᏅᏏᏓᏍᏗ, ᎾᏍᎩ ᏧᏓᎴᏅᏛ ᏂᎪᎯᎸᎾᏉ ᎢᏳᎵᏍᏙᏗ ᎨᏒᎢ.
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Hi-a-no na-gi-we-se-lv-gi, Hi-a na-s-gi ka-no-he-dv gv-dla-si-dv-e-di ni-ge-sv-na a-le u-do-hi-yu-hi-ya. U-gv-wi-yu-hi-no U-ne-la-nv-hi ge-tsi-lv-quo-di a-na-do-le-ho-s-gi u-na-tse-li-ga tsu-nv-se u-tse-li di-ka-hna-wa-di-do-hi gv-ni-ge-sv i-tsu-wa-ne-di-yi tsu-tse-li tsu-nv-si-da-s-di, na-s-gi tsu-da-le-nv-dv ni-go-hi-lv-na-quo i-yu-li-s-do-di ge-sv-i.
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| Behold, I come quickly: blessed is he that keepeth the sayings of the prophecy of this book.
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| ᎬᏂᏳᏉ ᏞᎩᏳ ᏓᏥᎷᏥ; ᎣᏏᏳ ᎢᏳᎵᏍᏓᏁᏗ ᎠᏍᏆᏂᎪᏗᏍᎩ ᎧᏃᎮᏛ ᎠᏙᎴᎰᏒᎯ ᎠᏂ ᎪᏪᎵᎯ ᎪᏪᎸᎢ.
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Gv-ni-yu-quo tle-gi-yu da-tsi-lu-tsi; o-si-yu i-yu-li-s-da-ne-di a-s-qua-ni-go-di-s-gi ka-no-he-dv a-do-le-ho-sv-hi a-ni go-we-li-hi go-we-lv-i.
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| And I John saw these things, and heard them. And when I had heard and seen, I fell down to worship before the feet of the angel which shewed me these things.
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| ᎠᎴ ᎠᏴ ᏣᏂ ᎠᎩᎪᎲᎩ ᎯᎠ ᎾᏍᎩ ᏧᏓᎴᏅᏛ, ᎠᎴ ᎠᏆᏛᎦᏅᎩ. ᎠᏆᏛᎦᏅᏃ ᎠᎴ ᎠᎩᎪᎲ, ᎡᎳᏗ ᎠᏆᏓᏅᏅᎩ ᏓᎦᏓᏙᎵᏍᏔᏂᏒᎩ ᏚᎳᏍᎬ ᎾᏍᎩ Ꮎ ᏗᎧᎿᎭᏩᏗᏙᎯ ᎯᎠ ᎾᏍᎩ ᏧᏓᎴᏅᏛ ᎠᎩᎾᏄᎪᏫᏎᎸᎯ.
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A-le a-yv Tsa-ni a-gi-go-hv-gi hi-a na-s-gi tsu-da-le-nv-dv, a-le a-qua-dv-ga-nv-gi. A-qua-dv-ga-nv-no a-le a-gi-go-hv, e-la-di a-qua-da-nv-nv-gi da-ga-da-do-li-s-ta-ni-sv-gi du-la-s-gv na-s-gi na di-ka-hna-wa-di-do-hi hi-a na-s-gi tsu-da-le-nv-dv a-gi-na-nu-go-wi-se-lv-hi.
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| Then saith he unto me, See thou do it not: for I am thy fellowservant, and of thy brethren the prophets, and of them which keep the sayings of this book: worship God.
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| ᎯᎠᏃ ᎾᎩᏪᏎᎸᎩ, ᎥᏞᏍᏗ. ᎠᏴᏰᏃ ᎤᏠᏱᏉ ᎡᎩᏂᏅᏏᏓᏍᏗ, ᎠᎴ ᎠᏴ ᎾᏍᎩ ᎠᎾᏙᎴᎰᏍᎩ ᏥᎨᏒ ᎢᏣᎵᏅᏟ, ᎠᎴ ᎾᏍᎩ ᎠᏂᏍᏆᏂᎪᏗᏍᎩ ᎧᏃᎮᏛ ᎠᏂ ᎪᏪᎵᎯ ᎪᏪᎸᎢ; ᎤᏁᎳᏅᎯ ᎯᏯᏓᏙᎵᏍᏓᏏ.
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Hi-a-no na-gi-we-se-lv-gi, V-tle-s-di. A-yv-ye-no u-tlo-yi-quo e-gi-ni-nv-si-da-s-di, a-le a-yv na-s-gi a-na-do-le-ho-s-gi tsi-ge-sv i-tsa-li-nv-tli, a-le na-s-gi a-ni-s-qua-ni-go-di-s-gi ka-no-he-dv a-ni go-we-li-hi go-we-lv-i; u-ne-la-nv-hi hi-ya-da-do-li-s-da-si.
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| And he saith unto me, Seal not the sayings of the prophecy of this book: for the time is at hand.
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| ᎯᎠᏃ ᎾᎩᏪᏎᎸᎩ, ᏞᏍᏗ ᏣᏍᏚᏅᎩ ᎧᏃᎮᏛ ᎠᏙᎴᎰᏒᎯ ᎠᏂ ᎯᎠ ᎪᏪᎵᎯ; ᎤᏍᏆᎸᎯᏗᏰᏃ.
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Hi-a-no na-gi-we-se-lv-gi, Tle-s-di tsa-s-du-nv-gi ka-no-he-dv a-do-le-ho-sv-hi a-ni hi-a go-we-li-hi; u-s-qua-lv-hi-di-ye-no.
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| He that is unjust, let him be unjust still: and he which is filthy, let him be filthy still: and he that is righteous, let him be righteous still: and he that is holy, let him be holy still.
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| ᎩᎶ ᎠᏍᎦᎾ ᎨᏎᏍᏗ, ᎠᏏᏉ ᎠᏍᎦᎾ ᎨᏎᏍᏗ; ᎩᎶᏃ ᎦᏓᎭ ᎨᏎᏍᏗ, ᎠᏏᏉ ᎦᏓᎭ ᎨᏎᏍᏗ; ᎩᎶᏃ ᎣᏏᏳ ᎢᏯᏛᏁᎯ ᎨᏎᏍᏗ, ᎠᏏᏉ ᎣᏏᏳ ᎢᏯᏛᏁᎯ ᎨᏎᏍᏗ; ᎩᎶᏃ ᎠᏍᎦᎾ ᏂᎨᏒᎾ ᎨᏎᏍᏗ, ᎠᏏᏉ ᎠᏍᎦᎾ ᏂᎨᏒᎾ ᎨᏎᏍᏗ.
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Gi-lo a-s-ga-na ge-se-s-di, a-si-quo a-s-ga-na ge-se-s-di; gi-lo-no ga-da-ha ge-se-s-di, a-si-quo ga-da-ha ge-se-s-di; gi-lo-no o-si-yu i-ya-dv-ne-hi ge-se-s-di, a-si-quo o-si-yu i-ya-dv-ne-hi ge-se-s-di; gi-lo-no a-s-ga-na ni-ge-sv-na ge-se-s-di, a-si-quo a-s-ga-na ni-ge-sv-na ge-se-s-di.
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| And, behold, I come quickly; and my reward is with me, to give every man according as his work shall be.
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| ᎠᎴ ᎬᏂᏳᏉ ᏞᎩᏳ ᏓᏥᎷᏥ; ᎠᎴ ᎠᏆᎫᏴᏗ ᏥᏰᎮᏍᏗ, ᎦᏥᏁᏗᏱ ᎾᏂᎥ ᎠᏂᏏᏴᏫᎭ ᎾᏍᎩᏯ ᏄᏍᏛ ᏚᏂᎸᏫᏍᏓᏁᎸᎢ.
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A-le gv-ni-yu-quo tle-gi-yu da-tsi-lu-tsi; a-le a-qua-gu-yv-di tsi-ye-he-s-di, ga-tsi-ne-di-yi na-ni-v a-ni-si-yv-wi-ha na-s-gi-ya nu-s-dv du-ni-lv-wi-s-da-ne-lv-i.
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| I am Alpha and Omega, the beginning and the end, the first and the last.
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| ᎠᏴ ᎡᎵᏆ ᎠᎴ ᎣᎻᎦ, ᏗᏓᎴᏅᏗᏍᎩ ᎠᎴ ᎠᏍᏆᏗᏍᏗᏍᎩ, ᎠᎴ ᎢᎬᏱᏱ ᎠᎴ ᎤᎵᏍᏆᎸᎢ.
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A-yv E-li-qua a-le O-mi-ga, di-da-le-nv-di-s-gi a-le a-s-qua-di-s-di-s-gi, a-le i-gv-yi-yi a-le u-li-s-qua-lv-i.
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| Blessed are they that do his commandments, that they may have right to the tree of life, and may enter in through the gates into the city.
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| ᎣᏏᏳ ᎢᏳᎾᎵᏍᏓᏁᏗ ᎾᏍᎩ Ꮎ ᎤᏤᎵ ᏗᎧᎿᎭᏩᏛᏍᏗ ᏂᎦᏪᏍᎬ ᎢᏯᎾᏛᏁᎯ, ᎾᏍᎩ ᎬᏂᏛ ᏡᎬ ᎤᎾᏤᎵ ᎢᏳᎵᏍᏙᏗᏱ, ᎠᎴ ᏫᎬᏩᏂᏴᏍᏗ ᎢᏳᎵᏍᏙᏗᏱ ᎦᎶᎯᏍᏗᏱ ᎪᏢᏒ ᎦᏚᎲᎢ.
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O-si-yu i-yu-na-li-s-da-ne-di na-s-gi na u-tse-li di-ka-hna-wa-dv-s-di ni-ga-we-s-gv i-ya-na-dv-ne-hi, na-s-gi gv-ni-dv tlu-gv u-na-tse-li i-yu-li-s-do-di-yi, a-le wi-gv-wa-ni-yv-s-di i-yu-li-s-do-di-yi ga-lo-hi-s-di-yi go-tlv-sv ga-du-hv-i.
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| For without are dogs, and sorcerers, and whoremongers, and murderers, and idolaters, and whosoever loveth and maketh a lie.
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| ᏙᏱᏗᏢᏰᏃ ᎠᏁᏙᎭ ᎩᎶ, ᎠᎴ ᏗᎾᏙᏂᏍᎩ, ᎠᎴ ᎤᏂᏁᎫᏥᏛ, ᎠᎴ ᏗᎾᏓᎯᎯ, ᎠᎴ ᎤᏁᎳᏅᎯ ᏗᏰᎸᎯ ᏗᎾᏓᏙᎵᏍᏓᏁᎯ, ᎠᎴ ᏂᎦᏗᏳ ᎣᏏᏳ ᎤᏂᏰᎸᎯ ᎦᏰᎪᎩ ᎨᏒ, ᎠᎴ ᎦᏰᎪᎩ ᎠᏃᏢᏍᎩ.
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Do-yi-di-tlv-ye-no a-ne-do-ha gi-lo, a-le di-na-do-ni-s-gi, a-le u-ni-ne-gu-tsi-dv, a-le di-na-da-hi-hi, a-le U-ne-la-nv-hi di-ye-lv-hi di-na-da-do-li-s-da-ne-hi, a-le ni-ga-di-yu o-si-yu u-ni-ye-lv-hi ga-ye-go-gi ge-sv, a-le ga-ye-go-gi a-no-tlv-s-gi.
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| I Jesus have sent mine angel to testify unto you these things in the churches. I am the root and the offspring of David, and the bright and morning star.
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| ᎠᏴ ᏥᏌ ᏗᏥᏅᏒ ᎠᏆᏤᎵ ᏗᎧᎿᎭᏩᏗᏙᎯ ᎬᏂᎨᏒ ᎢᏨᏁᏗᏱ ᎯᎠ ᎾᏍᎩ ᏧᏓᎴᏅᏛ ᏧᎾᏁᎶᏗ ᏚᎾᏓᏡᏩᏗᏒᎢ. ᎠᏴ ᏕᏫ ᎤᏪᏥ ᎠᎴ ᎾᏍᎩ ᎤᏁᏢᏔᏅᏛ ᎨᏒᎢ, ᎠᎴ ᏃᏈᏏ ᏧᎦᏙᏴᎢᏍᏗ ᎠᎴ ᏑᎾᎴ ᎡᎯ.
