20 research outputs found
Understanding of Authorship by the Post Graduate Medical Students at a Center in Bangladesh
No full textEducation on authorship was delivered and evaluated by pre test and post test questionnairen on 30 post graduate medical students at the Department of Anestheology, Dhaka Medical College, Bangladesh between January and June 2019 to understand the knowledge, skill and attitude of post graduate medical students on authorship. Result: Before intervention, majority (60%) of the students felt that who perform the research work should be the author of the article. But 40% students were divided and felt that who advised the design of the research (20%), who provided the grants (10%) and Chief/Head of the division (10%) should be the author of the article respectively. Maximum (70%) respondents did not know the order of authorship. Of 40% respondent felt that the PI should be always the first author and 40% don’t know the answer. Half of the students (50%) felt that keeping honorary author increased the opportunity of acceptance of publication. Of 36.7% and 13.3% of students felt that keeping honorary author increased the article’s value and made good relation to them to get some extra facility from them respectively. Of 20% participants were pressurized by lab head/head of department for inclusion of their name as an author. Half of the (56.7 %) respondents felt that the author’s contribution should be stated in the article. Only few 4 (13.3%) respondents said that their institute had guideline for authorship. However, after education 100% of students felt that who perform the research work should be only the author of the article. All (100%) respondents understood the order of authorship. Most of the students (86%) felt that PI should be always the first author. Of 100% respondent felt supervisor of the research should be the last author. All students (100%) felt author’s contribution should be mentioned in the article. All (100%) students did not want to include as author those who help in research design and secured grant; and they did not like to keep honorary author in their article. All (100%) students expressed that their institute had no guideline for authorship. After intervention, three groups of students were asked to write one page of article on Anesthesiology. Interestingly, they did not include any name in the author by line who were not participate or had any contribution in the writing. Conclusion: The comparative data between pre- and post-text have highlighted a general lack of understanding of the basic concept of authorship ethics which significantly improved after the intervention. The results also indicate that the education on authorship improved the awareness of postgraduate medical students in a particular centre
Understanding of Authorship by the Post Graduate Medical Students at a Center in Bangladesh
No full textEducation on authorship was delivered and evaluated by pre test and post test questionnairen on 30 post graduate medical students at the Department of Anestheology, Dhaka Medical College, Bangladesh between January and June 2019 to understand the knowledge, skill and attitude of post graduate medical students on authorship. Result: Before intervention, majority (60%) of the students felt that who perform the research work should be the author of the article. But 40% students were divided and felt that who advised the design of the research (20%), who provided the grants (10%) and Chief/Head of the division (10%) should be the author of the article respectively. Maximum (70%) respondents did not know the order of authorship. Of 40% respondent felt that the PI should be always the first author and 40% don’t know the answer. Half of the students (50%) felt that keeping honorary author increased the opportunity of acceptance of publication. Of 36.7% and 13.3% of students felt that keeping honorary author increased the article’s value and made good relation to them to get some extra facility from them respectively. Of 20% participants were pressurized by lab head/head of department for inclusion of their name as an author. Half of the (56.7 %) respondents felt that the author’s contribution should be stated in the article. Only few 4 (13.3%) respondents said that their institute had guideline for authorship. However, after education 100% of students felt that who perform the research work should be only the author of the article. All (100%) respondents understood the order of authorship. Most of the students (86%) felt that PI should be always the first author. Of 100% respondent felt supervisor of the research should be the last author. All students (100%) felt author’s