UNASSIGNED: The National Research Mentoring Network (NRMN) is a National Institutes of Health-funded program for diversifying the science, technology, engineering, math, and medicine research workforce through the provision of mentoring, networking, and professional development resources. The NRMN provides mentoring resources to members through its online platform-MyNRMN.
UNASSIGNED: MyNRMN helps members build a network of mentors. Our goal was to expand enrollment and mentoring connections, especially among those who have been historically underrepresented in biomedical training and the biomedical workforce.
UNASSIGNED: To improve the ease of enrollment, we implemented the split testing of iterations of our user interface for platform registration. To increase mentoring connections, we developed multiple features that facilitate connecting via different pathways.
UNASSIGNED: Our improved user interface yielded significantly higher rates of completed registrations (P<.001). Our analysis showed improvement in completed enrollments that used the version 1 form when compared to those that used the legacy form (odds ratio 1.52, 95% CI 1.30-1.78). The version 2 form, with its simplified, 1-step process and fewer required fields, outperformed the legacy form (odds ratio 2.18, 95% CI 1.90-2.50). By improving the enrollment form, the rate of MyNRMN enrollment completion increased from 57.3% (784/1368) with the legacy form to 74.5% (2016/2706) with the version 2 form. Our newly developed features delivered an increase in connections between members.
UNASSIGNED: Our technical efforts expanded MyNRMN\'s membership base and increased connections between members. Other platform development teams can learn from these efforts to increase enrollment among underrepresented groups and foster continuing, successful engagement.

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

Fish welfare is a critical issue that needs to be addressed by the rapidly growing aquaculture industry. Scientific knowledge regarding the natural behaviors of species and the conditions in which they are kept in farms is essential for improving their welfare in aquaculture. To provide a consistent overview of the welfare of farmed fish, the organization fair-fish has created the online platform fair-fish database, which gathers ethological knowledge categorized into profiles of farmed aquatic species. The WelfareChecks on this platform are profiles based on criteria that are rated based on the likelihood and potential of the species to experience a high level of welfare in aquaculture systems, together with the certainty about the findings. A score (WelfareScore) is calculated from these ratings, serving as a reference to identify knowledge gaps, assess welfare, and suggest ways to improve it. Here, we performed an in-depth analysis of the species with WelfareChecks already published in the fair-fish database based on their respective WelfareScores. In general, although just a small percentage of farmed aquatic species (~5%) have at least a 20% chance of experiencing a good level of welfare under minimal aquaculture conditions, 60% of them have at least some potential to achieve good welfare under high-standard conditions, with more than a third of the species (~37%) having at least a 20% potential. Despite that, several species exhibit a very high frequency of low chances and potential for experiencing good welfare levels under aquaculture conditions, besides a low degree of certainty based on literature reviews. Furthermore, many others show a very frequent occurrence of unclear or nonexistent knowledge in their profiles. The current welfare state is therefore poor for the majority of farmed aquatic species; yet, there is considerable potential for improvement. However, many species are very unlikely to achieve good welfare, even under high-standard conditions. Importantly, large knowledge gaps remain for an accurate assessment of the welfare of several farmed species.

During the COVID-19 pandemic, the extensive lockdown measures implemented for disease mitigation triggered a surge in round-the-clock social media use, giving rise to widespread concerns regarding its impact on sleep health. This meta-analysis examined the association between social media use and sleep disturbance during the pandemic, along with potential moderators. The dataset included 43 independent samples comprising 68,247 residents of 21 countries across 7 world regions. The three-level mixed-effects meta-analysis revealed a weak, positive overall effect size (r = 0.1296, 95% confidence interval: 0.0764-0.1828, k = 90). The magnitude of the effect size varied by the type of social media use: compulsive use exhibited a moderately strong effect size, whereas information-focused use showed marginal significance. The effect size was more pronounced in countries imposing stricter (vs. less strict) lockdown measures. Lockdown status also moderated this association, with a marginally significant effect size observed during lockdowns but a significant effect size after lockdowns. For demographics, samples involving emerging adults demonstrated moderately strong effect sizes, whereas those involving the general population had modest effect sizes. Notably, the interaction between the type of social media use and lockdown status was significant. Specifically, the positive association with information-focused use was significant only during lockdowns, whereas that with general use was significant after, but not during, lockdowns. However, compulsive use showed a moderately strong effect size both during and after lockdowns. These findings underscored the importance of considering multiple factors-such as the type of social media use, context, and demographics-when studying social media use and sleep health.

