搜索结果：hypotheses

We explore alternative hypotheses regarding the association between activity space racial composition and risk behavior among Black-identifying urban youth. Racial isolation perspectives argue that exposure to Black segregated neighborhoods limits access to mainstream institutions and influence, increasing participation in risk behavior (violence, delinquency, and substance/alcohol use). An alternative compelled mobility perspective argues that Black youth spend a substantial amount of time in low proportion Black, largely white neighborhoods seeking organizational resources typically less available in segregated areas. These exposures may lead to discrimination-related strain, detachment from conventional norms, and elevated physiological stress, increasing the likelihood of risk behavior compared to Black youth who spend more time in same-race dominated activity spaces. We test these competing hypotheses employing data from the Columbus, Ohio, USA-based Adolescent Health and Development in Context study on the geospatial exposures and both survey and Ecological Momentary Assessment (EMA)-reported behaviors of 506 Black youth ages 11-17. Contrary to the expectations of the isolation model, we find that greater exposure to residentially low proportion Black areas is associated with an increased likelihood of risk behavior for Black males. We consider implications of findings for extant theories and data collection approaches in research examining spatial effects on adolescent risk behavior.

A key goal in the microbiome field is to move from taxonomic associations towards mechanistic hypotheses about microbial gene function. However, most methods for linking microbiome changes to specific genes are biased towards finding marker genes, with weak evidence for functional relevance. Phylogenetic regression can address this issue and has been previously applied to changes in microbial prevalence, but many environments (such as the gut in health vs. disease) are characterized more by changes in abundance, which presents unique statistical challenges. We show that when applied to real differential abundances from metagenomes, phylogenetic regression has an anti-conservative bias, indicating inflated false positives. We develop an alternative non-parametric method called "robust permutration," designed specifically for differential abundance data, and evaluate its performance against phylogenetic regression as well as several other phylogenetic comparative methods in realistic simulations of metagenomic data. These results show that robust permutration is the most powerful method that appropriately controls the false positive rate. We further apply robust permutration to a human case-control study of liver cirrhosis, revealing that Lachnospiraceae abundance in disease is linked to a previously uncharacterized iron- sulfur transcription factor encoded near homologs of the butyryl-CoA oxygen oxidoreductase system, a recently discovered system for oxygen detoxification. This illustrates how robust, sensitive phylogenetic methods can enable the generation of new molecular hypotheses directly from metagenomic case-control data. Previously, we showed that phylogenetic regression can effectively detect genes associated with microbial presence or absence while correcting for evolutionary relationships. Unexpectedly, however, we here observe that this method can lead to high false positive rates when applied to microbial abundance data. In realistic simulations, other methods we test either have similar problems with false positives, or display very low power. We outline a new statistical test that better accounts for measurement uncertainty, outliers, and model violations, achieving more balanced sensitivity and accuracy than competing methods. Applying this test to a cirrhosis study reveals an uncharacterized transcription factor enriched in disease, with an apparent role in oxidative stress based on its sequence and gene neighborhood. This suggests a functional explanation for the observed taxonomic shifts, and demonstrates how improved phylogenetic methods could help inform future microbiome-targeted treatments.

Oropouche virus (OROV), an emerging orthobunyavirus in the Americas, has historically been associated with self-limited febrile illness in endemic Amazon basin regions. However, recent epidemiological updates from the Pan American Health Organization (PAHO) and World Health Organization (WHO) document a marked increase in case counts and geographic expansion, with over 16,000 confirmed cases reported in 2024 and continued transmission across multiple countries in 2025, including regions where transmission had not been previously recognized. The detection of cases in the Caribbean, Central America, and imported infections in North America and Europe underscores its evolving epidemiological profile and growing global relevance. In parallel with this expansion, neurological involvement-including meningitis and encephalitis-has been reported in a subset of patients. Experimental and ex vivo studies demonstrate neural permissiveness and suggest the capacity for interaction with central nervous system (CNS) tissue. Based on established principles of viral neuroimmunology, a conservative conceptual framework is proposed linking acute neuroimmune activation during OROV infection to potential neurological manifestations. Although long-term neurological sequelae have not been systematically characterized, existing clinical observations and biological mechanisms provide plausibility for further investigation. This framework is intended to generate testable hypotheses and guide prospective studies rather than establish causality.

