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ABSTRACT Considering that the human microbiota plays a critical role in health and disease, an accurate and high-resolution taxonomic classification is thus essential for meaningful microbiome analysis. In this study, we developed an automatic system, named MultiTax pipeline, for generating de novo taxonomy from full-length 16S rRNA sequences using the Genome Taxonomy Database and other existing reference databases. We first constructed the MultiTax-human database, a high-resolution resource specifically designed for human microbiome research and clinical applications. The database includes 842,649 high-quality full-length 16S rRNA sequences, extracted from multiple public repositories and human-related studies, offering a comprehensive and accurate portrayal of the human microbiome. To validate the MultiTax-human database, we profiled the human microbiome across various body sites, identified core microbial taxa, and tested its performance using an independent data set. Additionally, the database is equipped with a user-friendly web interface for easy querying and data exploration. The MultiTax-human database is poised to serve as a valuable tool for researchers, enhancing the precision of human microbiome studies and advancing our understanding of its impact on human health and diseases.IMPORTANCEUnderstanding the human microbiome, the collection of microorganisms in and on our bodies, is essential for advancing health research. Current methods often lack precision and consistency, hindering our ability to study these microorganisms effectively. Our study presents the MultiTax-human database, a high-resolution reference tool specifically designed for human microbiome research. By integrating data from multiple sources and employing advanced classification techniques, this database offers an accurate and detailed map of the human microbiome. This resource enhances the ability of researchers and clinicians to explore the roles of microorganisms in health and disease, potentially leading to improved diagnostics, treatments, and insights into various medical conditions.

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Summary: Alcohol dependence is a heritable disorder, yet its genetic basis and underlying mechanisms remain poorly understood, especially in Chinese population. In this study, we conducted gene-based and transcript-based association tests and found a significant association between ARVCF expression in the cortex and hippocampus of the brain and alcohol use in a cohort of 1,329 individuals with Chinese ancestry. Further analysis using the effective-median-based Mendelian randomization framework for inferring the causal genes (EMIC) revealed a causal relationship between ARVCF expression in the frontal cortex and alcohol use. Moreover, leveraging extensive European alcohol dependence data, our gene association tests and EMIC analysis showed that ARVCF expression in the nucleus accumbens was significantly associated with alcohol dependence. Finally, animal studies indicated that Arvcf knockout mice lacked conditioned place preference for alcohol. Together, our combined human genetic and animal studies indicate that ARVCF plays a crucial role in alcohol dependence.

Introduction: As one of the common psychiatric diseases, depression poses serious threats to human health. Although many genes have been nominated for depression, few of them were investigated in details at the molecular level. Objectives: To demonstrate Frizzled class receptor 6 (FZD6) functions in depression through disrupting Wnt/β-catenin signal pathway. Methods: The FZD6 edited cell line and mouse model were generated by using CRISPR/Cas9 technique. The expression of key genes and proteins in Wnt/β-catenin pathway was determined by qRT-PCR and Western blotting, respectively. Animal behavioral tests, including open field test (OFT), elevated plus maze test (EPM), forced swimming test (FST), tail suspension test (TST), and sucrose preference test (SPT), were employed to determine anxiety- and depressive-like behaviors. Immunofluorescent staining was used to assess cell proliferation in the hippocampus of mouse brain. Results: Among patients with depression, FZD6, one of the receptors of Wnt ligand, was significantly decreased. In CRISPR/Cas9-based FZD6 knockdown cells, we showed that FZD6 plays a significant role in regulating expression of genes involved in Wnt/β-catenin pathway. Subsequently behavioral studies on Fzd6 knockdown mice (with a 5-nucleotide deletion; Fzd6-Δ5) revealed significant changes in depressive symptoms, including increased immobility duration in FST, less preference of sucrose in SPT, reduction of distance traveled in OFT, and decreased time spent in open arms in EPM. Immunofluorescent staining showed decreased cell proliferation in the hippocampus of Fzd6-Δ5 mice with reduced number of Ki67+ and PCNA+ cells.Moreover, decreased Gsk3β mRNA expression, phosphorylated GSK3β, and cytoplasmic β-catenin in the hippocampus of Fzd6-Δ5 mice provided further evidence supporting the role of Fzd6 in depression. Conclusion: Together, above findings proved the significant role of FZD6 in depression through its effect on hippocampal cell proliferation and its ability to regulate canonical Wnt/β-catenin pathway.

