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A classic T-cell phenotype in systemic lupus erythematosus (SLE) is the downregulation and replacement of the CD3ζ chain that alters T-cell receptor signaling. However, genetic associations with SLE in the human CD247 locus that encodes CD3ζ are not well established and require replication in independent cohorts. Our aim was therefore to examine, localize and validate CD247-SLE association in a large multiethnic population. We typed 44 contiguous CD247 single-nucleotide polymorphisms (SNPs) in 8922 SLE patients and 8077 controls from four ethnically distinct populations. The strongest associations were found in the Asian population (11 SNPs in intron 1, 4.99 × 10(-4) < P < 4.15 × 10(-2)), where we further identified a five-marker haplotype (rs12141731-rs2949655-rs16859085-rs12144621-rs858554; G-G-A-G-A; P(hap) = 2.12 × 10(-5)) that exceeded the most associated single SNP rs858554 (minor allele frequency in controls = 13%; P = 4.99 × 10(-4), odds ratio = 1.32) in significance. Imputation and subsequent association analysis showed evidence of association (P < 0.05) at 27 additional SNPs within intron 1. Cross-ethnic meta-analysis, assuming an additive genetic model adjusted for population proportions, showed five SNPs with significant P-values (1.40 × 10(-3) < P< 3.97 × 10(-2)), with one (rs704848) remaining significant after Bonferroni correction (P(meta) = 2.66 × 10(-2)). Our study independently confirms and extends the association of SLE with CD247, which is shared by various autoimmune disorders and supports a common T-cell-mediated mechanism.

Adiponectin (APM1) is an adipocyte-derived peptide that contributes to glucose, lipid, and energy homeostasis. We assessed the genetic basis of plasma adiponectin in Hispanic-American and African-American families enrolled through the Insulin Resistance Atherosclerosis Study Family Study. A 10-cM genome scan was performed in two batches: an original set (set 1) consisting of 66 families (45 Hispanic American and 21 African American) and a replication set (set 2) consisting of 66 families (45 Hispanic American and 21 African American). Adiponectin levels were measured by radioimmunoassay in 1,727 individuals from 131 of 132 families. Linkage analysis was carried out in Hispanic Americans and African Americans separately in set 1, set 2, and the pooled set (set 1 plus set 2), with and without diabetic subjects. A major gene was mapped to 3q27 with a logarithm of odds (LOD) score of 8.21 in the Hispanic-American sample. Ninety-six unrelated individuals were screened for polymorphisms in the APM1 gene, and 18 single nucleotide polymorphisms (SNPs) were genotyped in the Hispanic-American sample. Plasma adiponectin level was modestly associated with two SNPs and their accompaning haplotypes. Incorporating each or both SNPs in the linkage analysis, however, did not significantly reduce the LOD score. Therefore, a quantitative trait locus at 3q27, likely distinct from the APM1 gene, contributes to the variation of plasma adiponectin levels in the Hispanic-American population. [ABSTRACT FROM AUTHOR]

Glucose homeostasis, a defining characteristic of physiological glucose metabolism, is the result of complex feedback relationships with both genetic and environmental determinants that influence insulin sensitivity and beta-cell function. Relatively little is known about the genetic basis of glucose homeostasis phenotypes or their relationship to risk of diabetes. Our group previously published a genome scan for glucose homeostasis traits in 284 African-American subjects from 21 pedigrees in the Insulin Resistance Atherosclerosis Study Family Study (IRASFS) and presented evidence for linkage to disposition index (DI) on chromosome 11q with a logarithm of odds (LOD) of 3.21 at 81 cM flanked by markers D11S2371 and D11S2002 (support interval from 71 to 96 cM). In this study, genotyping and analysis of an additional 214 African-American subjects in 21 pedigrees from the IRASFS yielded independent evidence of linkage to DI. When these two datasets were combined, a DI linkage peak was observed with an LOD of 3.89 at 78 cM (support interval from 67 to 89 cM). Fine mapping with 15 additional microsatellite markers in this 11q region for the entire 42 pedigrees resulted in an LOD score of 4.80 at 80 cM near marker D11S937 (support interval from 76 to 84 cM). In these 42 pedigrees, there was also suggestive evidence for linkage to acute insulin response (AIR) at two separate locations flanking the DI peak (64 cM, LOD 2.77, flanked by markers D11S4076 and D11S981; and 85 cM, LOD 2.54, flanked by markers D11S4172 and D11S2002). No evidence of linkage to the insulin sensitivity index (S(i)) was observed. Nine positional candidate genes were evaluated for association to DI and AIR. Among these candidates, single nucleotide polymorphisms (SNPs) in muscle glycogen phosphorylase showed evidence of association with DI (P < 0.011). In addition, SNPs in the pyruvate carboxylase gene showed evidence of association (P < 0.002) with AIR. Further analysis of these candidate genes, however, did not provide evidence that these SNPs accounted for the evidence of linkage to either DI or AIR. These detailed genetic analyses provide strong evidence of a DI locus on 11q in African-American pedigrees, with additional suggestive evidence of independent AIR loci in the same region. [ABSTRACT FROM AUTHOR]