Cognitive fatigue (CF), characterized by decrements in executive function and heightened subjective exhaustion resulting from prolonged cognitive exertion, has emerged as a critical determinant of athletic performance and psychophysiological wellbeing. Despite the exponential growth in research output, systematic quantitative analyses of the intellectual structure, evolutionary trajectory, and emerging frontiers within this domain remain scarce. Drawing upon the Web of Science Core Collection and Scopus databases, this study retrieved publications addressing exercise and cognitive fatigue from 1998 to 2025 using the search strategy: TS = ("physical activity" OR exercise OR sport*) AND TS = ("mental fatigue" OR "cognitive fatigue" OR "mental fog" OR "cognitive weariness*" OR "cognitive exhaustion*"). Following systematic screening, 820 articles were included for bibliometric analysis utilizing Bibliometrix and VOSviewer. Publication output rose modestly (1.84% annually), peaking in 2025 (n = 106), likely reflecting post-pandemic mental health research expansion and portable neurotechnology adoption. The US and China led productivity; the UK showed highest citation impact (48.16/article), suggesting influential contributions, though this may reflect publication timing and foundational works. Vrije Universiteit Brussel and University of Birmingham topped institutional output, reflecting sustained contributions within the Marcora, Meeusen, and Roelands traditions. Frontiers in Psychology was most influential. Keywords shifted from laboratory tasks to ecologically valid sport contexts ("football," "team sports"). Thematic evolution moved from "chronic fatigue syndrome" to "perceived exertion" and "depression," then to "executive function" and "combat sports"-indicating a gradual shift from descriptive symptoms to integrated cognitive-affective-physiological mechanisms. "Cognitive effort," "physical fatigue," and "executive function" occupied the motor themes quadrant in 2024-2025, signaling mature, structurally central topics. Findings suggest emerging brain-body-performance integration. This study traces an evolution from pathological fatigue measurement to executive function precision assessment. Bibliometric indicators reveal growing emphasis on cognitive effort as a motor modulator, with co-occurrence patterns identifying dual clusters around subjective perception and performance parameters. Whether this bibliographic convergence reflects validated physiological mechanisms or emerging theoretical hypotheses requires further primary experimental investigation. This analysis provides a complementary, field-level perspective that complements, rather than replaces, primary experimental research.

Positron emission tomography (PET) imaging is widely used in a number of clinical applications, including cancer and Alzheimer's disease (AD) diagnosis, monitoring of disease development, and treatment effect evaluation. Statistical modeling of PET imaging is essential to address continually emerging scientific questions in these research fields, including hypotheses related to evaluation of effects of disease modifying treatments on amyloid reduction in AD and associations between amyloid reduction and cognitive function, among many others. In this paper, we provide background information and tools for statisticians interested in developing statistical models for PET imaging to pre-process and prepare data for analysis. We introduce our novel pre-processing and visualization tool TRAECR (Template registration, MRI-PET co-Registration, Anatomical brain Extraction and COMBAT/RAVEL harmonization) to facilitate data preparation for statistical analysis.

Scapholunate dissociation is usually the result of failure of multiple wrist ligaments. With continued use other structures attenuate, which results in change in position of the carpal bones. It is presumed that load characteristics in the wrist joint change with changes in carpal bone position. This is thought to result in localized pressure overload and arthritic change. The purpose of this study was to evaluate radioscaphoid joint pressures and carpal kinematics after sectioning specific wrist ligaments. Our hypotheses are that there would be increased scaphoid flexion and ulnar deviation, increased lunate extension and radial deviation, increased contact pressure in the radioscaphoid fossa, and increased tendon forces. Eight cadaver wrists were instrumented with an electromagnetic motion tracking device and a pressure sensor was inserted into the radioscaphoid joint. Using a wrist joint motion simulator, motion and pressure data were obtained in the moving wrist in the intact state and after sectioning the dorsal radiocarpal, dorsal intercarpal, and scapholunate interosseous ligaments. After ligament sectioning there was increased scaphoid flexion, scaphoid ulnar deviation, lunate extension, and lunate radial deviation resulting in carpal instability. There was also an increase in pressure in the radioscaphoid fossa. Several specimens showed evidence of scaphoid subluxation. It is our conclusion that this combination of ligament sectioning produces scapholunate instability and increased pressures in the radioscaphoid fossa in the laboratory setting. We believe that if left untreated in the clinical setting, scapholunate advanced collapse could result.