contribution should be mentioned in the article. All (100%) students did not want to include as author those who help in research design and secured grant; and they did not like to keep honorary author in their article. All (100%) students expressed that their institute had no guideline for authorship. After intervention, three groups of students were asked to write one page of article on Anesthesiology. Interestingly, they did not include any name in the author by line who were not participate or had any contribution in the writing. Conclusion: The comparative data between pre- and post-text have highlighted a general lack of understanding of the basic concept of authorship ethics which significantly improved after the intervention. The results also indicate that the education on authorship improved the awareness of postgraduate medical students in a particular centre
Conformational, vibrational spectroscopic and quantum chemical studies on 5-methoxyindole-3-carboxaldehyde: A DFT approach
No full textCommunication – Efficient Federated Learning for LEO Satellite
No full textThe full text of this item is not available at this time because the author has placed this item under an embargo until May 16, 2027.In the realm of low Earth orbit (LEO) satellite constellations, the burgeoning need for robust, real-time data processing and machine learning (ML) capabilities to address global challenges is palpable. Traditional centralized ML confronts significant challenges in satellite networks. The bandwidth constraints of download links have a substantial impact on how long data takes to transfer between satellite and ground servers. This delay limits the ability to handle data in real time, which is crucial for many satellite-based applications. The connection of download links to satellites is erratic and irregular, resulting in relatively brief communication periods with ground stations. This limitation restricts the quantity of data that may be transmitted inside each window, hindering the timely and effective processing of satellite data. Satellites collect massive volumes of data, most of which is redundant or useless to specific purposes. Downloading this large amount of raw data to ground stations for processing is not only inefficient, but also impracticable due to bandwidth limitations. The bandwidth constraints of download links have a substantial impact on how long data takes to transfer between satellite and ground servers. This delay limits the ability to handle data in real time, which is crucial for many satellite-based applications. The connection of download links to satellites is erratic and irregular, resulting in relatively brief communication periods with ground stations. This limitation restricts the quantity of data that may be transmitted inside each window, hindering the timely and effective processing of satellite data. Satellites collect massive volumes of data, most of which is redundant or useless to specific purposes. Downloading this large amount of raw data to ground stations for processing is not only inefficient, but also impracticable due to bandwidth limitations. Federated Learning (FL) has gained traction in academia and industry in recent years. FL allows ground stations and satellites to collaborate on training a global ML model without having to share the raw images captured by the satellites. This strategy dramatically decreases the need to download vast amounts of data, addressing the bandwidth and latency issues associated with classic ML methods. However, federated learning still suffers from inevitable high communication latency, especially in satellite networks. To overcome this challenge, we have proposed a revolutionary approach to improving communication efficiency for ML training in satellite networks by using Delayed Gradient Averaging (DGA) algorithm. This technology enhances the scalability and responsiveness of satellite networks, allowing for more robust and efficient federated learning applications in space-based systems.
Moreover, our technique takes advantage of intra-plane inter-satellite links (ISLs) to build intra-orbit aggregations, which can further minimize communication costs. Numerical findings show that our proposed framework outperforms various baselines, demonstrating a significant progress in making satellite networks more efficient for machine learning applications. This novel technique, which combines DGA and intra-orbit aggregation via ISLs, opens new possibilities for using satellite networks to address global concerns by improving data processing and analytic capabilities.Electrical and Computer Engineerin