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A-yv Tsi-sa di-tsi-nv-sv a-qua-tse-li di-ka-hna-wa-di-do-hi gv-ni-ge-sv i-tsv-ne-di-yi hi-a na-s-gi tsu-da-le-nv-dv tsu-na-ne-lo-di du-na-da-tlu-wa-di-sv-i. A-yv De-wi u-we-tsi a-le na-s-gi u-ne-tlv-ta-nv-dv ge-sv-i, a-le no-qui-si tsu-ga-do-yv-i-s-di a-le su-na-le e-hi.
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| And the Spirit and the bride say, Come. And let him that heareth say, Come. And let him that is athirst come. And whosoever will, let him take the water of life freely.
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| ᎠᏓᏅᏙᏃ ᎠᎴ ᎠᏥᏰᎯ ᎡᎮᎾ ᎠᎾᏗᎭ. ᎠᎴ ᎠᏛᎩᏍᎩ ᎡᎮᎾ ᎠᏗᏍᎨᏍᏗ. ᎠᎴ ᎩᎶ ᎤᏔᏕᎩᏍᎩ ᏫᎦᎷᎩ; ᎠᎴ ᎩᎶ ᎤᏚᎵᏍᎨᏍᏗ ᎦᏁᎩᏍᎨᏍᏗ ᎠᎹ ᎬᏂᏛ ᎠᏓᏁᎯ ᏧᎬᏩᎶᏗ ᏂᎨᏒᎾ.
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A-da-nv-do-no a-le a-tsi-ye-hi E-he-na a-na-di-ha. A-le a-dv-gi-s-gi E-he-na a-di-s-ge-s-di. A-le gi-lo u-ta-de-gi-s-gi wi-ga-lu-gi; a-le gi-lo u-du-li-s-ge-s-di ga-ne-gi-s-ge-s-di a-ma gv-ni-dv a-da-ne-hi tsu-gv-wa-lo-di ni-ge-sv-na.
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| For I testify unto every man that heareth the words of the prophecy of this book, If any man shall add unto these things, God shall add unto him the plagues that are written in this book:
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| ᎬᏂᎨᏒᏰᏃ ᏂᎦᏥᏴᏁᎭ ᎾᏂᎥᏉ ᎩᎶ ᎠᎾᏛᎩᏍᎩ ᎧᏃᎮᏛ ᎠᏙᎴᎰᏒᎯ ᎠᏂ ᎪᏪᎵᎯ ᏥᎪᏪᎳ, ᎾᏍᎩ ᎢᏳᏃ ᎩᎶ ᎢᎧᏁᏉᏍᎨᏍᏗ ᎯᎠ ᎾᏍᎩ ᏧᏓᎴᏅᏛ ᎠᏂ ᎪᏪᎵᎯ ᏥᎪᏪᎳ, ᎤᏁᎳᏅᎯ ᎠᏎ ᎤᏁᏉᎡᏗ ᎨᏎᏍᏗ ᎤᏕᏯᏙᏗ ᎨᏒ ᎠᏂ ᎪᏪᎵᎯ ᏥᎪᏪᎳ;
|
|---|
Gv-ni-ge-sv-ye-no ni-ga-tsi-yv-ne-ha na-ni-v-quo gi-lo a-na-dv-gi-s-gi ka-no-he-dv a-do-le-ho-sv-hi a-ni go-we-li-hi tsi-go-we-la, na-s-gi i-yu-no gi-lo i-ka-ne-quo-s-ge-s-di hi-a na-s-gi tsu-da-le-nv-dv a-ni go-we-li-hi tsi-go-we-la, U-ne-la-nv-hi a-se u-ne-quo-e-di ge-se-s-di u-de-ya-do-di ge-sv a-ni go-we-li-hi tsi-go-we-la;
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| And if any man shall take away from the words of the book of this prophecy, God shall take away his part out of the book of life, and out of the holy city, and from the things which are written in this book.
|
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| ᎢᏳ ᎠᎴ ᎩᎶ ᎢᎪᏣᎴᏍᎨᏍᏗ ᎢᎦᏛ ᎧᏃᎮᏛ ᎪᏪᎵᎯ ᎯᎠ ᎾᏍᎩ ᎠᏙᎴᎰᏒᎯ ᎨᏒᎢ, ᎤᏁᎳᏅᎯ ᎠᏎ ᏓᏳᏬᏣᎳᎡᎵ ᎤᏤᎵ ᎨᏒ ᎬᏂᏛ ᎠᏓᏁᎯ ᎪᏪᎵᎯ, ᎠᎴ ᎦᎸᏉᏗᏳ ᎦᏚᎲᎢ, ᎠᎴ ᏧᏓᎴᏅᏛ ᎪᏪᎸ ᎠᏂ ᎪᏪᎵᎯ.
|
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I-yu a-le gi-lo i-go-tsa-le-s-ge-s-di i-ga-dv ka-no-he-dv go-we-li-hi hi-a na-s-gi a-do-le-ho-sv-hi ge-sv-i, U-ne-la-nv-hi a-se da-yu-wo-tsa-la-e-li u-tse-li ge-sv gv-ni-dv a-da-ne-hi go-we-li-hi, a-le ga-lv-quo-di-yu ga-du-hv-i, a-le tsu-da-le-nv-dv go-we-lv a-ni go-we-li-hi.
|
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|
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| He which testifieth these things saith, Surely I come quickly. Amen. Even so, come, Lord Jesus.
|
|---|
| ᎯᎠ ᎾᏍᎩ ᏧᏓᎴᏅᏛ ᎬᏂᎨᏒ ᎢᎬᏁᎯ, ᎯᎠ ᏂᎦᏪᎭ, ᎤᏙᎯᏳᎯᏯ ᏞᎩᏳ ᏓᏥᎷᏥ. ᎡᎺᏅ; ᎰᏩᏉ; ᏞᎩᏳ ᎯᎷᎩ ᏣᎬᏫᏳᎯ ᏥᏌ.
|
|---|
Hi-a na-s-gi tsu-da-le-nv-dv gv-ni-ge-sv i-gv-ne-hi, hi-a ni-ga-we-ha, u-do-hi-yu-hi-ya tle-gi-yu da-tsi-lu-tsi. E-me-nv; ho-wa-quo; tle-gi-yu hi-lu-gi Tsa-gv-wi-yu-hi Tsi-sa.
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| The grace of our Lord Jesus Christ be with you all. Amen.
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| ᎬᏩᎦᏘᏯ ᎤᏓᏙᎵᏍᏗ ᎨᏒ ᎤᎬᏫᏳᎯ ᏥᏌ ᎦᎶᏁᏛ ᎢᎦᏤᎵᎦ ᎢᏤᎳᏗᏙᎮᏍᏗ ᏂᏥᎥᎢ. ᎡᎺᏅ.
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Gv-wa-ga-ti-ya u-da-do-li-s-di ge-sv U-gv-wi-yu-hi Tsi-sa Ga-lo-ne-dv i-ga-tse-li-ga i-tse-la-di-do-he-s-di ni-tsi-v-i. E-me-nv.
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(.%) .±. experimental descriptors breadth values used antibody antibodies ;():–. HIV MLP number training correlation neural Random model classifier data set using binding []. models Sciences different network {border-style computed protein px;} structure total #ffffff;} Fig VRC antibody/antigen available obtained Table machine learning value values. accuracy germline mature reported Biology Neural antibodies. complex descriptors, given regressor virus (see Pathogens antigen atomistic energy hidden neutralizing predict true use Life antibody, coefficient computational descriptors. high neutralization Antibodies Artificial Computational contains database exact force modeling molecular results scoring set. vaccine viruses ENM_EXP HIV- MD_h Pearson Proteins: approximate artificial bnAbs broadly descriptor developed entropy estimate methods panel parameters time CATNAP Machine Seaman ability breadths crystallographic design estimated important known networks px; randomly requires set, train trained {font-family Bioinformatics. Molecular RFHA [], affinity antibodies, antigens available, bound breadth. calculated complexes consider correlated develop font-size function half input layer method model. models. regression second shows significantly statistical ./journal.pcbi..tTable Analysis Antibody BNC CDbs CHARMM Chemical Function, Information Medicine Structure, Viral decreases distance double; e.g. evaluating generated included information line new positive potential prediction present random sequence small study takes vaccination validation work zero μg/ml (Spearman (the Biology. FoldX HIV, Immune NIH- National Network Networks OpenMM PDB ZRANKr [,]. according analysis antibody. atomic average border-color border-width classifier. compared constant described divided does evaluate fraction functions greater highest highly hydrogen including large model, negative observed pair pairwise particular predicted protein/protein putative red regressor, relative seconds significant similar solid; suggests sum template test these, used, useful {font-style {font-weight (by ANC Broadly Comparison Health However, Immunodeficiency KarplusM. Laboratory Lentivirus: Methods). Microbial Microbiology Moreover, Multi-Layer Neutralizing Organisms Perceptron Physical Physics Proceedings Protein Proteins Retroviruses Review Sciences. VRC, VRC-PG Viruses Viruses: ZRANK [,], accurate acid amino approach basis bind bold calculate change column complex, complex. computation confusion containing courier; decrease defined effective em; estimating evaluated expected, false fit focus folding functions, good higher likely matrix measured minutes mode neighbors normal output outputs paper points pt;} ratio related relatively repeated report represent required sensitivity sequences single site studied surface years


