AmorProt: Amino Acid Molecular Fingerprints Repurposing based Protein Fingerprint

As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature based methods; (4) feature importance analysis; and (5) protein space analysis. Consequently, the significantly improved model performance and data set independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands

EnSiam: Self-Supervised Learning With Ensemble Representations

Recently, contrastive self-supervised learning, where the proximity of representations is determined based on the identities of samples, has made remarkable progress in unsupervised representation learning. SimSiam is a well-known example in this area, known for its simplicity yet powerful performance. However, it is known to be sensitive to changes in training configurations, such as hyperparameters and augmentation settings, due to its structural characteristics. To address this issue, we focus on the similarity between contrastive learning and the teacher-student framework in knowledge distillation. Inspired by the ensemble-based knowledge distillation approach, the proposed method, EnSiam, aims to improve the contrastive learning procedure using ensemble representations. This can provide stable pseudo labels, providing better performance. Experiments demonstrate that EnSiam outperforms previous state-of-the-art methods in most cases, including the experiments on ImageNet, which shows that EnSiam is capable of learning high-quality representations

Functionality-Driven Musculature Retargeting

We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation-ready, so we can physically simulate muscle-actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi-planar X-ray images and medical examinations.Comment: 15 pages, 20 figure

Employment status and its related factors in adults who experienced traumatic brain injury in Korea

脳外傷者の就業状況や脳外傷者の就業を促進する要因と妨げとなる要因を明らかにし,今後の就業援助のための示唆を得ることを目的に,韓国の地域職業リハビリテーションセンターに過去約5年間(1995～2000)求職相談に訪れた障害者,1542名のうち65名を脳外傷と同定し,その就業状況を公式記録から得て分析した.65名のうち,初回求職相談後2000年8月の調査時点までに一度でも就業を経験した者は36名であった.調査時点に就業中であった者は25名で,そのうち事業所に就業して1年以上勤続していた者は6名のみであった.求職相談後の就業にプラスに働いた要因は,居住地域がセンターの近隣または都市部であったこと,受傷後求職相談に訪れるまでの間に何らかの教育・職業訓練経験,就業経験があったこと,及び,求職相談時に就業斡旋と判定され,実際にセンターの就業斡旋を受けたことであった.また,勤続にプラスに働いた要因には障害が軽度で,障害受容ができたこと,家族,会社,職業リハビリテーション機関からのサポートがあったことで,マイナス要因には,本人の身体的・精神的能力の問題,作業の不適合性,会社内の対人関係の問題や物理的・制度的環境の不備,経済状況などがあった.The purpose of this research was to investigate the employment status of people with traumatic brain injury and factors related to their employability in Korea. From the official documents of a local vocational rehabilitation center, 65 people were identified as victims of head trauma among 1542 people with disabilities over 4 years and 8 months (1995-2000). Information about their demographical factors, disabilities, educational/vocational experience after disability, the vocational rehabilitation services of the center and vocational experience after registration was gathered and analyzed descriptively. The factors which influenced employment were also analyzed by logistic regression. Educational and/or job experience after the injury, accessibility to the vocational rehabilitation service, and job introductions from the center were related to their employment after registration to the center. The results were further analyzed with reference to the Canadian Model of Occupational Therapy (CMOP). Finally, this article suggests the necessity of a client-centered vocational rehabilitation program for people with traumatic brain injury in the community

A Cyber Command and Control Framework for Psychological Operation Using Social Media

Global threats, international terrorist groups and North Korea, paralyze political decisions by attacking and neutralizing the credibility of the main policy makers in the state and simultaneously manipulate the public opinion, which results in distrust and disconnection between each other. These threats use social media as their biggest core routine to conduct such attacks. This paper presents a series of processes and frameworks on how a commander should make a decision when performing a cyber psychological operation using social media. Based on the Endsley model, which is a situational awareness model, the paper compares the strengths and weaknesses of the three social media operations (IGMO, DeSMO, OSMO) performed by the military and proposes a guideline for performing an operation

Retinal Not Systemic Oxidative and Inflammatory Stress Correlated with VEGF Expression in Rodent Models of Insulin Resistance and Diabetes

To correlate changes between VEGF expression with systemic and retinal oxidative stress and inflammation in rodent models of obesity induced insulin resistance and diabetes

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fishers exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient usingin vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP) Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Shot Boundary Detection with Graph Theory using Keypoint Features and Color Histograms

The article of record as published may be found at http://dx.doi.org/10.1109/WACV.2015.161Published in: 2015 IEEE Winter Conference on Applications of Computer VisionThe TRECVID report of 2010 [14] evaluated video shot boundary detectors as achieving "excellent performance on [hard] cuts and gradual transitions." Unfortunately, while re-evaluating the state of the art of the shot boundary detection, we found that they need to be improved because the characteristics of consumer-produced videos have changed significantly since the introduction of mobile gadgets, such as smartphones, tablets and outdoor activity purposed cameras, and video editing software has been evolving rapidly. In this paper, we evaluate the best-known approach on a contemporary, publicly accessible corpus, and present a method that achieves better performance, particularly on soft transitions. Our method combines color histograms with key point feature matching to extract comprehensive frame information. Two similarity metrics, one for individual frames and one for sets of frames, are defined based on graph cuts. These metrics are formed into temporal feature vectors on which a SVM is trained to perform the final segmentation. The evaluation on said "modern" corpus of relatively short videos yields a performance of 92% recall (at 89% precision) overall, compared to 69% (91%) of the best-known method