BACKGROUND: With an overarching goal of increasing diversity and inclusion in biomedical sciences, the National Research Mentoring Network (NRMN) developed a web-based national mentoring platform (MyNRMN) that seeks to connect mentors and mentees to support the persistence of underrepresented minorities in the biomedical sciences. As of May 15, 2024, the MyNRMN platform, which provides mentoring, networking, and professional development tools, has facilitated more than 12,100 unique mentoring connections between faculty, students, and researchers in the biomedical domain.
OBJECTIVE: This study aimed to examine the large-scale mentoring connections facilitated by our web-based platform between students (mentees) and faculty (mentors) across institutional and geographic boundaries. Using an innovative graph database, we analyzed diverse mentoring connections between mentors and mentees across demographic characteristics in the biomedical sciences.
METHODS: Through the MyNRMN platform, we observed profile data and analyzed mentoring connections made between students and faculty across institutional boundaries by race, ethnicity, gender, institution type, and educational attainment between July 1, 2016, and May 31, 2021.
RESULTS: In total, there were 15,024 connections with 2222 mentees and 1652 mentors across 1625 institutions contributing data. Female mentees participated in the highest number of connections (3996/6108, 65%), whereas female mentors participated in 58% (5206/8916) of the connections. Black mentees made up 38% (2297/6108) of the connections, whereas White mentors participated in 56% (5036/8916) of the connections. Mentees were predominately from institutions classified as Research 1 (R1; doctoral universities-very high research activity) and historically Black colleges and universities (556/2222, 25% and 307/2222, 14%, respectively), whereas 31% (504/1652) of mentors were from R1 institutions.
CONCLUSIONS: To date, the utility of mentoring connections across institutions throughout the United States and how mentors and mentees are connected is unknown. This study examined these connections and the diversity of these connections using an extensive web-based mentoring network.

UNASSIGNED: High biodiversity in the tropics is good for ecosystem services; however, challenges in taxonomy and identification usually come from such high biodiversity. Spiders are no exception to the challenges. Identifying spiders in tropical places like Thailand is difficult and time consuming. To reduce the difficulty of identifying Thai spiders, a data retrieval system for geographical occurrence and photographic identification was conducted to deploy on an online platform, Spiders in Thailand (SIT) via the website \"spiderthailand.info\". This allows professional arachnologists and amateur spider lovers to visit and check the geographical distribution of Thai spiders and to quickly access pictures for comparative photographic identification. To facilitate Thai spider identification, there were two parts, the database and the website, which are connected to each other. Data of Thai spiders were extracted from the World Spider Catalog to build a database comprising geographical occurrence and pictures of spider species in Thailand. The database was then linked with the website to display data.
UNASSIGNED: The dataset of pictures and illustrations extracted from taxonomic literature of the World Spider Catalog were included in the database for connecting with the online platform, Spiders in Thailand (SIT) via the website \"spiderthailand.info\" which facilitated access to pictures and illustrations, expediting the identification of Thai spider specimens. Geographical occurrences of Thai spiders consisted of 1419 records belonging to 670 species of 228 genera and 50 families. Amongst those, 461 species from 133 genera of 41 families were distributed only in Thailand. Around Thailand, 756 geographical localities were reported for spider occurrences. From 76 provinces and one additional special administrative area (Bangkok), 58 provinces showed occurrence records of spiders and 18 provinces showed non-occurrence records. Those provinces of non-occurrence records of spiders were Amnat Charoen, Ang Thong, Bueng Kan, Chai Nat, Maha Sarakham, Mukdahan, Nakhon Phanom, Nong Bua Lam Phu, Nonthaburi, Phayao, Phichit, Phra Nakhon Si Ayutthaya, Samut Prakan, Samut Sakhon, Si Sa Ket, Sing Buri, Uthai Thani and Yasothon. Most spiders were reported from Chiang Mai Province.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects.
RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of \"interaction-affinity-binding sites\". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN.
CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.

OBJECTIVE: Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis.
METHODS: In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin.
RESULTS: The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin.
CONCLUSIONS: In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

The ability to listen is critical in the task of language learning. Although listening has the least pedagogical attention, the growing emphasis on communication and language proficiency makes listening skills prominent in the language classroom. This paper aims to analyse the effectiveness of the Blended model to improve teaching listening skills, by instigating a top-down approach through Cognitive Load Theory. The top-down approach aids the students with the background knowledge of the audio with information like context, situation, phrases, etc. The blended model enables the teacher to facilitate students through the technological platform to process their listening input. A questionnaire was adopted for data collection and a semi-structured interview was performed from 60 samples from prefinal year Engineering students selected through purposive sampling techniques and grouped as experimental N = 30 and control N = 30 groups. The experimental group was trained with a top-down approach with the support of LMS. The control group was provided with the same listening material but taught in the conventional method. The purpose of this study is to show the statistically significant impact of employing technology inside the language classroom to teach listening skills. Findings showed that samples in the experimental group could identify the relevant and non-relevant information from the audio, conceptualise the audio content and predict the information beforehand. The difficulties that the students and teachers faced and the remedial measures to overcome them are also discussed. The following objectives were established for the study through mixed methods of enhancing listening skills through Cognitive Load Theory (CLT). •To explore the effect of intervention through a top-down approach with the support of technology (LMS) on enhancing the listening skills of the students.•How the blending of synchronous and asynchronous and a top-down approach develops the predicting skills of the students during the listening comprehension exercises.•To adapt procedures involved in enhancing the self-paced learning efficacy and reducing listening anxiety in ESL learners.