Scalability Benchmark
N producer threads push a 4-byte integer into one same queue, N consumer threads pop the integers from the queue. All producers posts 1,000,000 messages in total. Total time to send and receive all the messages is measured. The benchmark is run for from 1 producer and 1 consumer up to (total-number-of-cpus / 2) producers/consumers to measure the scalabilty of different queues.
Latency Benchmark
One thread posts an integer to another thread through one queue and waits for a reply from another queue (2 queues in total). The benchmarks measures the total time of 100,000 ping-pongs, best of 10 runs. Contention is minimal here (1-producer-1-consumer, 1 element in the queue) to be able to achieve and measure the lowest latency. Reports the average round-trip time.
 MIT License. See the full licence in file LICENSE.


Pompeii Consulting
IT Consulting | IT Management
Air Canada Case Study
06 September 2016
Information Technology Valuation
01 September 2016
Otis Elevator Case Study
29 August 2016
Productivity Analysis
23 August 2016
IT and Business Organization Strategy
23 August 2016
Future
Page 2 of 5
Past


WITNESS LIST Public School Finance, Select Committee (Subcommittee on Bivins Subcommittee) November 7, 2001 -10:00A Public School Finance FOR: Creager, Keith Classroom Educator (Texas State Teachers Association), Amarillo, TX Turner, Terrie (Texas Association for the Gifted and Talented), Amarillo, TX ON: Abraham, Salem Financial Trader (Canadian I.S.D.), Canadian, TX Barnhart, Linda Kay Superintendent (Pringle-Morse C.I.S.D.), Amarillo, TX Cargill, David Assistant Superintendent (Amarillo I.S.D.), Amarillo, TX Carlisle, Anette Board President (Amarillo I.S.D.), Amarillo, TX Carpenter, John Real Estate (Highland Park I.S.D.), Dallas, TX Clemmons, Jack Superintendent (Lubbock I.S.D.), Lubbock, TX Coker, L. Ben Assistant Superintendent (Highland Park I.S.D.), Dallas, TX Dinsmore, Allan Superintendent (Miami I.S.D.), Miami, TX Gaskins, Jack Superintendent (Klondike I.S.D.), Lamesa, TX Grantham, Thomas Accountant (Miami I.S.D.), Miami, TX Hancock, Linda (Texas Classroom Teachers Association), Austin, TX Havens, Wayne Deputy Superintendent (Lubbock I.S.D.), Lubbock, TX Hoyt, Les Assistant Superintendent (Amarillo I.S.D.), Amarillo, TX Kitten, Sandy Staff Representative (Amarillo Federation of Teachers), Amarillo, TX Lovelady, Sam Board Member (Amarillo I.S.D.), Amarillo, TX Orr, Dawson School Administrator (Pampa I.S.D.), Pampa, TX Pierel, Carolyn Superintendent (Rankin I.S.D.), Rankin, TX Polnick, Dr. David Assistant Superintendent (Lubbock I.S.D.), Lubbock, TX Porter, Lee (Pampa I.S.D.), Pampa, TX Schroeder, Rod Superintendent (Amarillo I.S.D.), Amarillo, TX Veitenheimer, James Superintendent (Canyon I.S.D.), Canyon, TX Wisnoski, Joe Assistant Commissioner (Texas Education Agency), Austin, TX


| Surface |
|
| Dipole |
|
| Translucent Molecule |
|
Aqueous Solvation Energy at 298.15 K
| Property | Value | Unit |
| Total Solvation Energy | {{soltotal}} | kJ mol-1 |
| Polar Solvation Energy | {{solpolar}} | kJ mol-1 |
| Nonpolar Solvation Energy | {{solnonpolar}} | kJ mol-1 |
| Surface Area | {{solsurface}} | Å2 |
| Charge of Molecule | {{charge}} | |
| Dipole | {{soldipoletotal}} | Debye |


Coffee bean is a seed of coffee plant and the source for coffee. It is the pit inside the red or purple fruit often reffered to as cherry. Just like ordinary cherries, the coffee fruit is also a so-called stone fruit.
Cacao bean or simply cocoa is the dried and fully fermented seed of Theobroma cacao, from which cocoa solid powder and cocoa butter can be extracted.
The tea plant is an evergreen shrub that provides us with black, white, yellow, and green tea as well as oolong and pu-erh tea. It is the leaves and leaf buds that are commonly used to produce the teas we enjoy. ... The tea plant can live anywhere between 30 and 50 years.
I WILL BE VERY GLAD TO JOIN THE TEAM 1 IN THE STARTUP
!
It is worthy at this point to recognize and appreciate the effort of my all time Mentor "LEO"!
Full Stack Engineer
IT ACTUALLY COMES AT THE TIME I'M ABOUT TO INNITIATE SIMILAR PROJECT.


LLDB API Documentation
Generated on Fri Jul 19 2013 13:25:55 for LLVM by <IP_ADDRESS> Copyright © 2003-2013 University of Illinois at Urbana-Champaign. All Rights Reserved.