National Democratic Congress Parliamentary Primaries in the Tamale Central Constituency: A Delegate-Based Electoral Survey Analysis
No full textThe Tamale Central Constituency, a key political and commercial hub in Ghana’s Northern Region, is preparing for a parliamentary primary of the National Democratic Congress (NDC) following the passing of its sitting Member of Parliament (MP) on August 6, 2025. Several aspirants, including Dr. Abdul Rashid Abdul Rahaman, Prof. Alidu Seidu, Dr. Seidu Fiter, Lawyer Abdul Hanan Gundadow, Lawyer Abdul Rauf, Hajia Shamima Yakubu, and Alhassan Osman Gomda, have emerged as potential contenders for the September 6, 2025, internal primary election. This paper analyses delegate preferences, perceptions of candidate strength, and the factors likely to influence delegate choice. A survey of 344 delegates was conducted to determine support levels for aspirants and the factors likely to influence their choice. The findings suggest that delegate decision-making is structured around three latent dimensions, and these are loyalty and service (grassroots embeddedness, party service), campaign resources (financial/logistical support), and charisma and appeal (youth appeal, communication). While grassroots embeddedness remains critical, the role of financial capacity and youth appeal cannot be discounted. Interestingly, public speaking ability, often emphasised in media campaigns, was statistically insignificant, reflecting that internal party elections rely more on patron-client networks than on rhetorical skills. In conclusion, the findings highlight the political economy of delegate decision-making in the Tamale Central constituency and suggest that an aspirant who integrates grassroots engagement, party loyalty, and campaign capacity is best positioned to secure the nomination. Based on this conclusion, the paper argues for a transparent internal primary election and merit-based selection to strengthen the NDC’s competitiveness in the Tamale Central constituency
Machine learning approaches for addressing classification problems of four types of immune-peptides
No full text1 Introduction||2 Prediction of Anti-Inflammatory Peptides by Integrating Mulptle Complementary Features||3 Prediction of Proinflammatory Peptides by Fusing of Multiple Feature Representations||4 Prediction of Anti-Tubercular Peptides by Exploiting Amino Acid Pattern and Properties||5 Prediction of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features||6 Conclusions and PerspectivesPeptides play an important role in all aspects of the immunological reactions to invading cancer and pathogen cells. It has been known for over 40-years that peptides are critical influences in assembling the immune system against foreign invaders. Since then, new knowledge about the generation and function of peptides in immunology has supported efforts to harness the immune system to treat disease. Yet, with little immunological insight, most of the highly productive treatments, including vaccines, have been developed empirically. Nonetheless, increased knowledge of the biology of antigen processing as well as chemistry and pharmacological properties of antigenic and antimicrobial peptides has now permitted to development of drugs and vaccines. Due to advanced technologies, it is vitally important to develop automatic computational methods for rapidly and accurately predicting immune-peptides. In this thesis, the author focuses on the machine learning approaches for addressing classification problems of four types of immune-peptides （anti-inflammatory, proinflammatory, anti-tuberculosis, and linear B-cell peptides）.Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP （Predictor of Anti-Inflammatory Peptides） was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/. A proinflammatory peptide （PIP） is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate a set of informative probabilistic features by making the use of random forest models with eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by the linearly combined models of the informative probabilistic features. The generalization capability of our proposed method evaluated through independent test showed that ProIn-Fuse yielded an accuracy of 0.746, which was over 10% higher than those obtained by the state-of-the-art PIP predictors. Cross-validation and independent results consistently demonstrated that ProIn-Fuse is more precise and promising in the identification of PIPs than existing PIP predictors. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse/. We believe that the proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. Tuberculosis （TB） is a leading killer caused by Mycobacterium tuberculosis. Recently anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. Herein, we have developed an effective computational predictor iAntiTB （identification of anti-tubercular peptides） that integrates multiple feature vectors deriving from the amino acid sequences via Random Forest （RF） and Support Vector Machine （SVM） classifiers. The iAntiTB combined the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor we prepared the two datasets with different types of negative samples. The iAntiTB achieved AUC values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors. Thus, the iAntiTB is a robust and accurate predictor that is helpful for researchers working on peptide therapeutics and immunotherapy. All the employed datasets and software application are accessible at http://kurata14.bio.kyutech.ac.jp/iAntiTB/. Linear B-cell peptides are critically important for immunological applications such as vaccine design, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. The accurate identification of linear B-cell peptides remains challenging despite several decades of research. In this work, we have developed a novel predictor, iLBE （Identification of B-Cell Epitope）, by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon rank-sum test. Then the random forest （RF） algorithm used the optimal consecutive feature vectors to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. The performance of the final iLBE yielded an AUC score of 0.809 on the training dataset. It outperformed other existing prediction models on a comprehensive independent dataset. The iLBE is suggested to be a powerful computational tool to identify the linear B-cell peptides and development of penetrating diagnostic tests. A web application with curated datasets is freely accessible of iLBE at http://kurata14.bio.kyutech.ac.jp/iLBE/. Taken together, the above results suggest that our proposed predictors （PreAIP, ProIn-Fuse, iAntiTB, and iLBE） would be helpful computational resources for the prediction of anti-inflammatory, pro-inflammatory, tuberculosis, and linear B-cell peptides. / ペプチドは、癌や病原体細胞に対する免疫反応のあらゆる側面で重要な役割を果たす。ペプチドが外来の侵入物に対する免疫系を起動する上で決定的な影響を与えることは40年以上前から知られている。それ以来、免疫学におけるペプチドの生成と機能に関する新しい知見は、病気を治療するために免疫系を利用する研究を支えてきた。依然として、免疫学的洞察がほとんどないため、ワクチンを含む効率的治療法のほとんどは、経験的に開発されている。それでもなお、抗原プロセシングの生物学、ならびに抗原性および抗菌性ペプチドの化学・薬理学に関する知見の増加により、現在、薬物およびワクチンの開発が可能になっている。高度な技術により、免疫ペプチドを迅速かつ正確に予測するためのコンピュータ技術を開発することが非常に重要である。この論文では、著者は4種類の免疫ペプチド（抗炎症、炎症誘発性、抗結核、および線形B細胞エピトープ）の分類問題に対処するための機械学習アプローチに焦点を当てる。炎症性疾患および自己免疫疾患に対する治療用ペプチドは、多くの検討がなされてきた。しかし、生物学的実験による抗炎症ペプチドの探索は、多くの場合、時間と費用のかかる作業である。新しいin siloco予測器の開発は、in vitro実験に先立って、潜在的な抗炎症ペプチドを同定するために望まれている。ここでは、PreAIP（抗炎症ペプチドの予測器）と呼ばれる予測器が、複数の補完的機能を統合することによって開発された。一次配列、進化的および構造的情報を含むさまざまなタイプの特徴量を、ランダムフォレスト分類器を介して抽出した。最終的なPreAIPモデルは、10分割交差検定によるトレーニングデータセットで0.833のAUC値を達成した。これは、既存のモデルよりも優れた値である。さらに、独立の検証用データセットでAUC値0.840を達成し、提案された方法が2つの既存の予測器よりも優れていることを示した。これらの結果は、PreAIPが抗炎症ペプチドを同定するための正確な予測器であり、抗炎症ペプチド治療および生物医学研究の開発に貢献した。用いたデータセットとPreAIPは、http：//kurata14.bio.kyutech.ac.jp/PreAIP/から自由に利用できる。炎症誘発性ペプチド（PIP）は、免疫細胞から分泌されるシグナル伝達分子の一種であり、侵入する病原体に対する防御の第一線を担当する。多くの実験により、PIPはワクチンや免疫療法薬などにおいて重要な役割を果たすことが示されている。ハイスループットな生物実験に時間と費用が掛かることを考えると、効率的なコンピュータ予測は、PIPを短時間にかつ正確に特定するために大きな需要がある。したがって、この研究では、PIP識別性能を向上させるために、ProIn-Fuseと呼ばれる複数の特徴表現を組み合わせた計算モデルを提案した。具体的には、特徴表現学習モデルを利用して、8つのシーケンスエンコーディングスキームを備えたランダムフォレストモデルを利用することにより、確率的予測スコアを計算した。ProIn-Fuseは、確率的予測スコアの線形結合モデルによって構築された。提案手法の汎化性能を独立したテストデータで評価した結果、ProIn-Fuseの精度は0.746であり、これは最新のPIP予測器によって得られた精度よりも10％以上高かった。テストデータによる検証結果は、ProIn-Fuseが既存のPIP予測器よりも正確にPIP識別できることを示した。Webサーバー、データセット、および説明書は、http：//kurata14.bio.kyutech.ac.jp/ProIn-Fuse/から自由にアクセスできる。ProIn-Fuseは、ドラッグデザイン含む幅広いアプリケーションに応用できる。結核（TB）は、結核菌によって引き起こされる疾患である。最近、抗結核ペプチドは抗生物質耐性に対抗するための代替アプローチを提供している。ここでは、ランダムフォレスト（RF）およびサポートベクターマシン（SVM）分類器を用いてアミノ酸配列に由来する複数の特徴ベクトルを統合する効果的な予測器iAntiTB（抗結核ペプチドの識別）を開発した。iAntiTBは、線形回帰を介してRFスコアとSVMスコアを組み合わせて、予測精度を向上させた。ロバストで正確な予測器を作成するために、異なるタイプのネガティブサンプルを使用して2つのデータセットを準備した。iAntiTBは、1番目と2番目のデータセットのトレーニングデータセットでそれぞれ0.896と0.946のAUC値を達成した。iAntiTBは、他の既存の予測器の性能を上回った。このように、iAntiTBは、ペプチド治療および免疫療法に取り組んでいる研究者に役立つロバストで正確な予測器である。利用されたすべてのデータセットとソフトウェアアプリケーションは、http：//kurata14.bio.kyutech.ac.jp/iAntiTB/から自由にアクセスできる。線形B細胞エピトープは、ワクチンの設計、免疫診断テスト、抗体産生、疾患の診断や治療などの免疫学的応用に非常に重要である。線形B細胞エピトープの正確な同定は、数十年の研究にもかかわらず、依然として挑戦的課題のままである。本研究では、配列の進化的特徴や物理化学的特徴等を統合することにより、新規な線形B細胞エピトープ予測モデル（iLBE）を開発した。Wilcoxon順位和検定によって最適化した特徴ベクトル群をランダムフォレスト（RF）アルゴリズムを用いて学習して、線形B細胞エピトープの予測スコアを計算した。ロジスティック回帰を用いてRFスコアを組合せて、予測精度を高めた。iLBEは、トレーニングデータセットで0.809のAUCを達成し、独立のテストデータセットを用いた検定では、既存の予測モデルの性能を超えた。線形B細胞エピトープを同定する強力な計算ツールであるiLBEは、診断テストの開発に有用である。注釈付きデータセットを備えたiLBEモデルのウエブアプリケーションは自由にアクセスできるhttp://kurata14.bio.kyutech.ac.jp/iLBE/。九州工業大学博士学位論文 学位記番号：情工博甲第358号 学位授与年月日：令和3年3月25日令和2年
