We would like to thank the Vodafone Americas Foundation, the Bill and Melinda Gates Foundation, and the S&#227;o Paulo Research Foundation (FAPESP) for funding this research. We would also like to thank the many faculty members from the Department of Entomology at University of California, Riverside, for their advice on this project.&#160;

1昆虫コロニー、飼育<ol><li>モスキートコロニー、子育て<ol><li>野生に由来リアイエカ足根骨の、ネッタイイエカ、アカイエカstigmatosoma、とラボコロニーからネッタイシマカ大人、個人を捉えました。 </li><li>リア標準実験室条件下でエナメルパンにおける蚊の幼虫（27℃、16：8時間明：1時間夕暮れ/夜明け期間が濃い[LD]サイクル）、および接地げっ歯類飼料およびビール酵母の混合物を自由摂取彼らを養う（3：1、V：V）。 </li><li> 200ミリリットルカップに蚊蛹を集め、実験室に配置します。代わりに、出現の1週間以内に実験室に大人の蚊を吸引除去する。各実験室は同じ種/性別の20から40の個人が含まれていることを確認します。 </li><li> 10％のスクロースおよび水の混合物を餌大人の蚊不断 。食料を毎週交換してください。 </li><li> plの週2回綿のタオルを湿らせケージ内の湿度を維持するために、実験室の上にそれらをエース。また、全体的な湿度レベルを維持するために常時チャンバ内で水道水200mlのカップを置く。 </li><li>実験期間中、暗所[LD]サイクル、20.5から22℃、30〜50％RH：8時間明：16についての実験室を維持します。 </li></ol></li><li>イエバエとフルーツフライコロニー、子育て<ol><li>野生から派生ラボコロニーからのリアイエバエは 、個人を捉えました。実験チャンバーにおける野生ショウジョウバエオナジ個人やリアそれらをキャッチ。 </li><li>標準的な実験室条件下でのプラスチック浴槽内リアイエバエの幼虫（12時12時間の明：暗所[LD]サイクル、26℃、40％RH）水、ふすま粉、アルファルファ、酵母、および粉ミルクの混合物中。飼育室でのリアショウジョウバエオナジ幼虫と腐敗の混合物上に自由に摂取を養うフルーツ。 </li><li>出現の1週間以内に実験室に吸引し、成人イエバエ 。大人のショウジョウバエオナジ直接実験チャンバーで飼育することができます。データ収集の前に、各実験室はせいぜい10〜15の個別イエバエまたは20〜30の個別ショウジョウバエオナジが含まれていないことを確認してください。 </li><li>水に自由にアクセスさせて、砂糖と低脂肪ドライミルクの混合物を、成人イエバエを自由にフィード。果物を腐敗との混合物を、大人のショウジョウバエオナジ不断フィード 。食料を毎週交換してください。 </li><li>実験期間中、暗所[LD]サイクル、20.5から22℃、30〜50％RH：8時間明：16実験チャンバーを維持します。 </li></ol></li></ol> 2。実験チャンバー内のレコードのフライングサウンド<ol><li>実験室内のセットアップ 注：「実験的Chamber」は、データが記録された私たちの研究室で設計ケージを意味する。センサは、かなり安価である。バルクに建てられたときは、セットアップは10ドル未満のために製造することができた。 <ol><li> 67センチメートルL×22センチメートル幅x 24.75センチメートルHは、以下のサイズ：30センチメートルL×20センチメートル幅×20センチHの実験室はフォトトランジスタアレイとから構成され、実験室、大きなサイズのいずれかを構築フォトトランジスタ·アレイを指し示すレーザーライン。 注：さらに、チャンバーは、センサ装置だけでなく、昆虫へのアクセスを許可するように塩ビ配管の一部に取り付けられたスリーブを含むように変更されているKritterキーパーで構成されています。 </li><li>電子基板へのフォトトランジスタ·アレイを接続します。電子基板の出力は、デジタル音声レコーダに供給し、MP3形式のオーディオデータとして記録される。 図1.IIセンサの論理設計図1.Iチャンバーの物理的なバージョンを見る。 </li><l私&gt;昆虫が逃げるのを防ぐために、内部に取り付けられたメッシュ布で実験チャンバーの蓋を変更します。 注：昆虫は、レーザビームを横切って飛ぶと、その翼は、部分的に、光を閉塞する小さな光の変動を引き起こす。光変動は電流の変化としてフォトトランジスタ·アレイによって捕捉され、信号は、カスタム設計された電子ボードによってフィルタリングされ、増幅される。 </li></ol></li><li>飛翔昆虫プロデュースを録音する音にシステムの設定<ol><li>電源に実験室を接続します。電源をオンにします。 </li><li>実験室では、レーザ光やフォトアレイを見つける。フォトアレイへのレーザ光の位置を合わせます。適切な位置合わせを達成するために、レーザ光がすべての個別photodiodを中心とするまで、チャンバの内部にフォトアレイに取り付けられた磁石に対応する実験室の外側に磁石を用いたフォトアレイを調整するエス。 </li><li>システムが適切に設定されていることを確認する2健全性チェックを実行します。 注：最初のステップは、システムが電源が入っている、すべての配線が正しく接続されており、レーザーフォトアレイを指していることを確認することです。第二のステップは、虫 &#39;wingbeatsの音をキャプチャすることができます確保するために、レーザーの位置合わせの詳細検査を実施することである。 <ol><li>オーディオジャックに（むしろレコーダーより）ヘッドフォンを接続します。レーザー光の平面を破るために、フォトアレイの近くに、実験室の内外手プランジ。レーザー光がオンになっている（それは光の赤色光になる）と、あなたの手で光の面を数回を破ることを確認してください。あなたの手がで、光ビームの外に出るようにノイズレベルの変化を聞いてください。もし可聴差を検出する場合、センサは、大きな物体の動きによって生成される音を取り込むことができる。成功した場合、ヘッドホンが正しく接続されているかどうかを確認、それ以外の場合は、次の健全性チェックに移動し、レーザーはフォトアレイを指しているかどうか。実験室の内外運針の音が聞こえるまで、それに応じてフォトアレイを調整します。 </li><li>自動歯ブラシに電気配線の薄い部分を取り付けます。歯ブラシの電源を入れ、phototarrayに近く、実験室の内外配線が急落。それが動くように、レーザ光が配線の一部に当たっていることを確認してください。もし周波数の音の変化を検出した場合、配線の一部が、レーザ光 ​​の平面を遮断するとき、システムは次に、小さな物体の移動によって生成さ音、 すなわち 、昆虫の音をキャプチャする準備ができている。あなたは音の違いを検出しない場合には、レーザ光やフォトアレイを再整列させるために2.2.2に戻ってください。 </li></ol></li><li>システムが適切に設定された後、蓋を閉じて虫を追加します。 </li></ol></li><li>データ収集：飛翔昆虫プロデュースを録音サウンド<ol><li>レコーダーの電源を入れ、VOICを作る以下の情報を含む電子注釈：実験室における種名、昆虫、日時、現在の周囲RT、および相対湿度の年齢。録音を一時停止します。 </li><li>オーディオケーブルを使って、システムにレコーダーを接続し、録音を再開。録音を停止する、3日間記録してレコーダーにしておきます。 </li><li> PC上で新しいフォルダに記録計からデータをダウンロードしてください。データを削除することでレコーダーを空にします。 </li><li>残りの昆虫がオフに死亡し、ケージの中で生き残された5つ以上の昆虫がなくなるまで、上記の記録処理を繰り返します。 </li></ol></li></ol> 3センサデータ処理と飛翔昆虫プロデュース音の検出<ol><li>飛翔昆虫プロデュース音を検出するためのソフトウェアを使用してください。 