Emotional intelligence (EI) refers to a set of competencies to process, understand, and reason with affective information. Recent studies suggest ability measures of experiential and strategic EI differentially predict performance on non-emotional and emotionally laden tasks. To explore cognitive processes underlying these abilities further, we varied the affective context of a traditional letter-based "n"-back working-memory task. In study 1, participants completed 0-, 2-, and 3-back tasks with flanking distractors that were either emotional (fearful or happy faces) or non-emotional (shapes or letters stimuli). Strategic EI, but not experiential EI, significantly influenced participants' accuracy across all n-back levels, irrespective of flanker type. In Study 2, participants completed 1-, 2-, and 3-back levels. Experiential EI was positively associated with response times for emotional flankers at the 1-back level but not other levels or flanker types, suggesting those higher in experiential EI reacted slower on low-load trials with affective context. In Study 3, flankers were asynchronously presented either 300 ms or 1000 ms before probes. Results mirrored Study 1 for accuracy rates and Study 2 for response times. Our findings (a) provide experimental evidence for the distinctness of experiential and strategic EI and (b) suggest that each are related to different aspects of cognitive processes underlying working memory.

BackgroundCurrently, the prevalence of allergic rhinitis (AR) remains high and there is a great need to develop better and safer ways to alleviate AR symptoms. The Lactobacillus plantarum GUANKE probiotic was reported as an immunomodulator through maintaining Th1/Th2 balance. This study aimed to determine the efficacy of GUANKE in AR subjects.MethodsAdults aged from 18 to 60 years old and previously suffered from AR were recruited and received GUANKE probiotics treatment for 4 weeks. The questionnaires of Total nasal symptom scores (TNSS), total non-nasal symptom score (TNNSS), and rhinitis control assessment test (RCAT) were used to assess the effectiveness before and after treatment. The serum allergen-specific IgE and cytokines were also determined at baseline and after 4 weeks of probiotics administration.ResultsThe results showed that TNSS and TNNSS were significantly reduced and the RCAT score was significantly increased compared to baseline. The sub-symptom score of rhinorrhea, itching, sneezing, and tearing in each questionnaire also showed significant changes, and the serum IgE level was markedly decreased. We further measured inflammatory-related proteins in serum and found that a total of 20 proteins (6 upregulated and 14 downregulated) were significantly changed compared to baseline, including IL-4, IL-7, IL-20, IL-33, CXCL1, CXCL5, CXCL6, CXCL11, CCL4, CCL23, TGF-alpha, LAP-TGF-beta-1, MMP-1, MMP-10, AXIN1, NT-3, OSM, SCF, CD6, and NRTN. Enrichment analysis showed that these significantly altered proteins were mainly enriched in cytokine and chemokine-related signaling pathways.ConclusionTaken together, this study demonstrated the Lactobacillus plantarum GUANKE can serve as an effective immunobiotic for the treatment of AR, which is realized through maintaining the Th1/Th2 balance by modulating the functions of various cytokines and chemokines.

