Bacteriophage targeting of gut bacterium attenuates alcoholic liver disease.

Chronic liver disease due to alcohol-use disorder contributes markedly to the global burden of disease and mortality1-3. Alcoholic hepatitis is a severe and life-threatening form of alcohol-associated liver disease. The gut microbiota promotes ethanol-induced liver disease in mice4, but little is known about the microbial factors that are responsible for this process. Here we identify cytolysin-a two-subunit exotoxin that is secreted by Enterococcus faecalis5,6-as a cause of hepatocyte death and liver injury. Compared with non-alcoholic individuals or patients with alcohol-use disorder, patients with alcoholic hepatitis have increased faecal numbers of E. faecalis. The presence of cytolysin-positive (cytolytic) E. faecalis correlated with the severity of liver disease and with mortality in patients with alcoholic hepatitis. Using humanized mice that were colonized with bacteria from the faeces of patients with alcoholic hepatitis, we investigated the therapeutic effects of bacteriophages that target cytolytic E. faecalis. We found that these bacteriophages decrease cytolysin in the liver and abolish ethanol-induced liver disease in humanized mice. Our findings link cytolytic E. faecalis with more severe clinical outcomes and increased mortality in patients with alcoholic hepatitis. We show that bacteriophages can specifically target cytolytic E. faecalis, which provides a method for precisely editing the intestinal microbiota. A clinical trial with a larger cohort is required to validate the relevance of our findings in humans, and to test whether this therapeutic approach is effective for patients with alcoholic hepatitis.

Activating TiO2 through the Phase Transition-Mediated Hydrogen Spillover to Outperform Pt for Electrocatalytic pH-Universal Hydrogen Evolution.

Endowing conventional materials with specific functions that are hardly available is invariably of significant importance but greatly challenging. TiO2 is proven to be highly active for the photocatalytic hydrogen evolution while intrinsically inert for electrocatalytic hydrogen evolution reaction (HER) due to its poor electrical conductivity and unfavorable hydrogen adsorption/desorption behavior. Herein, the first activation of inert TiO2 for electrocatalytic HER is demonstrated by synergistically modulating the positions of d-band center and triggering hydrogen spillover through the dual doping-induced partial phase transition. The N, F co-doping-induced partial phase transition from anatase to rutile phase in TiO2 (AR-TiO2|(N,F)) exhibits extraordinary HER performance with overpotentials of 74, 80, and 142 mV at a current density of 10 mA cm-2 in 1.0 M KOH, 0.5 M H2SO4, and 1.0 M phosphate-buffered saline electrolytes, respectively, which are substantially better than pure TiO2, and even superior to the benchmark Pt/C catalysts. These findings may open a new avenue for the development of low-cost alternative to noble metal catalysts for electrocatalytic hydrogen production.

Stability-enhanced low-frequency fiber optic hydrophone based on an extrinsic Fabry-Perot interferometer.

Fiber optic hydrophones (FOHs) offer the notable advantage of electromagnetic interference resistance. Nevertheless, overcoming the challenge of sustaining stable, high-performance operation in intricate underwater settings at a low cost remains a considerable obstacle for them. To circumvent the restrictions noted above, we employed a miniaturized FOH, utilizing an easily fabricated extrinsic Fabry-Perot interferometer (EFPI) which is made up of a composite chromium-aluminum (Cr-Al) membrane and fiber. The linear demodulation also suppresses the drift issue in the output spectrum. The average sound pressure sensitivity of the sensor, according to experimental findings, is around -139.15 dB re 1 V/µPa, while the equivalent noise sound pressure at 1 kHz is 51.52 dB re 1 µPa/Hz1/2. This sensor has a lot of potential because of features like sensitive low-frequency response and noise performance.

Dissection of potential anti-osteoporosis mechanism of isopsoralen - a quality control marker in Psoraleae Fructus - by metabolite profiling and network pharmacology.