NOTE: When you refresh the page, random values change and result may be different...
 This data is random, since it is needed to add bluetooth service in this web app !!!
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Responsive layout
When making apps for a wide range of mobile devices, we're facing a huge number of different screen sizes, pixel densities and aspect ratios. How do you make a fully responsive app that adapts to these constraints? Fuse solves the challenge by using a basic set of layout rules and triggers, which you should know and understand in order to be effective in Fuse.
Units of measurement
There are several units of measurement you can use to describe element dimensions. The default unit is points (pt
). The advantage of using points is that an element with explicit dimensions appears the same size on screens of different pixel densities. There is also a percentage unit (%
), which can be used to define the size of an element to be a percentage of its available size. For the odd case where you need to specify the exact amount of pixels you want an element to occupy, we have the pixel (px
) unit. Note however that when using this unit of measurement, elements will have different sizes on different screen densities.
You can specify the unit of a number by appending either pt, px or % (note that we don't need to specify pt
as that becomes the unit if we only specify the number):
<Panel Width="50%" Heigh="100px" />
Using images in responsive layout
When using images in Fuse apps, you should try to make them match the intended target size on screen. Scaling the image up or down significantly will likely result in visible aliasing artefacts. You should use MultiDensityImageSource
to supply several versions of the same image for use on screens of different pixel densities. This lets us avoid up-/downscaling in the cases where the image would otherwise not match its intended size on screen as a result of changes in pixel density between screens.
Basic building blocks
Fuse comes with an extremly powerful layout system that lets you create UIs that fit on many different screen sizes and aspect ratios. UIs in Fuse are created by a hierarchy of various Panel
types, as well as other primitives like Rectangle
, Circle
and Image
. The various Panel
types, like StackPanel
, Grid
and DockPanel
, are used to position and size multiple elements relative to each other as well as relative to the available space.
Available space concept
Let's start with the concept of available space. As you might expect, when you have an empty application, the available space is the whole screen of the mobile device, including the area behind status and action bars:
<App>
</App>
Now, most of the visual elements placed inside of the app tag will by default try to be as big as possible and fill up all available space (there are exceptions, such as StackPanel
):
<App>
<Panel>
</Panel>
</App>
However, sometimes we don't want our elements to occupy all available space, but instead subdivide that space and distribute it amongst several elements:
<App>
<DockPanel>
<Panel Dock="Top" Height="56" Color="Blue" />
<Panel Dock="Bottom" Height="56" Color="Green" />
<Panel Color="Yellow">
</Panel>
</DockPanel>
</App>
In this example, the DockPanel
fills up all available space, then inside it is a 56pt tall blue Panel
that is docked to the top edge. The green Panel
is also 56pt high, but it is docked to the bottom edge. Since there are no horizontal constraints on the two panels (like the Width
property for example), they stretch to fill the full width of the available space, which in this case is the full width of the screen.
The yellow Panel
automatically takes up the remaining available space, which is now the whole area between the blue and green panels.
Assigning a Dock
property to an element within a DockPanel
ensures it takes up as little space as possible either vertically (when docked to the top or bottom) or horizontally (when docked to the left of right).
Alignment affects layout
With the available space concept settled firmly, let's move on and throw Alignment
into the mix!
<App>
<DockPanel>
<Panel Dock="Top" Height="56" Color="Blue" />
<Panel Dock="Bottom" Height="56" Color="Green" />
<Panel Color="Yellow">
<Panel Color="Red" Alignment="Top" Height="16" />
</Panel>
</DockPanel>
</App>
In this case, the nested red Panel
with Alignment="Top"
set will take up only 16pt vertically, and be aligned to the top of the yellow Panel
. When we assign an Alignment
to an element, it tries to stick to the particular side of its parent and take up as little space in the respective direction as possible. The red Panel
still stretches to fill the full width of the available space since there are no horizontal constraints set on it.
[callout info]
At this point you might wonder what the difference between "docking" something in a DockPanel
and aligning something is. Although the two have similar effects, the most important difference is that aligning something does not subdivide its parents available space. What we mean by this is that the red Panel
in the previous example does not "spend" its parent available space (as was done by the blue and green panels), it is instead just a way of declaring where panel should appear in the case where it is smaller (or bigger) than its parent in one or more dimensions.
[/callout]
If we remove the explicit Height
on the red Panel
and add some child elements with non-zero dimensions inside of it, the children will push the parent dimensions to accommodate the tallest child, the 200pt high maroon Panel
in this case:
<App>
<DockPanel>
<Panel Dock="Top" Height="56" Color="Blue" />
<Panel Dock="Bottom" Height="56" Color="Green" />
<Panel Color="Yellow">
<Panel Color="Red" Alignment="Top">
<Panel Color="Teal" Height="20" />
<Panel Color="Maroon" Height="200" />
</Panel>
</Panel>
</DockPanel>
</App>
Other Panel
types behave slightly differently. For example, let's take a look at StackPanel
:
<App>
<DockPanel>
<Panel Dock="Top" Height="56" Color="Blue" />
<Panel Dock="Bottom" Height="56" Color="Green" />
<Panel Color="Yellow">
<StackPanel Color="Red" Alignment="Top">
<Panel Color="Teal" Height="20" />
<Panel Color="Maroon" Height="200" />
</StackPanel>
</Panel>
</DockPanel>
</App>
A vertical StackPanel
stacks its children one after the other, and stretches to accommodate the combined height of all children. The red StackPanel
in the example above is now 220 points high and aligned to the top of the yellow Panel
. You can find more details on StackPanel
behaviour in StackPanel docs.
It's worth noting that aside from Alignment
, other visual properties have an effect on the layout calculation too. These include Width
, Height
, MinWidth
, MinHeight
, MaxWidth
, MaxHeight
, X
, Y
, Margin
, Padding
and a few more. When setting such properties, be sure to always test how your layout looks on different target devices.
List of commonly used layout controls
DockPanel
DockPanel
is one of the most frequently used layout containers. It lets us dock elements to its sides, consuming that space in the process so that the next item being docked only considers the remaining space. See the DockPanel docs for more details.
<DockPanel>
<Rect Dock="Left" Color="Red" Width="50" />
<Rect Dock="Top" Color="Blue" Height="100" />
<Rect Dock="Right" Color="Green" Width="100" />
<Rect Color="Yellow"/>
</DockPanel>
StackPanel
As the name suggests, you want to use a StackPanel
for stacking items. This is very useful when creating lists of items, but it's applicable in any situation when you need to stack elements either vertically or horizontally. We often use StackPanel
when we don't know exactly how many items we need to display. The StackPanel
can just grow to fit as many children as it is given (unlike the DockPanel
for example). Because of this, a StackPanel
can grow to become larger than its parent, overflowing its available size. See the StackPanel docs for more details.
<StackPanel Alignment="Top" Margin="8" ItemSpacing="4">
<Each Count="4">
<Panel Height="56">
<Text Value="The title of an item" Alignment="Center" Color="#fff" />
<Rectangle Layer="Background" Color="#18f" CornerRadius="2" />
</Panel>
</Each>
</StackPanel>
Grid
Grid
comes in handy when you need to build a responsive layout with flexible row or column sizes. By default, elements in a Grid
are placed in the order they appear in UX, from left to right, top to bottom. However, you have full control over how a Grid
behaves by using its properties RowCount
and ColumnCount
, or Rows
and Columns
. You can also explicitly tell each child to which row and column it should attach itself by using the Row
, Column
, RowSpan
and ColumnSpan
properties. See the Grid docs for more details.
<Grid RowCount="2" Columns="1*,2*">
<Panel Color="#18f">
<Text Value="Top 1/3" Alignment="Center" Color="#fff" />
</Panel>
<Panel Color="#f81">
<Text Value="Top 2/3" Alignment="Center" Color="#fff" />
</Panel>
<Panel ColumnSpan="2" Color="#1f8">
<Text Value="Bottom half" Alignment="Center" />
</Panel>
</Grid>
WrapPanel
The WrapPanel
is used when you need to lay out children one after another in a given orientation, and wrap around whenever you reach the end of the available space in a particular direction. By default it lays its children out horizontally, and wraps around vertically. See the WrapPanel docs for more details.
<App>
<WrapPanel>
<Panel Width="128" Height="56" Margin="4" Color="#18f" />
<Panel Width="160" Height="56" Margin="4" Color="#18f" />
<Panel Width="48" Height="56" Margin="4" Color="#18f" />
<Panel Width="48" Height="56" Margin="4" Color="#18f" />
<Panel Width="128" Height="56" Margin="4" Color="#18f" />
</WrapPanel>
</App>
Panel
A Panel
is arguably the simplest container in Fuse. You want to use Panel
when you want to position its children in the same area of available space without subdividing it like for example DockPanel
does. Details can be found here.
<Panel Height="56" Margin="24,0">
<TextInput Value="Some text" TextColor="#fff" Margin="10"/>
