Valley-dependent tunneling through electrostatically created quantum dots in heterostructures of graphene with hexagonal boron nitride

Kelvin probe force microscopy (KPFM) has been employed to probe charge carriers in a graphene/hexagonal boron nitride (hBN) heterostructure [Nano Lett, 21, 5013 (2021)]. We propose an approach for operating valley filtering based on the KPFM-induced potential $U_0$ instead of using external or induced pseudo-magnetic fields in strained graphene. Employing a tight-binding model, we investigate the parameters and rules leading to valley filtering in the presence of a graphene quantum dot (GQD) created by the KPFM tip. This model leads to a resolution of different transport channels in reciprocal space, where the electron transmission probability at each Dirac cone ($K_1$= -K and $K_2$ = +K) is evaluated separately. The results show that U0 and the Fermi energy $E_F$ control (or invert) the valley polarization, if electrons are allowed to flow through a given valley. The resulting valley filtering is allowed only if the signs of $E_F$ and $U_0$ are the same. If they are different, the valley filtering is destroyed and might occur only at some resonant states affected by $U_0$. Additionally, there are independent valley modes characterizing the conductance oscillations near the vicinity of the resonances, whose strength increases with $U_0$ and are similar to those occurring in resonant tunneling in quantum antidots and to the Fabry-Perot oscillations. Using KPFM, to probe the charge carriers, and graphene-based structures to control valley transport, provides an efficient way for attaining valley filtering without involving external or pseudo-magnetic fields as in previous proposals

Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils.

Bioaugmentation aims to use the capacities of specific bacterial strains inoculated into sites to enhance pollutant biodegradation. Bioaugmentation results have been mixed, which has been attributed to poor inoculant growth and survival in the field, and, consequently, moderate catalytic performance. However, our understanding of biodegradation activity mostly comes from experiments conducted under laboratory conditions, and the processes occurring during adaptation and invasion of inoculants into complex environmental microbiomes remain poorly known. The main aim of this work was thus to study the specific and different cellular reactions of an inoculant for bioaugmentation during adaptation, growth and survival in natural clean and contaminated non-sterile soils, in order to better understand factors limiting bioaugmentation. As inoculant we focused on the monoaromatic compound-degrading bacterium Pseudomonas veronii 1YdBTEX2. The strain proliferated in all but one soil types in presence and in absence of exogenously added toluene. RNAseq and differential genome-wide gene expression analysis illustrated both a range of common soil responses such as increased nutrient scavenging and recycling, expression of defense mechanisms, as well as environment-specific reactions, notably osmoprotection and metal homeostasis. The core metabolism of P. veronii remained remarkably constant during exponential growth irrespective of the environment, with slight changes in cofactor regeneration pathways, possibly needed for balancing defense reactions. P. veronii displayed a versatile global program, enabling it to adapt to a variety of soil environments in the presence and even in absence of its target pollutant toluene. Our results thus challenge the widely perceived dogma of poor survival and growth of exogenous inoculants in complex microbial ecosystems such as soil and provide a further basis to developing successful bioaugmentation strategies

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from <sup>14</sup> C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems

Immuno-Transcriptomic Profiling of Blood and Tumor Tissue Identifies Gene Signatures Associated with Immunotherapy Response in Metastatic Bladder Cancer.

Blood-based biomarkers represent ideal candidates for the development of non-invasive immuno-oncology-based assays. However, to date, no blood biomarker has been validated to predict clinical responses to immunotherapy. In this study, we used next-generation sequencing (RNAseq) on bulk RNA extracted from whole blood and tumor samples in a pre-clinical MIBC mouse model. We aimed to identify biomarkers associated with immunotherapy response and assess the potential application of simple non-invasive blood biomarkers as a therapeutic decision-making assay compared to tissue-based biomarkers. We established that circulating immune cells and the tumor microenvironment (TME) display highly organ-specific transcriptional responses to ICIs. Interestingly, in both, a common lymphocytic activation signature can be identified associated with the efficient response to immunotherapy, including a blood-specific CD8+ T cell activation/proliferation signature which predicts the immunotherapy response

Acute Effects of Moderate versus High-Intensity Strength Exercise on Attention and Mood States in Female Physical Education Students