注：ソフトウェア（検出アルゴリズム）は、リアルタイムよりもはるかに高速である。それは、DATの3日間、 すなわち 、記録セッションを処理するために3時間未満のかかりA、2.00 GHzの8 GBのRAMでのインテル（R）コア™の​​CPUを搭載した標準的なマシンで。 <ol><li>レコーディング·セッションからのデータを含む各フォルダには、昆虫の音を検出するための検出ソフトウェアを実行します。ソフトウェア、オープンMATLABを実行し、DATADIRは 、記録データのディレクトリですコマンドウィンドウで「circandian_wbf（DATADIR）」を 、入力します。その後、開始するには &quot;Enter&quot;を押してください。 注：参照番号16から検出ソフトウェアのcircadian_wbfをダウンロードしてください。 </li><li>アルゴリズムが終了するまで、検出結果をチェックした後、待ってください。アルゴリズムはDATADIRは、前の手順と同じです」DATADIRの _extf」という名前の新 ​​しいフォルダに検出されたすべての昆虫の音を出力します。各サウンドファイルは、もともと生の記録から抽出された1秒長いオーディオクリップで、デジタルフィルタでノイズを除去するために適用。各検出された音の発生時間」という名前のファイルに保存されDATADIR _time.mat＆＃8221 ;. 図2で検出された虫の音の例を観察します。 </li></ol></li><li>検出アルゴリズム<ol><li>記録を通ってスライドするように0.1秒長いスライディングウィンドウを使用します。スライディングウィンドウは、記録の最初から始まります。各ウィンドウには、次の手順に従ってください。 <ol><li>現在のウィンドウの基本周波数を計算します。 </li><li>基本周波数が1200ヘルツ、100ヘルツの範囲内にある場合は、次の操作を行います。 <ol><li>レコーディングから現在のウィンドウを中心と1秒の長いオーディオクリップを抽出します。クリップ内のノイズを除去し、フォルダ「DATADIRの _extf」に濾過オーディオを保存するためにデジタルフィルタを適用します。 </li><li>ファイル&quot;DATADIRの _time」に現在のウィンドウの発生時刻を保存します。 </li><li>すぐに抽出されたオーディオクリップを、以下の点にスライディングウィンドウを移動します。 </li></ol></li><li>根本的なFRの場合equencyは単に転送スライディングウィンドウを0.01秒に移動、1200 Hzの100Hzの範囲内ではありません。 </li></ol></li><li>スライディングウィンドウは録画の最後に到達するまで、このプロセスを繰り返します。 </li></ol></li></ol> 4。昆虫の分類<ol><li>ただフライングサウンドを使用して、ベイズ分類 注：ベイズ分類は、最も可能性の高いクラスにオブジェクトを分類する確率的分類器である。 <ol><li>サウンド機能の計算<ol><li>各昆虫音、離散フーリエ変換（DFT）を用いて、音の周波数スペクトルを計算する。 2000 Hzの100ヘルツ：周波数範囲に対応するだけのデータ点を含むように周波数スペクトルを切り捨てる。切り捨てられた周波数スペクトルは、その後昆虫音の「代表」としての分類に使用される。 NOTE：DFT周波数領域、時間領域の信号に変換するアルゴリズムである。それは、BUIですLT-におけるほとんどのプログラミングライブラリ内の関数であり、は、たった1行のコードを使用してプログラムで呼び出すことができます。 </li></ol></li><li>ベイズ分類器を訓練<ol><li>サウンド機能を使用して事後確率分布を学習するのkNN密度推定法14を使用してください。 kNNのアプローチでは、トレーニング段階は、訓練データセットを構築することである。 <ol><li>ランダム昆虫のそれぞれの種のために収集されたデータから虫の音の数をサンプリングする。 </li><li> 4.1.1項の手順に従って、各サンプリングした音のために切り捨てられた周波数スペクトルを計算する。一緒にサンプルを「クラスラベル（昆虫種名）を持つ切り捨てられたスペクトルは、トレーニングデータセットを作曲。 </li></ol></li></ol></li><li>未知の昆虫を分類するためにベイズ分類器を使用して、 <ol><li>未知の昆虫音の切断周波数スペクトルを計算する。 </li><li> unknowの切断スペクトルとの間のユークリッド距離を計算nのオブジェクトとトレーニングデータセット内のすべての切り捨てスペクトル。 </li><li>上位k（本論文ではk = 8）のトレーニングデータセット内の未知のオブジェクトの最も近い隣人を検索します。クラスとしてラベル付けされる上位k最近傍の割合としてクラスに属する未知の昆虫音の事後確率を計算します。 </li><li>最も高い事後確率を持つクラスに未知の物体を分類します。 </li></ol></li></ol></li><li>昆虫概日リズム：クラシファイアに機能を追加<ol><li> 、昆虫のそれぞれの種のための概日リズムである昆虫の音の発生時間、クラス付き分布は、こちらをご覧ください。 <ol><li>検出結果から各音（のCF 3.2節）の発生時刻を取得します。 </li><li>それぞれの種、昆虫音発生時間のヒストグラムを構築する。 </li><li>ヒストグラムの面積が1になるようにヒストグラムを正規化​​します。通常の化されたヒストグラムは、所定の種の概日リズムである。これは、特定の期間内飛行中、その種の昆虫を観測する確率を伝える。 </li></ol></li><li> 「昆虫の音」と概日リズムを組み合わせることで、未知の「昆虫の音を」分類<ol><li>未知の昆虫の音が発生した特定の時点を考えると、クラスの概日リズムに基づいて、クラスの昆虫を観察する確率を得る。 注：概日リズムが確率分布である。それは、一日の特定の時間に、昆虫の特定の種によって生成される音を検出する確率を指定する配列です。時間が与えられた後、それで、人は単に確率を得るために、配列を確認することができます。 </li><li>未知の音は、サウンド機能を使用して、クラスに属する事後確率を計算するためのセクション4.1.2の手順に従います。あちこちに結果を事後確率を掛け新しい事後確率を得るために、前の手順をメートル。 </li><li>最高の新しい事後確率を持つクラスを &quot;未知の音」を分類します。 </li></ol></li></ol></li><li>クラシファイアにワン·モア·フィーチャーを追加します。昆虫地理分布<ol><li> 、いずれの史料で出版されたデータから、対象の種の地理的分布を学ぶ関連文献、または単に現場の技術/生物学者から最初の手の知識を収集します。 図7に示すように、デモのために、グラフィック分布のシミュレーションを使用しています。 </li><li> 「フライング音」2つの追加機能を使用して「未知の虫の音」を分類します。 <ol><li>昆虫の音が傍受された地理的位置を考えると、種のグラフィックディストリビューションを使用して、特定の場所でクラスから昆虫を観察する確率を計算する。 <ol><li>セクション4.2.2 Aの手順に従ってくださいndは「未知の音が「音の機能と概日リズムを使用したクラスに属する事後確率を計算する。新しい事後確率を得るために、前のステップからの結果は、このステップの結果を乗算する。 </li></ol></li><li>最高の新しい事後確率を持つクラスを &quot;未知の音」を分類します。 </li></ol></li></ol></li><li>機能を追加するための一般的なフレームワーク<ol><li>主な分類子としてだけで音機能を使用していますベイズ分類を考えてみましょう。分類器に新しい機能を追加するには、以下の手順に従ってください。 <ol><li>トレーニング段階では、新機能のクラス付き密度関数を学習。 </li><li>分類段階では、「未知の音」の新機能で与えられた、前のステップで学習した密度関数を使用してクラスに特徴を観測する確率を計算する。 PRに新しい確率を掛け新しい事後確率を得るために、単に奇数の機能に基づいて計算されたクラスに所属する「未知の音」に対応するevious事後確率。最高の新しい事後確率を持つクラスに未知の物体を分類します。 </li></ol></li></ol></li></ol>