<Rectangle Color="#18f" CornerRadius="2" />
</Panel>
ScrollView
A ScrollView
is generally used to display content that is bigger than the screen of the device. A prominent example are lists of items. Note that a ScrollView
can only have a single visual child. Since ScrollView
by default occupies the whole available space, we usually place a StackPanel
inside it in order to make a scrollable list.
<ScrollView>
<StackPanel Margin="8" ItemSpacing="4">
<Each Count="24">
<Panel Height="56">
<Text Value="The title of an item" Alignment="Center" Color="#fff" />
<Rectangle Color="#18f" CornerRadius="2" />
</Panel>
</Each>
</StackPanel>
</ScrollView>
Adapting to different screen sizes and orientations
When we're making cross-platform applications that target a broad range of device models, it makes sense to use the additional screen real estate you get when running on larger devices, such as tablets. It is also wise to adapt certain views to whether it is displayed in the portrait or landscape device orientation. Fuse has a set of triggers to handle this: WhileWindowPortrait
, WhileWindowLandscape
and WhileWindowSize
. These can be combined to describe very detailed use cases. We will show you how in the next section!
Hands-on
It's time we use what we've learnt so far and replicate a real-world app design in Fuse. As a popular example, let's take a look at how the Google Docs application behaves on different screens. There are of course many subtle details that change, but here we will replicate the basic structure of the app layout.
On iPhone 6, it has two items in a row while in portrait, and changes that to 3 items when rotated to landscape.
On iPad, it has three items in a row while in portrait, and changes that to 5 items when rotated to landscape.
Let's start with the basic app structure. The first things we take care of are the status and bottom bars - we do not want our app content to be drawn behind them, so we put a DockPanel
in the app and dock a StatusBarBackground
and BottomBarBackground
to its top and bottom. Further more, we set the background color of the status bar to the blue tone that's going to be used for the whole top section of the app.
<App Background="#eee">
<!-- iOS specific status bar styling -->
<iOS.StatusBarConfig Style="Light" />
<DockPanel>
<StatusBarBackground Dock="Top" Background="#4285f4" />
<BottomBarBackground Dock="Bottom" />
</DockPanel>
</App>
Next we add the blue top bar itself that holds our action buttons. Since we already have our parent DockPanel
, we only need to specify Dock="Top"
on the element and give it a height, and it gets aligned to the very top of the available space, right below StatusBarBackground
. We won't add any icons for now, but note that we conveniently made the element a DockPanel
so you can easily dock things inside of it later.
Then we add another empty Panel
which automatically fills the remaining available space, and is the container that we will put the rest of our content in.
<App Background="#eee">
<!-- iOS specific status bar styling -->
<iOS.StatusBarConfig Style="Light" />
<DockPanel>
<StatusBarBackground Dock="Top" Background="#4285f4" />
<BottomBarBackground Dock="Bottom" />
<DockPanel Dock="Top" Height="56" Color="#4285f4">
<!-- this is the blue top bar that will hold the icons -->
</DockPanel>
<Panel>
<!-- this fills the remaining available space -->
</Panel>
</DockPanel>
</App>
Looking back at the app screenshots, we see that the content area is scrollable, which means that we will have to use a ScrollView
. However, there is this red "plus" button that should stay on top of the ScrollView
, and not move with it as it scrolls. Because of that, we use a Panel
as the parent of the content area, because it allows us to put its children on top of each other:
<Panel>
<Circle Width="56" Height="56" Margin="16" Color="#db4437" Alignment="BottomRight">
<Shadow />
</Circle>
<ScrollView>
</ScrollView>
</Panel>
The scrollable content in the screenshots appears to be arranged in a grid formation with equally sized columns. It makes sense to use a Grid
here, since that allows us to change the number of columns our content is going to be put into, and the Grid
will adjust as necessary. As we outlined at the top, the number of columns will depend on the screen size of the device the app runs on.
First we need to reserve some space on top of the Grid
for a title and an ordering toggle that you can add later. To do that, we first put a StackPanel
inside of the ScrollView
. In the StackPanel
, we put a 56 points tall Panel
and the Grid
that will hold the items. We specify our Grid
to have 2 equally sized columns by default (using the ColumnCount
property), and give it a ux:Name
so we can refer to it later.
<Panel>
<Circle Width="56" Height="56" Margin="16" Color="#db4437" Alignment="BottomRight">
<Shadow />
</Circle>
<ScrollView>
<StackPanel>
<Panel Height="56" />
<Grid ux:Name="itemGrid" ColumnCount="2" Margin="4" CellSpacing="4">
</Grid>
</StackPanel>
</ScrollView>
</Panel>
The next thing we want to do is to add some placeholder content for the items so that we can style them. To achieve that, we use the Each
class. Each
is most frequently used for data-binding to arrays of data, but in our case we'll use it as a tool to repeat the same UX multiple times using its Count
property.
Inside the Each
, we want to define how a single "box", that represents an item, looks like. The Each
object will take care of creating 20 instances of it for us. What's tricky about the list items is that their size should adapt to the varying number of columns in the parent Grid
as the screen size or orientation changes. This means that we can't set explicit Width
or Height
on them. Luckily, there's one thing that is certain: we know they need to stay square. We can achieve this by setting BoxSizing="FillAspect"
and Aspect="1"
on the item. This will stretch the item to fill the available space within the Grid
cell that it gets assigned to.
<Grid ux:Name="itemGrid" ColumnCount="2" Margin="4" CellSpacing="4">
<Each Count="20">
<DockPanel BoxSizing="FillAspect" Aspect="1">
</DockPanel>
</Each>
</Grid>
Finally we add some content to the grid items. Note how we set Layer="Background"
on the Rectangle
that serves as the white background of the item - this ensures that it is sent to the background and does not participate in the layout calculation done by its parent DockPanel
.
<Grid ux:Name="itemGrid" ColumnCount="2" Margin="4" CellSpacing="4">
<Each Count="20">
<DockPanel BoxSizing="FillAspect" Aspect="1">
<Panel Dock="Bottom" Height="40" Color="#fafafa" Margin="1">
<Text Value="Title" Alignment="CenterLeft" Margin="8,0" />
</Panel>
<Text Value="preview" Alignment="Center" TextColor="#bbb" />
<Rectangle Color="#fff" Layer="Background">
<Stroke Width="1" Color="#eaeaea" />
</Rectangle>
</DockPanel>
</Each>
</Grid>
To make the basic app structure complete, we need to make the Grid
adapt to various screen sizes and device orientations. Here we want to use the general approach of describing varying states as deviations from the rest state. Our rest state in this case is a phone in portrait orientation that has two items on each row in the Grid
.
The deviations we need to describe then are "phone in landscape", "tablet in portrait" and "tablet in landscape". We start by creating a new WhileTablet
trigger that is going to evaluate to true when the screen size in any direction is larger than or equal to 600 points. We chose the number so that it matches Material design guidelines.
Then we couple the new WhileTablet
trigger with WhileWindowPortait
and WhileWindowLandscape
to describe the "how many items per row" rules we set out at the top of the Hands-on section.
<WhileWindowSize ux:Class="WhileTablet" GreaterThan="599,1" />
<WhileWindowPortrait>
<WhileTablet>
<Change itemGrid.ColumnCount="3" />
</WhileTablet>
</WhileWindowPortrait>
<WhileWindowLandscape>
<WhileTablet>
<Change itemGrid.ColumnCount="5" />
</WhileTablet>
<WhileTablet Invert="true">
<Change itemGrid.ColumnCount="3" />
</WhileTablet>
</WhileWindowLandscape>
With that, our Google Docs app mockup is done. Take a look at the full code below, give it a go and see how it works on different devices!
<App Background="#eee">
<iOS.StatusBarConfig Style="Light" />
<DockPanel>
<StatusBarBackground Dock="Top" Background="#4285f4" />
<BottomBarBackground Dock="Bottom" />
<DockPanel Dock="Top" Height="56" Color="#4285f4">
<!-- this is the blue top bar that will hold the icons -->
</DockPanel>
<Panel>
<Circle Width="56" Height="56" Margin="16" Color="#db4437" Alignment="BottomRight">
<Shadow />
</Circle>
<ScrollView>
<StackPanel>
<Panel Height="56" />
<Grid ux:Name="itemGrid" ColumnCount="2" Margin="4" CellSpacing="4">
<Each Count="20">
<DockPanel BoxSizing="FillAspect" Aspect="1">
<Panel Dock="Bottom" Height="40" Color="#fafafa" Margin="1">
<Text Value="Title" Alignment="CenterLeft" Margin="8,0" />
</Panel>
<Text Value="preview" Alignment="Center" TextColor="#bbb" />
<Rectangle Color="#fff" Layer="Background">
<Stroke Width="1" Color="#eaeaea" />
</Rectangle>
</DockPanel>
</Each>
</Grid>
</StackPanel>
</ScrollView>
</Panel>
<WhileWindowSize ux:Class="WhileTablet" GreaterThan="599,1" />
<WhileWindowPortrait>
<WhileTablet>
<Change itemGrid.ColumnCount="3" />
</WhileTablet>
</WhileWindowPortrait>
<WhileWindowLandscape>
<WhileTablet>
<Change itemGrid.ColumnCount="5" />
</WhileTablet>
<WhileTablet Invert="true">
<Change itemGrid.ColumnCount="3" />
</WhileTablet>
</WhileWindowLandscape>
</DockPanel>
</App>


| Date | # of Feedings | Average | Total | Diaper Count | Nursing (min) |
|---|---|---|---|---|---|
| {{day.date}} | {{day.numberOfFeedings}} | {{day.milkVolumeAverageMilliliters}}ml / {{day.milkVolumeAverageOunces}}oz | {{day.milkVolumeTotalMilliliters}}ml / {{day.milkVolumeTotalOunces}}oz | {{day.diaperCount}} | {{day.nursingDurationMinutes}} |


This is my paragraph. I'm using a p tag
Hemingway wrote The Sun Also Rises.
This is my Week Three paragraph. I'm using a p tag
Falkner wrote Tender is the Night and The Great Gatsby.