BackgroundsTobacco smoking is an important risk factor for coronary artery disease (CAD), but the genetic mechanisms linking smoking to CAD remain largely unknown.MethodsWe analyzed summary data from the genome-wide association study (GWAS) of the UK Biobank for CAD, plasma lipid concentrations (n = 184,305), and smoking (n = 337,030) using different biostatistical methods, which included LD score regression and Mendelian randomization (MR).ResultsWe identified SNPs shared by CAD and at least one smoking behavior, the genes where these SNPs are located were found to be significantly enriched in the processes related to lipoprotein metabolic, chylomicron-mediated lipid transport, lipid digestion, mobilization, and transport. The MR analysis revealed a positive correlation between smoking cessation and decreased risk for CAD when smoking cessation was considered as exposure (p = 0.001), and a negative correlation between the increased risk for CAD and smoking cessation when CAD was considered as exposure (p = 2.95E-08). This analysis further indicated that genetic liability for smoking cessation increased the risk of CAD.ConclusionThese findings inform the concomitant conditions of CAD and smoking and support the idea that genetic liabilities for smoking behaviors are strongly associated with the risk of CAD.

Trials of decision aids developed for use in shared decision making find that patients engaged in that process tend to choose more conservative treatment for preference-sensitive conditions. Shared decision making is a collaborative process in which clinicians and patients discuss trade-offs and benefits of specific treatment options in light of patients' values and preferences. Decision aids are paper, video, or web-based tools intended to help patients match personal preferences with available treatment options. We analyzed data for 2012-15 about patients within the ten High Value Healthcare Collaborative member systems who were exposed to condition-specific decision aids in the context of consultations for hip and knee osteoarthritis, with the intention that the aids be used to support shared decision making. Compared to matched patients not exposed to the decision aids, those exposed had two-and-a-half times the odds of undergoing hip replacement surgery and nearly twice the odds of undergoing knee replacement surgery within six months of the consultation. These findings suggest that health care systems adopting decision aids developed for use in shared decision making, and used in conjunction with hip and knee osteoarthritis consultations, should not expect reduced surgical utilization.

Abstract Immunotherapy utilizing chimeric antigen receptor T cell (CAR-T) therapy holds promise for hematologic malignancies, however, response rates and associated immune-related adverse effects widely vary among patients. Here we show, by comparing diversity and composition of the gut microbiome during different CAR-T therapeutic phases in the clinical trial ChiCTR1800017404, that the gut flora characteristically differs among patients and according to treatment stages, and might also reflect patient response to therapy in relapsed/refractory multiple myeloma (MM; n = 43), acute lympholastic leukemia (ALL; n = 23) and non-Hodgkin lymphoma (NHL; n = 12). We observe significant temporal differences in diversity and abundance of Bifidobacterium, Prevotella, Sutterella, and Collinsella between MM patients in complete remission (n = 24) and those in partial remission (n = 11). Furthermore, we find that patients with severe cytokine release syndrome present with higher abundance of Bifidobacterium, Leuconostoc, Stenotrophomonas, and Staphylococcus, which is reproducible in an independent cohort of 38 MM patients. This study has important implications for understanding the biological role of the microbiome in CAR-T treatment responsiveness of hematologic malignancy patients, and may guide therapeutic intervention to increase efficacy. The success rate of CAR-T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here we show that therapeutic response in MM, ALL and NHL, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

IntroductionSubmassive hepatic necrosis (SMHN, defined as necrosis of 15–90% of the entire liver on explant) is a likely characteristic pathological feature of ACLF in patients with hepatitis B cirrhosis. We aimed to comprehensively explore microbiome and bile acids patterns across enterhepatic circulation and build well-performing machine learning models to predict SMHN status.MethodsBased on the presence or absence of SMHN, 17 patients with HBV-related end-stage liver disease who received liver transplantation were eligible for inclusion. Serum, portal venous blood, and stool samples were collected for comparing differences of BA spectra and gut microbiome and their interactions. We adopted the random forest algorithm with recursive feature elimination (RF-RFE) to predict SMHN status.ResultsBy comparing total BA spectrum between SMHN (−) and SMHN (+) patients, significant changes were detected only in fecal (P = 0.015). Compared with the SMHN (+) group, the SMHN (−) group showed that UDCA, 7-KLCA, 3-DHCA, 7-KDCA, ISOLCA and α-MCA in feces, r-MCA, 7-KLCA and 7-KDCA in serum, γ-MCA and 7-KLCA in portal vein were enriched, and TUDCA in feces was depleted. PCoA analysis showed significantly distinct overall microbial composition in two groups (P = 0.026). Co-abundance analysis showed that bacterial species formed strong and broad relationships with BAs. Among them, Parabacteroides distasonis had the highest node degree. We further identified a combinatorial marker panel with a high AUC of 0.92.DiscussionOur study demonstrated the changes and interactions of intestinal microbiome and BAs during enterohepatic circulation in ACLF patients with SMHN. In addition, we identified a combinatorial marker panel as non-invasive biomarkers to distinguish the SMHN status with high AUC.