Standard methods to measure recreational water quality require at least 24 hr to obtain results, making it impossible to assess the quality of water within a single day. Methods to measure recreational water quality in

Background: Breast cancer incidence rates have historically been 4-7 times higher in the United States than in China or Japan, although the reasons remain elusive. When Chinese, Japanese, or Filipino women migrate to the United States, breast cancer risk rises over several generations and approaches that among U.S. Whites.Purpose: Our objective was to quantify breast cancer risks associated with the various migration patterns of Asian-American women.Methods: A population-based, case-control study of breast cancer among women of Chinese, Japanese, and Filipino ethnicities, aged 20-55 years, was conducted during 1983-1987 in San Francisco-Oakland, California, Los Angeles, California, and Oahu, Hawaii. We successfully interviewed 597 case subjects (70% of those eligible) and 966 control subjects (75%).Results: A sixfold gradient in breast cancer risk by migration patterns was observed. Asian-American women born in the West had a breast cancer risk 60% higher than Asian-American women born in the East. Among those born in the West, risk was determined by whether their grandparents, especially grandmothers, were born in the East or the West. Asian-American women with three or four grandparents born in the West had a risk 50% higher than those with all grandparents born in the East. Among the Asian-American women born in the East, breast cancer risk was determined by whether their communities prior to migration were rural or urban and by the number of years subsequently lived in the West. Migrants from urban areas had a risk 30% higher than migrants from rural areas. Migrants who had lived in the West for a decade or longer had a risk 80% higher than more recent migrants. Risk was unrelated to age at migration for women migrating at ages less than 36 years. Ethnic-specific incidence rates of breast cancer in the migrating generation were clearly elevated above those in the countries of origin, while rates in Asian-Americans born in the West approximated the U.S. White rate.Conclusions: Exposure to Western lifestyles had a substantial impact on breast cancer risk in Asian migrants to the United States during their lifetime. There was no direct evidence of an especially susceptible period, during either menarche or early reproductive life.Implications: Because heterogeneity in breast cancer risk in these ethnic populations is similar to that in international comparisons and because analytic epidemiologic studies offer the opportunity to disentangle correlated exposures, this study should provide new insights into the etiology of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Effective communication is founded on bidirectional participation from families and healthcare providers. In adult medicine, bidirectional communication promotes treatment adherence and builds the family-provider relationship. However, the relationship between communication styles in pediatrics remains poorly understood, particularly in culturally diverse settings. This study aims to investigate parent-provider communication dynamics and parental involvement during diagnostic cancer communication in Guatemala., Procedure: This qualitative study included 20 families of children with cancer and 10 providers at Unidad Nacional de Oncología Pediátrica in Guatemala. Psychoeducation and diagnostic conversations between parents, psychologists, and oncologists were recorded and thematically analyzed using a priori and novel codes exploring communication behaviors, parental engagement, and interpersonal dynamics., Results: Participating parents had children with various diagnoses. Only 15% of fathers and 5% of mothers reported education beyond primary school. Providers spoke 68% of words during psychoeducation and 85% of words during diagnosis conversations. Providers used supportive communication behaviors providing explanations, demonstrating verbal attentiveness, and soliciting questions and non-supportive behaviors including paternalistic talk. Parental participation was considered active when they asked questions, expressed hopes or concerns, or asserted their opinions, and non-active when participation was limited to brief responses to closed-ended questions. Supportive provider communication often encouraged active participation; non-supportive communication did not. Furthermore, active parental participation prompted supportive communication from providers, while non-active participation did not., Conclusions: Our findings highlight the bidirectional nature of effective communication, establishing that provider communication styles both influence and are influenced by parental participation, and emphasizing the importance of supportive provider communication for patient-centered care., (© 2024 Wiley Periodicals LLC.)