Breast cancer immunity depends on more than the number of immune cells in a tumor. It is also shaped by where those cells sit, which neighbors they contact, and what functional states they adopt locally. Tumor-associated macrophages (TAMs) and T cells are a key pairing in this setting. Depending on tissue context, their crosstalk may support cytotoxic immunity, reinforce immune exclusion, promote T-cell exhaustion, or weaken therapeutic response. Spatial technologies now allow these states to be examined in intact tumor sections rather than inferred from dissociated or bulk samples. Antibody-based imaging approaches, including imaging mass cytometry, MIBI, and CODEX, together with high-plex transcriptomic platforms such as MERFISH, Xenium, CosMx, Visium, GeoMx, and related methods, have revealed inflamed, excluded, myeloid-rich, stromal-barrier, and tertiary lymphoid structure-associated niches in breast cancer. However, spatial maps alone cannot establish mechanism. Cells that lie close together may not necessarily interact, and computational tools, including ligand-receptor scoring, graph-based neighborhood modeling, and spatial biomarker prediction, can only prioritize candidate macrophage-T cell programs. Functional validation remains essential. In this mini review, we discuss how spatial omics, computational modeling, organoid and explant cultures, microfluidic models, perturbation assays, and therapeutic testing can be linked to study macrophage-T cell crosstalk. We highlight a practical workflow in which spatial maps generate hypotheses, experimental systems test causality, and post-treatment profiling determines whether candidate interactions are remodeled by therapy.

The comparative efficacy of acceptance and commitment therapy (ACT) relative to other bona fide psychotherapies has been obscured by methodological limitations and variability in how ACT and active comparators are operationalized. We therefore conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing full-model ACT (therapist-delivered, multi-session interventions targeting the core ACT processes) to bona fide psychotherapies. We identified 34 RCTs meeting our inclusion criteria. In line with prior critiques of the ACT literature, we identified several common methodological shortcomings, such as lack of preregistration of primary outcomes and hypotheses, inadequate statistical power for detecting differential effects, limited use of intent-to-treat analyses, and interpretive overreach. Random-effects three-level meta-analyses indicated that full-model ACT was not superior to bona fide psychotherapies on mental and behavioral health outcomes (g = -0.01, p = .86). Secondary, hypothesis-generating post hoc analyses examined clinical significance, ACT-targeted processes, and an exploratory TOST comparison against a prespecified equivalence region (g = ±0.20). Current RCT evidence does not justify recommending full-model ACT over other established treatments. Further progress will require more rigorous comparative trials and idiographic designs that better align with the ACT model.

The molecular architecture underlying diverse vertebrate sex-determining systems remains elusive despite fragmentary evidence of changes in upstream regulators and downstream mediators. Here we modeled species-specific regulatory networks of urogonadal development for turtles with contrasting mechanisms [Apalone spinifera - ZZ/ZW genotypic sex determination (GSD), and Chrysemys picta - temperature-dependent sex determination (TSD)] using matched data from time-course sampling. We uncovered key steps in the evolutionary transition of sex determination by testing for conservation or divergence of network modular components. Specifically, we tested these alternative hypotheses: first, transcription factor (TF) hubs and their target genes are conserved between species (null H0); second, the same TF hub acquired a new set of target genes in a species, retaining or not ancestral functions (H1 and variants); third, a new TF hub took over the regulation of the former gene targets of an ancestral TF (H2); and finally, complete overhaul occured where both ancestral TF hubs and their target genes were replaced in a species (H3). Results implicate primary cilia as integrators of environmental signals underlying TSD, because known thermosensitive TSD components (e.g., calcium-redox, pSTAT3, Wnt/Rspo1/β-catenin, Dhh) overrepresented in our results are linked to primary cilia. TFs that evolved between species also regulate primary cilia and point to key changes in their sensory machinery that accompanied TSD-GSD transitions (e.g., calcium/ion channels or membrane transport components in Chrysemys versus structural elements and ciliogenesis in Apalone). This novel Primary Cilia Integration hypothesis expands current models of epigenetic regulation of turtle sexual development, the evolution of plasticity versus canalization, and warrants functional validation.

