CAManim: Animating end-to-end network activation maps.

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users.

Northern Nevada Public Health: Utilizing the Public Health Workforce Calculator and Workforce Capacity Self-assessment Tools to Develop a Framework for Workforce Investment.

Context: Health departments nationally are critically understaffed and lack infrastructure support. By examining current staffing and allocations through a Foundational Public Health Services (FPHS) lens at the Northern Nevada Public Health (NNPH), there is an opportunity to make a strong case for greater investment if current dedicated full-time equivalents are inadequate and to guide which investments in public health workforce are prioritized.

Objective: To assess the use of the Public Health Workforce Calculator (calculator) and other tools to identify and prioritize FPHS workforce needs in a field application.

The Transformative Potential of AI in Obstetrics and Gynaecology.

The transformative power of artificial intelligence (AI) is reshaping diverse domains of medicine. Recent progress, catalyzed by computing advancements, has seen commensurate adoption of AI technologies within obstetrics and gynaecology. We explore the use and potential of AI in three focus areas: predictive modelling for pregnancy complications, Deep learning-based image interpretation for precise diagnoses, and large language models enabling intelligent health care assistants.

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode.

The soybean cyst nematode (SCN) [ Ichinohe] is a devastating pathogen of soybean [ (L.) Merr.] that is rapidly becoming a global economic issue.

Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI).

The identification of novel drug-target interactions (DTI) is critical to drug discovery and drug repurposing to address contemporary medical and public health challenges presented by emergent diseases. Historically, computational methods have framed DTI prediction as a binary classification problem (indicating whether or not a drug physically interacts with a given protein target); however, framing the problem instead as a regression-based prediction of the physiochemical binding affinity is more meaningful. With growing databases of experimentally derived drug-target interactions (e.

Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction.

The soybean crop, (L.) Merr., is consumed by humans, , worldwide.

Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome.

Background: Understanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 ("coronavirus disease 2019"), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs.

RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction.

MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA-gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP.

PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes.

The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter- and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm.

In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences.

Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics.

Insights into the suitability of utilizing brown rats (Rattus norvegicus) as a model for healing spinal cord injury with epidermal growth factor and fibroblast growth factor-II by predicting protein-protein interactions.

The stimulation of the proliferation and differentiation of neural stem cells (NSCs) offers the possibility of a renewable source of replacement cells to treat numerous neurological diseases including spinal cord injury, traumatic brain injury and stroke. Epidermal growth factor (EGF) and fibroblast growth factor-2 (FGF-2) have been used to stimulate NSCs to renew, expand, and produce precursors for neural repair within an adult brown rat (Rattus norvegicus). To provide greater insight into the interspecies protein-protein interactions between human FGF-2 and EGF proteins and native R.

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction.

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles.

Designing anti-Zika virus peptides derived from predicted human-Zika virus protein-protein interactions.

The production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs).

A Real-world What-Where-When Memory Test.

Episodic memory is a complex memory system which allows recall and mental re-experience of previous episodes from one's own life. Real-life episodic memories are about events in their spatiotemporal context and are typically visuospatial, rather than verbal. Yet often, tests of episodic memory use verbal material to be recalled (word lists, stories).

The interaction between hepatitis B virus X protein and AIB1 oncogene is required for the activation of NFκB signal transduction.

We identified the interaction between HBV X (HBx) protein and the oncogene AIB1 (amplified in breast cancer 1). A serine/proline motif (SSPSPS) in HBx was found to be required for the interaction. Two LXD motifs [LLXX(X)L, X means any amino acids], LLRNSL and LLDQLHTLL in AIB1 were also found to be involved in the HBx-AIB1 interaction.

Vaccinia virus K1L and C7L inhibit antiviral activities induced by type I interferons.

Cellular tropism of vaccinia virus (VACV) is regulated by host range genes, including K1L, C7L, and E3L. While E3L is known to support viral replication by antagonizing interferon (IFN) effectors, including PKR, the exact functions of K1L and C7L are unclear. Here, we show that K1L and C7L can also inhibit antiviral effectors induced by type I IFN.

The interaction between thymine DNA glycosylase and nuclear receptor coactivator 3 is required for the transcriptional activation of nuclear hormone receptors.

The T:G mismatch specific DNA glycosylase (TDG) is known as an important enzyme in repairing damaged DNA. Recent studies also showed that TDG interacts with a p160 protein, steroid receptor coactivator 1 or nuclear receptor coactivator 1 (SRC1), and is involved in transcriptional activation of the estrogen receptor. However, whether other members of the p160 family are also involved in TDG-interaction and signal transduction regulation remains to be seen.

The molecular basis of color vision in colorful fish: four long wave-sensitive (LWS) opsins in guppies (Poecilia reticulata) are defined by amino acid substitutions at key functional sites.

Background: Comparisons of functionally important changes at the molecular level in model systems have identified key adaptations driving isolation and speciation. In cichlids, for example, long wavelength-sensitive (LWS) opsins appear to play a role in mate choice and male color variation within and among species. To test the hypothesis that the evolution of elaborate coloration in male guppies (Poecilia reticulata) is also associated with opsin gene diversity, we sequenced long wavelength-sensitive (LWS) opsin genes in six species of the family Poeciliidae.