We know a job applicant’s social category affects an employer’s likelihood of hiring them, but we do not know whether, or how, employers update their beliefs regarding members of these social categories. I examine how prior negative and positive hiring experiences of employees from particular countries affects an employer’s subsequent likelihood of hiring applicants from those countries. Analyses of over 26 million applications, from freelancers worldwide, for over 2.2 million jobs on an online labor market demonstrate that employers react more strongly to negative hiring experiences than positive ones. Employers are 14% less likely (versus 8% more likely) to hire freelancers from a country following a prior negative (versus positive) experience. The similarity of the prior job moderates this effect. Prior negative experiences with similar jobs (versus dissimilar jobs) lead employers to be 92% less likely (versus 7% less likely) to hire from that country. Conversely, positive experiences with similar jobs (versus dissimilar jobs) lead employers to be 23% more likely (versus 3% more likely) to subsequently hire from that country. The consequences for switching countries, following negative experiences, are analyzed and wage differences, made to compensate for employer reactions, are calculated. Contributions to the hiring discrimination, impression formation, and gig-economy literatures are discussed.

We propose and test a theory of how decisions not to hire reproduce sex segregation through what we term proportional prejudice. We hypothesize that employers are less likely to hire anyone when the applicant pool contains a large proportion of gender atypical applicants – that is, applicants from a different gender than the typical job holder – because they view this as a signal of a poor quality applicant pool. Analyses, of over seven million job applications for over 700,000 jobs by over 200,000 freelancers on an online platform for contract labor support our contention. A survey experiment isolates the mechanism: Applicant pools with a larger proportion of gender atypical applicants were perceived as less likely to contain people who “seemed skilled enough for the job.” We conclude by demonstrating how our theory explains the mixed findings as to whether gender atypical job seekers are disadvantaged in the hiring process.

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression. [ABSTRACT FROM AUTHOR]