<ol>
	<li>Kahn, M. C., Celestin, W., Offenhauser, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recording+of+sounds+produced+by+certain+disease-carrying+mosquitoes.">Recording of sounds produced by certain disease-carrying mosquitoes.</a> Science. 101 (2622), 335-336 (1945).</li><li>Reed, S. C., Williams, C. M., Chadwick, L. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency+of+wing-beat+as+a+character+for+separating+species+races+and+geographic+varieties+of+Drosophila.">Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila.</a> Genetics. 27 (3), 349 (1942).</li><li>Belton, P., Costello, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flight+sounds+of+the+females+of+some+mosquitoes+of+Western+Canada.">Flight sounds of the females of some mosquitoes of Western Canada.</a> Entomologia experimentalis et applicata. 26 (1), 105-114 (1979).</li><li>Mankin, R. W., Machan, R., Field Jones, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=testing+of+a+prototype+acoustic+device+for+detection+of+Mediterranean+fruit+flies+flying+into+a+trap.">testing of a prototype acoustic device for detection of Mediterranean fruit flies flying into a trap.</a> Proc. 7th Int Symp Fruit Flies of Economic Importance. , 10-15 (2006).</li><li>Raman, D. R., Gerhardt, R. R., Wilkerson, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+insect+flight+sounds+in+the+field+Implications+for+acoustical+counting+of+mosquitoes.">Detecting insect flight sounds in the field Implications for acoustical counting of mosquitoes.</a> Transactions of the ASABE. 50 (4), 1481 .</li><li>Moore, A., Miller, J. R., Tabashnik, B. E., Gage, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+identification+of+flying+insects+by+analysis+of+wingbeat+frequencies.">Automated identification of flying insects by analysis of wingbeat frequencies.</a> Journal of economic entomology. 79 (6), 1703-1706 (1986).</li><li>Moore, A., Miller, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+identification+of+optically+sensed+aphid+(Homoptera:+Aphidae)+wingbeat+waveforms.">Automated identification of optically sensed aphid (Homoptera: Aphidae) wingbeat waveforms.</a> Annals of the Entomological Society of America. 95 (1), 1-8 (2002).</li><li>Li, Z., Zhou, Z., Shen, Z., Yao, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+identification+of+mosquito+(diptera:+Culicidae)+wingbeat+waveform+by+artificial+neural+network.">Automated identification of mosquito (diptera: Culicidae) wingbeat waveform by artificial neural network.</a> Artificial Intelligence Applications and Innovations. , 483-489 (2005).</li><li>Moore, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+neural+network+trained+to+identify+mosquitoes+in+flight.">Artificial neural network trained to identify mosquitoes in flight.</a> Journal of insect behavior. 4 (3), 391-396 (1991).</li><li>Halevy, A., Norvig, P., Pereira, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+unreasonable+effectiveness+of+data.">The unreasonable effectiveness of data.</a> Intelligent Systems, IEEE. 24 (2), 8-12 (2009).</li><li>Banko, M., Brill, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mitigating+the+paucity-of-data+problem+exploring+the+effect+of+training+corpus+size+on+classifier+performance+for+natural+language+processing.">Mitigating the paucity-of-data problem exploring the effect of training corpus size on classifier performance for natural language processing.</a> , 1-5 (2001).</li><li>Shotton, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+human+pose+recognition+in+parts+from+single+depth+images.">Real-time human pose recognition in parts from single depth images.</a> Communications of the ACM. 56 (1), 116-124 (2013).</li><li>Ilyas, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Classifying+insects+on+the+fly.">Classifying insects on the fly.</a> Ecological Informatics. , (2013).</li><li>Mack, Y. P., Rosenblatt, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multivariate+k-nearest+neighbor+density+estimates.">Multivariate k-nearest neighbor density estimates.</a> Journal of Multivariate Analysis. 9 (1), 1-15 (1979).</li><li>Benedict, M., Robinson, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+first+releases+of+transgenic+mosquitoes+an+argument+for+the+sterile+insect+technique.">The first releases of transgenic mosquitoes an argument for the sterile insect technique.</a> TRENDS in Parasitology. 19 (8), 349-355 (2003).</li><li>. Supporting Materials Available from: <a target="_blank" href="https://sites.google.com/site/insectclassification">https://sites.google.com/site/insectclassification</a></li><li>Chen, Y., Why, A., Batista, G., Mafra-Neto, A., Keogh, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flying+Insect+Classification+with+Inexpensive+Sensors.">Flying Insect Classification with Inexpensive Sensors.</a> Journal of Insect. 27 (5), 657-677 (2014).</li></ol>

The authors declare that they have no competing financial interests.

