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We consider the problem of learning causal structures in sparse high-dimensional settings that may be subject to the presence of (potentially many) unmeasured confounders, as well as selection bias. Based on the structure found in common families of large random networks and examining the representation of local structures in linear structural equation models (SEM), we propose a new local notion of sparsity for consistent structure learning in the presence of latent and selection variables, and develop a new version of the Fast Causal Inference (FCI) algorithm with reduced computational and sample complexity, which we refer to as local FCI (lFCI). The new notion of sparsity allows the presence of highly connected hub nodes, which are common in real-world networks, but problematic for existing methods. Our numerical experiments indicate that the lFCI algorithm achieves state-of-the-art performance across many classes of large random networks, and its performance is superior to that of existing methods for networks containing hub nodes.

Background and ObjectivesPlasma proteomics may elucidate novel insights into the pathophysiology of ischemic stroke (IS), identify biomarkers of IS risk, and guide development of nascent prevention strategies. We evaluated the relationship between the plasma proteome and IS risk in the population-based Cardiovascular Health Study (CHS).MethodsEligible CHS participants were free of prevalent stroke and underwent quantification of 1,298 plasma proteins using the aptamer-based SOMAScan assay platform from the 1992–1993 study visit. Multivariable Cox proportional hazards regression was used to evaluate associations between a 1-SD increase in the log2-transformed estimated plasma protein concentrations and incident IS, adjusting for demographics, IS risk factors, and estimated glomerular filtration rate. For proteins independently associated with incident IS, a secondary stratified analysis evaluated associations in subgroups defined by sex and race. Exploratory analyses evaluated plasma proteomic associations with cardioembolic and noncardioembolic IS and proteins associated with IS risk in participants with left atrial dysfunction but without atrial fibrillation.ResultsOf 2,983 eligible participants, the mean age was 74.3 (±4.8) years, 61.2% were women, and 15.4% were Black. Over a median follow-up of 12.6 years, 450 participants experienced an incident IS. N-terminal probrain natriuretic peptide (NTproBNP, adjusted HR 1.37, 95% CI 1.23–1.53,p= 2.08 × 10−08) and macrophage metalloelastase (MMP12, adjusted HR 1.30, 95% CI 1.16–1.45,p= 4.55 × 10−06) were independently associated with IS risk. These 2 associations were similar in men and women and in Black and non-Black participants. In exploratory analyses, NTproBNP was independently associated with incident cardioembolic IS, E-selectin with incident noncardioembolic IS, and secreted frizzled-related protein 1 with IS risk in participants with left atrial dysfunction.DiscussionIn a cohort of older adults, NTproBNP and MMP12 were independently associated with IS risk. We identified plasma proteomic determinants of incident cardioembolic and noncardioembolic IS and found a novel protein associated with IS risk in those with left atrial dysfunction.

In this study, a new mathematical model is presented to solve the flexible flow shop problem where transportation is reliable and there are constraints on intermediate buffers, budgets, and human resource learning effects. Firstly, the model is validated to confirm the accuracy of its performance. Then, since it is an NP-hard one, two metaheuristic algorithms, namely, MOSA and MOEA/D, are rendered to solve mid- and large-scale problems. To confirm their accuracy of performance, two small-scale problems are solved using GAMS exact solution software, and the obtained results have been compared with the output of the algorithms. Since the problem in this study is multiobjective, five comparative indices are used to compare the performance of algorithms. The results show that the answers achieved using the metaheuristic algorithms are very close to the ones achieved via the GAMS exact program. Therefore, the proposed algorithms are validated, and it is proved that they are accurately designed and useable in solving the real-world problems (which have mid- and large-scale) in logical calculation time. By comparing the obtained results, it can be seen that the MOEA/D algorithm performs better in terms of computational time (CPU time) and Mean ideal distance (MID). The MOSA algorithm also performs better according to the index Spread of nondominated solutions (SNS), diversity metric (DM), and number of Pareto solutions (NPS). Considering the confirmation of precision and accuracy of performance of the proposed algorithms, it can be concluded that MOSA and MOEA/D are useful in solving the mid- and large-scale modes of the problem in the study, which is very applicable in the real world.