Driven by the global rise in obesity and lifestyle transitions, cardiovascular-kidney-metabolic (CKM) syndrome has emerged as a pathophysiological continuum characterized by metabolic dysregulation and involving multi-organ interactions. Within the comprehensive management of CKM syndrome, dietary patterns represent a cornerstone of intervention due to their high modifiability and cost-effectiveness. Adopting the perspective of the CKM syndrome pathophysiological continuum, this narrative review provides a thematic overview of the current literature on the Mediterranean, DASH, plant-based, and ketogenic diets in relation to metabolic syndrome, type 2 diabetes mellitus, chronic kidney disease, and cardiovascular disease. Evidence indicates that the Mediterranean and DASH diets, through established anti-inflammatory, antioxidant, and endothelial protective mechanisms, are the most consistently supported dietary patterns for CKM risk mitigation. The efficacy of plant-based diets is strictly quality-dependent: while healthful patterns rich in whole grains and vegetables are associated with improved cardiorenal outcomes, unhealthful patterns dominated by refined carbohydrates may exacerbate metabolic derangements. Although the ketogenic diet may improve glucose metabolism in the short term, concerns regarding elevated low-density lipoprotein cholesterol, potential hepatotoxicity, and limited long-term adherence suggest that its role may be more relevant in selected short-term settings than as a sustained long-term dietary pattern. Furthermore, structured dietary quality indices may provide useful tools for characterizing dietary exposure in relation to CKM and for generating mechanistic hypotheses. By integrating clinical and mechanistic evidence, this review outlines a stage-oriented conceptual framework to discuss how different dietary patterns may relate to distinct phases of the CKM syndrome.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive tumor of the digestive system with a very low five-year survival rate. The limited efficacy and significant toxicity of existing chemotherapy regimens make the development of novel natural therapeutic agents an urgent priority. Lycopene is a natural carotenoid that has been shown to inhibit multiple cancers. However, research specifically targeting PDAC remains relatively scarce. This study first employed bibliometric analysis to examine the research landscape and emerging trends in lycopene-related cancer research from 2016 to 2026. Subsequently, network pharmacology methods are applied to screen potential lycopene targets and PDAC-related targets from databases such as CTD, ChEMBL and HERB. Following the identification of overlapping targets, drug-target and protein-protein interaction (PPI) networks are constructed, as well as a disease network. The mechanisms were explored using Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Molecular docking was used to predict the potential interactions between lycopene and representative hub targets, and molecular dynamics simulations were performed for selected high-ranking docking complexes to provide supportive information on complex-level conformational stability. In vitro experiments were then conducted to evaluate the predicted anti-PDAC effects and to perform focused validation of apoptosis-related proteins and the PI3K/Akt/P53 signaling axis. Publications on lycopene research in the field of cancer have shown a sustained upward trend. The focus of this research has gradually shifted from areas such as oxidative stress and antioxidant effects towards anti-cancer mechanisms. A total of 132 overlapping targets for lycopene's anti-PDAC activity were screened, leading to the identification of 10 core targets, including BCL2, AKT1, and TP53. GO enrichment analysis revealed that these targets are involved in biological processes such as the response to oxidative stress and cellular senescence. Meanwhile, KEGG enrichment analysis identified the PI3K-Akt signaling pathway as a key pathway. Molecular docking results showed that the binding energies of lycopene with core targets such as TP53 and BCL2 were below -4.5 kcal/mol. Molecular dynamics simulations provided supportive evidence for the conformational stability of representative lycopene-target complexes. In vitro experiments showed that lycopene inhibited the proliferation and migration of PDAC cells and promoted apoptosis-associated cell death, accompanied by decreased p-PI3K and p-AKT expression and increased P53 expression. This study systematically combined bibliometrics, network pharmacology, molecular docking, representative molecular dynamics simulations, and focused experimental validation to explore the potential anti-PDAC activity of lycopene. The inflammation-related hub targets identified by network analysis provide additional hypotheses for future experimental investigation. These findings provide preliminary mechanistic evidence for further preclinical investigation of lycopene in PDAC, but its translational application will require optimized formulations, pharmacokinetic validation, and in vivo efficacy studies to overcome its limited bioavailability.