The Thermal Evolved Gas Analyzer (TEGA) analysis of surface and icy subsurface Phoenix landing site soils consisted of low (300–700°C) and high (>700°C) temperature CO2 evolutions that were attributed to organic carbon (83–1,484 μgC/g) and Ca‐rich carbonate (1.1–2.6 wt.%). Total carbon abundances ranged from 1,143 to 4,905 µgC/g, which is the highest soil carbon concentration so far detected on Mars. Low temperature CO2 was attributed to oxidized organic C (e.g., oxalates, acetates), while hydrocarbon combustion was indicated in two soils by the detection of coevolved CO2 and O2 (perchlorate). Combustion reactions may have prevented the detection of hydrocarbon masses in the Phoenix landing site soils. Organic C was likely derived from meteoritic and igneous/hydrothermal sources, but microbiological sources cannot be excluded. CO2 evolved at high temperatures was consistent with Ca‐rich carbonate along with possible minor contributions from macromolecular organic carbon and mineral/glass vesicle CO2. Carbon detected in the Phoenix landing site soil and other landing site soils and sands (e.g., Gale/Jezero craters) would be consistent with global organic C and carbonate in soils and sand across Mars. However, oxidizing water thin films derived from the near‐surface ice in the Phoenix soils favor Ca‐carbonate over Fe‐carbonate, which is likely more stable in the ice‐free regions of Mars (e.g., Gale/Jezero craters). The global carbon budget on Mars inferred from these results emphasizes that Mars Sample Return should yield carbon bearing soil/rock that would allow the identification of the origin of carbon and any possible connections to ancient martian microbiology. Plain Language Summary: The Phoenix Lander's Thermal Evolved Gas Analyzer (TEGA) analyzed surface and subsurface icy soils of the martian northern plains in 2008. The TEGA instrument heated scooped soils to 1,000°C and measured evolved gases (e.g., CO2, O2, H2O) to quantify the presence of volatile bearing phases (e.g., organics, carbonates, salts). Some TEGA results have been previously published; however, this report describes unreported TEGA results. The TEGA instrument detected low (300–700°C) and high (>700°C) temperature CO2 evolutions that were attributed to organic carbon and Ca‐rich carbonate. Total carbon abundances as high as 4,905 µgC/g were detected which is the highest soil carbon concentration detected on Mars to date. Organic C sources include meteoritic and igneous/hydrothermal sources, but microbiological origins cannot be excluded. Ca‐rich carbonate may have been inherited from local sediments or was derived from dissolved CO2 reactions with the soil in migrating water thin‐films from subsurface ice. The carbon detections at the Phoenix and other landing sites suggest that Mars Sample Return of surface soil/rock should contain carbon that can be used to identify the origin(s) of carbon. Key Points: Phoenix landing site soils have the highest soil carbon concentration (up to 4,905 µgC/g) of any soil measured on MarsOrganic C (83–1,484 μgC/g) was likely derived from meteoritic sources with possible contributions from igneous/hydrothermal sourcesCa‐carbonate (1.1–2.6 wt.%) was inherited from local sediments or derived from soil reactions with dissolved CO2 in water thin‐films [ABSTRACT FROM AUTHOR]

Although various susceptibility genes have been revealed to influence tobacco smoking, the underlying regulatory mechanisms between genetic variants and smoking are poorly understood. In this study, we investigated cis-expression quantitative trait loci (cis-eQTLs) and methylation quantitative trait loci (mQTLs) for 56 candidate smoking-linked genes using the BrainCloud cohort samples. An eQTL was revealed to significantly affect EGLN2 expression in the European sample and two mQTLs were respectively detected in CpG sites in NRXN1 and CYP2A7. Interestingly, we found for the first time that the minor allele of the single nucleotide polymorphism (SNP) rs3745277 located in CYP2A7P1 (downstream of CYP2B6) significantly decreased methylation at the CpG site for CYP2A7 (cg25427638; P = 5.31 × 10–7), reduced expression of CYP2B6 (P = 0.03), and lowered the percentage of smokers (8.8% vs. 42.3%; Odds Ratio (OR) = 0.14, 95% Confidence Interval (CI): 0.02–0.62; P = 4.47 × 10–3) in a dominant way for the same cohort sample. Taken together, our findings resulted from analyzing genetic variation, DNA methylation, mRNA expression, and smoking status together using the same participants revealed a regulatory mechanism linking mQTLs to the smoking phenotype. Moreover, we demonstrated the presence of different regulatory effects of low-frequency and common variants on mRNA expression and DNA methylation.

Although numerous susceptibility loci for depression have been identified in recent years, their biological function and molecular mechanism remain largely unknown. By using an exome-wide association study for depressive symptoms assessed by the Center for Epidemiological Studies Depression (CES-D) score, we discovered a novel missense single nucleotide polymorphism (SNP), rs61753730 (Q152E), located in the fourth exon of the frizzled class receptor 6 gene (FZD6), which is a potential causal variant and is significantly associated with the CES-D score. Computer-based in silico analysis revealed that the protein configuration and stability, as well as the secondary structure of FZD6 differed greatly between the wild-type (WT) and Q152E mutant. We further found that rs61753730 significantly affected the luciferase activity and expression of FZD6 in an allele-specific way. Finally, we generated Fzd6-knockin (Fzd6-KI) mice with rs61753730 mutation using the CRISPR/Cas9 genome editing system and found that these mice presented greater immobility in the forced swimming test, less preference for sucrose in the sucrose preference test, as well as decreased center entries, center time, and distance traveled in the open filed test compared with WT mice after exposed to chronic social defeat stress. These results indicate the involvement of rs61753730 in depression. Taken together, our findings demonstrate that SNP rs61753730 is a novel functional variant and plays an important role in depressive symptoms.