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies., Competing Interests: Declaration of interests G.B. is a member of the advisory board of Neuron., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Understanding the behavioral and neural dynamics of social interactions is a goal of contemporary neuroscience. Many machine learning methods have emerged in recent years to make sense of complex video and neurophysiological data that result from these experiments. Less focus has been placed on understanding how animals process acoustic information, including social vocalizations. A critical step to bridge this gap is determining the senders and receivers of acoustic information in social interactions. While sound source localization (SSL) is a classic problem in signal processing, existing approaches are limited in their ability to localize animal-generated sounds in standard laboratory environments. Advances in deep learning methods for SSL are likely to help address these limitations, however there are currently no publicly available models, datasets, or benchmarks to systematically evaluate SSL algorithms in the domain of bioacoustics. Here, we present the VCL Benchmark: the first large-scale dataset for benchmarking SSL algorithms in rodents. We acquired synchronized video and multi-channel audio recordings of 767,295 sounds with annotated ground truth sources across 9 conditions. The dataset provides benchmarks which evaluate SSL performance on real data, simulated acoustic data, and a mixture of real and simulated data. We intend for this benchmark to facilitate knowledge transfer between the neuroscience and acoustic machine learning communities, which have had limited overlap.

The bacterial stressosome is a supramolecular multiprotein complex that acts as a critical signal integration and transduction hub, orchestrating cellular responses to environmental stimuli. Recent studies have resolved near-atomic stressosome structures from various bacterial species, revealing assemblies that should be capable of altering their configuration in response to external changes. Further genetic, biochemical, and cell biology research has elucidated interactions and phosphorylation status within the stressosome complex as well as its subcellular localization and mobility within living cells. These insights enhance our comprehension of the stressosome pathways and their roles in directing various survival responses during environmental stress., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards, and actions, where the extent of tuning depends on ongoing task demands. However, it remains uncertain whether such diverse tuning corresponds to distinct functions within the hippocampal network or if a more generic computation can account for these observations. To disentangle the contribution of externally driven cues versus internal computation, we developed a task in mice where space, auditory tones, rewards, and context were juxtaposed with changing relevance. High-density electrophysiological recordings revealed that neurons were tuned to each of these modalities. By comparing movement paths and action sequences, we observed that external variables had limited direct influence on hippocampal firing. Instead, spiking was influenced by online action plans modulated by goal uncertainty. Our results suggest that internally generated cell assembly sequences are selected and updated by action plans toward deliberate goals. The apparent tuning of hippocampal neuronal spiking to different sensory modalities might emerge due to alignment to the afforded action progression within a task rather than representation of external cues.

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior., Competing Interests: Competing Interest Statement The authors declare no competing financial interests.