Benzodiazepines and sedative hypnotics such as zolpidem ("z-drugs") are commonly prescribed for anxiety and sleep disorders. Epidemiologic evidence links their use to increased risk of venous thromboembolism. We investigated venous thromboembolism risk among concomitant users of individual benzodiazepines/z-drugs (examined separately) with other prescription medications to generate data-driven hypotheses about drug interactions resulting in clinically meaningful harm to inform future etiologic studies of specific drug combinations. We conducted a series of self-controlled case series studies within a 50% random sample of US Medicaid and Medicare data. Each cohort comprised person-time exposed to a benzodiazepine/z-drug, dichotomized into focal versus referent periods based on concomitant drug use versus non-use. We used conditional Poisson regression to estimate incidence rate ratios for hospital or emergency department presentation for venous thromboembolism. We generated ratios of incidence rate ratios, leveraging negative control analyses of eye drop-concomitant drug pairs, to minimize confounding by indication for the concomitant drug. We used semi-Bayes shrinkage to minimize false positives. Among 1590 self-controlled case series studies involving 8853 individuals with venous thromboembolism, 38 (2.4%) potential drug interaction signals were identified before calibration. After adjustment for multiple testing and negative control findings, five (0.3%) signals remained, involving gabapentin combined with eszopiclone, lorazepam, clonazepam, or alprazolam, and apixaban combined with diazepam (ratio of incidence rate ratio range: 1.92-3.57). Four (80%) involved concurrent use of gabapentin, a medication largely used to treat neuropathic pain. Most benzodiazepine/z-drug combinations conferred no increased venous thromboembolism risk. However, concurrent use of a benzodiazepine/z-drug with gabapentin may increase the relative rate of venous thromboembolism up to 3.5-fold. As this work was hypothesis generating, a future etiologic study should confirm this potential drug interaction.

Antimicrobial resistance (AMR) has emerged as a major threat to global public health, while conventional research methods face severe bottlenecks in deciphering its complex mechanisms and accelerating new drug development. Artificial intelligence (AI), particularly deep learning, is revolutionizing AMR research by enabling the processing of high-dimensional multi-omics data, uncovering hidden patterns, and generating novel hypotheses. This review systematically elaborates on the biomedical big data ecosystem that drives the AI revolution, including multi-omics data, phenotypic and clinical data, and literature-based knowledge data. We then discuss in detail cutting-edge AI methods and their applications in multi-level resistance mechanism analysis (knowledge-enhanced retrieval, resistance gene identification, and phenotype prediction) and intelligent design of novel antimicrobial molecules. Furthermore, we analyze core challenges in data quality, algorithm interpretability, clinical translation, and ethical governance. Finally, we propose key future directions, such as building equitable data ecosystems, developing interpretable AI models, and deepening interdisciplinary collaborations. This review aims to provide researchers with a comprehensive perspective on the current landscape, existing challenges, and future paths for AI applications in the AMR field. 抗微生物药物耐药性(antimicrobial resistance, AMR)已经成为全球公共卫生的重大威胁，传统的研究方法在解析其复杂机制、加速新药研发方面遇到了严峻的问题。人工智能(artificial intelligence, AI)技术，尤其是深度学习，由于可以处理高维多模态数据、发现隐藏的模式、产生新的假设，正在对AMR的策略产生革命性的影响。本文对驱动AI革命的生物医学大数据生态进行了系统阐述，包括多组学数据、表型和临床数据、文献知识数据;详细论述了AI在多层次耐药性机制解析(知识增强检索、耐药基因识别、表型预测)和新型抗菌分子智能设计中的前沿方法和实践;最后对目前数据质量、算法可解释性、临床转化、伦理监管等核心挑战进行了剖析，并对未来构建公平数据生态、开发可解释模型、深化跨学科合作等关键发展方向进行了展望。本文能够帮助相关研究人员全面了解AI在AMR领域的应用全景、存在的问题和未来的发展方向。.