The presented study evaluates the performance with the balanced scorecard approach and fuzzy Delphi in the Mostazafan Foundation. The main goal of the present study is to consider the most important key indicators of human resource performance for designing a business dashboard based on multi-criteria decision-making in the Mostazafan Foundation. For this, the main contributions of the article are (1) identifying the main effective human resource performance indicators using the Delphi method and (2) presenting an integration framework using multi-criteria decision-making according to the determined indicators. The target population of the study was the senior and middle managers of the Mostazafan Foundation. The questionnaire was completed by 30 experts and Cronbach’s coefficient for the questionnaire was calculated to be 0.87. Finally, after using the fuzzy Delphi method, the key indicators for evaluating the performance of the Mostazafan Foundation were obtained and ranked using AHP. The results show the ranking of indicators from the financial perspective: the organization’s profit per employee (per capita profit), the organization’s income per employee (per capita income), and the cost of compensation for total services per employee; from the customer’s point of view: the level of commitment and belonging of employees to the organization; the client’s complaint rate and employees’ perceptions of using their capabilities in the organization; in terms of internal processes: agility in hiring the right staff, key employees by market standards; and in terms of growth and learning, the percentage of key employees with a development plan, the rate of staff with a coach, and the rate of trained staff.

Background: Nanotechnology plays a very important role in producing dental compounds. In recent years, a variety of nanoparticles have been used to enhance the properties of these compounds. However, less attention has been paid to the implications of the toxic effects of metallic nanoparticles. The purpose of this systematic review was to review studies evaluating the toxic effects of metal nanoparticles used in dentistry to determine whether long-term use of these compounds has toxic effects. Methods: Electronic search of studies in Pubmed, Scopus, Medline, and google scholar databases was performed until December 2020, according to PRISMA guidelines (preferential reporting items for systematic review and meta-analysis). Results: During the initial research, 325 papers were collected. After reviewing the texts, eliminating duplicates, and applying inclusion criteria, only 10 of these were selected for qualitative review. The results of the studies showed that the use of short-term metallic nanoparticles does not result in increased cytotoxicity. Conclusion: Dental compounds containing metallic nanoparticles had higher cytotoxic activity than unmodified raw materials, but overall, during the short-term review, these studies did not indicate a significant difference in cytotoxicity.

Microglia, the innate immune cells of the brain, influence Alzheimer’s disease (AD) progression and are potential therapeutic targets. However, microglia exhibit diverse functions, the regulation of which is not fully understood, complicating therapeutics development. To better define the transcriptomic phenotypes and gene regulatory networks associated with AD, we enriched for microglia nuclei from 12 AD and 10 control human dorsolateral prefrontal cortices (7 males and 15 females, all aged >60 years) before single-nucleus RNA sequencing. Here we describe both established and previously unrecognized microglial molecular phenotypes, the inferred gene networks driving observed transcriptomic change, and apply trajectory analysis to reveal the putative relationships between microglial phenotypes. We identify microglial phenotypes more prevalent in AD cases compared with controls. Further, we describe the heterogeneity in microglia subclusters expressing homeostatic markers. Our study demonstrates that deep profiling of microglia in human AD brain can provide insight into microglial transcriptional changes associated with AD.

Objective The purpose of this study was to analyze quantitative values of normal and abnormal marrow on T1-weighted images of spine, to propose a ratio for T1 values of abnormal to normal vertebrae, and to assess whether this ratio could be helpful in predicting presence of neoplastic lesions in the spine. Materials and Methods One-hundred randomly selected magnetic resonance imagings of lumbar spine without infection, fracture, and tumor were selected to form normal cohort. A second cohort of 100 metastasis of lumbar spine was identified. Ratio of T1 value of vertebral body to the T1 value of the inferior vertebral body was performed for normal cohort from D11 to L5. Ratio of T1 value of metastasis to adjacent normal vertebral body was done for metastatic cohort. Data was analyzed using standard t-test and kappa was performed for intra- and inter-observer reliability. Results A decline in T1 value of abnormal to normal marrow was seen in patients with metastasis that was statistically significant. We call this the T1 ratio of marrow (TROM). The sensitivity and accuracy with the cutoff value of TROM at 0.7 (92% sensitivity, 97.1% accuracy) are better than at 0.6 (75% sensitivity, 96.2% accuracy) or 0.5 (47% sensitivity, 93.2% accuracy). A subset analysis of the other T1 hypointense benign lesions including atypical hemangiomas and focal marrow hyperplasia, however, revealed overlapping TROM values with the metastatic cohort. Conclusion Using the TROM on T1-weighted images could not confidently differentiate malignant from benign T1 hypointense lesions of the spine.