Background: MUC1 and UMOD pathogenic variants cause autosomal dominant tubulointerstitial kidney disease (ADTKD). MUC1 is expressed in kidney, nasal mucosa and respiratory tract, while UMOD is expressed only in kidney. Due to haplo-insufficiency ADTKD- MUC1 patients produce approximately 50% of normal mucin-1., Methods: To determine whether decreased mucin-1 production was associated with an increased COVID-19 risk, we sent a survey to members of an ADTKD registry in September 2021, after the initial, severe wave of COVID-19. We linked results to previously obtained ADTKD genotype and plasma CA15-3 (mucin-1) levels and created a longitudinal registry of COVID-19 related deaths., Results: Surveys were emailed to 637 individuals, with responses from 89 ADTKD- MUC1 and 132 ADTKD- UMOD individuals. 19/83 (23%) ADTKD- MUC1 survey respondents reported a prior COVID-19 infection vs. 14/125 (11%) ADTKD- UMOD respondents (odds ratio (OR) 2.35 (95%CI 1.60-3.11, P = 0.0260). Including additional familial cases reported from survey respondents, 10/41 (24%) ADTKD- MUC1 individuals died of COVID-19 vs. 1/30 (3%) with ADTKD- UMOD , with OR 9.21 (95%CI 1.22-69.32), P = 0.03. The mean plasma mucin-1 level prior to infection in 14 infected and 27 uninfected ADTKD- MUC1 individuals was 7.06±4.12 vs. 10.21±4.02 U/mL ( P = 0.035). Over three years duration, our longitudinal registry identified 19 COVID-19 deaths in 360 ADTKD- MUC1 individuals (5%) vs. 3 deaths in 478 ADTKD- UMOD individuals (0.6%) ( P = 0.0007). Multivariate logistic regression revealed the following odds ratios (95% confidence interval) for COVID-19 deaths: ADTKD- MUC1 8.4 (2.9-29.5), kidney transplant 5.5 (1.6-9.1), body mass index (kg/m 2 ) 1.1 (1.0-1.2), age (y) 1.04 (1.0-1.1)., Conclusions: Individuals with ADTKD- MUC1 are at an eight-fold increased risk of COVID-19 mortality vs. ADTKD- UMOD individuals. Haplo-insufficient production of mucin-1 may be responsible.

In the gastric pathogen Helicobacter pylori, post-transcriptional regulation relies strongly on the activity of the essential ribonuclease RNase J. Here, we elucidated the crystal and cryo-EM structures of RNase J and determined that it assembles into dimers and tetramers in vitro. We found that RNase J extracted from H. pylori is acetylated on multiple lysine residues. Alanine substitution of several of these residues impacts on H. pylori morphology, and thus on RNase J function in vivo. Mutations of Lysine 649 modulates RNase J oligomerization in vitro, which in turn influences ribonuclease activity in vitro. Our structural analyses of RNase J reveal loops that gate access to the active site and rationalizes how acetylation state of K649 can influence activity. We propose acetylation as a regulatory level controlling the activity of RNase J and its potential cooperation with other enzymes of RNA metabolism in H. pylori., (© 2023. The Author(s).)

Background: Positive, supportive responses to child maltreatment disclosure are critical for victims to receive appropriate resources and support for healing. Young people often prefer to disclose to their peers, frequently on social media platforms., Objective: We assessed young people's use of TalkLife, an online peer-to-peer support platform, to respond to the disclosure of child maltreatment., Methods: We conducted a qualitative content analysis of 1090 comments on childhood maltreatment-related posts on TalkLife between 2013 and 2020. We used an iterative, team-based qualitative content analysis approach to understand how peers responded to maltreatment disclosure., Findings: Peer responses tended to be supportive, including asking questions about the abuse and offering advice, emotional support, and other positive responses. Most commonly, peers advised the victim to report, focus on their strengths instead of the abuse, reach out to adults for more support, or confront the perpetrator. On occasion, however, peers began an irrelevant discussion, joked about the situation, or even directly attacked the discloser., Conclusions: Learning about child maltreatment disclosures on social media builds the foundation for research to assist in identifying and applying interventions on online platforms. Further, these findings can inform programs that teach how to provide healthy responses to child maltreatment disclosures., (Copyright © 2023. Published by Elsevier Ltd.)