Lets get to know Mohit!!
So Hi, fellows!! I am Mohit Kumar, I come from the beautiful and diverse country of India.
Let me talk first about my all time interests!
All Timers
My all time interests include watching movies, especially sci-fi; reading biographies and history, watching cricket, acting and my first love DANCING!!
Lets now talk about my favorie actors!
- Dwayne Johnson
- Shah Rukh Khan
- Hrithik Roshan
- Al Pacino
- Robert Downey Jr.
- Varun Dhawan
My top 5 favourite drinks
- Pepsi/Coke
- BournVita(still a kid.. hahahah)
- Coffee
- Mixed/Mango Juice
- Lemonade
Lets know my FOOD PREFERENCES
| Food |
Preference |
| North Indian |
Very much |
| South Indian |
Very much |
| Mughlai |
Very much |
| Chinese |
To some extent |
| Italian |
Never tasted as such |


The Elves, who called themselves the Quendi, and who in lore are commonly referred to as the Eldar (adj. Eldarin), were the first and eldest of the Children of Ilúvatar, and are considered to be the fairest and wisest of any race of Arda given sapience.
The Maiar (singular Maia; Quenya, pronounced [ˈmaɪ. ar]) were those spirits which descended to Arda to help the Valar shape the World. They were numerous, yet not many were named, and few also took visible shapes in Middle-Earth.
The race of Men in Tolkien's fictional world is the second race of beings, the "younger children", created by the One God, Ilúvatar. Like Elves, Men first awoke in the East of Middle-earth, spreading all over the continent and developing a variety of cultures and ethnicities. Unlike Tolkien's elves, Men are mortal.
Dwarves were long-lived, with a lifespan of some 250 years. ... The Dwarves are described as "the most redoubtable warriors of all the Speaking Peoples" – a warlike race who fought fiercely against their enemies, including other Dwarves.
Hobbits, also known as Halflings, were an ancient mortal race that lived in Middle-earth. Although their exact origins are unknown, they were initially found in the northern regions of Middle-earth and below the Vales of Anduin. Most of their race eventually founded the land of the Shire.
Orcs were the primary foot soldiers of the Dark Lords' armies and sometimes the weakest (but most numerous) of their servants. They were created by the first Dark Lord, Morgoth, before the First Age and served him and later his successor in their quest to dominate Middle-earth.

Learn more about your drinking buddy here.
| Measurements | Ingredients |
|---|
Rate your drink!
Saved!
| Drink | Companion | Rating |
|---|


date_range
Date of donation
attach_money
Amount
MAD
Percent of Participation in Case:
Case!
Donator!


+++ title = "Roy the guide" slug = "roy-the-guide" date = 2007-07-11T12:00:00+10:00 tags = ["locals", "eccentric-encounters", "tourguides"] locations = ["peru/canon-del-colca"] draft = false summary = """
Roy was our guide on our three-day hike in the Cañon del Colca, and he's one of the funniest and craziest guides I've had so far on my trip. Every time we asked him how much hiking we have left for the day, he'd say something like: "nueve horas mas" ("nine hours more"). Sometimes it was hard to tell whether he was joking, or being serious. But we all loved his humour, and he did a great job of leading us through the canyon, and of then helping us get safely back into Arequipa at the end of the trip.
""" +++Roy was our guide on our three-day hike in the Cañon del Colca, and he's one of the funniest and craziest guides I've had so far on my trip. Every time we asked him how much hiking we have left for the day, he'd say something like: "nueve horas mas" ("nine hours more"). Sometimes it was hard to tell whether he was joking, or being serious. But we all loved his humour, and he did a great job of leading us through the canyon, and of then helping us get safely back into Arequipa at the end of the trip.
{{< galleryphoto "2007-07-11-12-00-00--roy-in-the-canyon.jpg" >}}


SignalP 4.1 server predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
get_signalp(data, ...) # S3 method for character get_signalp( data, org_type = c("euk", "gram-", "gram+"), Dcut_type = c("default", "sensitive", "user"), Dcut_noTM = 0.45, Dcut_TM = 0.5, method = c("best", "notm"), minlen = NULL, trunc = 70L, splitter = 1000L, attempts = 2, progress = FALSE, ... ) # S3 method for data.frame get_signalp(data, sequence, id, ...) # S3 method for list get_signalp(data, ...) # S3 method for default get_signalp(data = NULL, sequence, id, ...) # S3 method for AAStringSet get_signalp(data, ...)
| data | A data frame with protein amino acid sequences as strings in one column and corresponding id's in another. Alternatively a path to a .fasta file with protein sequences. Alternatively a list with elements of class |
|---|---|
| ... | currently no additional arguments are accepted apart the ones documented bellow. |
| org_type | One of c("euk", "gram-", "gram+"), defaults to "euk". Which model should be used for prediction. |
| Dcut_type | One of c("default", "sensitive", "user"), defaults to "default". The default cutoff values for SignalP 4 are chosen to optimize the performance measured as Matthews Correlation Coefficient (MCC). This results in a lower sensitivity (true positive rate) than SignalP 3.0 had. Setting this argument to "sensitive" will yield the same sensitivity as SignalP 3.0. This will make the false positive rate slightly higher, but still better than that of SignalP 3.0. |
| Dcut_noTM | A numeric value, with range 0 - 1, defaults to 0.45. For experimenting with cutoff values. |
| Dcut_TM | A numeric value, with range 0 - 1, defaults to 0.5. For experimenting with cutoff values. |
| method | One of c("best", "notm"), defaults to "best". Signalp 4.1 contains two types of neural networks. SignalP-TM has been trained with sequences containing transmembrane segments in the data set, while SignalP-noTM has been trained without those sequences. Per default, SignalP 4.1 uses SignalP-TM as a preprocessor to determine whether to use SignalP-TM or SignalP-noTM in the final prediction (if 4 or more positions are predicted to be in a transmembrane state, SignalP-TM is used, otherwise SignalP-noTM). An exception is Gram-positive bacteria, where SignalP-TM is used always. If you are confident that there are no transmembrane segments in your data, you can get a slightly better performance by choosing "Input sequences do not include TM regions", which will tell SignalP 4.1 to use SignalP-noTM always. |
| minlen | An integer value corresponding to the minimal predicted signal peptide length, at default set to 10. SignalP 4.0 could, in rare cases, erroneously predict signal peptides shorter than 10 residues. These errors have in SignalP 4.1 been eliminated by imposing a lower limit on the cleavage site position (signal peptide length). The minimum length is by default 10, but you can adjust it. Signal peptides shorter than 15 residues are very rare. If you want to disable this length restriction completely, enter 0 (zero). |
| trunc | An integer value corresponding to the N-terminal truncation of input sequence, at default set to 70. By default, the predictor truncates each sequence to max. 70 residues before submitting it to the neural networks. If you want to predict extremely long signal peptides, you can try a higher value, or disable truncation completely by entering 0 (zero). |
| splitter | An integer indicating the number of sequences to be in each .fasta file that is to be sent to the server. Default is 1000. Change only in case of a server side error. Accepted values are in range of 1 to 2000. |
| attempts | Integer, number of attempts if server unresponsive, at default set to 2. |
| progress | Boolean, whether to show the progress bar, at default set to FALSE. |
| sequence | A vector of strings representing protein amino acid sequences, or the appropriate column name if a data.frame is supplied to data argument. If .fasta file path, or list with elements of class "SeqFastaAA" provided to data, this should be left blank. |
| id | A vector of strings representing protein identifiers, or the appropriate column name if a data.frame is supplied to data argument. If .fasta file path, or list with elements of class "SeqFastaAA" provided to data, this should be left blank. |
https://services.healthtech.dtu.dk/service.php?SignalP-4.1
A data frame with columns:
Character, as from input
Numeric, C-score (raw cleavage site score). The output from the CS networks, which are trained to distinguish signal peptide cleavage sites from everything else. Note the position numbering of the cleavage site: the C-score is trained to be high at the position immediately after the cleavage site (the first residue in the mature protein).
Integer, position of Cmax. position immediately after the cleavage site (the first residue in the mature protein).
Numeric, Y-score (combined cleavage site score), A combination (geometric average) of the C-score and the slope of the S-score, resulting in a better cleavage site prediction than the raw C-score alone. This is due to the fact that multiple high-peaking C-scores can be found in one sequence, where only one is the true cleavage site. The Y-score distinguishes between C-score peaks by choosing the one where the slope of the S-score is steep.
Integer, position of Ymax
Numeric, S-score (signal peptide score). The output from the SP networks, which are trained to distinguish positions within signal peptides from positions in the mature part of the proteins and from proteins without signal peptides.
Integer, position of Smax
Numeric, The average S-score of the possible signal peptide (from position 1 to the position immediately before the maximal Y-score)
Numeric, D-score (discrimination score). A weighted average of the mean S and the max. Y scores. This is the score that is used to discriminate signal peptides from non-signal peptides.
Character, does the sequence contain a N-sp
Numeric, as from input, Dcut_noTM if SignalP-noTM network used and Dcut_TM if SignalP-TM network used
Character, which network was used for the prediction: SignalP-noTM or SignalP-TM
Logical, did SignalP predict the presence of a signal peptide
Integer, length of the predicted signal peptide.
This function creates temporary files in the working directory.
Petersen TN. Brunak S. Heijne G. Nielsen H. (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nature Methods 8: 785-786
library(ragp) signalp_pred <- get_signalp(data = at_nsp[1:10,], sequence, Transcript.id) signalp_pred#> id Cmax Cmax.pos Ymax Ymax.pos Smax Smax.pos Smean Dmean is.sp #> 1 ATCG00660.1 0.210 30 0.162 30 0.260 2 0.146 0.154 N #> 2 AT2G43600.1 0.860 23 0.894 23 0.984 13 0.930 0.913 Y #> 3 AT2G28410.1 0.779 23 0.826 23 0.940 15 0.877 0.853 Y #> 4 AT2G22960.1 0.701 23 0.790 23 0.948 15 0.891 0.844 Y #> 5 AT2G19580.1 0.422 26 0.586 26 0.885 17 0.798 0.671 Y #> 6 AT2G19690.2 0.797 29 0.870 29 0.987 18 0.952 0.914 Y #> 7 AT2G19690.1 0.797 29 0.870 29 0.987 18 0.952 0.914 Y #> 8 AT2G33130.1 0.318 33 0.530 27 0.990 16 0.933 0.748 Y #> 9 AT2G05520.1 0.633 24 0.782 24 0.990 16 0.966 0.881 Y #> 10 AT2G05520.2 0.633 24 0.782 24 0.990 16 0.966 0.881 Y #> Dmaxcut Networks.used is.signalp sp.length #> 1 0.450 SignalP-noTM FALSE 30 #> 2 0.450 SignalP-noTM TRUE 23 #> 3 0.450 SignalP-noTM TRUE 23 #> 4 0.450 SignalP-noTM TRUE 23 #> 5 0.500 SignalP-TM TRUE 26 #> 6 0.450 SignalP-noTM TRUE 29 #> 7 0.450 SignalP-noTM TRUE 29 #> 8 0.450 SignalP-noTM TRUE 27 #> 9 0.450 SignalP-noTM TRUE 24 #> 10 0.450 SignalP-noTM TRUE 24