Cross-docking is the main operation of unloading products from incoming trucks, regrouping products in relation to their destination, and loading directly onto shipping trucks, reducing warehousing, picking, transportation costs, and delivery times. This is the intended logistics technology. In this paper, we present a new bi-objective mixed-integer mathematical model for truck scheduling problems in cross-docking systems. The goal of the proposed mixed-integer mathematical model is to minimize the total operation time (makespan) and cost of moving cargo within the terminal. The performance of the proposed model is compared with that of the available model to solve small instances. The results showed that in solving small size of problem, the proposed model in this study is more efficient and we found better solutions. An evolutionary algorithm called the nondominated sorting genetic algorithm (NSGA-II) has been proposed to solve larger instances due to computational complexity. To evaluate the proposed algorithm, a comparative analysis of benchmark instances was performed and the efficiency of the above algorithm was compared to the nondominated ranked algorithm (NRGA) based on the index designed in the literature. The statistical hypothesis testing (t-test) is used for determining the best algorithm based on the average runtime and average number of Pareto solutions. Using the Taguchi method, the proposed algorithms are tuned. Considering a temporary storage space and the multiple receiving and shipping docks is the main contribution of the paper. Finally, for evaluating algorithms, multicriteria decision-making (MCDM) technique and statistical method are used. The results show the suitable performance of presented model.

BackgroundIn the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology.Methods and ResultsIn the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations.ConclusionThe CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.

IntroductionThe aim of this meta-analysis was to assess the effect of ongoing statin treatment on susceptibility to influenza virus infection and on influenza-associated mortality.Material and methodsStudies investigating the impact of statin treatment on influenza prevalence and mortality were searched in PubMed-Medline, Scopus, ISI Web of Knowledge, Embase, Proquest, OVID, EBSCO and CINAHLdatabases (up to June 30, 2020). Fixed- and random-effects model and generic inverse variance method were used for quantitative data synthesis.ResultsIn the meta-analysis of 15 arms of 3 eligible studies including flu-vaccinated and unvaccinated patients, treatment with statins was associated with a reduction of influenza virus prevalence [Odds Ratio (OR) 0.85, 95% Confidence Interval (CI) 0.73-0.99; p 0.040]. No significant effect of statins on the susceptibility to influenza infection was observed in the distinct communities of either vaccinated or unvaccinated subjects. Among 9 arms of 6 eligible studies, the use of statins among patients with influenza was associated with a reduced mortality [OR (95% CI), 0.68 (0.56, 0.82); pConclusionsA reduced influenza prevalence and anincreased survivalfrom influenza infection was observed in patients on ongoing statin treatment. Further research is needed to define the possible role of statins as adjunctive therapy in patients with influenza infection.

Although alterations in xenobiotic metabolism are considered causal in the development of bladder cancer, the precise mechanisms involved are poorly understood. In this study, we used high-throughput mass spectrometry to measure over 2,000 compounds in 58 clinical specimens, identifying 35 metabolites which exhibited significant changes in bladder cancer. This metabolic signature distinguished both normal and benign bladder from bladder cancer. Exploratory analyses of this metabolomic signature in urine showed promise in distinguishing bladder cancer from controls and also nonmuscle from muscle-invasive bladder cancer. Subsequent enrichment-based bioprocess mapping revealed alterations in phase I/II metabolism and suggested a possible role for DNA methylation in perturbing xenobiotic metabolism in bladder cancer. In particular, we validated tumor-associated hypermethylation in the cytochrome P450 1A1 (CYP1A1) and cytochrome P450 1B1 (CYP1B1) promoters of bladder cancer tissues by bisulfite sequence analysis and methylation-specific PCR and also by in vitro treatment of T-24 bladder cancer cell line with the DNA demethylating agent 5-aza-2′-deoxycytidine. Furthermore, we showed that expression of CYP1A1 and CYP1B1 was reduced significantly in an independent cohort of bladder cancer specimens compared with matched benign adjacent tissues. In summary, our findings identified candidate diagnostic and prognostic markers and highlighted mechanisms associated with the silencing of xenobiotic metabolism. The metabolomic signature we describe offers potential as a urinary biomarker for early detection and staging of bladder cancer, highlighting the utility of evaluating metabolomic profiles of cancer to gain insights into bioprocesses perturbed during tumor development and progression. Cancer Res; 71(24); 7376–86. ©2011 AACR.

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The patient is a 33-year-old Iranian woman without a significant past medical history with the full range of narcolepsy symptoms that started within two weeks after her recovery from COVID-19. Sleep studies revealed increased sleep latency and three sleep-onset rapid eye movement events, compatible with a narcolepsy-cataplexy diagnosis.