The sensor/classification framework described here allows the inexpensive and scalable classification of flying insects. The accuracies achievable by the system are good enough to allow the development of commercial products and could be a useful tool in entomological research.
The ability to use inexpensive, noninvasive sensors to accurately and automatically classify flying insects would have significant implications for entomological research. For example, by deploying the system in the field to count and classify insect vectors, the system can provide real-time counts of the target species, producing real-time information that can be used to plan intervention/suppression programs to combat malaria. Moreover, the system can automatically separate insects by sex, and thus it can be used to free entomologists working on the Sterile Insect Technique15 from the tedious and time-consuming task of manually sexing the insects.
In using this system, the most critical step is to properly set up the sensor for data collection. If the laser and the photo array are not properly aligned, the data will be very noisy. After the insects are placed in the cage, the photo array should always be fine-tuned using the magnets on the outside of the cage. Note that flashing lights, camera flashes and vibrations near the cages will introduce noise to the data. Therefore, to obtain clean data, place the cage in a dark room, and wherever necessary, place dry towels under the cages in order to diminish the level of vibration.
The classifier presented in this work used just two additional features. However, there may be dozens of additional features that could help improve the classification performance. As the potential features are domain and application specific, the users could choose features based on their specific need or applications. The general framework of the classifier allows users to easily add features to the classifier to improve the classification performance.
To encourage the adoption and extension of our ideas, we are making all code, data, and sensor schematics freely available at the UCR Computational Entomology Page16-17. Moreover, within the limits of our budget, we will continue our practice of giving a complete system (as shown in&#160;Figure 1) to any research entomologist who requests one.

自動的に飛行の付帯音を使って昆虫を分類する考え方は、最も初期のコンピュータの時代と市販のオーディオ録音機器1にさかのぼる。しかし、ほとんど進展は、介在十年でこの問題について説明した。この追求の進展の欠如は、いくつかの関連する要因に起因することができます。 まず、有効なセンサの欠如は、データ収集が困難になった。データを収集するためのほとんどの努力は、音響マイクロフォン2-5を使用している。このようなデバイスは、非常にまばらな、低品質のデータで、その結果、環境中のノイズや周囲の騒音を巻くことは非常に敏感である。 第二に、これらのデータ品質の問題を配合することは、多くの研究者が非常に複雑な分類モデル、特にニューラルネットワーク6-8を学習することを試みてきたという事実である。例の単なる十で、複雑な分類モデルを学習しようとすると、オーバーフィッティングのためのレシピです。 第三に、データ取得の困難は、多くの研究者は300のインスタンス9以下のように少数のように、非常に限られたデータに分類モデルを構築することを試みていることを意味している。しかし、正確な分類モデルを構築するために、より多くのデータが10-13優れていることが知られている。 この作品は、すべての3つの問題に対処します。オプティカル（ではなく音響 ）センサーは、ノイズや周囲の音を巻くために、完全な不変性で、数メートル先から昆虫の飛行の&quot;音&quot;を記録するために使用することができます。これらのセンサは、以前のすべての努力よりもはるかに多くのデータを組み合わせて、ラベルされた訓練事例の数百万の記録を許可されたため、以前の研究努力を悩ませてきた、オーバーフィッティングを回避している。原則に基づいた方法は、その分類モデルへの追加的な情報の取り込みを可能にするの下に表示されます。この追加情報は、日常のようにようにすることができ時刻などから入手しやすいが、その場合でも、モデルの精度が大幅に利益を生み出す。最後に、私たちが収集した膨大な量のデータは、私たちは、シンプル、正確かつ堅牢な分類子を生成するために&quot; データの不当な有効性 」10を利用できるようにすることが実証されている。 要約すると、飛ぶ昆虫の分類は研究室で作成した怪しげな主張を越えて移動し、現在、現実の展開のための準備ができています。この作品で提示センサーとソフトウェアは、世界中の研究を加速するための強力なツールを研究者に提供します。

<table border="0" cellpadding="0" cellspacing="0" width="1293">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:328px;">Name of Material/ Equipment</td>
			<td style="width:237px;">Company</td>
			<td style="width:257px;">Catalog Number</td>
			<td style="width:471px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:328px;">Audio Recorder: ICD-PX312</td>
			<td style="width:237px;">Sony</td>
			<td style="width:257px;">4-267-065-11(2)</td>
			<td>With a 8 GB microSD extra memory</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:328px;">Insectary</td>
			<td style="width:237px;">Lee&#39;s Aquarium &#38; Pet Products.</td>
			<td style="width:257px;">20088 HerpHaven&#174;, Large Rectangle</td>
			<td style="width:471px;">14 1/2&#34; Long x 8 3/4&#34; Wide x 9 3/4&#34; high. Modified to house insects.&#160;</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:328px;">Laser Line Generator, 650nm (red)</td>
			<td style="width:237px;">Apinex (www.apinex.com)</td>
			<td style="width:257px;">LN60-650</td>
			<td style="width:471px;">5mW. This is a low powered laser, similar to a teachers lasers pointer</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:328px;">Photodiode Array</td>
			<td style="width:237px;">VISHAY SEMICONDUCTOR&#160; TEFD4300&#160; PIN PHOTODIODE, 650NM, 20DEG, T1</td>
			<td style="width:257px;">TEFD4300</td>
			<td style="width:471px;">We made a custom array of 15 of these Photodiodes wired in parallel</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;">Analogue to Digital Convertor Integrated Circuit&#160;</td>
			<td style="width:237px;">Custom made in our lab</td>
			<td style="width:257px;"></td>
			<td style="width:471px;">We made this item ourselves, but an easily available commercial product, Gino PCF8591 AD/DA Converter, provides the same functionality.&#160;</td>
		</tr>
	</tbody>
</table>

flying insect detection and classification with inexpensive sensors

正確に飛翔昆虫を分類できた安価な、非侵襲的なシステムは、昆虫学の研究にとって重要な意味を持っており、両方の医療や農業昆虫学のためのベクトルと害虫駆除の多くの有用なアプリケーションの開発を可能にするであろう。この与えられた、最後の60年は、このタスクを対象とした数多くの研究努力を見てきました。しかし、今日まで、本研究のいずれも、持続的な影響を与えなかった。本研究では、擬似的な音響光学センサは、優れたデータを生成できることを示している。昆虫の飛行動作に内因性および外因性の両方その追加機能には、昆虫分類を改善するために利用することができます。ことは、ベイジアン分類アプローチは、効率的にオーバーフィッティングに非常に堅牢である分類モデルを学習することができ、そして一般的な分類の枠組みを簡単に特徴の任意の数を組み込むことができます。私たちは、以前のすべての作品のコンビを矮小大規模実験での知見を実証昆虫の数と見なさ種の数によって測定されるように、定義さ。