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Introduction: Autosomal dominant tubulointerstitial kidney disease (ADTKD) is an increasingly recognized cause of chronic kidney disease. ADTKD pregnancy outcomes have not previously been described., Methods: A cross-sectional survey was sent to women from ADTKD families., Results: Information was obtained from 85 afffected women (164 term pregnancies) and 23 controls (50 pregnancies). Only 16.5% of genetically affected women knew they had ADTKD during pregnancy. Eighteen percent of ADTKD mothers had hypertension during pregnancy versus 12% in controls ( p   =  0.54) and >40% in comparative studies of chronic kidney disease in pregnancy. Eleven percent of births of ADTKD mothers were <37 weeks versus 0 in controls ( p  < 0.0001). Cesarean section occurred in 19% of pregnancies in affected women versus 38% of unaffected individuals ( p   =  0.06). Only 12% of babies required a neonatal intensive care unit stay., Conclusions: ADTKD pregnancies had lower rates of hypertension during pregnancy versus other forms of chronic kidney disease, which may have contributed to good maternal and fetal outcomes., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ('remap') in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference., Competing Interests: IL, LG, AW No competing interests declared, (© 2023, Low et al.)

Background: In high-income countries, hope facilitates parental coping and builds the clinical relationship between families of children with cancer and their clinicians. However, the manifestation of hope in low- and middle-income countries (LMICs) remains poorly understood. Our study explores Guatemalan parents' experiences with hope during the pediatric oncology diagnostic process and aims to identify discrete actions clinicians take to support hope., Methods: This qualitative study utilized audio-recordings of the diagnostic process and an additional semi-structured interview for 20 families of children with cancer at Unidad Nacional de Oncología Pediátrica in Guatemala. Spanish audio-recordings were translated into English, transcribed, and coded using a priori and novel codes. Thematic content analysis using constant comparative methods explored parents' hopes and concerns., Results: At diagnosis, Guatemalan parents expressed both hopes and concerns related to the entire cancer continuum. Throughout the diagnostic process, hope grew as concerns were alleviated. Clinicians supported hope by creating a supportive environment, providing information, affirming religious beliefs, and empowering parents. These strategies helped parents shift their focus from fear and uncertainty toward hope for their child's future. Parents expressed that establishing hope improved mood, promoted acceptance, and enabled them to care for themselves and their children., Conclusion: These results confirm the relevance of supporting hope in pediatric oncology settings in LMICs and suggest that culture informs hope-related needs. Supporting hope is critical across cultures and can be integrated into clinical conversation using the four processes identified by our results., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

It is challenging to find a conventional nanofabrication technique that can consistently produce soft polymeric matter of high surface area and nanoscale morphology in a way that is scalable, versatile, and easily tunable. Here, the capabilities of a universal method for fabricating diverse nano- and micro-scale morphologies based on polymer precipitation templated by the fluid streamlines in multiphasic flow are explored. It is shown that while the procedure is operationally simple, various combinations of its intertwined mechanisms can controllably and reproducibly lead to the formation of an extraordinary wide range of colloidal morphologies. By systematically investigating the process conditions, 12 distinct classes of polymer micro- and nano-structures including particles, rods, ribbons, nanosheets, and soft dendritic colloids (dendricolloids) are identified. The outcomes are interpreted by delineating the physical processes into three stages: hydrodynamic shear, capillary and mechanical breakup, and polymer precipitation rate. The insights into the underlying fundamental mechanisms provide guidance toward developing a versatile and scalable nanofabrication platform. It is verified that the liquid shear-based technique is versatile and works well with many chemically diverse polymers and biopolymers, showing potential as a universal tool for simple and scalable nanofabrication of many morphologically distinct soft matter classes., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

The deposition of coatings with hierarchical morphology from hydrophobic and hydrophilic polymers is a common approach for making superhydrophobic and superhydrophilic coatings. The water-repellent, water-wicking, and anti-icing coatings reported here are made from a class of materials called soft dendritic colloids (SDCs). The branched, nanofibrous SDCs are produced in suspension through nonsolvent-induced phase separation in a turbulent medium. The properties of coatings formed by drying ethanol suspensions of SDCs made of polystyrene, polyvinyl alcohol, and polyester are compared. The highly branched SDC morphology creates entangled, porous coating layers with strong physical adhesion to the substrate due to the multitude of nanofiber sub-contacts analogous to the "gecko leg effect". Polystyrene SDC coatings show excellent superhydrophobicity but weaker adhesion due to low surface energy. Alternatively, polyvinyl alcohol SDC coatings show superhydrophilicity and strong adhesion from their high surface energy. Two strategies to improve the adhesivity and cohesivity of the SDCs layers are shown effective - use of intertwined networks and of silicone droplet microbinders. The water repulsion, together with the air trapped in the blended superhydrophobic coatings also makes them effective against ice nucleation and adhesion. Finally, these SDCs make thin, flexible, and durable nonwovens with similar properties., (© 2022 The Authors. Macromolecular Rapid Communications published by Wiley-VCH GmbH.)