Background: Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer’s disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models. Objective: This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage. Results: Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories. Conclusion: The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

Background: Pulmonary arterial hypertension (PAH) is a complex disease characterized by progressive right ventricular (RV) failure leading to significant morbidity and mortality. Investigating metabolic features and pathways associated with RV dilation, mortality, and measures of disease severity can provide insight into molecular mechanisms, identify subphenotypes, and suggest potential therapeutic targets. Methods: We collected data from a prospective cohort of PAH participants and performed untargeted metabolomic profiling on 1045 metabolites from circulating blood. Analyses were intended to identify metabolomic differences across a range of common metrics in PAH (eg, dilated versus nondilated RV). Partial least squares discriminant analysis was first applied to assess the distinguishability of relevant outcomes. Significantly altered metabolites were then identified using linear regression, and Cox regression models (as appropriate for the specific outcome) with adjustments for age, sex, body mass index, and PAH cause. Models exploring RV maladaptation were further adjusted for pulmonary vascular resistance. Pathway enrichment analysis was performed to identify significantly dysregulated processes. Results: A total of 117 participants with PAH were included. Partial least squares discriminant analysis showed cluster differentiation between participants with dilated versus nondilated RVs, survivors versus nonsurvivors, and across a range of NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels, REVEAL 2.0 composite scores, and 6-minute-walk distances. Polyamine and histidine pathways were associated with differences in RV dilation, mortality, NT-proBNP, REVEAL score, and 6-minute walk distance. Acylcarnitine pathways were associated with NT-proBNP, REVEAL score, and 6-minute walk distance. Sphingomyelin pathways were associated with RV dilation and NT-proBNP after adjustment for pulmonary vascular resistance. Conclusions: Distinct plasma metabolomic profiles are associated with RV dilation, mortality, and measures of disease severity in PAH. Polyamine, histidine, and sphingomyelin metabolic pathways represent promising candidates for identifying patients at high risk for poor outcomes and investigation into their roles as markers or mediators of disease progression and RV adaptation.

ObjectivesThe use of synthetic data to supplement clinical trial placebo groups or for trial planning is rapidly gaining interest. However, there is not yet an established framework for generating synthetic data for these purposes. In this work we test two approaches to generating synthetic placebo arms for ALS trials with survival being the primary outcome variable.MethodsFor the first approach, we extracted sample subsets from the UK MND register (n = 308) using an evolutionary algorithm such that the subset baseline variables matched a target trials group, either people enrolled in LiCALS (n = 106) or people included in the PRO-ACT database (n = 171). We also applied trial specific exclusion criteria where possible or alternatively we applied a custom ‘time filter’. For the second approach, survival was predicted for LiCALS participants using the ENCALS model. Survival probabilities from each method were compared to real placebo participants using Kaplan-Meier analysis and the log rank test.ResultsWe found that the synthetic placebo groups derived from the MND register matched the target trials outcomes very well. The ENCALS model produces synthetic placebo groups that are significantly different to the real placebo groups. However, when participants are censored at 6 month intervals, the ENCALS synthetic group matches the target group very well between 24 and 48 months, indicating a possible timeframe that this method could be utilised.ConclusionBoth the register based approach and the ENCALS prognostic model generated synthetic placebo groups that matched placebo groups from historical trials. These methods need to be validated in prospective trials.

For many years, people and even some doctors believed that opium has beneficial effects on cardiovascular disease and diabetes. The effect of opium as a stimulator or inhibitor on the metabolic reg...

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotype’s ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer’s disease.

Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result, these approaches may detect spurious differential connections induced by the effect of the covariates on both the disease condition and the network. To address this issue, we propose a general covariate-adjusted test for differential network analysis. Our method assesses differential network connectivity by testing the null hypothesis that the network is the same for individuals who have identical covariates and only differ in disease status. We show empirically in a simulation study that the covariate-adjusted test exhibits improved type-I error control compared with na\"ive hypothesis testing procedures that do not account for covariates. We additionally show that there are settings in which our proposed methodology provides improved power to detect differential connections. We illustrate our method by applying it to detect differences in breast cancer gene co-expression networks by subtype., Comment: 42 pages, 3 figures

The Dehdez forest provides various services for both humans and animals, including food and timber production, carbon storage, air purification, wildlife habitation, and socio-cultural benefit. Dur...