To show the code used click either the code tab on the right side above each box or at the top of the document
library(vegan)
library(MASS)
library(ggplot2)
library(plyr)
library(dplyr)
library(magrittr)
library(scales)
library(grid)
library(reshape2)
library(phyloseq)
library(randomForest)
library(knitr)
library(ape)
library(ggpubr)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#000000","#CC79A7")
theme_set(theme_bw(base_size = 18)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))
biom=import_biom("C:\\Users\\Joe Receveur\\Documents\\Virtual Box\\BeninSwab\\Outputs\\BeninSwabR.biom",parseFunction= parse_taxonomy_greengenes)
meta=read.table("C:\\Users\\Joe Receveur\\Documents\\Virtual Box\\BeninSwab\\Metadata\\sample-metadata.txt",header=TRUE)
meta=sample_data(meta)
sample_names(meta)=meta$SampleID
physeq=merge_phyloseq(biom,meta)
physeq <- subset_taxa(physeq, Family != "mitochondria" & Class != "Chloroplast")
GP= rarefy_even_depth(physeq, sample.size = 17500,
rngseed = FALSE, replace = TRUE, trimOTUs = TRUE, verbose = TRUE)
GP
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 673 taxa and 50 samples ]
## sample_data() Sample Data: [ 50 samples by 9 sample variables ]
## tax_table() Taxonomy Table: [ 673 taxa by 13 taxonomic ranks ]
physeq=GP
sample_data(physeq)$Timepoint = factor(sample_data(physeq)$Timepoint, levels = c("DayOfDeparture","ArrivalBenin","DepartingBenin","ArrivalGeneva","DepartingGeneva","ArrivalHome","1WeekPost","2WeekPost")) #fixes x-axis labels
Study Design: Samples were taken from four locations (Ears, Eyes, Nares, and Mouth) on four subjects during international travel to Benin and Switzerland over the course of two weeks and one week after returning.
Only Mouth and Nares samples have been sequenced from the following timepoints: DayOfDeparture, ArrivalBenin, DepartingBenin, ArrivalGeneva, DepartingGeneva, ArrivalHome, 1WeekPost, 2WeekPost
plot_richness(physeq, x="Timepoint",color="Timepoint", measures=c("Simpson"))+geom_boxplot(aes(x=Timepoint, y=value, color=Timepoint), alpha=0.05)
GPr = transform_sample_counts(physeq, function(x) x / sum(x) ) #transform samples based on relative abundance
GPrPhylum=tax_glom(GPr, "Phylum")
PhylumLevel = filter_taxa(GPrPhylum, function(x) mean(x) > 1e-3, TRUE) #filter out any taxa lower tha 0.1%
GPrFamily=tax_glom(GPr,"Family")
FamilyLevel = filter_taxa(GPrFamily,function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
GPrGenus=tax_glom(GPr,"Genus")
GenusLevel = filter_taxa(GPrGenus, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
Phylum Level stacked bar graph
df <- psmelt(PhylumLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Phylum", "Timepoint","Swab"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
cdataplot=ggplot(Trtdata, aes(x=Timepoint,y=mean))+geom_bar(aes(fill = Phylum),colour="black", stat="identity",position = "fill")+ facet_grid(~Swab, labeller=labeller(Sample_Type = labels))+xlab("Treatment")+ylab("Relative Abundance (> 0.1%)") + theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+theme(axis.title.x=element_blank())+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+ scale_fill_manual(values=cbPalette)
cdataplot
physeq=subset_samples(physeq,Timepoint!="2WeekPost")
sample_data(physeq)$Timepoint =factor(sample_data(physeq)$Timepoint, levels = c("DayOfDeparture","ArrivalBenin","DepartingBenin","ArrivalGeneva","DepartingGeneva","ArrivalHome","1WeekPost","2WeekPost"))
LInt=subset_samples(physeq,Swab=="Mouth")
LInt=subset_samples(LInt,Timepoint!="2WeekPost")
GPr = transform_sample_counts(LInt, function(x) x / sum(x) ) #transform samples based on relative abundance
GPrPhylum=tax_glom(GPr, "Phylum")
PhylumLevel = filter_taxa(GPrPhylum, function(x) mean(x) > 1e-3, TRUE) #filter out any taxa lower tha 0.1%
GPrFamily=tax_glom(GPr,"Family")
FamilyLevel = filter_taxa(GPrFamily, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
GPrGenus=tax_glom(GPr,"Genus")
GenusLevel = filter_taxa(GPrGenus, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
Stars (*) on plot represent unadjusted p values for the test for each taxa (Wilcox or KW test depending on number of groups), pairwise adjusted p values are in the table below (FDR adjustment) Table is filtered to only show taxa with a adjusted p value below 0.1
df <- psmelt(PhylumLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Phylum", "Timepoint"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
p <- ggbarplot(df, x = "Timepoint", y = "Abundance",add = c("mean_se"),#"mean_se"
color = "black", palette = "cbPalette", facet.by="Phylum",
line.color = "gray", line.size = 0.4, short.panel.labs = TRUE, p.adjust.method = "fdr", fill= "Timepoint") + stat_compare_means(aes(group = Timepoint), label = "..p.signif..",label.y = 7)
p+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab("Relative abundance (> 0.1%)")+ theme(legend.position="none")
Means=compare_means(Abundance ~ Timepoint, data = df,
group.by = "Phylum", p.adjust.method = "fdr")
#head(Means)
keeps <- c("Phylum","group1","group2","p.format","p.adj","method","p.signif")
keeps=Means[keeps]
#keeps
test3 <- list('Phylum'= keeps$Phylum,'group1'=keeps$group1,'group2'= keeps$group2 ,'p'=keeps$p.format,'p.adj'=keeps$p.adj,p.signif=keeps$p.signif,'Method'=keeps$method)
test3= as.data.frame(test3)
#test3
FilteredResults<-test3[!(test3$p.adj>0.1),]
FilteredResults
## [1] Phylum group1 group2 p p.adj p.signif Method
## <0 rows> (or 0-length row.names)
df <- psmelt(FamilyLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Family", "Timepoint"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
p <- ggbarplot(df, x = "Timepoint", y = "Abundance",add = c("mean_se"),#"mean_se"
color = "black", palette = "cbPalette", facet.by="Family",
line.color = "gray", line.size = 0.4, short.panel.labs = TRUE, p.adjust.method = "bonferroni", fill= "Timepoint") + stat_compare_means(aes(group = Timepoint), label = "..p.signif..",label.y = 7)
p+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab("Relative abundance (> 1%)")+ theme(legend.position="none")
Means=compare_means(Abundance ~ Timepoint, data = df,
group.by = "Family", p.adjust.method = "fdr")
#head(Means)
keeps <- c("Family","group1","group2","p.format","p.adj","method","p.signif")
keeps=Means[keeps]
#keeps
test3 <- list('Family'= keeps$Family,'group1'=keeps$group1,'group2'= keeps$group2 ,'p'=keeps$p.format,'p.adj'=keeps$p.adj,p.signif=keeps$p.signif,'Method'=keeps$method)
test3= as.data.frame(test3)
#test3
FilteredResults<-test3[!(test3$p.adj>0.1),]
FilteredResults
## [1] Family group1 group2 p p.adj p.signif Method
## <0 rows> (or 0-length row.names)
df <- psmelt(GenusLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Genus", "Timepoint"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
p <- ggbarplot(df, x = "Timepoint", y = "Abundance",add = c("mean_se"),#"mean_se"
color = "black", palette = "cbPalette", facet.by="Genus",
line.color = "gray", line.size = 0.4, short.panel.labs = TRUE, p.adjust.method = "fdr", fill= "Timepoint") + stat_compare_means(aes(group = Timepoint), label = "..p.signif..",label.y = 7)
p+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab("Relative abundance (> 1%)")+ theme(legend.position="none")
Means=compare_means(Abundance ~ Timepoint, data = df,
group.by = "Genus", p.adjust.method = "fdr")
#head(Means)
keeps <- c("Genus","group1","group2","p.format","p.adj","method","p.signif")
keeps=Means[keeps]
#keeps
test3 <- list('Genus'= keeps$Genus,'group1'=keeps$group1,'group2'= keeps$group2 ,'p'=keeps$p.format,'p.adj'=keeps$p.adj,p.signif=keeps$p.signif,'Method'=keeps$method)
test3= as.data.frame(test3)
#test3
FilteredResults<-test3[!(test3$p.adj>0.1),]
FilteredResults
## [1] Genus group1 group2 p p.adj p.signif Method
## <0 rows> (or 0-length row.names)
LInt=subset_samples(physeq,Swab=="Nares")
LInt=subset_samples(LInt,Swab!="2WeekPost")
GPr = transform_sample_counts(LInt, function(x) x / sum(x) ) #transform samples based on relative abundance
GPrPhylum=tax_glom(GPr, "Phylum")
PhylumLevel = filter_taxa(GPrPhylum, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
GPrFamily=tax_glom(GPr,"Family")
FamilyLevel = filter_taxa(GPrFamily, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
GPrGenus=tax_glom(GPr,"Genus")
GenusLevel = filter_taxa(GPrGenus, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
Stars (*) on plot represent unadjusted p values for the test for each taxa (Wilcox or KW test depending on number of groups), pairwise adjusted p values are in the table below (FDR adjustment) Table is filtered to only show taxa with a adjusted p value below 0.1
df <- psmelt(PhylumLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Phylum", "Timepoint"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
p <- ggbarplot(df, x = "Timepoint", y = "Abundance",add = c("mean_se"),#"mean_se"
color = "black", palette = "cbPalette", facet.by="Phylum",
line.color = "gray", line.size = 0.4, short.panel.labs = TRUE, p.adjust.method = "fdr", fill= "Timepoint") + stat_compare_means(aes(group = Timepoint), label = "..p.signif..",label.y = 7)
p+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab("Relative abundance (> 1%)")+ theme(legend.position="none")
Means=compare_means(Abundance ~ Timepoint, data = df,
group.by = "Phylum", p.adjust.method = "fdr")
#head(Means)
keeps <- c("Phylum","group1","group2","p.format","p.adj","method","p.signif")
keeps=Means[keeps]
#keeps
test3 <- list('Phylum'= keeps$Phylum,'group1'=keeps$group1,'group2'= keeps$group2 ,'p'=keeps$p.format,'p.adj'=keeps$p.adj,p.signif=keeps$p.signif,'Method'=keeps$method)
test3= as.data.frame(test3)
#test3
FilteredResults<-test3[!(test3$p.adj>0.1),]
FilteredResults
## [1] Phylum group1 group2 p p.adj p.signif Method
## <0 rows> (or 0-length row.names)
df <- psmelt(FamilyLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Family", "Timepoint"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
p <- ggbarplot(df, x = "Timepoint", y = "Abundance",add = c("mean_se"),#"mean_se"
color = "black", palette = "cbPalette", facet.by="Family",
line.color = "gray", line.size = 0.4, short.panel.labs = TRUE, p.adjust.method = "bonferroni", fill= "Timepoint") + stat_compare_means(aes(group = Timepoint), label = "..p.signif..",label.y = 7)
p+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab("Relative abundance (> 1%)")+ theme(legend.position="none")
Means=compare_means(Abundance ~ Timepoint, data = df,
group.by = "Family", p.adjust.method = "fdr")
#head(Means)
keeps <- c("Family","group1","group2","p.format","p.adj","method","p.signif")
keeps=Means[keeps]
#keeps
test3 <- list('Family'= keeps$Family,'group1'=keeps$group1,'group2'= keeps$group2 ,'p'=keeps$p.format,'p.adj'=keeps$p.adj,p.signif=keeps$p.signif,'Method'=keeps$method)
test3= as.data.frame(test3)
#test3
FilteredResults<-test3[!(test3$p.adj>0.1),]
FilteredResults
## [1] Family group1 group2 p p.adj p.signif Method
## <0 rows> (or 0-length row.names)
df <- psmelt(GenusLevel)
df$Abundance=df$Abundance*100
Trtdata <- ddply(df, c("Genus", "Timepoint"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
p <- ggbarplot(df, x = "Timepoint", y = "Abundance",add = c("mean_se"),#"mean_se"
color = "black", palette = "cbPalette", facet.by="Genus",
line.color = "gray", line.size = 0.4, short.panel.labs = TRUE, p.adjust.method = "fdr", fill= "Timepoint") + stat_compare_means(aes(group = Timepoint), label = "..p.signif..",label.y = 7)
p+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab("Relative abundance (> 0.1%)")+ theme(legend.position="none")
Means=compare_means(Abundance ~ Timepoint, data = df,
group.by = "Genus", p.adjust.method = "fdr")
#head(Means)
keeps <- c("Genus","group1","group2","p.format","p.adj","method","p.signif")
keeps=Means[keeps]
#keeps
test3 <- list('Genus'= keeps$Genus,'group1'=keeps$group1,'group2'= keeps$group2 ,'p'=keeps$p.format,'p.adj'=keeps$p.adj,p.signif=keeps$p.signif,'Method'=keeps$method)
test3= as.data.frame(test3)
#test3
FilteredResults<-test3[!(test3$p.adj>0.1),]
FilteredResults
## [1] Genus group1 group2 p p.adj p.signif Method
## <0 rows> (or 0-length row.names)
-All ordinations were conducted using Jensen-Shannon distance
-Ellipses represent 95% CI for the mean of each group
ord=ordinate(physeq,"PCoA", "jsd")
ordplot=plot_ordination(physeq, ord,"samples", color="Timepoint")+geom_point(size=4)+scale_colour_manual(values=cbPalette)+scale_fill_manual(values=cbPalette)
ordplot+ stat_ellipse(type= "norm",geom = "polygon", alpha = 1/4, aes(fill = Timepoint))+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
facet_wrap(~Swab)#+ theme(legend.justification=c(1,0), legend.position=c(1,0))
ord=ordinate(physeq,"PCoA", "jsd")
ordplot=plot_ordination(physeq, ord,"samples", color="Swab")+geom_point(size=4)+scale_colour_manual(values=cbPalette)+scale_fill_manual(values=cbPalette)
ordplot+ stat_ellipse(type= "norm",geom = "polygon", alpha = 1/4, aes(fill = Swab))+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
facet_wrap(~Timepoint)#+ theme(legend.justification=c(1,0), legend.position=c(1,0))
See QIIME outputs for other distance metrics
GPdist=phyloseq::distance(physeq, "jsd")
adonis(GPdist ~ Swab*Timepoint, as(sample_data(physeq), "data.frame"))
##
## Call:
## adonis(formula = GPdist ~ Swab * Timepoint, data = as(sample_data(physeq), "data.frame"))
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Swab 1 3.7610 3.7610 67.593 0.63596 0.001 ***
## Timepoint 6 0.1944 0.0324 0.582 0.03287 0.873
## Swab:Timepoint 6 0.1223 0.0204 0.366 0.02068 0.989
## Residuals 33 1.8362 0.0556 0.31049
## Total 46 5.9138 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Last updated on October 29, 2017.
This privacy policy is designed to provide you with information regarding data collection and use in connection with
our products and services.
A. INFORMATION WE COLLECT
1. Why do we collect data?
We collect data in order to effectively provide useful products and services to you and others. We also use this
data to improve the quality of those products and services. such as finding near station to your location.
2. What kind of data to you collect?
We collect information such as:
Location data. When you enable location-based features of our products, we may collect location data, such as GPS
coordinates. We may also collect location data derived from your IP address.
When you are asked to provide personal data, you may decline. But if you choose not to provide data that is
necessary to provide a service, you may not be able to use some features or services.
3. Do you share the data you collect? Why?
No. This datas are only accessable from your devices.
C. MISCELLANEOUS
1. Children's privacy
Our services are not intended for children under the age of 3.
2. Changes to the Privacy Policy
Please see the date at the top of this page for information about when it was last updated. We encourage you to
periodically review this page for the latest information on our privacy practices.
3. How do I contact you?
If you have any questions about this policy, please write at info[ at ] yazdipour.github.io