RATIONALE: Isopsoralen (ISO), a quality control marker (Q-marker) in Psoraleae Fructus, is proven to present an obvious anti-osteoporosis effect. Until now, the metabolism and anti-osteoporosis mechanisms of ISO have not been fully elucidated, greatly restricting its drug development. METHODS: The metabolites of ISO in rats were profiled by using ultrahigh-performance liquid chromatography coupled with time-of-flight mass spectrometry. The potential anti-osteoporosis mechanism of ISO in vivo was predicted by using network pharmacology. RESULTS: A total of 15 metabolites were characterized in rats after ingestion of ISO (20 mg/kg/day, by gavage), including 2 in plasma, 12 in urine, 6 in feces, 1 in heart, 3 in liver, 1 in spleen, 1 in lung, 3 in kidney, and 2 in brain. The pharmacology network results showed that ISO and its metabolites could regulate AKT1, SRC, NFKB1, EGFR, MAPK3, etc., involved in the prolactin signaling pathway, ErbB signaling pathway, thyroid hormone pathway, and PI3K-Akt signaling pathway. CONCLUSIONS: This is the first time for revealing the in vivo metabolism features and potential anti-osteoporosis mechanism of ISO by metabolite profiling and network pharmacology, providing data for further verification of pharmacological mechanism.

Characterization of metabolic features and potential anti-osteoporosis mechanism of pinoresinol diglucoside using metabolite profiling and network pharmacology.

RATIONALE: Eucommia cortex is the core herb in traditional Chinese medicine preparations for the treatment of osteoporosis. Pinoresinol diglucoside (PDG), the quality control marker and the key pharmacodynamic component in Eucommia cortex, has attracted global attention because of its definite effects on osteoporosis. However, the in vivo metabolic characteristics of PDG and its anti-osteoporotic mechanism are still unclear, restricting its development and application. METHODS: Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was used to analyze the metabolic characteristics of PDG in rats, and its anti-osteoporosis targets and mechanism were predicted using network pharmacology. RESULTS: A total of 51 metabolites were identified or tentatively characterized in rats after oral administration of PDG (10 mg/kg/day), including 9 in plasma, 28 in urine, 13 in feces, 10 in liver, 4 in heart, 3 in spleen, 11 in kidneys, and 5 in lungs. Furan-ring opening, dimethoxylation, glucuronidation, and sulfation were the main metabolic characteristics of PDG in vivo. The potential mechanism of PDG against osteoporosis was predicted using network pharmacology. PDG and its metabolites could regulate BCL2, MARK3, ALB, and IL6, involving PI3K-Akt signaling pathway, estrogen signaling pathway, and so on. CONCLUSIONS: This study was the first to demonstrate the metabolic characteristics of PDG in vivo and its potential anti-osteoporosis mechanism, providing the data for further pharmacological validation of PDG in the treatment of osteoporosis.

Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.

Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.

The heritability of blood-based biomarkers related to risk of Alzheimer's disease in a population-based sample of early old-age men.

INTRODUCTION: Despite their increased application, the heritability of Alzheimer's disease (AD)-related blood-based biomarkers remains unexplored. METHODS: Plasma amyloid beta 40 (Aß40), Aß42, the Aß42/40 ratio, total tau (t-tau), and neurofilament light (NfL) data came from 1035 men 60 to 73 years of age (µ = 67.0, SD = 2.6). Twin models were used to calculate heritability and the genetic and environmental correlations between them. RESULTS: Additive genetics explained 44% to 52% of Aß42, Aß40, t-tau, and NfL. The Aß42/40 ratio was not heritable. Aß40 and Aß42 were genetically near identical (rg  = 0.94). Both Aß40 and Aß42 were genetically correlated with NfL (rg  = 0.35 to 0.38), but genetically unrelated to t-tau. DISCUSSION: Except for Aß42/40, plasma biomarkers are heritable. Aß40 and Aß42 share mostly the same genetic influences, whereas genetic influences on plasma t-tau and NfL are largely unique in early old-age men. The absence of genetic associations between the Aßs and t-tau is not consistent with the amyloid cascade hypothesis.

CO2-assisted Controllable Synthesis of PdNi Nanoalloys for Highly Selective Hydrogenation of Biomass-derived 5-Hydroxymethylfurfural.

