Topological term in the non-linear σ\sigma model of the SO(5) spin chains

We show that there is a topological (Berry phase) term in the non-linear $\sigma$ model description of the SO(5) spin chain. It distinguishes the linear and projective representations of the SO(5) symmetry group, in exact analogy to the well-known $\theta$-term of the SO(3) spin chain. The presence of the topological term is due to the fact that $\pi_2(\frac{SO(5)}{SO(3)\times SO(2)})= \mathbb{Z}$. We discuss the implication of our results on the spectra of the SO(5) spin chain, and connect it with a recent solvable SO(5) spin model which exhibits valence bond solid ground state and edge degeneracy.Comment: 12 pages, 1 figure; the publication versio

Depressive symptoms in patients with irritable bowel syndrome: A meta-analysis of comparative studies

Depression is common in patients with irritable bowel syndrome (IBS), but the reported prevalence across different studies is inconsistent. This meta-analysis systematically examined the presence and severity of depressive symptoms in patients with IBS. Two investigators independently performed a literature search. The pooled depressive symptom severity was calculated using a random effects model. Subgroup, sensitivity and meta-regression analyses were conducted to examine the moderating factors of the development of depressive symptoms. Twenty four studies (n=2,837) comparing depressive symptoms between IBS patients (n=1,775) and healthy controls (n=1,062) were identified; 14 (58.3%) studies were rated as high quality. Compared to healthy controls, IBS patients had more frequent (OR=9.21, 95%CI: 4.56-18.57, P\u3c0.001; I2=76%) and more severe depressive symptoms (n=1,480, SMD=2.02, 95%CI: 1.56-2.48, P\u3c0.001; I2=94%). Subgroup analyses revealed that patients with all IBS subtypes had more severe depressive symptoms than controls. In addition, versions of the Hamilton Depression Rating Scale (HAM-D) and IBS diagnostic criteria were significantly associated with depressive symptom severity. Meta-regression analyses revealed that female gender, younger age and small sample size were significantly associated with more severe depressive symptoms. In conclusion, meta-analytic data showed that IBS patients had more frequent and severe depressive symptoms than healthy controls. Adequate screening and treatment for depression should be developed and implemented in this patient population

The aminobisphosphonate pamidronate controls influenza pathogenesis by expanding a γδ T cell population in humanized mice

As shown in humanized mice, a population of Vγ9Vδ2 T cells can reduce the severity and mortality of disease caused by infection with human and avian influenza viruses

Experiences of environmental services workers in a tertiary hospital in Asia during the COVID-19 pandemic: a qualitative study

BackgroundThe Coronavirus Disease 2019 (COVID-19) pandemic has had a significant impact on all walks of life, in particular, environmental services workers in healthcare settings had higher workload, increased stress and greater susceptibility to COVID-19 infections during the pandemic. Despite extensive literature describing the impact of the pandemic on healthcare workers such as doctors and nurses, studies on the lived experiences of environmental services workers in healthcare settings are sparse and none has been conducted in the Asian context. This qualitative study thus aimed to examine the experiences of those who worked for a year of the COVID-19 pandemic.MethodsA purposive sample of environmental services workers was recruited from a major tertiary hospital in Singapore. Semi-structured interviews were conducted in-person, lasting around 30min, and included open-ended questions pertaining to five main domains: work experiences during COVID-19, training and education needs, resource and supplies availability, communication with management and other healthcare staff, and perceived stressors and support. These domains were identified based on team discussions and literature review. The interviews were recorded and transcribed for thematic analysis, as guided by Braun and Clarke.ResultsA total of 12 environmental services workers were interviewed. After the first seven interviews, no new themes emerged but an additional five interviews were done to ensure data saturation. The analysis yielded three main themes and nine subthemes, including (1) practical and health concerns, (2) coping and resilience, and (3) occupational adaptations during the pandemic. Many expressed confidence in the preventive efficacy of proper PPE, infection control practice and COVID-19 vaccination in protecting them against COVID-19 and severe illness. Having prior experience with infectious disease outbreaks and previous training in infection control and prevention appeared to be useful as well for these workers. Despite the various challenges presented by the pandemic, they could still find meaning in their everyday work by positively impacting the wellbeing of patients and other healthcare workers in the hospital.ConclusionBesides uncovering the concerns shared by these workers, we identified helpful coping strategies, resilience factors and certain occupational adaptations, which have implications for future pandemic planning and readiness