In this manuscript, we study the nearly flat approximation of a conformally invariant gravitational theory in metric measure space (MMS). In addition, we investigate the transformation of the energy-momentum tensor in this context and obtain the vacuum solution of MMS and its weak field limit in the spherically symmetric coordinates. We show that while it is already a vacuum solution, it can simulate dark matter when restricted to the framework of general relativity, i.e., a symmetry-broken conformal frame. This is done by employing a density function which is an essential part of MMS. We derive an equation for the density function for a general profile of a rotation curve obtained from observations. Specifically, the density function corresponding to two well-known profiles PSS and NFW are provided., Comment: 12 pages

In this study, Glutathione-responsive hydrogel, MIPs and their related NIPs were synthesized by precipitation polymerization method. Effect of HEMA/MAA and EGDMA/TRIM as functional monomer and crosslinking agents on size, morphology, drug loading and releasing were studied. BACy as a disulfide cross-linking agent was used in praparation of Glutathione-responsive hydrogel. The size and morphology of MIPs and NIPs were examined by FE-SEM technique. Results showed EGDMA-synthesized MIPs had the smaller size than TRIM-prepared MIPs. In order to measure the capacity of MIPs or NIPs for binding to cisplatin, static and dynamic adsorption experiments were done. The amounts of cisplatin were measured by HPLC method. The binding capacity of drug by MIPs is observed to be higher than NIPs. The EGDMA-synthesized polymers showed a high percentage of adsorption. NIPs were saturated much sooner than MIPs. In vitro release assays of cisplatin were done in PBS at pH = 7.4 and temperature of 37 °C. The results showed that increase of the amount of GSH cause to increase release of the drug from hydrogel.

Diets included high-fat (HFD) and high calories intake is correlated with greater risk of obesity and oxidative stress, which lead to increase the risk of related diseases such as cardiovascular and metabolic disease. In the present study, we have examined the hypolipidemic activity of Hypericum Scabrum extract on HFD fed rats. Fifty-four male Wistar rats divided into six groups: 1) control, 2) H. Scabrum extract (100 mg/kg gavage per day), 3) H. Scabrum extract (300 mg/kg), 4) HFD, 5) HFD and H. Scabrum extract (100 mg/kg), 6) HFD and H. Scabrum extract (300 mg/kg). The groups were fed their diet and treatment for 3 months. Biochemical analysis showed elevated lipid serum profile in HFD rats compared to control group. H. Scabrum extract supplementation significantly ameliorated triglyceride, total cholesterol and LDL-cholesterol. H. Scabrum extract supplementation leading to increase HDL-cholesterol in HFD treated groups. This experiment showed that H. Scabrum extract decreased HFD complications and might be beneficial herbal drug for treatment of hyperlipidemia and obesity.

The incidence rate of cancer is steadily increasing all around the world, and there is an urgent need to develop novel and more effective treatment strategies. Recently, bacterial therapy has been investigated as a new approach to target cancer, and is becoming a serious option. Streptococcus strains are among the most common and well-studied virulent bacteria that cause a variety of human infections. Everyone has experienced a sore throat during their lifetime, or has been asymptomatically colonized by streptococci. The ability of Streptococcus bacteria to fight cancer was discovered more than 100 years ago, and over the years has undergone clinical trials, but the mechanism is not yet completely understood. Recently, several animal models and human clinical trials have been reported. Streptococcal strains can have an intrinsic anti-tumor activity, or can activate the host immune system to fight the tumor. Bacteria can selectively accumulate and proliferate in the hypoxic regions of solid tumors. Moreover, the bacteria can be genetically engineered to secrete toxins or enzymes that can specifically attack the tumors.

Background: Norwalk virus is one of the most common causes of viral gastroenteritis. The aquatic products are potential sources of contamination with this virus. Objectives: The main objective of the study was to investigate the presence of the Norwalk virus in different aquatic animals in Khuzestan provinces, Iran. Materials and Methods: A total of 40 pieces of fish (silver carp, common carp, big head, and grass carp species) and 10 pieces of shrimps were caught from ponds, and the samples were transferred to the laboratory in ice bags. After the separation of the intestine, the content of the intestine was extracted using two sterile filters. Then, the supernatant was used for reverse transcription polymerase chain reaction (RT-PCR) using Calicivirus-specific primers (p289/ p290). Then, Norwalk virus-specific primers (NVp36/NVp35) were detected in Calicivirus positive samples. Results: The results showed 8% (4 samples) and 6% (3 samples) of the samples were infected with Calicivirus (p289/p290 genes) and Norwalk virus (NVp36/NVp35 genes), respectively. Calicivirus positive samples included 2 common carp, 1 silver carp, and 1 shrimp. Norwalk virus-positive samples included 2 common carp and 1 shrimp. In other words, the highest prevalence of virus was observed in aquatic fish feeding from the bottom of the pool. Due to the fact that this species is bred with other species and considering that this virus lives in the gastrointestinal tract, the ingestion of feces of other infected organisms can lead to the increase of this virus in the digestive system of carp. Conclusion: Therefore, due to the importance of Norwalk as a zoonotic agent and the possibility of human infection through consumption of aquatic products, preventive measures such as not using animal manure for fertilization and preventing the growth of phytoplankton in aquaculture ponds and cooking meat properly are suggested.