The selective hydrogenation of 5-hydroxymethylfurfural (HMF) to 2,5-bishydroxymethyltetrahydrofuran (BHMTHF), a vital fuel precursor and solvent, is crucial for biomass refining. Herein, we report highly selective and stable PdNi nanoalloy catalysts for this deep hydrogenation process. A CO2-assisted green method was developed for the controllable synthesis of various bimetallic and monometallic catalysts. The PdNi/SBA-15 catalysts with various Pd/Ni ratios exhibited a volcano-like trend between BHMTHF yield and Pd/Ni ratio. Among all catalysts tested, Pd2Ni1/SBA-15 achieved the best performance, converting 99.0% of HMF to BHMTHF with 96.0% selectivity, surpassing previously reported catalysts. Additionally, the Pd2Ni1/SBA-15 catalyst maintained excellent stability even after five recycling runs. Catalyst characterizations (e.g., HAADF-STEM) and DFT calculations confirmed the successful formation of the alloy structure with electron transfer between Ni and Pd, which accounts for the remarkable performance and stability of the catalyst. This work paves the way for developing highly selective and stable alloy catalysts for biomass valorization.

Plasma-Enabled Process with Single-Atom Catalysts for Sustainable Plastic Waste Transformation.

The escalating issue of plastic waste generation has prompted the search for an effective solution to address these challenges. In this study, we present a novel plasma-enabled strategy for the rapid breakdown of various types of plastic wastes, including mixtures, into high-value carbon nanomaterials and hydrogen. The H2 yield and selectivity achieved through the implemented catalyst-free plasma-enabled strategy are 14.2 and 5.9 times higher, respectively, compared to those obtained with conventional thermal pyrolysis under similar conditions. It is noteworthy that this catalyst-free plasma alone approach yields a significantly higher energy yield of H2 (gH2/kWh) compared to other pyrolysis processes. By coupling plasma pyrolysis with thermal catalytic process, employing of 1 wt . % M/CeO2 (M=Fe, Co, and Ni) atomically dispersed catalysts can further enhance hydrogen production. Specifically, the 1 wt . % Co/CeO2 catalyst demonstrated excellent catalytic performance throughout the 10 cycles of plastic waste decomposition, achieving the highest H2 yield of 46.7 mmol/gplastic (equivalent to 64.4 % of theoretical H2 production) and nearly 100 % hydrogen atom recovery efficiency at the 7th cycle. Notably, the H2 yield achieved over the atomically dispersed Fe on CeO2 surface (1 wt . % Fe/CeO2) in the integrated plasma-thermal catalytic process is comparable to that obtained with Fe particles on CeO2 surface (10 wt . % Fe/CeO2). This outcome, demonstrated with single-atom catalysts, offers a promising avenue for cost-effective and efficient chemical plastic recycling. Through a combination of experimental and computational efforts, we have provided an in-depth understanding of the catalytic mechanisms of the investigated single atom catalysts in the developed plasma-enabled process. This innovative and straightforward approach provides a promising and expedient strategy for continuously converting diverse plastic waste streams, including mixed and contaminated sources, into high-value products conducive to a circular plastic economy.

Light-Reinforced Key Intermediate for Anticoking To Boost Highly Durable Methane Dry Reforming over Single Atom Ni Active Sites on CeO2.

Dry reforming of methane (DRM) has been investigated for more than a century; the paramount stumbling block in its industrial application is the inevitable sintering of catalysts and excessive carbon emissions at high temperatures. However, the low-temperature DRM process still suffered from poor reactivity and severe catalyst deactivation from coking. Herein, we proposed a concept that highly durable DRM could be achieved at low temperatures via fabricating the active site integration with light irradiation. The active sites with Ni-O coordination (NiSA/CeO2) and Ni-Ni coordination (NiNP/CeO2) on CeO2, respectively, were successfully constructed to obtain two targeted reaction paths that produced the key intermediate (CH3O*) for anticoking during DRM. In particular, the operando diffuse reflectance infrared Fourier transform spectroscopy coupling with steady-state isotopic transient kinetic analysis (operando DRIFTS-SSITKA) was utilized and successfully tracked the anticoking paths during the DRM process. It was found that the path from CH3* to CH3O* over NiSA/CeO2 was the key path for anticoking. Furthermore, the targeted reaction path from CH3* to CH3O* was reinforced by light irradiation during the DRM process. Hence, the NiSA/CeO2 catalyst exhibits excellent stability with negligible carbon deposition for 230 h under thermo-photo catalytic DRM at a low temperature of 472 °C, while NiNP/CeO2 shows apparent coke deposition behavior after 0.5 h in solely thermal-driven DRM. The findings are vital as they provide critical insights into the simultaneous achievement of low-temperature and anticoking DRM process through distinguishing and directionally regulating the key intermediate species.