Two experiments are presented here. For both experiments, the data used were randomly sampled from a dataset that contains over 100,000 objects.
The first experiment shows the ability of the proposed classifier to accurately classify different species/sexes of insects. As the classification accuracy depends on the insects to be classified, a single absolute value for classification accuracy will not give the reader a good intuition about the performance of the system. Instead, rather than reporting the classifier&#8217;s accuracy on a fixed set of insects, the classifier was applied to datasets with an incrementally increasing number of species, and therefore increasing classification difficulty.
The dataset began with just 2&#160;species of insects; then at each step, one more species (or a single sex of a sexually dimorphic species) was added and the classifier was used to classify the increased number of species (the new dataset). A total of ten classes of insects (different sexes from the same species counting as different classes) were considered, with 5,000 exemplars in each class.
The classifier used both insect-sound (frequency spectrum) and time-of-intercept for classification. Table 1 shows the classification accuracy measured at each step and the relevant class added at that step.
According to Table 1, the classifier achieves more than 96% accuracy when classifying no more than 5&#160;species of insects, significantly higher than the default rate of 20% accuracy. Even when the number of classes considered increases to 10, the classification accuracy is never lower than 79%, again significantly higher than the default rate of 10%. Note that the ten classes are not easy to separate, even by human inspection. Among the ten species, eight of them are mosquitoes, with six of them being from the same genus.
The second experiment is to show how accurately the system can sex flying insects, specifically, to distinguish male Ae. aegypti mosquitoes from females. For the first part of the experiment, assume that the &#8220;misclassification cost&#8221; of misclassifying males as females is the same as the cost of misclassifying females as males. With this assumption, the classification results are shown in Table 2.I. The classification accuracy to sex Ae. aegypti is about 99.4%.
For the second part of experiment, assume the cost is not asymmetric, i.e., misclassification of females as males is much more costly than the reverse. With this assumption, the decision threshold of the classifier was changed to reduce the number of high-cost misclassifications. With the threshold properly adjusted, the classification results in Table 2.II were achieved. Of 2,000 insects in the experiment, twenty-two males, and zero females were misclassified.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/52111/52111fig1highres.jpg" width="500" /> 
Figure 1. (I) One of the experimental cages used to gather the data. (II) A logical version of the sensor setup with the components annotated. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52111/52111fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52111/52111fig2highres.jpg" /> 
Figure 2. (I) An example of a 1 sec audio clip containing a &#8220;flying insect sound&#8221; generated by the sensor. The sound was produced by a female Cx. stigmatosoma. The insect sound is highlighted in red/bold. (II) The &#8220;insect sound&#8221;, which has been cleaned and saved into a 1-sec&#160;long audio clip by centering the insect signal and padding with 0s elsewhere. (III) The frequency spectrum of the insect sound, obtained using Discrete Fourier Transform. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52111/52111fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52111/52111fig3highres.jpg" /> 
Figure 3.&#160;A Bayesian network that uses a single feature for classification.
<img alt="Figure 4" src="/files/ftp_upload/52111/52111fig4highres.jpg" /> 
Figure 4.&#160;A Bayesian network that uses two independent features for classification
<img alt="Figure 5" src="/files/ftp_upload/52111/52111fig5highres.jpg" /> 
Figure 5.&#160;The&#160;circadian rhythms of Cx. stigmatosoma (female), Cx. tarsalis (male), and Ae. aegypti (female), learned based on observations generated by the sensor that were collected over 1&#160;month duration. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52111/52111fig5highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/52111/52111fig6highres.jpg" /> 
Figure 6.&#160;A Bayesian network that uses three independent features for classification.
<img alt="Figure 7" src="/files/ftp_upload/52111/52111fig7highres.jpg" /> 
Figure 7.&#160;The assumptions of the geographic distributions of each insect species and sensor locations in the simulation to demonstrate the effectiveness of using location-of-intercept feature in classification. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52111/52111fig7highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/52111/52111fig8highres.jpg" /> 
Figure 8.&#160;The general Bayesian network that uses n features for classification, where n is a positive integer. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52111/52111fig8highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Step</td>
			<td>Species Added&#160;</td>
			<td>Classification Accuracy</td>
			<td></td>
			<td>Step</td>
			<td>Species Added&#160;</td>
			<td>Classification Accuracy</td>
		</tr>
		<tr>
			<td>1</td>
			<td>Ae. aegypti &#9794;</td>
			<td>N/A</td>
			<td></td>
			<td>6</td>
			<td>Cx. quinquefasciatus &#9794;</td>
			<td>92.69%</td>
		</tr>
		<tr>
			<td>2</td>
			<td>Musca domestica</td>
			<td>98.99%</td>
			<td></td>
			<td>7</td>
			<td>Cx. stigmatosoma &#9792;</td>
			<td>89.66%</td>
		</tr>
		<tr>
			<td>3</td>
			<td>Ae. aegypti &#9792;</td>
			<td>98.27%</td>
			<td></td>
			<td>8</td>
			<td>Cx. tarsalis &#9794;</td>
			<td>83.54%</td>
		</tr>
		<tr>
			<td>4</td>
			<td>Cx. stigmatosoma &#9794;</td>
			<td>97.31%</td>
			<td></td>
			<td>9</td>
			<td>Cx. quinquefasciatus&#160;&#9792;</td>
			<td>81.04%</td>
		</tr>
		<tr>
			<td>5</td>
			<td>Cx. tarsalis &#9792;</td>
			<td>96.10%</td>
			<td></td>
			<td>10</td>
			<td>Drosophila simulans</td>
			<td>79.44%</td>
		</tr>
	</tbody>
</table>
Table 1.&#160;Classification accuracy with increasing number of classes.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td></td>
			<td></td>
			<td colspan="2">Predicted class</td>
			<td></td>
			<td colspan="2"></td>
			<td colspan="2">Predicted class</td>
		</tr>
		<tr>
			<td colspan="2">I (Symmetric cost)</td>
			<td>female</td>
			<td>male</td>
			<td></td>
			<td colspan="2">II (Asymmetric cost)</td>
			<td>female</td>
			<td>male</td>
		</tr>
		<tr>
			<td rowspan="2">Actual class</td>
			<td>female</td>
			<td>993</td>
			<td>7</td>
			<td></td>
			<td rowspan="2">Actual class</td>
			<td>female</td>
			<td>1,000</td>
			<td>0</td>
		</tr>
		<tr>
			<td>male</td>
			<td>5</td>
			<td>995</td>
			<td></td>
			<td>male</td>
			<td>22</td>
			<td>978</td>
		</tr>
	</tbody>
</table>
Table 2. (I) The confusion matrix for sex discrimination of Ae. aegypti mosquitoes with the decision threshold for females is set at 0.5 (i.e., same cost assumption). (II) The confusion matrix of sex the same mosquitoes, with the decision threshold for females set at 0.1.