The presumed benefits of exercise/physical activity on the brain are an important public health issue. However, the experimental approach to understanding the effects of physical activity on the brain, and more particularly on cognitive functions, has only been studied recently. In particular, females remain underrepresented in the research, despite having a specific training/exercise adaptation/response. The aim of the present study was to examine the acute effects of high- and moderate-intensity strength exercise (3 sets of 8–10 repetitions and 3 sets of 6 repetitions, respectively, with each session lasting approximately 30 min) on attention and mood states in female physical education students. Forty-six female physical education students (Mage = 20.02 ± 1.05 years, MBody Mass Index = 21.07) volunteered to participate in this study. They were divided into three groups: a moderate-intensity strength exercise group (MISEG: n = 15), a high-intensity strength exercise group (HISEG: n = 16), and a control group (CG: n = 15). Attention and psychological states were assessed using the d2 test, Rating of Perceived Exertion (RPE) and the Brunel Mood Scale (BRUMS) questionnaire, respectively, before and after each session. The data showed that in the MISEG attention increased, in terms of concentration (p = 0.05). RPE values, fatigue and confusion were higher for the HISEG than the CG (p < 0.05) and the MISEG (p < 0.05). Vigour was higher for the MISEG than other groups (p < 0.05). In conclusion, moderate-intensity resistance exercise is an appropriate method to improve attention in female participants. The elevated cognitive performance may be due to the changes in RPE and mood states (fatigue, vigour and confusion subscales)

Molecular epidemiology of hcv among health care workers of khyber pakhtunkhwa

<p>Abstract</p> <p>Background</p> <p>Studies of the molecular epidemiology and risk factors for hepatitis C virus (HCV) in health care workers (HCWs) of Peshawar, Khyber Pakhtunkhwa region are scarce. Lack of awareness about the transmission of HCV and regular blood screening is contributing a great deal towards the spread of hepatitis C. This study is an attempt to investigate the prevalence of HCV and its possible association with both occupational and non-occupational risk factors among the HCWs of Peshawar.</p> <p>Results</p> <p>Blood samples of 824 HCWs, aged between 20-59 years were analysed for anti-HCV antibodies, HCV RNA and HCV genotypes by Immunochromatographic tests and PCR. All relevant information was obtained from the HCWs with the help of a questionnaire. The study revealed that 4.13% of the HCWs were positive for HCV antibodies, while HCV RNA was detected in 2.79% of the individuals. The most predominant HCV genotype was 3a and 2a.</p> <p>Conclusion</p> <p>A program for education about occupational risk factors and regular blood screening must be implemented in all healthcare setups of Khyber Pakhtunkhwa province in order to help reduce the burden of HCV infection.</p

Effects of different lower-limb sensory stimulation strategies on postural regulation – A systematic review and meta-analysis

Systematic reviews of balance control have tended to only focus on the effects of single lower-limb stimulation strategies, and a current limitation is the lack of comparison between different relevant stimulation strategies. The aim of this systematic review and meta-analysis was to examine evidence of effects of different lower-limb sensory stimulation strategies on postural regulation and stability. Moderate- to high- pooled effect sizes (Unbiased (Hedges’ g) standardized mean differences (SMD) = 0.31 – 0.66) were observed with the addition of noise in a Stochastic Resonance Stimulation Strategy (SRSS), in three populations (i.e., healthy young adults, older adults, and individuals with lower-limb injuries), and under different task constraints (i.e., unipedal, bipedal, and eyes open). A Textured Material Stimulation Strategy (TMSS) enhanced postural control in the most challenging condition – eyes-closed on a stable surface (SMD = 0.61), and in older adults (SMD = 0.30). The Wearable Garments Stimulation Strategy (WGSS) showed no or adverse effects (SMD = -0.68 – 0.05) under all task constraints and in all populations, except in individuals with lower-limb injuries (SMD = 0.20). Results of our systematic review and meta-analysis revealed that future research could consider combining two or more stimulation strategies in intervention treatments for postural regulation and balance problems, depending on individual need

Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance

Tumour brain: pre‐treatment cognitive and affective disorders caused by peripheral cancers

People that develop extracranial cancers often display co-morbid neurological disorders, such as anxiety, depression and cognitive impairment, even before commencement of chemotherapy. This suggests bidirectional crosstalk between non-CNS tumours and the brain, which can regulate peripheral tumour growth. However, the reciprocal neurological effects of tumour progression on brain homeostasis are not well understood. Here, we review brain regions involved in regulating peripheral tumour development and how they, in turn, are adversely affected by advancing tumour burden. Tumour-induced activation of the immune system, blood–brain barrier breakdown and chronic neuroinflammation can lead to circadian rhythm dysfunction, sleep disturbances, aberrant glucocorticoid production, decreased hippocampal neurogenesis and dysregulation of neural network activity, resulting in depression and memory impairments. Given that cancer-related cognitive impairment diminishes patient quality of life, reduces adherence to chemotherapy and worsens cancer prognosis, it is essential that more research is focused at understanding how peripheral tumours affect brain homeostasis