Liver Stiffness on Magnetic Resonance Elastography and the MEFIB Index and Liver-Related Outcomes in Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Individual Participants.

BACKGROUND & AIMS: Magnetic resonance elastography (MRE) is an accurate biomarker of liver fibrosis; however, limited data characterize its association with clinical outcomes. We conducted an individual participant data pooled meta-analysis on patients with nonalcoholic fatty liver disease to evaluate the association between liver stiffness on MRE and liver-related outcomes. METHODS: A systematic search identified 6 cohorts of adults with nonalcoholic fatty liver disease who underwent a baseline MRE and were followed for hepatic decompensation, hepatocellular carcinoma, and death. Cox and logistic regression were used to assess the association between liver stiffness on MRE and liver-related outcomes, including a composite primary outcome defined as varices needing treatment, ascites, and hepatic encephalopathy. RESULTS: This individual participant data pooled meta-analysis included 2018 patients (53% women) with a mean (± standard deviation) age of 57.8 (±14) years and MRE at baseline of 4.15 (±2.19) kPa, respectively. Among 1707 patients with available longitudinal data with a median (interquartile range) of 3 (4.2) years of follow-up, the hazard ratio for the primary outcome for MRE of 5 to 8 kPa was 11.0 (95% confidence interval [CI]: 7.03-17.1, P < .001) and for ≥ 8 kPa was 15.9 (95% CI: 9.32-27.2, P < .001), compared with those with MRE <5 kPa. The MEFIB index (defined as positive when MRE ≥3.3 kPa and Fibrosis-4 ≥1.6) had a robust association with the primary outcome with a hazard ratio of 20.6 (95% CI: 10.4-40.8, P < .001) and a negative MEFIB had a high negative predictive value for the primary outcome, 99.1% at 5 years. The 3-year risk of incident hepatocellular carcinoma was 0.35% for MRE <5 kPa, 5.25% for 5 to 8 kPa, and 5.66% for MRE ≥8 kPa, respectively. CONCLUSION: Liver stiffness assessed by MRE is associated with liver-related events, and the combination of MRE and Fibrosis-4 has excellent negative predictive value for hepatic decompensation. These data have important implications for clinical practice.

Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network.

Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif identification in high-throughput omics data by learning kernel length from data adaptively. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. Meanwhile, vConv could be readily integrated into multi-layer neural networks as an 'in-place replacement' of canonical convolutional layer. All source codes are freely available on GitHub for academic usage.

Common antibiotics, azithromycin and amoxicillin, affect gut metagenomics within a household.

BACKGROUND: The microbiome of the human gut serves a role in a number of physiological processes, but can be altered through effects of age, diet, and disturbances such as antibiotics. Several studies have demonstrated that commonly used antibiotics can have sustained impacts on the diversity and the composition of the gut microbiome. The impact of the two most overused antibiotics, azithromycin, and amoxicillin, in the human microbiome has not been thoroughly described. In this study, we recruited a group of individuals and unrelated controls to decipher the effects of the commonly used antibiotics amoxicillin and azithromycin on their gut microbiomes. RESULTS: We characterized the gut microbiomes by metagenomic sequencing followed by characterization of the resulting microbial communities. We found that there were clear and sustained effects of the antibiotics on the gut microbial community with significant alterations in the representations of Bifidobacterium species in response to azithromycin (macrolide antibiotic). These results were supported by significant increases identified in putative antibiotic resistance genes associated with macrolide resistance. Importantly, we did not identify these trends in the unrelated control individuals. There were no significant changes observed in other members of the microbial community. CONCLUSIONS: As we continue to focus on the role that the gut microbiome plays and how disturbances induced by antibiotics might affect our overall health, elucidating members of the community most affected by their use is of critical importance to understanding the impacts of common antibiotics on those who take them. Clinical Trial Registration Number NCT05169255. This trial was retrospectively registered on 23-12-2021.