Watch this Scientific Journal Video about Flying Insect Detection and Classification with Inexpensive Sensors at JoVE.com

Flying Insect Detection and Classification with Inexpensive Sensors

We proposed a system that uses inexpensive, noninvasive pseudo-acoustic optical sensors to automatically and accurately detect, count, and classify flying insects based on their flying sound.

安価なセンサで昆虫の検出と分類をフライング

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow ...

flying-insect-detection-and-classification-with-inexpensive-sensors

Nanyang Technological University

Research

JoVE Journal

Bioengineering

25.0K ビュー.  University of California, Riverside. We proposed a system that uses inexpensive, noninvasive pseudo-acoustic optical sensors to automatically and accurately detect, count, and classify flying insects based on their flying sound.

ビデオ: Flying Insect Detection and Classification with Inexpensive Sensors

Fluorescence detection methods for microfluidic droplet platforms

The development of microfluidic platforms for performing chemistry and biology has in large part been driven by a range of potential benefits that accompany system miniaturisation. Advantages include the ability to efficiently process nano- to femoto- liter volumes of sample, facile integration of functional components, an intrinsic predisposition towards large-scale multiplexing, enhanced analytical throughput, improved control and reduced instrumental footprints.1
In recent years much interest has focussed on the development of droplet-based (or segmented flow) microfluidic systems and their potential as platforms in high-throughput experimentation.2-4 Here water-in-oil emulsions are made to spontaneously form in microfluidic channels as a result of capillary instabilities between the two immiscible phases. Importantly, microdroplets of precisely defined volumes and compositions can be generated at frequencies of several kHz. Furthermore, by encapsulating reagents of interest within isolated compartments separated by a continuous immiscible phase, both sample cross-talk and dispersion (diffusion- and Taylor-based) can be eliminated, which leads to minimal cross-contamination and the ability to time analytical processes with great accuracy. Additionally, since there is no contact between the contents of the droplets and the channel walls (which are wetted by the continuous phase) absorption and loss of reagents on the channel walls is prevented.
Once droplets of this kind have been generated and processed, it is necessary to extract the required analytical information. In this respect the detection method of choice should be rapid, provide high-sensitivity and low limits of detection, be applicable to a range of molecular species, be non-destructive and be able to be integrated with microfluidic devices in a facile manner. To address this need we have developed a suite of experimental tools and protocols that enable the extraction of large amounts of photophysical information from small-volume environments, and are applicable to the analysis of a wide range of physical, chemical and biological parameters. Herein two examples of these methods are presented and applied to the detection of single cells and the mapping of mixing processes inside picoliter-volume droplets. We report the entire experimental process including microfluidic chip fabrication, the optical setup and the process of droplet generation and detection.

Droplet-based microfluidic platforms are promising candidates for high throughput experimentation since they are able to generate picoliter, self-compartmentalized vessels inexpensively at kHz rates. Through integration with fast, sensitive and high resolution fluorescence spectroscopic methods, the large amounts of information generated within these systems can be efficiently extracted, harnessed and utilized.

マイクロ流体液滴のプラットフォームのための蛍光検出法

The development of microfluidic platforms for performing chemistry and biology has in large part been driven by a range of potential benefits that ...

生物工学

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Circulating tumor cells (CTCs) are those cells that have separated from a macroscopic tumor and spread through the blood and lymph systems to seed secondary tumors1,2,3. CTCs are indicators of metastatic disease and their detection in blood samples may be used to diagnose cancer and monitor a patient&prime;s response to therapy. Since CTCs are rare, comprising about one tumor cell among billions of normal blood cells in advanced cancer patients, their detection and enumeration is a difficult task. We exploit the presence of pigment in most melanoma cells to generate photoacoustic, or laser induced ultrasonic waves in a custom flow cytometer for detection of circulating melanoma cells (CMCs)4,5. This process entails separating a whole blood sample using centrifugation and obtaining the white blood cell layer. If present in whole blood, CMCs will separate with the white blood cells due to similar density. These cells are resuspended in phosphate buffered saline (PBS) and introduced into the flowmeter. Rather than a continuous flow of the blood cell suspension, we induced two phase flow in order to capture these cells for further study. In two phase flow, two immiscible liquids in a microfluidic system meet at a junction and form alternating slugs of liquid6,7. PBS suspended white blood cells and air form microliter slugs that are sequentially irradiated with laser light. The addition of a surfactant to the liquid phase allows uniform slug formation and the user can create different sized slugs by altering the flow rates of the two phases. Slugs of air and slugs of PBS with white blood cells contain no light absorbers and hence, do not produce photoacoustic waves. However, slugs of white blood cells that contain even single CMCs absorb laser light and produce high frequency acoustic waves. These slugs that generate photoacoustic waves are sequestered and collected for cytochemical staining for verification of CMCs.

We have developed a flow cytometer using laser induced ultrasound to detect circulating melanoma cells as an early indicator of metastatic disease.

光音響流量測定法を用いて循環メラノーマ細胞の検出と分離

Circulating tumor cells (CTCs) are those cells that have separated from a macroscopic tumor and spread through the blood and lymph systems to seed ...

