Differential Toxicity of Arsenic in Daphnia pulex Under Phosphorus and Food Limitation.

The on-going anthropogenic degradation of freshwater habitats has drastically altered the environmental supply of both nutrients and common pollutants. Most organisms living in these altered habitats experience interactive effects of various stressors that can initiate adjustments at multiple levels impacting their fitness. Hence, studies measuring response to a single environmental parameter fail to capture the complexities of the status quo. We tested both the individual and the interactive effect of arsenic (As) exposure, food quantity, and dietary phosphorus (P)-supply on six life-history traits (Juvenile Growth Rate; Adult Growth Rate; Age and Size at Maturity, Lifespan, and Fecundity) as surrogates for organismal fitness in the keystone aquatic grazer Daphnia pulex. We also tested the effect of food quantity and P-supply on somatic As accumulation in Daphnia. Our results indicated an influence of P-supply on neonatal growth and an influence of As and food quantity on growth and maintenance later in life. Maturation was strongly influenced by all three variables, with no reproduction observed in the presence of two or more environmental stressors. We found a strong interaction between As and dietary P, with increased P-supply intensifing the toxicity effect of As. No such effects were seen between As and food quantity, indicating a differential role of quantity versus quality on As toxicity. We found a nominal effect of diet on somatic As accumulation. The results from the present study emphasize the importance of considering such interactions between co-occurring environmental stressors and the dietary status of organisms, to better predict and manage impacts and risks associated with common environmental toxicants in highly vulnerable ecosystems. Environ Toxicol Chem 2024;00:1-13. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Development of a liver disease-specific large language model chat interface using retrieval-augmented generation.

BACKGROUND AND AIMS: Large language models (LLMs) have significant capabilities in clinical information processing tasks. Commercially available LLMs, however, are not optimized for clinical uses and are prone to generating hallucinatory information. Retrieval-augmented generation (RAG) is an enterprise architecture that allows the embedding of customized data into LLMs. This approach "specializes" the LLMs and is thought to reduce hallucinations. APPROACH AND RESULTS: We developed "LiVersa," a liver disease-specific LLM, by using our institution's protected health information-complaint text embedding and LLM platform, "Versa." We conducted RAG on 30 publicly available American Association for the Study of Liver Diseases guidance documents to be incorporated into LiVersa. We evaluated LiVersa's performance by conducting 2 rounds of testing. First, we compared LiVersa's outputs versus those of trainees from a previously published knowledge assessment. LiVersa answered all 10 questions correctly. Second, we asked 15 hepatologists to evaluate the outputs of 10 hepatology topic questions generated by LiVersa, OpenAI's ChatGPT 4, and Meta's Large Language Model Meta AI 2. LiVersa's outputs were more accurate but were rated less comprehensive and safe compared to those of ChatGPT 4. RESULTS: We evaluated LiVersa's performance by conducting 2 rounds of testing. First, we compared LiVersa's outputs versus those of trainees from a previously published knowledge assessment. LiVersa answered all 10 questions correctly. Second, we asked 15 hepatologists to evaluate the outputs of 10 hepatology topic questions generated by LiVersa, OpenAI's ChatGPT 4, and Meta's Large Language Model Meta AI 2. LiVersa's outputs were more accurate but were rated less comprehensive and safe compared to those of ChatGPT 4. CONCLUSIONS: In this demonstration, we built disease-specific and protected health information-compliant LLMs using RAG. While LiVersa demonstrated higher accuracy in answering questions related to hepatology, there were some deficiencies due to limitations set by the number of documents used for RAG. LiVersa will likely require further refinement before potential live deployment. The LiVersa prototype, however, is a proof of concept for utilizing RAG to customize LLMs for clinical use cases.

Development of a Liver Disease-Specific Large Language Model Chat Interface using Retrieval Augmented Generation.