Liver transplantation is a life-saving treatment for children who are in liver failure. The survival rate index is used to assess the success rate of liver transplantation. The study aimed to assess the survival rate of liver transplantation in children. We searched 5 international databases in this study, including Medline/PubMed, ProQuest, Scopus, Web of Knowledge, and Google Scholar, for published articles by the end of 2020. Also, meta-regression analysis was performed based on the year of the study, and subgroup analysis was performed according to continents. A total of 425 titles were reviewed. Based on the results, 96 articles were entered in the meta-analysis. Established on the random-effect model, the survival rates of 1, 3, 5, and 10 years of transplantation were 86.62%, 77.74%, 73.95%, and 68.60%, respectively. Also, based on the meta-regression results, there was a relationship between the year of the study and the survival rate, as the study year gets more recent, the survival rate is increased. This study can provide documented and comprehensive evidence which can be the basis of many policies and decisions in various sectors of health development, including evaluating treatment options and health interventions in transplantation.

Microglia contribute to Alzheimer’s Disease (AD) progression and are candidate therapeutic targets for disease modulation. Single cell transcriptomics demonstrate microglia adopt multiple phenotypes. However, identifying the human AD microglia profile was limited by small numbers. We employed fluorescence activated nuclei sorting prior to single-nucleus RNA-seq on human prefrontal cortices boosting the number of microglia analyzed per subject. We observed microglia subpopulations previously unrecognized in human brain and mapped their transcriptomic relationships by trajectory inference. We identified three clusters enriched for endolysosomal pathways, one of which showed differential expression of AD GWAS genes, in addition to genes implicated in nucleic acid detection and interferon signaling. We histologically demonstrated a microglia subpopulation with cytosolic nucleic acid and altered endolysosome morphology in AD tissue. Analysis of the “homeostatic” microglia cluster revealed a uniquely AD subpopulation. Our study demonstrates the value of deeply profiling microglia to explore the biological implications of microglia transcriptomic diversity.

Background and Objectives: The objective of this study was to determine molecular characterization and genetic diversity of colistin-resistant A. baumannii clinical isolates in Intensive Care Unit hospitalized patients. Materials and Methods: A total of 127 A. baumannii clinical isolates were evaluated for antimicrobial susceptibility. PCR reaction and sequencing were performed for the detection of mutations in pmrAB and lpx ACD genes. Results: Based on antimicrobial susceptibility testing, 40.94% and 33.85% of the isolates were MDR and XDR respec- tively whereas 3.93% of them were found to be PDR. Results of agar dilution MIC and E-test indicated that 76% of the isolates were sensitive to colistin. All of the isolates were positive for bla OXA-51 and 50% of them were positive for both blaOXA-23-like and bla OXA-143-like genes while only 25% of the isolates were positive for bla OXA-72 . None of them were positive for the bla OXA-58-like gene. There is no mutation in pmrA. The V162A substitution for pmrB gene was repeated in two isolates, and E394D and Y292H substitutions in lpxA were observed in two isolates; also, C120R and F165L substitutions in lpxC gene was repeated in two isolates. Analysis of phylogenetic tree based on alterations in lpxACD and pmrB genes indicated the appearance of new isolates compared to the reference strain ATCC17978 A. baumannii isolates. Conclusion: The present study indicated the prevalence of MDR and XDR A. baumannii isolates and the emergence of PDR isolates in the northwest portion of Iran. The appearance of colistin-resistant isolates with new mutations in pmrB, lpxACD genes indicates new resistance mechanisms.