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Licensed under the Apache 2.0 License.
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is
distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
Special Thanks to /u/Slateclean for supplying touchscreens, so development can continue!


User documentation¶
What is Permon?¶
Permon is a tool to display live line charts in a clear, uncluttered way. Permon comes prepackaged with a lot of useful stats for monitoring the performance of your PC. It is developed with a focus on only showing you things you care about, not everything you can monitor in your system.
Permon is developed in Python 3. There is a good chance you already have Python on your system. If
not, install it (recommended: Miniconda. Run pip
install permon
in your preferred command line to install permon. Permon can then be started from the command line.
Synopsis¶
usage: permon [-h] [--version] {terminal,native,browser,config,password} ...
Positional Arguments¶
| subcommand | Possible choices: terminal, native, browser, config, password the subcommand to launch. When When When |
Named Arguments¶
| --version | show permon’s version and exit Default: False |
Sub-commands:¶
terminal¶
Starts permon’s terminal frontend.
permon terminal [-h] [--fps FPS] [--verbose] [--buffer-size BUFFER_SIZE]
[stats [stats ...]]
Positional Arguments¶
| stats | which stats to display If none are given, take those from the config file (core.cpu_usage, core.ram_usage, core.read_speed, core.write_speed, core.vram_usage) Default: [‘core.cpu_usage’, ‘core.ram_usage’, ‘core.read_speed’, ‘core.write_speed’, ‘core.vram_usage’] |
Named Arguments¶
| --fps | the frames per second the display moves with |
| --verbose | whether to enable verbose logging Default: True |
| --buffer-size | the number of points displayed on the screen at any time |
native¶
Starts permon’s native frontend.
permon native [-h] [--fps FPS] [--verbose] [--buffer-size BUFFER_SIZE]
[stats [stats ...]]
Positional Arguments¶
| stats | which stats to display If none are given, take those from the config file (core.cpu_usage, core.ram_usage, core.read_speed, core.write_speed, core.vram_usage) Default: [‘core.cpu_usage’, ‘core.ram_usage’, ‘core.read_speed’, ‘core.write_speed’, ‘core.vram_usage’] |
Named Arguments¶
| --fps | the frames per second the display moves with |
| --verbose | whether to enable verbose logging Default: True |
| --buffer-size | the number of points displayed on the screen at any time |
browser¶
Starts permon’s browser frontend.
permon browser [-h] [--port PORT] [--ip IP] [--no-browser]
[--certfile CERTFILE] [--keyfile KEYFILE] [--fps FPS]
[--verbose] [--buffer-size BUFFER_SIZE]
[stats [stats ...]]
Positional Arguments¶
| stats | which stats to display If none are given, take those from the config file (core.cpu_usage, core.ram_usage, core.read_speed, core.write_speed, core.vram_usage) Default: [‘core.cpu_usage’, ‘core.ram_usage’, ‘core.read_speed’, ‘core.write_speed’, ‘core.vram_usage’] |
Named Arguments¶
| --port | the port permon will listen on Default: 1234 |
| --ip | the IP address permon will listen on Default: “localhost” |
| --no-browser | don’t open permon in a browser after startup Default: False |
| --certfile | the path to an SSL/TLS certificate file |
| --keyfile | the path to a private key file for usage with SSL/TLS |
| --fps | the frames per second the display moves with |
| --verbose | whether to enable verbose logging Default: True |
| --buffer-size | the number of points displayed on the screen at any time |
config¶
Command to interact with the configuration of permon. Default configuration:
{
"stats": [
"core.cpu_usage",
"core.ram_usage",
"core.read_speed",
"core.write_speed"
],
"colors": [
"#ed5565",
"#ffce54",
"#48cfad",
"#sd9cec",
"#ec87c0",
"#fc6e51",
"#a0d468",
"#4fc1e9",
"#ac92ec"
],
"verbose": true,
"password": null
}
Stats can either be specified by their tag name like in the default config or
by a dictionary with keys tag
and settings
where settings
is a
dictionary consisting of keys and values specifying the value of settings
for the respective stat e. g.
...
"stats": [
{
"tag": "jupyter.ram_usage",
"settings": {
"connection info": "",
"query interval [s]": 1
}
}
]
...
permon config [-h] {edit,show,reset}
Positional Arguments¶
| command | Possible choices: edit, show, reset Which command to run. When When When |
password¶
Open dialog to set permon’s password. The password is currently only used for authentication in the browser frontend.
permon password [-h]
Stats¶
Permon comes prepackaged with some useful stats to monitor the performance of your PC. You can also add custom stats to Permon. See Extending permon with custom stats to see how to do that. The following stats are always part of Permon:
-
class
permon.backend.stats.core.
CPUStat
¶ tag:
core.cpu_usage
settings: none
Tracks the CPU usage of the user. Also returns the top contributors to the CPU usage.
-
class
permon.backend.stats.core.
RAMStat
¶ tag:
core.ram_usage
settings: none
Tracks the RAM usage of the user. Also returns the top contributors to the RAM usage.
-
class
permon.backend.stats.core.
GPUStat
¶ tag:
core.vram_usage
settings: none
Tracks the current vRAM usage of the user. Currently only works for NVIDIA GPUs and
nvidia-smi
must be installed. To check if it is installed, typenvidia-smi
in your command line and see if the command was found.
-
class
permon.backend.stats.core.
ReadStat
¶ tag:
core.read_speed
settings: none
Tracks the disk read speed of the user.
-
class
permon.backend.stats.core.
WriteStat
¶ tag:
core.write_speed
settings: none
Tracks the disk write speed of the user.
-
class
permon.backend.stats.core.
CPUTempStat
¶ tag:
core.cpu_temp
settings: none
Tracks the CPU temperature of the user.
-
class
permon.backend.stats.jupyter.
JupyterRAMUsage
¶ tag:
jupyter.ram_usage
settings:
{ "connection info": "", "query interval [s]": 1 }
Tracks the RAM usage of all variables in a user-specified jupyter notebook. If no connection info is given in the settings, take the kernel with the latest start date.
connection info
must be a string containing the info displayed when running%connect_info
in a jupyter notebook (you can directly copy-paste it).query interval [s]
specifies how often the thread running in the jupyter notebook should read the variables. The lower this is, the higher the resolution of the stat but it might start affecting the speed of your notebook when too low.Note that RAM tracked in this way is not equal to the actual RAM the OS needs because some further optimization is done by e. g. numpy to reduce the OS memory usage.
Extending permon with custom stats¶
Permon can be extended by adding custom stats. Custom stats have to be put in the stats
subdirectory of the user
config directory. You can find your user config directory by executing permon config show
and looking for Config directory.
Hint
On Linux, execute cd `permon config show | grep -o "\S*/permon$"`
to directly change your directory to permon’s config directory.
In the config directory, make a subdirectory stats
and create a file called <name>.permon.py
in the subdirectory where <name>
is the name for
your suite of stats e. g. custom.permon.py
.
The tag of your stat will then be <name>.<base_tag>
where <base_tag>
is the static base_tag
attribute
of your stat class.
You can now start creating stats in this file. They will automatically be discovered by permon. A simple custom stat could look like this:
import math
from permon.backend import Stat
class SineStat(Stat):
name = 'Sine'
base_tag = 'sine'
def __init__(self, fps):
self.t = 0
super(SineStat, self).__init__(fps)
def get_stat(self):
# get_stat is called once per frame to fetch the latest value for the stat
self.t += 1 / self.fps
return math.sin(self.t)
@property
def maximum(self):
# sets the maximum value for the stat
# if the stat has no fixed maximum, make it return None
return 1
@property
def minimum(self):
# sets the minimum value for the stat
# if the stat has no fixed minimum, make it return None
return -1
All stats must inherit from the permon.backend.Stat
base class. Also, the parent constructor should be called at the end of __init__
to make sure get_stat
can run successfully (the parent constructor needs to call it once to check whether the stat has a contributor breakdown).
View your first custom stat by running permon native custom.sine
(or, of course, use another frontend).
Adding settings to a stat¶
Stats can have settings to conventienly change some aspects of the stat. Extending the example from above:
import math
from permon.backend import Stat
class SineStat(Stat):
name = 'Sine'
base_tag = 'sine'
default_settings = {
'speed': 1.
}
def __init__(self, fps):
self.t = 0
super(SineStat, self).__init__(fps)
def get_stat(self):
self.t += 1 / self.fps * self.settings['speed']
return math.sin(self.t)
@property
def maximum(self):
return 1
@property
def minimum(self):
return -1
Note that default_settings
only stores the default settings. In the calculation, self.settings
is used because it stores the settings that
are entered in the UI.
Every setting must have a default value. Every value should be a basic python data type (string, float, integer, …) because
permon will automatically cast the user-entered string to the type specified in the default_settings
.
If you need more advanced data types like JSON, make the default value a string and manually convert it to the needed data type afterwards.
Making a stat conditionally available¶
Some stats are not always available, like permon.backend.stats.JupyterRAMUsage()
which needs nvidia-smi
installed.
To add some logic to check whether your stat is available or not, implement the Stat.check_availability()
method.
This method must raise a permon.exceptions.StatNotAvailableError()
if the stat is not available. cls.settings
can also be used
in Stat.check_availability()
to e. g. disallow some settings. Further expanding the example from above:
import math
from permon.backend import Stat
from permon import exceptions
class SineStat(Stat):
name = 'Sine'
base_tag = 'sine'
default_settings = {
'speed': 1.
}
@classmethod
def check_availability(cls):
if cls.settings['speed'] <= 0:
raise exceptions.StatNotAvailableError(
'Speed must be greater than zero.')
def __init__(self, fps):
self.t = 0
super(SineStat, self).__init__(fps)
def get_stat(self):
self.t += 1 / self.fps * self.settings['speed']
return math.sin(self.t)
@property
def maximum(self):
return 1
@property
def minimum(self):
return -1
Adding a contributor breakdown to a stat¶
Stats can have a contributor breakdown. A contributor breakdown shows what contributes to the stat, e. g. how much of the CPU different
processes need for permon.backend.stats.CPUUsage()
.
import math
from permon.backend import Stat
class SineModulationStat(Stat):
name = 'Sine Modulation'
base_tag = 'sine_modulation'
def __init__(self, fps):
self.t = 0
super(SineModulationStat, self).__init__(fps)
def get_stat(self):
self.t += 1 / self.fps
sine1 = 1 + math.sin(self.t)
sine2 = 1 + math.sin(self.t * 10)
return sine1 + sine2, [
('sine1', sine1),
('sine2', sine2)
]
@property
def maximum(self):
# both sines have a maximum of 2 because they are offset by +1
return 4
@property
def minimum(self):
return 0
Stats with a contributor breakdown must return the stat value and a list containing tuples of (name, value)
for every contributor (sine1 and sine2 in the above example).
Permon will automatically handle the rest.
Warning
The contributor breakdown of stats with a minimum of less than zero might have unexpected behaviour because the range of contributors is not well-defined in that case.
Negative contributors are also not handled well at the moment.
That already covers the full functionality of any stat. To see how the prepackaged stats are implemented, see the source on github.


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Joanne Roberts
Flips the model of Knowledge Management on its head, suggesting a focus on ignorance would be more effective, especially, for example, within innovation processes as it takes focus away from the known.


Looking for a quick and easy way to get together with your friends? You've come to the right place.
In order to send out invites to your friends, we need to know who your are.


2. Editor coordinates system. Positions and offsets
Every caret in the editor has a set of properties describing it’s coordinates. These properties can be accessed by obtaining a caret model instance. Working with caret positions and it’s logical and visual properties will be explained in the sample below.
2.1. Pre-requirements
Access to the Editor is performed through an action.
2.2. Accessing caret positions
To get an access to caret positions an instance of CaretModel should be obtained.
public class EditorAreaIllustration extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
final Editor editor = anActionEvent.getRequiredData(CommonDataKeys.EDITOR);
CaretModel caretModel = editor.getCaretModel();
}
@Override
public void update(AnActionEvent e) {
//...
}
}
2.3. Logical position
LogicalPosition.java represents a line and a column of the current logical position of the caret. Logical positions ignore folding - for example, if the top 10 lines of the document are folded, the 10th line in the document will have the line number 10 in its logical position.
public class EditorAreaIllustration extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
final Editor editor = anActionEvent.getRequiredData(CommonDataKeys.EDITOR);
CaretModel caretModel = editor.getCaretModel();
LogicalPosition logicalPosition = caretModel.getLogicalPosition();
}
@Override
public void update(AnActionEvent e) {
//...
}
}
Logical position may store additional parameters that define its mapping to VisualPosition.java. Rationale is that single logical pair matches soft wrap-introduced virtual space, i.e. different visual positions correspond to the same logical position. It’s convenient to store exact visual location details within the logical position in order to relief further ‘logical position’ -> ‘visual position’ mapping.
2.4. Visual position
VisualPosition.java represent a visual position and may very from the corresponding logical position. Visual positions take folding into account - for example, if the top 10 lines of the document are folded, the 10th line in the document will have the line number 1 in its visual position.
public class EditorAreaIllustration extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
final Editor editor = anActionEvent.getRequiredData(CommonDataKeys.EDITOR);
CaretModel caretModel = editor.getCaretModel();
LogicalPosition logicalPosition = caretModel.getLogicalPosition();
VisualPosition visualPosition = caretModel.getVisualPosition();
}
@Override
public void update(AnActionEvent e) {
//...
}
}
2.5. Offset
An absolute offset for a given caret position is accessible through CaretModel as well
public class EditorAreaIllustration extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
final Editor editor = anActionEvent.getRequiredData(CommonDataKeys.EDITOR);
CaretModel caretModel = editor.getCaretModel();
LogicalPosition logicalPosition = caretModel.getLogicalPosition();
VisualPosition visualPosition = caretModel.getVisualPosition();
int offset = caretModel.getOffset();
}
@Override
public void update(AnActionEvent e) {
//...
}
}
2.6. Displaying position values
To display the actual values of logical anf visual positions we add an
Messages.showInfoMessage()
call that will show them in form of notification after the action is performed.
public class EditorAreaIllustration extends AnAction {
@Override
public void actionPerformed(AnActionEvent anActionEvent) {
final Editor editor = anActionEvent.getRequiredData(CommonDataKeys.EDITOR);
CaretModel caretModel = editor.getCaretModel();
LogicalPosition logicalPosition = caretModel.getLogicalPosition();
VisualPosition visualPosition = caretModel.getVisualPosition();
int offset = caretModel.getOffset();
Messages.showInfoMessage(logicalPosition.toString() + "\n" +
visualPosition.toString() + "\n" +
"Offset: " + offset, "Caret Parameters Inside The Editor");
}
@Override
public void update(AnActionEvent e) {
//...
}
}
Check out, compile, and run the Editor Basics Plugin, then move carets, invoke EditorAreaIllustration action, and see how logical and visual positions are related dependently on folding.
Find the action in the context menu:
Perform the action to see caret positions:


Hi! It's Gisu Chung.
I received a master's degree in the department of Electrical & Electronic Engineering from Dongguk University, in 2021.
During the master's degree, I researched efficient network compression methods for utilizing CNN in real-time inference.
Also, I studied video data augmentation strategy for 3D CNN-based approach such as video action recognition task.
Recently, I've worked as a Machine Learning Engineer Intern at On-device & AI moderation squad in Hyperconnect ML Team.
For more, check out my CV and contact me by email.


mushroomwarjava
Classes
botIA
Circle
GameOver
gameSetting
gameUI
GenerateUnity
helpPage
IA
mushroomUI
MushroomWarJava
Player
stateOfGame
targetHouse