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. DESIGN: Field application of the calculator in conjunction with the use of FPHS workforce capacity self-assessment tools. SETTING: NNPH. PARTICIPANTS: NNPH and Public Health Foundation (PHF). INTERVENTION: From June 2022 through April 2023, PHF collaborated with NNPH, serving Washoe County, to provide expertise and assistance as NNPH undertook an assessment of its workforce needs based upon the FPHS model. MAIN OUTCOME MEASURES: Comparison of the calculator output with FPHS workforce capacity self-assessment tools. RESULTS: The calculator and the FPHS capacity self-assessment process yielded complementary FPHS workforce capacity gap data. The use of a structured and transparent process, coupled with additional tools that included prioritizing needs, provided a viable and sustainable process for public health workforce investment planning. NNPH successfully utilized the results to bolster a supplemental funding request and a state public health appropriation. CONCLUSIONS: The use of the calculator and an FPHS workforce capacity self-assessment in a facilitated and structured process such as that used by NNPH to identify staffing priorities may hold promise as an approach that could be used to support decision-making and justification for infrastructure resources when funding for public health increases in the future.

Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, we investigated the application of deep learning models in early detection of fetal kidney anomalies. To provide an enhanced interpretation of those models' predictions, we proposed an adapted two-class representation and developed a multi-class model interpretation approach for problems with more than two labels and variable hierarchical grouping of labels. Additionally, we employed the explainable AI (XAI) visualization tools Grad-CAM and HiResCAM, to gain insights into model predictions and identify reasons for misclassifications. The study dataset consisted of 969 ultrasound images from unique patients; 646 control images and 323 cases of kidney anomalies, including 259 cases of unilateral urinary tract dilation and 64 cases of unilateral multicystic dysplastic kidney. The best performing model achieved a cross-validated area under the ROC curve of 91.28% ± 0.52%, with an overall accuracy of 84.03% ± 0.76%, sensitivity of 77.39% ± 1.99%, and specificity of 87.35% ± 1.28%. Our findings emphasize the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.

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. We also provide recommendations for the ethical implementation, governance of AI, and promote research into AI explainability, which are crucial for responsible AI integration and deployment. AI promises a revolutionary era of personalized health care in obstetrics and gynaecology.

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

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

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.g. Davis, Binding-DB, and Kiba), deep learning-based DTI predictors can be effectively leveraged to achieve state-of-the-art (SOTA) performance. In this work, we formulated a DTI competition as part of the coursework for a senior undergraduate machine learning course and challenged students to generate component DTI models that might surpass SOTA models and effectively combine these component models as part of a meta-model using the Reciprocal Perspective (RP) multi-view learning framework. Following 6 weeks of concerted effort, 28 student-produced component deep-learning DTI models were leveraged in this work to produce a new SOTA RP-DTI model, denoted the Meta Undergraduate Student DTI (MUSDTI) model. Through a series of experiments we demonstrate that (1) RP can considerably improve SOTA DTI prediction, (2) our new double-cold experimental design is more appropriate for emergent DTI challenges, (3) that our novel MUSDTI meta-model outperforms SOTA models, (4) that RP can improve upon individual models as an ensembling method, and finally, (5) RP can be utilized for low computation transfer learning. This work introduces a number of important revelations for the field of DTI prediction and sequence-based, pairwise prediction in general.

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

The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.

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. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics. METHODS: We leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments. RESULTS: The all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions. CONCLUSION: We publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

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. We here demonstrate that miRNA target prediction can be significantly improved ([Formula: see text]) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA-gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA-gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and https://doi.org/10.5683/SP2/LD8JKJ.

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. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter- and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) inter-species predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross- and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas.

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. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available.

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. norvegicus proteins, we have utilized the Massively Parallel Protein-Protein Interaction Prediction Engine (MP-PIPE) in an attempt to computationally shed light on the pathways potentially driving neurosphere proliferation. This study determined similar and differing protein interaction pathways between the two growth factors and the proteins in R. norvegicus compared with the proteins in H. sapiens. The protein-protein interactions predicted that EGF and FGF-2 may behave differently in rats than in humans. The identification and improved understanding of these differences may help to improve the clinical translation of NSC therapies from rats to humans.