Bacterial Detection &amp; Identification Using Electrochemical Sensors

Electrochemical sensors are widely used for rapid and accurate measurement of blood glucose and can be adapted for detection of a wide variety of analytes. Electrochemical sensors operate by transducing a biological recognition event into a useful electrical signal. Signal transduction occurs by coupling the activity of a redox enzyme to an amperometric electrode. Sensor specificity is either an inherent characteristic of the enzyme, glucose oxidase in the case of a glucose sensor, or a product of linkage between the enzyme and an antibody or probe.
Here, we describe an electrochemical sensor assay method to directly detect and identify bacteria. In every case, the probes described here are DNA oligonucleotides. This method is based on sandwich hybridization of capture and detector probes with target ribosomal RNA (rRNA). The capture probe is anchored to the sensor surface, while the detector probe is linked to horseradish peroxidase (HRP). When a substrate such as 3,3',5,5'-tetramethylbenzidine (TMB) is added to an electrode with capture-target-detector complexes bound to its surface, the substrate is oxidized by HRP and reduced by the working electrode. This redox cycle results in shuttling of electrons by the substrate from the electrode to HRP, producing current flow in the electrode.

Bacterial Detection & Identification Using Electrochemical Sensors

We describe an electrochemical sensor assay method for rapid bacterial detection and identification. The assay involves a sensor array functionalized with DNA oligonucleotide capture probes for ribosomal RNA (rRNA) species-specific sequences. Sandwich hybridization of target rRNA with the capture probe and a horseradish peroxidase-linked DNA oligonucleotide detector probe produces a measurable amperometric current.

電気化学センサを用いた細菌検出＆識別

Electrochemical  sensors are widely used for rapid and accurate measurement of blood glucose and  can be adapted for detection of a wide variety of ...

Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria

A structurally transformed lytic bacteriophage having a broad host range of Staphylococcus aureus strains and a penicillin-binding protein (PBP 2a) antibody conjugated latex beads have been utilized to create a biosensor designed for discrimination of methicillin resistant (MRSA) and sensitive (MSSA) S. aureus species 1,2. The lytic phages have been converted into phage spheroids by contact with water-chloroform interface. Phage spheroid monolayers have been moved onto a biosensor surface by Langmuir-Blodgett (LB) technique 3. The created biosensors have been examined by a quartz crystal microbalance with dissipation tracking (QCM-D) to evaluate bacteria-phage interactions. Bacteria-spheroid interactions led to reduced resonance frequency and a rise in dissipation energy for both MRSA and MSSA strains. After the bacterial binding, these sensors have been further exposed to the penicillin-binding protein antibody latex beads. Sensors analyzed with MRSA responded to PBP 2a antibody beads; although sensors inspected with MSSA gave no response. This experimental distinction determines an unambiguous discrimination between methicillin resistant and sensitive S. aureus strains. Equally bound and unbound bacteriophages suppress bacterial growth on surfaces and in water suspensions. Once lytic phages are changed into spheroids, they retain their strong lytic activity and show high bacterial capture capability. The phage and phage spheroids can be utilized for testing and sterilization of antibiotic resistant microorganisms. Other applications may include use in bacteriophage therapy and antimicrobial surfaces.

Lytic phage biosensors and antibody beads are able to discriminate between methicillin resistant (MRSA) and sensitive staphylococcus bacteria. The phages were immobilized by a Langmuir-Blodgett method onto a surface of a quartz crystal microbalance sensor and worked as broad range staphylococcus probes. Antibody beads recognize MRSA.

抗生物質耐性ブドウ球菌の細菌の検出のためのバイオセンサー

A structurally transformed lytic bacteriophage having a broad host range of Staphylococcus aureus strains and a penicillin-binding protein (PBP 2a) ...

Glutamine Flux Imaging Using Genetically Encoded Sensors

Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. A tremendous variety of such sensors for biological molecules became available in the past 15 years, some of which became indispensable tools that are used routinely in many laboratories.
One of the exciting applications of genetically encoded sensors is the use of these sensors in investigating cellular transport processes. Properties of transporters such as kinetics and substrate specificities can be investigated at a cellular level, providing possibilities for cell-type specific analyses of transport activities. In this article, we will demonstrate how transporter dynamics can be observed using genetically encoded glutamine sensor as an example. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed.

This article will demonstrate how to monitor glutamine dynamics in live cells using FRET. Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed for genetically encoded glutamine sensors.

遺伝的にコードされたセンサを用いたグルタミンフラックスイメージング

Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. A tremendous variety of such sensors for ...

A Microfluidic-based Electrochemical Biochip for Label-free DNA Hybridization Analysis

Miniaturization of analytical benchtop procedures into the micro-scale provides significant advantages in regards to reaction time, cost, and integration of pre-processing steps. Utilizing these devices towards the analysis of DNA hybridization events is important because it offers a technology for real time assessment of biomarkers at the point-of-care for various diseases. However, when the device footprint decreases the dominance of various physical phenomena increases. These phenomena influence the fabrication precision and operation reliability of the device. Therefore, there is a great need to accurately fabricate and operate these devices in a reproducible manner in order to improve the overall performance. Here, we describe the protocols and the methods used for the fabrication and the operation of a microfluidic-based electrochemical biochip for accurate analysis of DNA hybridization events. The biochip is composed of two parts: a microfluidic chip with three parallel micro-channels made of polydimethylsiloxane (PDMS), and a 3 x 3 arrayed electrochemical micro-chip. The DNA hybridization events are detected using electrochemical impedance spectroscopy (EIS) analysis. The EIS analysis enables monitoring variations of the properties of the electrochemical system that are dominant at these length scales. With the ability to monitor changes of both charge transfer and diffusional resistance with the biosensor, we demonstrate the selectivity to complementary ssDNA targets, a calculated detection limit of 3.8 nM, and a 13% cross-reactivity with other non-complementary ssDNA following 20 min&#160;of incubation. This methodology can improve the performance of miniaturized devices by elucidating on the behavior of diffusion at the micro-scale regime and by enabling the study of DNA hybridization events.

We present a microfluidic-based electrochemical biochip for DNA hybridization detection. Following ssDNA probe functionalization, the specificity, sensitivity, and detection limit are studied with complementary and non-complementary ssDNA targets. Results illustrate the influence of the DNA hybridization events on the electrochemical system, with a detection limit of 3.8 nM.

ラベルフリーDNAハイブリダイゼーション分析のためのマイクロ流体ベースの電気化学バイオチップ

Miniaturization of analytical benchtop procedures into the micro-scale provides significant advantages in regards to reaction time, cost, and integration ...