Background: Large language models (LLMs) have significant capabilities in clinical information processing tasks. Commercially available LLMs, however, are not optimized for clinical uses and are prone to generating incorrect or hallucinatory information. Retrieval-augmented generation (RAG) is an enterprise architecture that allows embedding of customized data into LLMs. This approach "specializes" the LLMs and is thought to reduce hallucinations. Methods: We developed "LiVersa," a liver disease-specific LLM, by using our institution's protected health information (PHI)-complaint text embedding and LLM platform, "Versa." We conducted RAG on 30 publicly available American Association for the Study of Liver Diseases (AASLD) guidelines and guidance documents to be incorporated into LiVersa. We evaluated LiVersa's performance by comparing its responses versus those of trainees from a previously published knowledge assessment study regarding hepatitis B (HBV) treatment and hepatocellular carcinoma (HCC) surveillance. Results: LiVersa answered all 10 questions correctly when forced to provide a "yes" or "no" answer. Full detailed responses with justifications and rationales, however, were not completely correct for three of the questions. Discussions: In this study, we demonstrated the ability to build disease-specific and PHI-compliant LLMs using RAG. While our LLM, LiVersa, demonstrated more specificity in answering questions related to clinical hepatology - there were some knowledge deficiencies due to limitations set by the number and types of documents used for RAG. The LiVersa prototype, however, is a proof of concept for utilizing RAG to customize LLMs for clinical uses and a potential strategy to realize personalized medicine in the future.

Distribution and Quantity of Sites of John Cunningham Virus Persistence in Immunologically Healthy Patients: Correlation With John Cunningham Virus Antibody and Urine John Cunningham Virus DNA.

Importance: Although seroepidemiological studies indicate that greater than 50% of the population has been infected with John Cunningham virus (JCV), the sites of JCV persistence remain incompletely characterized. Objective: To determine sites of JCV persistence in immunologically healthy individuals. Design, Setting, and Participants: Tissue specimens from multiple sites including brain, renal, and nonrenal tissues were obtained at autopsy performed in the Department of Pathology at the University of Kentucky from 12 immunologically healthy patients between February 9, 2011, and November 27, 2012. Quantitative polymerase chain reaction was performed on the tissue specimens and urine. Serum JCV antibody status was determined by enzyme-linked immunosorbent assay. Main Outcomes and Measures: The detection and quantification of JCV from the tissues by quantitative polymerase chain reaction illuminated sites of viral persistence. These results were correlated with JCV antibody levels. Results: Autopsies were performed on 12 individuals, 10 men and 2 women, ranging in age from 25 to 75 years (mean, 55.3 years). Seven of 12 individuals were JCV antibody seropositive based on absorbance units. Serostatus was associated with amounts of JCV DNA in urine and its tissue distribution. John Cunningham virus DNA was found in 75% of genitourinary tissue samples from donors (18 of 24) with high JCV antibody levels, 13.3% of donors with low levels i(4 of 30), and 0% of seronegative persons (0 of 32). In nongenitourinary tissues, JCV DNA was detected in 45.1% of tissue samples of donors (32 of 71) with high JCV, 2.2% of donors with low JCV serostatus (2 of 93), and 0% of seronegative persons (0 of 43). Genitourinary tissues had higher copy numbers than other sites. John Cunningham virus DNA was detected in urine of seronegative individuals in a research-grade assay. Conclusions and Relevance: Persistent (latent or actively replicating) JCV infection mostly predominates in genitourinary tissues but distributes in other tissues at low copy number. The distribution and copy numbers of the virus appear to correlate with urinary JCV shedding and serostatus.

Usf1, a suppressor of the circadian Clock mutant, reveals the nature of the DNA-binding of the CLOCK:BMAL1 complex in mice.

Genetic and molecular approaches have been critical for elucidating the mechanism of the mammalian circadian clock. Here, we demonstrate that the ClockΔ19 mutant behavioral phenotype is significantly modified by mouse strain genetic background. We map a suppressor of the ClockΔ19 mutation to a ∼900 kb interval on mouse chromosome 1 and identify the transcription factor, Usf1, as the responsible gene. A SNP in the promoter of Usf1 causes elevation of its transcript and protein in strains that suppress the Clock mutant phenotype. USF1 competes with the CLOCK:BMAL1 complex for binding to E-box sites in target genes. Saturation binding experiments demonstrate reduced affinity of the CLOCKΔ19:BMAL1 complex for E-box sites, thereby permitting increased USF1 occupancy on a genome-wide basis. We propose that USF1 is an important modulator of molecular and behavioral circadian rhythms in mammals. DOI:http://dx.doi.org/10.7554/eLife.00426.001.