The intestinal microbiota regulate the brain function of the host through the production of a myriad of metabolites and are associated with various neurological diseases. Understanding the intestinal microbiome of patients with prolonged disorders of consciousness (DoC) is important for the evaluation and treatment of the disease. To investigate the differences in the intestinal microbiome and short-chain fatty acids (SCFAs) among patients in a vegetative state (VS), a minimally conscious state (MCS), and emerged from MCS (EMCS), as well as the influence of antibiotics on these patients, 16S ribosomal RNA (16S rRNA) sequencing and targeted lipidomics were performed on fecal samples from patients; in addition, analysis of the electroencephalogram (EEG) signals was performed to evaluate the brain function of these patients. The results showed that the intestinal microbiome of the three groups differed greatly, and some microbial communities showed a reduced production of SCFAs in VS patients compared to the other two groups. Moreover, reduced microbial communities and five major SCFAs, along with attenuated brain functional connectivity, were observed in MCS patients who were treated with antibiotics compared to those who did not receive antibiotic treatment, but not in the other pairwise comparisons. Finally, three genus-level microbiota—Faecailbacterium, Enterococcus, and Methanobrevibacter—were considered as potential biomarkers to distinguish MCS from VS patients, with high accuracy both in the discovery and validation cohorts. Together, our findings improved the understanding of patients with prolonged DoC from the intestinal microbiome perspective and provided a new reference for the exploration of therapeutic targets.

Can employers learn to hire? This article conceptualizes hiring as a dynamic experiential learning process. Instead of examining hiring as a point in time decision, I investigate whether and how employers’ past hiring experiences affect their future decisions. Drawing on evidence from a global online market for contract labor, I argue that employers revise their beliefs regarding job applicants from a particular social category following a negative hiring experience from that social category. I analyze over 16 Million applications from freelancers worldwide for over 2.2 Million jobs from 557,416 employers. I find that employers who have a negative hiring experience with a freelancer from a particular country are subsequently less likely to hire other freelancers from that country. This effect is stronger on hiring for identical subsequent jobs and weaker for other jobs. Most strikingly, evidence from the actual hiring switches following a negative experience and a simulation using data from the observed distribution of freelancers on the platform demonstrate that employers unnecessarily oversteer away from countries given the narrow distribution of observable ability among freelancers. Switches do not result in hiring from a “better” country.

It is well recognized that organizations play a central role in generating inequality in employment outcomes between women and men. Women are often disadvantaged relative to men when they enter firms either because they are more likely to enter into lower paying positions or into roles that offer less advancement opportunities. What is less well-understood are the mechanisms though which women may be able to overcome these disadvantages. One theoretical solution to this problem is for women to undertake less typical career paths within the firm and move to more fecund jobs and job ladders that offer more opportunity for advancement. However, there is a risk to moving atypically, as erratic careers are often viewed negatively. We investigate this question with monthly observations of 53,311 exempt U.S. employees at a West Coast Fortune 500 tech company over an eight year period, from 2008 to 2015. We first demonstrate that jobs disproportionately staffed by women are, on average, of lower pay and lower advancement opportunities within the firm. We find, erratic career mobility, defined as a sequence of atypical job moves, results in differential outcomes for women and men. Specifically, women who move erratically are promoted faster than similarly erratic men. However, this effect is the opposite for performance appraisals. More erratic mobility by women results in lower performance appraisals than similarly erratic men. Mobility is therefore a double-edged sword for women - we refer to this as the dilemma of mobility.