Detection of Exosomal Biomarker by Electric Field-induced Release and Measurement (EFIRM)

Exosomes are microvesicular structures that play a mediating role in intercellular communication. It is of interest to study the internal cargo of exosomes to determine if they carry disease discriminatory biomarkers. For performing exosomal analysis, it is necessary to develop a method for extracting and analyzing exosomes from target biofluids without damaging the internal content.
Electric field-induced release and measurement (EFIRM) is a method for specifically extracting exosomes from biofluids, unloading their cargo, and testing their internal RNA/protein content. Using an anti-human CD63 specific antibody magnetic microparticle, exosomes are first precipitated from biofluids. Following extraction, low-voltage electric cyclic square waves (CSW) are applied to disrupt the vesicular membrane and cause cargo unloading. The content of the exosome is hybridized to DNA primers or antibodies immobilized on an electrode surface for quantification of molecular content.
The EFIRM method is advantageous for extraction of exosomes and unloading cargo for analysis without lysis buffer. This method is capable of performing specific detection of both RNA and protein biomarker targets in the exosome. EFIRM extracts exosomes specifically based on their surface markers as opposed to size-based techniques.
Transmission electron microscopy (TEM) and assay demonstrate the functionality of the method for exosome capture and analysis. The EFIRM method was applied to exosomal analysis of 9 mice injected with human lung cancer H640 cells (a cell line transfected to express the exosome marker human CD63-GFP) in order to test their exosome profile against 11 mice receiving saline controls. Elevated levels of exosomal biomarkers (reference gene GAPDH and protein surface marker human CD63-GFP) were found for the H640 injected mice in both serum and saliva samples. Furthermore, saliva and serum samples were demonstrated to have linearity (R = 0.79). These results are suggestive for the viability of salivary exosome biomarkers for detection of distal diseases.

Detection of Exosomal Biomarker by Electric Field-induced Release and Measurement EFIRM

Exosomes are microvesicular structures found within biofluids that potentially carry important disease discriminatory biomarkers. Here, a novel method is used to specifically extract exosomes and rapidly test the exosomal cargo for both RNA/protein targets following the disruption of exosomes using non-uniform electric cyclic square waves.

電界誘起リリースおよび測定によるエキソソームのバイオマーカーの検出（EFIRM）

Exosomes are microvesicular structures that play a mediating role in intercellular communication. It is of interest to study the internal cargo of ...

Label-free Single Molecule Detection Using Microtoroid Optical Resonators

Detecting small concentrations of molecules down to the single molecule limit has impact on areas such as early detection of disease, and fundamental studies on the behavior of molecules. Single molecule detection techniques commonly utilize labels such as fluorescent tags or quantum dots, however, labels are not always available, increase cost and complexity, and can perturb the events being studied. Optical resonators have emerged as a promising means to detect single molecules without the use of labels. Currently the smallest particle detected by a non-plasmonically-enhanced bare optical resonator system in solution is a 25 nm polystyrene sphere1. We have developed a technique known as Frequency Locking Optical Whispering Evanescent Resonator (FLOWER) that can surpass this limit and achieve label-free single molecule detection in aqueous solution2. As signal strength scales with particle volume, our work represents a &#62; 100x improvement in the signal to noise ratio (SNR) over the current state of the art. Here the procedures behind FLOWER are presented in an effort to increase its usage in the field.

We have developed a label-free biosensing system based on optical resonator technology known as Frequency Locking Optical Whispering Evanescent Resonator (FLOWER) that is capable of detecting single molecules in solution. Here the procedures behind this work are described and presented.

マイクロトロイドの光共振器を用いたラベルフリー単一分子検出

Detecting small concentrations of molecules down to the single molecule limit has impact on areas such as early detection of disease, and fundamental ...

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry

Increasing evidence supports transcellular propagation of toxic protein aggregates, or proteopathic seeds, as a mechanism for the initiation and progression of pathology in several neurodegenerative diseases, including Alzheimer's disease and the related tauopathies. The potentially critical role of tau seeds in disease progression strongly supports the need for a sensitive assay that readily detects seeding activity in biological samples. 
By combining the specificity of fluorescence resonance energy transfer (FRET), the sensitivity of flow cytometry, and the stability of a monoclonal cell line, an ultra-sensitive seeding assay has been engineered and is compatible with seed detection from recombinant or biological samples, including human and mouse brain homogenates. The assay employs monoclonal HEK 293T cells that stably express the aggregation-prone repeat domain (RD) of tau harboring the disease-associated P301S mutation fused to either CFP or YFP, which produce a FRET signal upon protein aggregation. The uptake of proteopathic tau seeds (but not other proteins) into the biosensor cells stimulates aggregation of RD-CFP and RD-YFP, and flow cytometry sensitively and quantitatively monitors this aggregation-induced FRET. The assay detects femtomolar concentrations (monomer equivalent) of recombinant tau seeds, has a dynamic range spanning three orders of magnitude, and is compatible with brain homogenates from tauopathy transgenic mice and human tauopathy subjects. With slight modifications, the assay can also detect seeding activity of other proteopathic seeds, such as &#945;-synuclein, and is also compatible with primary neuronal cultures. The ease, sensitivity, and broad applicability of FRET flow cytometry makes it useful to study a wide range of protein aggregation disorders.

Cell-to-cell transfer of protein aggregates, or proteopathic seeds, may underlie the progression of pathology in neurodegenerative diseases. Here, a novel FRET flow cytometry assay is described that enables specific and sensitive detection of seeding activity from recombinant or biological samples.

FRETフローサイトメトリーでProteopathic播種活動の高感度検出

Increasing evidence supports transcellular propagation of toxic protein aggregates, or proteopathic seeds, as a mechanism for the initiation and ...

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. This step is necessary for the generation of small molecular weight peptides, generally with MW &#60; 3,000-4,000 Da, that fall within the effective scan range of mass spectrometry instrumentation. Conventional protocols involve O/N enzymatic digestion at 37 &#186;C. Recent advances have led to the development of a variety of strategies, typically involving the use of a microreactor with immobilized enzymes or of a range of complementary physical processes that reduce the time necessary for proteolytic digestion to a few minutes (e.g., microwave or high-pressure). In this work, we describe a simple and cost-effective approach that can be implemented in any laboratory for achieving fast enzymatic digestion of a protein. The protein (or protein mixture) is adsorbed on C18-bonded reversed-phase high performance liquid chromatography (HPLC) silica particles preloaded in a capillary column, and trypsin in aqueous buffer is infused over the particles for a short period of time. To enable on-line MS detection, the tryptic peptides are eluted with a solvent system with increased organic content directly in the MS ion source. This approach avoids the use of high-priced immobilized enzyme particles and does not necessitate any aid for completing the process. Protein digestion and complete sample analysis can be accomplished in less than ~3 min and ~30 min, respectively.

A quick protocol for proteolytic digestion with an in-house built flow-through tryptic microreactor coupled to an electrospray ionization (ESI) mass spectrometer is presented. The fabrication of the microreactor, the experimental setup and the data acquisition process are described.

フロースルーマイクロリアクターとMS検出のためのタンパク質の高速酵素処理

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. ...