Renal cell carcinoma (RCC) is a highly heterogeneous disease in which distinct molecular subtypes exhibit characteristic genomic, metabolic, and microenvironmental features that influence therapeutic response. Substantial inter-patient variability exists within each subtype, resulting in markedly different clinical outcomes even among tumours of the same histological category. Proteomics provides a direct readout of tumour biology and pathway activity, complementing genomic information and enabling the identification of patient-specific actionable vulnerabilities. We applied a Total Protein Approach (TPA)-based prescriptomics framework that integrates absolute quantitative proteomics with curated drug-target knowledge to nominate patient-specific drug-repurposing options, positioned as coadjuvants to the prevailing standard of care. Seventeen human kidney tissue specimens, seven clear cell RCC (ccRCC), five papillary RCC (pRCC), and five normal adjacent tissues (NAT), were retrieved from the publicly available PRIDE repository (PXD023296) and reanalysed by TPA-based absolute quantification applied to previously acquired label-free LC-MS/MS data. Differential expression analysis between each tumour subtype and NAT identified subtype-specific upregulated proteins, wich were intersected with Therapeutic Target Database (TTD) to nominate FDA-approved drugs targeting dysregulated proteins as candidate repurposing strategies. ccRCC and pRCC produced distinct proteome-wide upregulation profiles consistent with their known biological drivers. TPA index stratification nominated bempedoic acid (ACLY inhibitor) and tipiracil hydrochloride (TYMP inhibitor) as patient-stratified candidates for ccRCC, and auranofin (TXNRD1 inhibitor), bempedoic acid, and mipomersen (APOB-directed antisense oligonucleotide) for pRCC. ACLY was the only top-priority target shared across both subtypes, pointing to a candidate cross-subtype metabolic vulnerability. Secondary candidates emerged from protein-protein interaction network analysis in both subtypes.. This study presents a quantitative proteomics framework for translating individual-patient proteomic dysregulation into coadjuvant drug-repurposing hypotheses across the principal RCC subtypes. By combining the TPA for absolute protein quantification with prescriptomics-guided drug-target mapping, we show that ccRCC and pRCC harbour distinct, individually stratifiable therapeutic vulnerabilities. These findings provide a proof-of-concept for proteomics-based treatment stratification in RCC and establish a scalable framework that, pending functional validation, could inform personalised therapeutic decision-making across RCC subtypes.

We hypothesized that a dynamic surveillance strategy guided by circulating tumor DNA (ctDNA) methylation would increase the rate of curative-intent therapy for recurrence in patients with nonmetastatic colorectal cancer (CRC) after curative resection. The FIND trial (ClinicalTrials.gov identifier: NCT05904665) is a prospective, multicenter, randomized, phase III study. Patients with nonmetastatic CRC were randomly assigned to ctDNA-guided surveillance or standard computed tomography (CT)-based monitoring. In the ctDNA-guided group, a positive ctDNA result triggered immediate CT imaging; if negative, bimonthly CT continued alongside quarterly ctDNA testing. After two consecutive ctDNA-negative results, imaging reverted to standard frequency. The primary end point was the proportion of patients with recurrence receiving curative-intent metastasis-directed therapy. Among 584 eligible patients (289 ctDNA-guided, 295 control) in the modified intention-to-treat population, with a median follow-up of 23.3 months, recurrence rates were similar (18.0% v 18.6%, P = .919). The ctDNA-guided group had a significantly higher rate of curative-intent treatment (48.1% v 23.6%, relative risk 2.03, P = .008). The median time to clinical recurrence was significantly shorter in the ctDNA-guided group than in the control group (9.5 v 13.4 months; P < .001), representing a lead time of 3.9 months. Among recurrences confined to the liver and/or lungs, the ctDNA-guided group showed higher curative resection rates (42.3% v 18.2%, P = .002). These patients had more favorable hepatic metastatic features: fewer lesions (&#x2264;3: 75.0% v 28.6%, P = .005), smaller tumor size (&#x2264;3 cm: 90.0% v 57.1%, P = .033), and more unilobar disease (80.0% v 28.6%, P = .002). ctDNA methylation-guided dynamic surveillance improves the rate of curative-intent therapy for recurrence in patients with initially nonmetastatic CRC through earlier detection of resectable metastases, pending validation of long-term survival benefit in future analyses with mature data.

Tropical regions may face drastic changes under climate change. Bryophytes, with already low biomass in tropical lowlands are likely to be severely affected. Their low biomass is hypothesized to result from a combination of low-light conditions in the rainforest understorey and under clouds, inactivity during daylight due to fast drying, and high respiration rates during warm nights. Warming is likely to further affect carbon balances, despite increased atmospheric CO2 and/or possible acclimatization to higher temperatures. We conducted a climate-change simulation experiment in a Costa Rican rainforest understory and used the carbon-exchange model PoiCarb to simulate bryophyte carbon balances in the experimental conditions. Warming negatively affected the studied species, and increasing CO2 could not compensate sufficiently for this. No acclimation of CO2-exchange rates to increased temperature was detected. Warming increased the vapour pressure deficit in the chambers, probably aggravating the negative warming effects while not accurately reflecting future climate conditions. Modelling revealed that the reduced carbon balance under warming was primarily driven by drying, with higher respiration rates also contributing. Warming is likely to challenge tropical-lowland bryophyte growth even more, threatening biodiversity through direct bryophyte species losses and cascading effects of decreased bryophyte abundance and diversity on other rainforest components.