Lytic transglycosylases (Ltgs) are glycan strand cleaving enzymes whose role is poorly understood in the genesis of the bacterial envelope. They play multiple roles in all stages of a bacterial life cycle, by creating holes in the peptidoglycan that is necessary for cell division and separation. Here, we review recent advances in understanding the suitability of Ltgs as antibacterial drug targets. We specifically highlight a known inhibitor bulgecin A that is able to inhibit the function of structurally diverse Ltgs, as well as synergize with beta-lactams to improve its efficacy in antibiotic insensitive strains. Discovery of new antibiotics or new targets has been challenging. These studies could provide a viable path toward designing broad-spectrum inhibitors that targets Ltgs., Competing Interests: Conflict of interest statement Nothing declared., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity ( K d ) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the K d of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a K d of ∼22.69 nM (which is very close to the experimental K d of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a K d of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

The central relaxin-3/RXFP3 system plays important roles in stress responses, feeding, and motivation for reward. However, exploration of its therapeutic applications has been hampered by the lack of small molecule ligands and the cross-activation of RXFP1 in the brain and RXFP4 in the periphery. Herein, we report the first structure-activity relationship studies of a series of novel nonpeptide amidinohydrazone-based agonists, which were characterized by RXFP3 functional and radioligand binding assays. Several potent and efficacious RXFP3 agonists (e.g., 10d ) were identified with EC 50 values <10 nM. These compounds also had high potency at RXFP4 but no agonist activity at RXFP1, demonstrating > 100-fold selectivity for RXFP3/4 over RXFP1. In vitro ADME and pharmacokinetic assessments revealed that the amidinohydrazone derivatives may have limited brain permeability. Collectively, our findings provide the basis for further optimization of lead compounds to develop a suitable agonist to probe RXFP3 functions in the brain.

Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interested in surveying large collections of networks that are trained on, or biologically adapted to, similar tasks. A standardized set of analysis tools is now needed to identify how network-level covariates-such as architecture, anatomical brain region, and model organism-impact neural representations (hidden layer activations). Here, we provide a rigorous foundation for these analyses by defining a broad family of metric spaces that quantify representational dissimilarity. Using this framework, we modify existing representational similarity measures based on canonical correlation analysis and centered kernel alignment to satisfy the triangle inequality, formulate a novel metric that respects the inductive biases in convolutional layers, and identify approximate Euclidean embeddings that enable network representations to be incorporated into essentially any off-the-shelf machine learning method. We demonstrate these methods on large-scale datasets from biology (Allen Institute Brain Observatory) and deep learning (NAS-Bench-101). In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.

In this study, highly porous, ultrasoft polymeric mats mimicking human tissues were formed from novel polyurethane soft dendritic colloids (PU SDCs). PU SDCs have a unique fibrillar morphology controlled by antisolvent precipitation. When filtered from suspension, PU SDCs form mechanically robust nonwoven mats. The stiffness of the SDC mats can be tuned for physiological relevance. The unique physiochemical characteristics of the PU SDC particles dictate the mechanical properties resulting in tunable elastic moduli ranging from 200 to 800 kPa. The human lung A549 cells cultured on both stiff and soft PU SDC membranes were found to be viable, capable of supporting the air-liquid interface (ALI) cell culture, and maintained barrier integrity. Furthermore, A549 cellular viability and uptake efficiency of aerosolized tannic acid-coated gold nanoparticles (Ta-Au) was found to depend on elastic modulus and culture conditions. Ta-Au nanoparticle uptake was twofold and fourfold greater on soft PU SDCs, when cultured at submerged and ALI conditions, respectively. The significant increase in endocytosed Ta-Au resulted in a 20% decrease in viability, and a 4-fold increase in IL-8 cytokine secretion when cultured on soft PU SDCs at ALI. Common tissue culture materials exhibit super-physiological elastic moduli, a factor found to be critical in analyzing nanomaterial cellular interactions and biological responses.

Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic 'noise' and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure - including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions - and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits., Competing Interests: Conflict of interest statement Nothing declared., (Copyright © 2021. Published by Elsevier Ltd.)

Neurons in the medial entorhinal cortex alter their firing properties in response to environmental changes. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique episodes. However, it is unknown how the entorhinal circuit as a whole transitions between different representations when sensory information is not delineated into discrete contexts. Here we describe rapid and reversible transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of neurons, differentially affected navigational cell types, and correlated with changes in running speed. Despite widespread changes in spatial coding, remapping comprised a translation along a single dimension in population-level activity space, enabling simple decoding strategies. These findings provoke reconsideration of how the medial entorhinal cortex dynamically represents space and suggest a remarkable capacity of cortical circuits to rapidly and substantially reorganize their neural representations., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The design of hydrogels where multiple interpenetrating networks enable enhanced mechanical properties can broaden their field of application in biomedical materials, 3D printing, and soft robotics. We report a class of self-reinforced homocomposite hydrogels (HHGs) comprised of interpenetrating networks of multiscale hierarchy. A molecular alginate gel is reinforced by a colloidal network of hierarchically branched alginate soft dendritic colloids (SDCs). The reinforcement of the molecular gel with the nanofibrillar SDC network of the same biopolymer results in a remarkable increase of the HHG's mechanical properties. The viscoelastic HHGs show >3× larger storage modulus and >4× larger Young's modulus than either constitutive network at the same concentration. Such synergistically enforced colloidal-molecular HHGs open up numerous opportunities for formulation of biocompatible gels with robust structure-property relationships. Balance of the ratio of their precursors facilitates precise control of the yield stress and rate of self-reinforcement, enabling efficient extrusion 3D printing of HHGs.

Synthetic indole cannabinoids characterized by a 2',2'-dimethylindan-5'-oyl group at the indole C3 position constitute a new class of ligands possessing high affinity for human CB 2 receptors at a nanomolar concentration and a good selectivity index. Starting from the neutral antagonist 4 , the effects of indole core modification on the pharmacodynamic profile of the ligands were investigated. Several N1 side chains afforded potent and CB 2 -selective neutral antagonists, notably derivatives 26 (R 1 = n -propyl, R 2 = H) and 35 (R 1 = 4-pentynyl, R 2 = H). Addition of a methyl group at C2 improved the selectivity for the CB 2 receptor. Moreover, C2 indole substitution may control the CB 2 activity as shown by the functionality switch in 35 (antagonist) and 49 (R 1 = 4-pentynyl, R 2 = CH 3 , partial agonist).

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity ( K d ) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Ascochyta lentis causes ascochyta blight in lentil (Lens culinaris Medik.) and yield loss can be as high as 50%. With careful agronomic management practices, fungicide use, and advances in breeding resistant lentil varieties, disease severity and impact to farmers have been largely controlled. However, evidence from major lentil producing countries, Canada and Australia, suggests that A. lentis isolates can change their virulence profile and level of aggressiveness over time and under different selection pressures. In this paper, we describe the first genome assembly for A. lentis for the Australian isolate Al4, through the integration of data from Illumina and PacBio SMRT sequencing. The Al4 reference genome assembly is almost 42 Mb in size and encodes 11,638 predicted genes. The Al4 genome comprises 21 full-length and gapless chromosomal contigs and two partial chromosome contigs each with one telomere. We predicted 31 secondary metabolite clusters, and 38 putative protein effectors, many of which were classified as having an unknown function. Comparison of A. lentis genome features with the recently published reference assembly for closely related A. rabiei show that genome synteny between these species is highly conserved. However, there are several translocations and inversions of genome sequence. The location of secondary metabolite clusters near transposable element and repeat-rich genomic regions was common for A. lentis as has been reported for other fungal plant pathogens., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Sparse sequences of neural spikes are posited to underlie aspects of working memory [1], motor production [2], and learning [3, 4]. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience [5-7]. Promising recent work [4, 8] utilized a convolutive nonnegative matrix factorization model [9] to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model predictions or estimated parameters. We address each of these shortcomings by developing a point process model that characterizes fine-scale sequences at the level of individual spikes and represents sequence occurrences as a small number of marked events in continuous time. This ultra-sparse representation of sequence events opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, which have been experimentally observed in neural circuits [10]. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.