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. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method.

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). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.

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 Real-World What-Where-When memory test requires participants to hide a total of 16 different objects in 16 different locations over two temporal occasions, 2 h apart. Another two hours later, they are then asked to recall which objects (What) they had hidden in which locations (Where) and on which of the two occasions (When). In addition to counting the number of correctly recalled complete what-where-when combinations, this task can also be used to test real-world spatial memory and object memory. This task is sensitive to normal cognitive aging, and correlates well with performance on other episodic memory tasks, while at the same time providing more ecological validity and being cheap and easy to run.

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. The HBx-AIB1 interaction was important for the activation of NFκB signal transduction, the HBx mutant that did not interact with AIB1showed dramatically lower NFκB activation activity than the WT HBx. These findings contribute to the new understanding on signal transduction activation mechanisms of HBx.

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. In this study, we employed the mammalian two-hybrid system to investigate the interaction between TDG and another member of the p160 family, nuclear receptor coactivator 3 (NCoA-3). We found that a DXXD motif from aa 294-297 within TDG was responsible for the TDG-NCoA-3 interaction, we also found that a LLXXXL motif (X means any amino acid) from aa 1029-1037 (LLRNSL) and a merged LLXXL motif (LLDQLHTLL) from aa 1053-1061 in NCoA-3 were important for the TDG-NCoA-3 interactions. Mutation of the two aspartic acids (aa 294 and 297) into two alanines in TDG significantly affected the interaction and subsequent transcriptional activation of several steroid hormone receptors including, estrogen-, androgen- and progesterone- receptors in Huh7 cells. We also identified that mutations of NCoA-3 at either leucines 1029-1030 or 1053-1054 (replaced by alanines) also reduced the interaction activity between TDG and NCoA1. These data indicated that the TDG-NCoA-3 interaction is important for broad range activation of steroid hormone nuclear receptors, and may also contribute significantly to further understanding of TDG-related nuclear receptor regulation.

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. In human Huh7 and MCF-7 cells, a VACV mutant lacking both K1L and C7L (vK1L-C7L-) replicated as efficiently as wild-type (WT) VACV, even in the presence of IFN. However, pretreating the cells with type I IFN, while having very little effect on WT VACV, blocked the replication of vK1L-C7L- at the step of intermediate viral gene translation. Restoring either K1L or C7L to vK1L(-)C7L(-) fully restored the IFN resistance phenotype. The deletion of K1L and C7L from VACV did not affect the ability of the virus to inhibit IFN signaling or its ability to inhibit the phosphorylation of PKR and the alpha subunit of eukaryotic initiation factor 2, indicating that K1L and C7L function by antagonizing an IFN effector(s) but with a mechanism that is different from those of IFN antagonists previously identified for VACV. Mutations of K1L that inactivate the host range function also rendered K1L unable to antagonize IFN, suggesting that K1L supports VACV replication in mammalian cells by antagonizing the same antiviral factor(s) that is induced by IFN in Huh7 cells.

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. RESULTS: Sequences of four LWS opsin genes were amplified from the guppy genome and from mRNA isolated from adult guppy eyes. Variation in expression was quantified using qPCR. Three of the four genes encode opsins predicted to be most sensitive to different wavelengths of light because they vary at key amino acid positions. This family of LWS opsin genes was produced by a diversity of duplication events. One, an intronless gene, was produced prior to the divergence of families Fundulidae and Poeciliidae. Between-gene PCR and DNA sequencing show that two of the guppy LWS opsins are linked in an inverted orientation. This inverted tandem duplication event occurred near the base of the poeciliid tree in the common ancestor of Poecilia and Xiphophorus. The fourth sequence has been uncovered only in the genus Poecilia. In the guppies surveyed here, this sequence is a hybrid, with the 5' end most similar to one of the tandem duplicates and the 3' end identical to the other. CONCLUSION: Enhanced wavelength discrimination, a possible consequence of opsin gene duplication and divergence, might have been an evolutionary prerequisite for color-based sexual selection and have led to the extraordinary coloration now observed in male guppies and in many other poeciliids.