Lack of association between genetic polymorphisms within DUSP12 - ATF6 locus and glucose metabolism related traits in a Chinese population

<p>Abstract</p> <p>Background</p> <p>Genome-wide linkage studies in multiple ethnic populations found chromosome 1q21-q25 was the strongest and most replicable linkage signal in the human chromosome. Studies in Pima Indian, Caucasians and African Americans identified several SNPs in <it>DUSP12 </it>and <it>ATF6</it>, located in chromosome 1q21-q23, were associated with type 2 diabetes.</p> <p>Methods</p> <p>We selected 19 single nucleotide polymorphisms (SNPs) that could tag 98% of the SNPs with minor allele frequencies over 0.1 within <it>DUSP12-ATF6 </it>region. These SNPs were genotyped in a total of 3,700 Chinese Han subjects comprising 1,892 type 2 diabetes patients and 1,808 controls with normal glucose regulation.</p> <p>Results</p> <p>None of the SNPs and haplotypes showed significant association to type 2 diabetes in our samples. No association between the SNPs and quantitative traits was observed either.</p> <p>Conclusions</p> <p>Our data suggests common SNPs within <it>DUSP12</it>-<it>ATF6 </it>locus may not play a major role in glucose metabolism in the Chinese.</p

The density-matrix renormalization group

The density-matrix renormalization group (DMRG) is a numerical algorithm for the efficient truncation of the Hilbert space of low-dimensional strongly correlated quantum systems based on a rather general decimation prescription. This algorithm has achieved unprecedented precision in the description of one-dimensional quantum systems. It has therefore quickly acquired the status of method of choice for numerical studies of one-dimensional quantum systems. Its applications to the calculation of static, dynamic and thermodynamic quantities in such systems are reviewed. The potential of DMRG applications in the fields of two-dimensional quantum systems, quantum chemistry, three-dimensional small grains, nuclear physics, equilibrium and non-equilibrium statistical physics, and time-dependent phenomena is discussed. This review also considers the theoretical foundations of the method, examining its relationship to matrix-product states and the quantum information content of the density matrices generated by DMRG.Comment: accepted by Rev. Mod. Phys. in July 2004; scheduled to appear in the January 2005 issu

PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 Are Associated with Type 2 Diabetes in a Chinese Population

Recent advance in genetic studies added the confirmed susceptible loci for type 2 diabetes to eighteen. In this study, we attempt to analyze the independent and joint effect of variants from these loci on type 2 diabetes and clinical phenotypes related to glucose metabolism.Twenty-one single nucleotide polymorphisms (SNPs) from fourteen loci were successfully genotyped in 1,849 subjects with type 2 diabetes and 1,785 subjects with normal glucose regulation. We analyzed the allele and genotype distribution between the cases and controls of these SNPs as well as the joint effects of the susceptible loci on type 2 diabetes risk. The associations between SNPs and type 2 diabetes were examined by logistic regression. The associations between SNPs and quantitative traits were examined by linear regression. The discriminative accuracy of the prediction models was assessed by area under the receiver operating characteristic curves. We confirmed the effects of SNPs from PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 on risk for type 2 diabetes, with odds ratios ranging from 1.114 to 1.406 (P value range from 0.0335 to 1.37E-12). But no significant association was detected between SNPs from WFS1, FTO, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30 and type 2 diabetes. Analyses on the quantitative traits in the control subjects showed that THADA SNP rs7578597 was association with 2-h insulin during oral glucose tolerance tests (P = 0.0005, empirical P = 0.0090). The joint effect analysis of SNPs from eleven loci showed the individual carrying more risk alleles had a significantly higher risk for type 2 diabetes. And the type 2 diabetes patients with more risk allele tended to have earlier diagnostic ages (P = 0.0006).The current study confirmed the association between PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 and type 2 diabetes. These type 2 diabetes risk loci contributed to the disease additively

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at \sqrt{s}=13 TeV

This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 \hbox {fb}^{-1} of pp collision data at \sqrt{s}=13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of Z\rightarrow \mu \mu and J/\psi \rightarrow \mu \mu decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of |\eta |<2.7