Identification of causal genes, networks, and transcriptional regulators of REM sleep and wake.

STUDY OBJECTIVE: Sleep-wake traits are well-known to be under substantial genetic control, but the specific genes and gene networks underlying primary sleep-wake traits have largely eluded identification using conventional approaches, especially in mammals. Thus, the aim of this study was to use systems genetics and statistical approaches to uncover the genetic networks underlying 2 primary sleep traits in the mouse: 24-h duration of REM sleep and wake. DESIGN: Genome-wide RNA expression data from 3 tissues (anterior cortex, hypothalamus, thalamus/midbrain) were used in conjunction with high-density genotyping to identify candidate causal genes and networks mediating the effects of 2 QTL regulating the 24-h duration of REM sleep and one regulating the 24-h duration of wake. SETTING: Basic sleep research laboratory. PATIENTS OR PARTICIPANTS: Male [C57BL/6J × (BALB/cByJ × C57BL/6J*) F1] N(2) mice (n = 283). INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The genetic variation of a mouse N2 mapping cross was leveraged against sleep-state phenotypic variation as well as quantitative gene expression measurement in key brain regions using integrative genomics approaches to uncover multiple causal sleep-state regulatory genes, including several surprising novel candidates, which interact as components of networks that modulate REM sleep and wake. In particular, it was discovered that a core network module, consisting of 20 genes, involved in the regulation of REM sleep duration is conserved across the cortex, hypothalamus, and thalamus. A novel application of a formal causal inference test was also used to identify those genes directly regulating sleep via control of expression. CONCLUSION: Systems genetics approaches reveal novel candidate genes, complex networks and specific transcriptional regulators of REM sleep and wake duration in mammals.

Pharmacological validation of candidate causal sleep genes identified in an N2 cross.

Despite the substantial impact of sleep disturbances on human health and the many years of study dedicated to understanding sleep pathologies, the underlying genetic mechanisms that govern sleep and wake largely remain unknown. Recently, the authors completed large-scale genetic and gene expression analyses in a segregating inbred mouse cross and identified candidate causal genes that regulate the mammalian sleep-wake cycle, across multiple traits including total sleep time, amounts of rapid eye movement (REM), non-REM, sleep bout duration, and sleep fragmentation. Here the authors describe a novel approach toward validating candidate causal genes, while also identifying potential targets for sleep-related indications. Select small-molecule antagonists and agonists were used to interrogate candidate causal gene function in rodent sleep polysomnography assays to determine impact on overall sleep architecture and to evaluate alignment with associated sleep-wake traits. Significant effects on sleep architecture were observed in validation studies using compounds targeting the muscarinic acetylcholine receptor M3 subunit (Chrm3) (wake promotion), nicotinic acetylcholine receptor alpha4 subunit (Chrna4) (wake promotion), dopamine receptor D5 subunit (Drd5) (sleep induction), serotonin 1D receptor (Htr1d) (altered REM fragmentation), glucagon-like peptide-1 receptor (Glp1r) (light sleep promotion and reduction of deep sleep), and calcium channel, voltage-dependent, T type, alpha 1I subunit (Cacna1i) (increased bout duration of slow wave sleep). Taken together, these results show the complexity of genetic components that regulate sleep-wake traits and highlight the importance of evaluating this complex behavior at a systems level. Pharmacological validation of genetically identified putative targets provides a rapid alternative to generating knock out or transgenic animal models, and may ultimately lead towards new therapeutic opportunities.

Non-laser capture microscopy approach for the microdissection of discrete mouse brain regions for total RNA isolation and downstream next-generation sequencing and gene expression profiling.