Gene polymorphism in IL17A and gene-gene interaction in the IL23R/IL17A axis are associated with susceptibility to coronary artery disease.

AIMS: Studies have confirmed that the IL-23R/IL-17A axis plays an important role in the development of autoimmune and inflammatory diseases. However, its role in coronary artery disease (CAD) remains unclear. Here, we conducted a large sample case-control study to investigate the association between the IL23R/IL17A axis and CAD in the Chinese Han population. METHODS: Two SNPs, rs2275913: G>A (IL17A) and rs6682925: T>C (IL23R), were genotyped in 3042 CAD cases and 3216 controls using the high-resolution melt technology (HRM). Logistic regression analyses were used to adjust the traditional risk factors for CAD and perform the gene interaction analyses. Multiple linear regression analyses were used to study the relationships between the selected SNPs and the levels of serum lipids. In addition, meta-analysis also was performed for the association between rs6682925 and rs2275913 with CAD in different popolations. RESULTS: Our case-control and meta-analysis for single SNPs demonstrated that the frequencies of the alleles and the distribution of the genotypes had no significant differences in CAD cases compared with controls. In the stratified analysis, we observed that the frequency of the IL17A rs2275913-A allele was more epidemic in early-onset CAD than in the controls (Padj = 0.005, OR = 1.209, 95% CI: 1.059-1.382), and the minor allele C of rs6682925 was associated with a decreased level of serum total cholesterol under a recessive model (Padj = 0.011). We demonstrated a significant interaction between rs6682925 and rs2275913 and CAD in the Chinese Han population. Four genotypes (CTGG, CCAA, CCAG, CCGG) were significantly associated with CAD (Padj = 2.94 × 10-4, OR = 0.619, 95% CI: 0.478-0.803; Padj = 0.01, OR = 1.808, 95% CI: 1.152-1.869; Padj = 6 × 10-6, OR = 2.179, 95% CI: 1.558-3.049; Padj = 0.001, OR = 1.883, 95% CI: 1.282-2.762, respectively). CONCLUSION: Our study found no single SNP of rs2275913 in IL17A and rs6682925 in IL23R was associated with CAD in the Chinese population, but the interaction of them were significantly associated with CAD susceptibility, highlighting the key role of the IL-23R/IL-17A axis in the development of CAD. In addition, we also found rs2275913 was associated with early-onset CAD and rs6682925 was associated with total cholesterol levels, which will contribute to the clinical stratified management of this common disease.

Bioinformatics analysis of markers based on m6 A related to prognosis combined with immune invasion of renal clear cell carcinoma.

The incidence rate of renal cell carcinoma (RCC) is about 3% of all adult cancers. Of these, the Kidney clear cell renal cell carcinoma (KIRC) is the most common type, accounting for about 70%-75% of RCC. KIRC is difficult to be detected in time clinically. KIRC still has no effective treatment at this stage. We combined high-throughput bioinformatics analysis to obtained the structural sequence transcriptome data, relevant clinical information, and m6 A gene map of KIRC patients from genomics TCGA database. Pearson's correlation analysis was used to explore m6 A related gene long noncoding RNAs (lncRNAs), and then univariate Cox regression analysis was performed to screen the prognostic role of KIRC patients. Lasso-Cox regression was performed to establish the lncRNAs risk model associated with m6 A.LINC02154 and AC016773.2, Z98200.2, AL161782.1, EMX2OS, AC021483.2, CD27-AS1, AC006213.3 were iidentif. Compared with the low-risk group, the overall survival of patients in the high-risk group was significantly worse. Analyzing whether there are differences in immune cells between high-risk and low-risk subgroups. There were CD4 memory resting, Monocytes, Macrophages M1, Dendritic cells activated, Mast cells resting, which had higher infiltrations in the low-risk group. We performed Go enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis and gene set enrichment analysis enrichment analysis. Overall, our results suggest that the component of m6A-related lncRNAs in the prognostic signal may be a key mediator in the immune microenvironment of KIRC, which represents a promising therapeutic effect.