Extant hiring research has generally focused on understanding outcomes for employees and not on outcomes for employers. I theorize on how employer cognitive hiring decision processes affect their likelihood of extending an offer of employment. I argue that greater variety in the job experiences of candidates in the applicant pool complicates employer comparison processes. Hiring is a two-stage process and I predict that comparison difficulties materialize among a winnowed down consideration set of candidates in this second stage. More experienced employers have less difficulty with variety because they have better constructed preferences. Regression analyses from an online market for contract labor on over 640,000 job postings by over 170,000 employers support my contentions. Greater variety in job experiences among job candidates in the applicant pool leads to a lower likelihood a job offer will be extended to any of them. This relationship is completely mediated by the variety in job candidates in the second stage consideration set. The more experience an employer has in hiring in a domain, the less of an issue variety becomes. Results utilizing an instrumental variable and several supporting analyses are also reported. Contributions to the study of evaluation in markets, hiring, and cognitive processes of categorization are discussed.

Extant hiring research has generally focused on understanding outcomes for employees and not on outcomes for employers. I theorize on how employer cognitive hiring decision processes affect their likelihood of extending an offer of employment. I argue that greater variety in the job experiences of candidates in the applicant pool complicates employer comparison processes. Hiring is a two-stage process and I predict that comparison difficulties materialize among a winnowed down consideration set of candidates in this second stage. More experienced employers have less difficulty with variety because they have better constructed preferences. Regression analyses from an online market for contract labor on over 640,000 job postings by over 170,000 employers support my contentions. Greater variety in job experiences among job candidates in the applicant pool leads to a lower likelihood a job offer will be extended to any of them. This relationship is completely mediated by the variety in job candidates in the second stage consideration set. The more experience an employer has in hiring in a domain, the less of an issue variety becomes. Results utilizing an instrumental variable and several supporting analyses are also reported. Contributions to the study of evaluation in markets, hiring, and cognitive processes of categorization are discussed.

Qingqing Chen,1,2 Dandan Yin,1,3 Pei Li,1,3 Yan Guo,1,3 Desong Ming,2 Yuling Lin,2 Xiaoli Yan,2 Zhishan Zhang,2 Fupin Hu1,3 1Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, People’s Republic of China; 2The Affiliated Quanzhou First Hospital of Fujian Medical University, Quanzhou, People’s Republic of China; 3Key Laboratory of Clinical Pharmacology of Antibiotics, Ministry of Health, Shanghai, People’s Republic of ChinaCorrespondence: Zhishan ZhangDepartments of Clinical Laboratory, The Affiliated Quanzhou First Hospital of Fujian Medical University, No. 248 East Street, Quanzhou City, Fujian Province 362002, People’s Republic of ChinaTel +86-595-22277352Fax +86-595-22278131Email 15859775058@139.comFupin HuInstitute of Antibiotics, Huashan Hospital, Fudan University, 12 M. Wulumuqi Road, Shanghai 200040, People’s Republic of ChinaTel +86-21-52888186Fax +86-21-62488290Email hufupin@fudan.edu.cnAbstract: A linezolid-resistant E.faecalis strain harboring optrA and cfr resistance genes were isolated from a patient in china, which had no mutations in rplC, rplD, rplV, and 23S rRNA gene. Transformation indicated that optrA and cfr were located on two different plasmids and both could be transferred to recipient strain, resulting in the increase of MICs of linezolid and chloramphenicol. Cfr, carried by an 11,872-bp plasmid, was enclosed with an IS 110 transposase in upstream and an IS 3-like transposase in downstream, while optrA was on an 8357-bp plasmid. As far as we know, this is the first report of an E.faecalis clinical strain co-harboring optrA and cfr in China.Keywords: E.faecalis, linezolid, cfr, optrA

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.