Early detection of Alzheimer’s disease (AD) during the Mild Cognitive Impairment (MCI) stage could enable effective intervention to slow down disease progression. Computer-aided diagnosis of AD relies on a sufficient amount of biomarker data. When this requirement is not fulfilled, transfer learning can be used to transfer knowledge from a source domain with more amount of labeled data than available in the desired target domain. In this study, an instance-based transfer learning framework is presented based on the gradient boosting machine (GBM). In GBM, a sequence of base learners is built, and each learner focuses on the errors (residuals) of the previous learner. In our transfer learning version of GBM (TrGB), a weighting mechanism based on the residuals of the base learners is defined for the source instances. Consequently, instances with different distribution than the target data will have a lower impact on the target learner. The proposed weighting scheme aims to transfer as much information as possible from the source domain while avoiding negative transfer. The target data in this study was obtained from the Mount Sinai dataset which is collected and processed in a collaborative 5-year project at the Mount Sinai Medical Center. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset was used as the source domain. The experimental results showed that the proposed TrGB algorithm could improve the classification accuracy by 1.5 and 4.5% for CN vs. MCI and multiclass classification, respectively, as compared to the conventional methods. Also, using the TrGB model and transferred knowledge from the CN vs. AD classification of the source domain, the average score of early MCI vs. late MCI classification improved by 5%.

Our prior work shows that azinphos-methyl pesticide exposure is associated with altered oral microbiomes in exposed farmworkers. Here we extend this analysis to show the same association pattern is also evident in their children. Oral buccal swab samples were analyzed at two time points, the apple thinning season in spring-summer 2005 for 78 children and 101 adults and the non-spray season in winter 2006 for 62 children and 82 adults. The pesticide exposure for the children were defined by the farmworker occupation of the cohabitating household adult and the blood azinphos-methyl detection of the cohabitating adult. Oral buccal swab 16S rRNA sequencing determined taxonomic microbiota proportional composition from concurrent samples from both adults and children. Analysis of the identified bacteria showed significant proportional changes for 12 of 23 common oral microbiome genera in association with azinphos-methyl detection and farmworker occupation. The most common significantly altered genera had reductions in the abundance of Streptococcus, suggesting an anti-microbial effect of the pesticide. Principal component analysis of the microbiome identified two primary clusters, with association of principal component 1 to azinphos-methyl blood detection and farmworker occupational status of the household. The children's buccal microbiota composition clustered with their household adult in ∼95% of the households. Household adult farmworker occupation and household pesticide exposure is associated with significant alterations in their children's oral microbiome composition. This suggests that parental occupational exposure and pesticide take-home exposure pathways elicit alteration of their children's microbiomes.

This paper concerns the development of an inferential framework for high-dimensional linear mixed effect models. These are suitable models, for instance, when we have $n$ repeated measurements for $M$ subjects. We consider a scenario where the number of fixed effects $p$ is large (and may be larger than $M$), but the number of random effects $q$ is small. Our framework is inspired by a recent line of work that proposes de-biasing penalized estimators to perform inference for high-dimensional linear models with fixed effects only. In particular, we demonstrate how to correct a `naive' ridge estimator in extension of work by B\"uhlmann (2013) to build asymptotically valid confidence intervals for mixed effect models. We validate our theoretical results with numerical experiments, in which we show our method outperforms those that fail to account for correlation induced by the random effects. For a practical demonstration we consider a riboflavin production dataset that exhibits group structure, and show that conclusions drawn using our method are consistent with those obtained on a similar dataset without group structure., Comment: 30 pages, 3 figures

We present a framework for learning Granger causality networks for multivariate categorical time series based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a ...

The optical properties of glassy liquid crystals (GLC’s) designed to provide high resistance to laser-induced damage are explored. GLC’s readily enable freezing of the anisotropic ordering of liqui...

Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at given time points. We propose a three-stage procedure for consistent estimation of both structural change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and propose a penalized least square estimator using a total variation penalty. We show that the proposed penalized estimation method over-estimates the number of change points. We then propose a backward selection criterion in conjunction with a penalized least square estimator to tackle this issue. In the last step of our procedure, we estimate the VAR parameters in each of the segments. We prove that the proposed procedure consistently detects the number of change points and their locations. We also show that the procedure consistently estimates the VAR parameters. The performance of the method is illustrated through several simulation scenarios and real data examples., arXiv admin note: substantial text overlap with arXiv:1708.02736