Alcohol use and cognitive aging in middle-aged men: The Vietnam Era Twin Study of Aging.

OBJECTIVE: To determine associations of alcohol use with cognitive aging among middle-aged men. METHOD: 1,608 male twins (mean 57 years at baseline) participated in up to three visits over 12 years, from 2003-2007 to 2016-2019. Participants were classified into six groups based on current and past self-reported alcohol use: lifetime abstainers, former drinkers, very light (1-4 drinks in past 14 days), light (5-14 drinks), moderate (15-28 drinks), and at-risk drinkers (>28 drinks in past 14 days). Linear mixed-effects regressions modeled cognitive trajectories by alcohol group, with time-based models evaluating rate of decline as a function of baseline alcohol use, and age-based models evaluating age-related differences in performance by current alcohol use. Analyses used standardized cognitive domain factor scores and adjusted for sociodemographic and health-related factors. RESULTS: Performance decreased over time in all domains. Relative to very light drinkers, former drinkers showed worse verbal fluency performance, by -0.21 SD (95% CI -0.35, -0.07), and at-risk drinkers showed faster working memory decline, by 0.14 SD (95% CI 0.02, -0.20) per decade. There was no evidence of protective associations of light/moderate drinking on rate of decline. In age-based models, light drinkers displayed better memory performance at advanced ages than very light drinkers (+0.14 SD; 95% CI 0.02, 0.20 per 10-years older age); likely attributable to residual confounding or reverse association. CONCLUSIONS: Alcohol consumption showed minimal associations with cognitive aging among middle-aged men. Stronger associations of alcohol with cognitive aging may become apparent at older ages, when cognitive abilities decline more rapidly.

Survey response over 15 years of follow-up in the Millennium Cohort Study.

BACKGROUND: Patterns of survey response and the characteristics associated with response over time in longitudinal studies are important to discern for the development of tailored retention efforts aimed at minimizing response bias. The Millennium Cohort Study, the largest and longest running cohort study of military personnel and veterans, is designed to examine the long-term health effects of military service and experiences and thus relies on continued participant survey responses over time. Here, we describe the response rates for follow-up survey data collected over 15 years and identify characteristics associated with follow-up survey response and mode of response (paper vs. web). METHOD: Patterns of follow-up survey response and response mode (web, paper, none) were examined among eligible participants (n=198,833), who were initially recruited in four panels from 2001 to 2013 in the Millennium Cohort Study, for a follow-up period of 3-15 years (2004-2016). Military and sociodemographic factors (i.e., enrollment panel, sex, birth year, race and ethnicity, educational attainment, marital status, service component, service branch, pay grade, military occupation, length of service, and time deployed), life experiences and health-related factors (i.e., military deployment/combat experience, life stressors, mental health, physical health, and unhealthy behaviors) were used to examine follow-up response and survey mode over time in multivariable generalized estimating equation models. RESULTS: Overall, an average response rate of 60% was observed across all follow-up waves. Factors associated with follow-up survey response over time included increased educational attainment, married status, female sex, older age, military deployment (regardless of combat experience), and higher number of life stressors, mental health issues, and physical health diagnoses. CONCLUSION: Despite the challenges associated with collecting multiple waves of follow-up survey data from members of the U.S. military during and after service, the Millennium Cohort Study has maintained a relatively robust response rate over time. The incorporation of tailored messages and outreach to those groups least likely to respond over time may improve retention and thereby increase the representativeness and generalizability of collected survey data.

Modeling homophily in dynamic networks with application to HIV molecular surveillance.