The cannabinoid (CB) receptors (CB 1 R and CB 2 R) represent a promising therapeutic target for several indications such as nociception and obesity. The ligands with nonselectivity can be traced to the high similarity in the binding sites of both cannabinoid receptors. Therefore, the need for selectivity, potency, and G-protein coupling bias has further complicated the design of desired compounds. The bias of currently studied cannabinoid agonists is seldom investigated, and agonists that do exhibit bias are typically nonselective. However, certain long-chain endocannabinoids represent a class of selective and potent CB 1 R agonists. The binding mode for this class of compounds has remained elusive, limiting the implementation of its binding features to currently studied agonists. Hence, in the present study, the binding poses for these long-chain cannabinoids, along with other interesting ligands, with the receptors have been determined, by using a combination of molecular docking and molecular dynamics (MD) simulations along with molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The binding poses for the long-chain cannabinoids implicate that a site surrounded by the transmembrane (TM)2, TM7, and extracellular loop (ECL)2 is vital for providing the long-chain ligands with the selectivity for CB 1 R, especially I267 of CB 1 R (corresponding to L182 of CB 2 R). Based on the obtained binding modes, the calculated relative binding free energies and selectivity are all in good agreement with the corresponding experimental data, suggesting that the determined binding poses are reasonable. The computational strategy used in this study may also prove fruitful in applications with other GPCRs or membrane-bound proteins.

Androgen-deprivation therapy (ADT) is only a palliative measure, and prostate cancer invariably recurs in a lethal, castration-resistant form (CRPC). Prostate cancer resists ADT by metabolizing weak, adrenal androgens to growth-promoting 5α-dihydrotestosterone (DHT), the preferred ligand for the androgen receptor (AR). Developing small-molecule inhibitors for the final steps in androgen metabolic pathways that utilize 17-oxidoreductases required probes that possess fluorescent groups at C-3 and intact, naturally occurring functionality at C-17. Application of the Pictet-Spengler condensation to substituted 4-(2-aminoethyl)coumarins and 5α-androstane-3-ones furnished spirocyclic, fluorescent androgens at the desired C-3 position. Condensations required the presence of activating C-7 amino or N,N-dialkylamino groups in the 4-(2-aminoethyl)coumarin component of these condensation reactions. Successful Pictet-Spengler condensation, for example, of DHT with 9-(2-aminoethyl)-2,3,6,7-tetrahydro-1H,5H,11H-pyrano[2,3-f]pyrido[3,2,1-ij]quinolin-11-one led to a spirocyclic androgen, (3R,5S,10S,13S,17S)-17-hydroxy-10,13-dimethyl-1,2,2',3',4,5,6,7,8,8',9,9',10,11,12,12',13,13',14,15,16,17-docosahydro-7'H,11'H-spiro-[cyclopenta[a]phenanthrene-3,4'-pyrido[3,2,1-ij]pyrido[4',3':4,5]pyrano[2,3-f]quinolin]-5'(1'H)-one. Computational modeling supported the surrogacy of the C-3 fluorescent DHT analog as a tool to study 17-oxidoreductases for intracrine, androgen metabolism., Competing Interests: Conflicts of interest CL and DSW have partial ownership in a for-profit venture, Epionc, Inc., that seeks to develop small-molecule inhibitors for cancer treatment. In accord with University of Kentucky policies, CL and DSW have disclosed this work to the University of Kentucky’s Intellectual Property Committee and to a Conflict of Interest Oversight Committee.