The subthalamic region consists of a complex intersection of many different axonal pathways. Emerging hypotheses in deep brain stimulation (DBS) for Parkinson's disease (PD) suggest that direct stimulation of specific axonal pathways may be linked to the control of specific motor symptoms (e.g. cerebellothalamic (CT) - tremor; motor hyperdirect (mHD) - bradykinesia; pallidothalamic (PT) - rigidity). However, the typical frontal DBS lead trajectory limits opportunities to co-activate all of these pathways. We used advanced biophysical DBS models to evaluate the theoretical utility of a parietal lead trajectory into the subthalamic region that could facilitate activation of the PT, mHD, and CT pathways. We compared a typical frontal DBS lead trajectory with a traditional 8-contact directional DBS lead to the parietal alternative with a 16-contact directional DBS lead. The analyses were performed within the context of the CIT168 human atlas brain populated with the Petersen axonal pathway models. PT, mHD, and CT fibers are distributed in an anterior-to-posterior fashion within the subthalamic white matter. Given this anatomical feature, traditional frontal trajectories limit opportunities for multi-pathway DBS because the electrode contacts are primarily aligned dorsal-ventrally. Alternatively, the span of DBS contacts along a parietal trajectory can provide better opportunities to activate all of the pathways of interest, while also avoiding unwanted activation of the internal capsule. Parietal DBS trajectories warrant consideration in PD therapy as the research concepts of pathway-targeted stimulation begin migrating into clinical practice.

Positive social comparative feedback during motor skill practice is hypothesized to enhance motor learning by triggering a dopaminergic response. Individual differences in dopamine-related genes impact dopamine neurotransmission and may influence responsiveness to motor practice conditions that target dopaminergic pathways. The purpose of this study was to examine the impact of dopamine genotype on learning of a motor sequence task under two different feedback conditions: response time only feedback or response time with positive social comparison. Fifty-two adults practiced a joystick-based motor sequence task over two consecutive days. On Day 1, participants were randomized to receive either 1) response time only feedback (i.e., "You completed the block in 80 seconds") or 2) positive social comparative feedback (i.e., "You completed the block in 80 seconds. You were faster than others"). Motor learning was assessed by retention performance, or the change in response time from the first block of Day 1 to the first block on Day 2. Saliva samples were used to genotype for dopamine receptors DRD1, DRD2 and DRD3 and COMT. Individual genes were scored (0-2) and summed to create a polygene score (0-8). Participants were then categorized as having Low (1-4) or High (5-8) dopamine neurotransmission. A significant interaction was found between time, summary polygene group and feedback group (p&#x2009;=&#x2009;0.048). The Low dopamine group showed greater improvements with response time only feedback versus positive social comparative feedback. The High dopamine group showed similar improvements in response time regardless of feedback type. This suggests that feedback targeting dopaminergic pathways may not be beneficial for individuals with lower dopamine neurotransmission. Our findings suggest that dopaminergic genetics may impact the efficacy of positive social comparative feedback on motor learning in low dopamine genotypes.

The presence of SARS-CoV-2 RNA in blood has been proposed as a marker of severe COVID-19, but it is unclear whether RNAemia mediates the pathway toward worsening disease. We hypothesized that RNAemia is associated with severe disease and distinct gene expression patterns are associated with RNAemia and severe COVID-19. These RNAemia-associated patterns may identify COVID-19 treatment targets. We analyzed 202 hospitalized COVID-19 participants from a multi-center U.S. Military Health System cohort using digital droplet PCR (ddPCR) to quantify SARS-CoV-2 RNA in plasma and performed host RNA sequencing of peripheral blood. Differential gene expression (DGE) logistic regression models were used to assess associations among RNAemia, host gene expression, and disease severity. RNAemia was detected in 39.1% of participants and was associated with severe disease (54% vs. 32% in RNAemia-negative participants; p <0.001). In final adjusted models, independent predictors of severity included RNAemia (adjusted Odds Ratio [aOR] range 1.99-2.24, all p &#x2264; 0.04), as well as host genes ADAMTS2 (aOR = 1.58, p < 0.001), OLAH (aOR = 1.55, p < 0.001), and PCSK9 (aOR = 1.60, p < 0.001). RNAemia is an independent predictor of COVID-19 severity. However, host gene expression changes associated with RNAemia, particularly involving OLAH, PCSK9, and ADAMTS2, had stronger statistical evidence of severe outcomes than RNAemia itself. PCSK9 is an intervenable treatment target worth further study.