BACKGROUND: Efforts to control the HIV epidemic can benefit from knowledge of the relationships between the characteristics of people who have transmitted HIV and those who became infected by them. Investigation of this relationship is facilitated by the use of HIV genetic linkage analyses, which allows inference about possible transmission events among people with HIV infection. Two persons with HIV (PWH) are considered linked if the genetic distance between their HIV sequences is less than a given threshold, which implies proximity in a transmission network. The tendency of pairs of nodes (in our case PWH) that share (or differ in) certain attributes to be linked is denoted homophily. Below, we describe a novel approach to modeling homophily with application to analyses of HIV viral genetic sequences from clinical series of participants followed in San Diego. Over the 22-year period of follow-up, increases in cluster size results from HIV transmissions to new people from those already in the cluster-either directly or through intermediaries. METHODS: Our analytical approach makes use of a logistic model to describe homophily with regard to demographic, clinical, and behavioral characteristics-that is we investigate whether similarities (or differences) between PWH in these characteristics are associated with their sequences being linked. To investigate the performance of our methods, we conducted on a simulation study for which data sets were generated in a way that reproduced the structure of the observed database. RESULTS: Our results demonstrated strong positive homophily associated with hispanic ethnicity, and strong negative homophily, with birth year difference. The second result implies that the larger the difference between the age of a newly-infected PWH and the average age for an available cluster, the lower the odds of a newly infected person joining that cluster. We did not observe homophily associated with prior diagnosis of sexually transmitted diseases. Our simulation studies demonstrated the validity of our approach for modeling homophily, by showing that the estimates it produced matched the specified values of the statistical network generating model. CONCLUSIONS: Our novel methods provide a simple and flexible statistical network-based approach for modeling the growth of viral (or other microbial) genetic clusters from linkage to new infections based on genetic distance.

Short daylight photoperiod alleviated alarm substance-stimulated fear response of zebrafish.

Photoperiod has been well-documented to be involved in regulating many activities of animals. However, whether photoperiod takes part in mood control, such as fear response in fish and the underlying mode(s) of action remain unclear. In this study, adult zebrafish males and females (Danio rerio) were exposed to different photoperiods, Blank (12 h light: 12 h dark), Control (12 h light: 12 h dark), Short daylight (SD, 6 h light: 18 h dark) and Long daylight (LD, 18 h light: 6 h dark) for 28 days. After exposure, fear response of the fish was investigated using a novel tank diving test. After alarm substance administration, the onset to higher half, total duration in lower half and duration of freezing in SD-fish were significantly decreased, suggesting that short daylight photoperiod is capable of alleviating fear response in zebrafish. In contrast, comparing with the Control, LD didn't show significant effect on fear response of the fish. Further investigation revealed that SD increased the levels of melatonin (MT), serotonin (5-HT) and dopamine (DA) in the brain while decreased the plasma level of cortisol comparing to the Control. Moreover, the expressions of genes in MT, 5-HT and DA pathways and HPI axis were also altered consistently. Our data indicated that short daylight photoperiod might alleviate fear response of zebrafish probably through interfering with MT/5-HT/DA pathways and HPI axis.

Shielding Protection by Mesoporous Catalysts for Improving Plasma-Catalytic Ambient Ammonia Synthesis.

Plasma catalysis is a promising technology for decentralized small-scale ammonia (NH3) synthesis under mild conditions using renewable energy, and it shows great potential as an alternative to the conventional Haber-Bosch process. To date, this emerging process still suffers from a low NH3 yield due to a lack of knowledge in the design of highly efficient catalysts and the in situ plasma-induced reverse reaction (i.e., NH3 decomposition). Here, we demonstrate that a bespoke design of supported Ni catalysts using mesoporous MCM-41 could enable efficient plasma-catalytic NH3 production at 35 °C and 1 bar with >5% NH3 yield at 60 kJ/L. Specifically, the Ni active sites were deliberately deposited on the external surface of MCM-41 to enhance plasma-catalyst interactions and thus NH3 production. The desorbed NH3 could then diffuse into the ordered mesopores of MCM-41 to be shielded from decomposition due to the absence of plasma discharge in the mesopores of MCM-41, that is, "shielding protection", thus driving the reaction forward effectively. This promising strategy sheds light on the importance of a rational design of catalysts specifically for improving plasma-catalytic processes.