The purpose of this study was to prepare the affordable and novel materials as the efficient and effective antimicrobials. Therefore, the metal–organic framework (MOF) with the formula of [Zn(HCOO)3][C2H8N], polyethylene glycol (PEG), and nitrogen-doped carbon quantum dots (N-CQDs) was chosen to synthesize the two novel mixed-matrix membranes (MMMs). [Zn(HCOO)3][C2H8N]/PEG and N-CQDs@[Zn(HCOO)3][C2H8N]/PEG as the novel MMMs were simply synthesized in a few steps and their antimicrobial activity was investigated against the Gram-negative bacteria, Escherichia coli (E. coli). Furthermore, a novel method was used for the synthesis of [Zn(HCOO)3][C2H8N]. The particles were characterized by the Fourier-transform infrared spectroscopy, the X-ray powder diffraction, the CHNS elemental analysis, and the field-emission scanning electron microscopy. The cytotoxicity and antimicrobial activity of the particles were also studied. The result of MTT (methylthiazolyldiphenyl-tetrazolium bromide) assay showed that these novel MMMs have a good biocompatibility unlike the MOF ([Zn(HCOO)3][C2H8N]) alone. Moreover, the [Zn(HCOO)3][C2H8N]/PEG has a moderate antimicrobial activity against E. coli under the irradiation of UV or the ambient light. However, N-CQDs@[Zn(HCOO)3][C2H8N]/PEG as the novel mixed-matrix membrane (MMM) has excellent antimicrobial activity without the presence of antibiotics which is due to its high quantum yield under the irradiation of UV light. Therefore, this research enhances the biocompatibility of [Zn(HCOO)3][C2H8N] through producing MMM. Furthermore, the antimicrobial activity of MOF/PEG was increased in terms of cost-effectiveness by encapsulating the N-CQDs.

Background: The widespread use of social networks among students seems to provide an appropriate opportunity to track the effects of the media on their educational capabilities. Objectives: We aimed to investigate the effect of virtual education (via Telegram messaging service during six months) on the knowledge of orthopedic residents of Mashhad University of Medical Sciences in the field of spine surgery. Methods: This quasi-experiment study was conducted on 25 orthopedic residents with an Mean±SD age of 31.4±4.1 years. During a 6-month training course, 85 educational spinal cases were presented in the form of riddles, video questions, or case presentations and were discussed among orthopedic residents. Before and after the intervention, a reliable and valid exam comprising 70 multiple choice questions about different topics of spinal surgery was taken to evaluate the level of students’ knowledge. We used the annual residency promotion exam to compare the correlation coefficients obtained by Fischer exact test, paired t test, and independent t test for statistical analysis. Results: The Mean±SD score of the assistants’ knowledge in the pretest and post-test exams were 42.8±6 and 45.2±9.2 (out of a total score of 70). Despite the obvious increase, the values were not statistically significant (P=0.09, t=-1.7). The intervention was associated with an increase in the level of knowledge in 17 residents (68%) while in 8 (32%), this 6-month virtual training course has had no significant effect. In this regard, age, marital status, housing, activity level, or level of residency (from the first to fourth year) had no significant relationship with knowledge improvement. Conclusion: virtual spinal education for six months using the social network of Telegram could improve the level of knowledge in most orthopedic residents, although the observed difference was not statistically significant.

Introduction: Osteoporosis falls among the major general health issues, specifically in the elderly, and is a widespread disease these days. According to various studies, good nutrition plays a significant role in osteoporosis prevention and treatment. The aim of this study was to conduct an extensive literature review on the effects of different nutrients to understand how macronutrients, micronutrients, and non-nutritive substances affect bone health. Methodology: To find relevant studies, the main keyword “osteoporosis” was searched in combination with “zinc,” “vitamin K,” “phosphorus,” “vitamin D,” “calcium,” “lipid,” “protein,” and “phytoestrogens” in PubMed (MEDLINE), Web of Science, SID, and Iran Medex databases. Findings: The most important element for bone health is calcium, which has a direct link to the bone mass density (BMD). In the case of calcium deficiency, high phosphorus content can damage bone tissue. The acceptable ratio of phosphorus to calcium is 0.5-1.5:1. Vitamin D is another important nutrient for bones; serum levels of vitamin D less than 20 ng/ml reduce bone density and increase the risk of fracture. High protein intake results in calcium excretion and loss of bone mass. In addition, calcium deficiency increases the risk of osteoporosis, specifically in the elderly. According to the literature, there is an inverse correlation between saturated fats and BMD. Vitamin K and magnesium deficiencies are correlated with BMD reduction and increased risk of osteoporosis. Copper and zinc are used as co-factors in the formation of collagen and elastin, and in mineralization of bone. As a result, deficiency of these elements may disrupt the process of incorporating minerals into the bone matrix. Conclusion: Good nutrition may play a significant role in osteoporosis prevention and treatment. Indeed, a healthy diet containing calcium (1,200 mg/day); vitamin D (600 IU); and certain amounts of protein, magnesium, and vitamin K can contribute greatly to bone health.