As technological platforms, approaches such as next-generation sequencing, microarray, and qRT-PCR have great promise for expanding our understanding of the breadth of molecular regulation. Newer approaches such as high-resolution RNA sequencing (RNA-Seq)(1) provides new and expansive information about tissue- or state-specific expression such as relative transcript levels, alternative splicing, and micro RNAs(2-4). Prospects for employing the RNA-Seq method in comparative whole transcriptome profiling(5) within discrete tissues or between phenotypically distinct groups of individuals affords new avenues for elucidating molecular mechanisms involved in both normal and abnormal physiological states. Recently, whole transcriptome profiling has been performed on human brain tissue, identifying gene expression differences associated with disease progression(6). However, the use of next-generation sequencing has yet to be more widely integrated into mammalian studies. Gene expression studies in mouse models have reported distinct profiles within various brain nuclei using laser capture microscopy (LCM) for sample excision(7,8). While LCM affords sample collection with single-cell and discrete brain region precision, the relatively low total RNA yields from the LCM approach can be prohibitive to RNA-Seq and other profiling approaches in mouse brain tissues and may require sub-optimal sample amplification steps. Here, a protocol is presented for microdissection and total RNA extraction from discrete mouse brain regions. Set-diameter tissue corers are used to isolate 13 tissues from 750-µm serial coronal sections of an individual mouse brain. Tissue micropunch samples are immediately frozen and archived. Total RNA is obtained from the samples using magnetic bead-enabled total RNA isolation technology. Resulting RNA samples have adequate yield and quality for use in downstream expression profiling. This microdissection strategy provides a viable option to existing sample collection strategies for obtaining total RNA from discrete brain regions, opening possibilities for new gene expression discoveries.

Sleeping through the night: the consolidation of self-regulated sleep across the first year of life.

OBJECTIVE: To investigate the consolidation of infants' self-regulated nocturnal sleep over the first year, to determine when infants first sleep through the night from 24:00 to 05:00 hours (criterion 1), for 8 hours (criterion 2), or between 22:00 and 06:00 hours (the family-congruent criterion 3). METHODS: This was a prospective longitudinal study with repeated measures. Parents of 75 typically developing infants completed sleep diaries for 6 days each month for 12 months. Accuracy of parent reports were assessed by using videosomnography. RESULTS: The largest mean increase (504 minutes) in self-regulated sleep length occurred from 1 to 4 months. The survival function decreased most rapidly (indicating greatest probability of meeting criteria) for criterion 1 at 2 months, criterion 2 at 3 months, and criterion 3 at 4 months. A 50% probability of meeting criteria 1 and 2 occurred at 3 months and at 5 months for criterion 3. The hazard function identified 2 months (criteria 1 and 2) and 3 months (criterion 3) as the most likely ages for sleeping through the night. At 12 months, 11 infants did not meet criteria 1 or 2, whereas 21 failed to meet criterion 3. CONCLUSIONS: The most rapid consolidation in infant sleep regulation occurs in the first 4 months. Most infants are sleeping through the night at 2 and 3 months, regardless of the criterion used. The most developmentally and socially valid criterion for sleeping through is from 22:00 to 0:600 hours. At 5 months, more than half of infants are sleeping concurrently with their parents.

Uncovering the genetic landscape for multiple sleep-wake traits.

Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior.

Dihadron tomography of high-energy nuclear collisions in next-to-leading order perturbative QCD.

Dihadron spectra in high-energy heavy-ion collisions are studied within the next-to-leading order perturbative QCD parton model with modified jet fragmentation functions due to jet quenching. High-p(T) back-to-back dihadrons are found to originate mainly from jet pairs produced close and tangential to the surface of the dense matter. However, a substantial fraction also comes from jets produced at the center with finite energy loss. Consequently, high-p(T) dihadron spectra are found to be more sensitive to the initial gluon density than the single hadron spectra that are more dominated by surface emission. A simultaneous chi(2) fit to both the single and dihadron spectra can be achieved within a range of the energy loss parameter E(0)=1.6-2.1 GeV/fm. Because of the flattening of the initial jet production spectra at square root s=5.5 TeV, high p(T) dihadrons are found to be more robust as probes of the dense medium.