The Rules of Human T Cell Fate in vivo.

The processes governing lymphocyte fate (division, differentiation, and death), are typically assumed to be independent of cell age. This assumption has been challenged by a series of elegant studies which clearly show that, for murine cells in vitro, lymphocyte fate is age-dependent and that younger cells (i.e., cells which have recently divided) are less likely to divide or die. Here we investigate whether the same rules determine human T cell fate in vivo. We combined data from in vivo stable isotope labeling in healthy humans with stochastic, agent-based mathematical modeling. We show firstly that the choice of model paradigm has a large impact on parameter estimates obtained using stable isotope labeling i.e., different models fitted to the same data can yield very different estimates of T cell lifespan. Secondly, we found no evidence in humans in vivo to support the model in which younger T cells are less likely to divide or die. This age-dependent model never provided the best description of isotope labeling; this was true for naïve and memory, CD4+ and CD8+ T cells. Furthermore, this age-dependent model also failed to predict an independent data set in which the link between division and death was explored using Annexin V and deuterated glucose. In contrast, the age-independent model provided the best description of both naïve and memory T cell dynamics and was also able to predict the independent dataset

Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis

Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82–86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity

Targeting Listeria monocytogenes consensus sequence of internalin genes using an antisense molecule

As an intracellular pathogen, Listeria monocytogenes can enter host cells where it can replicate and escape detection and eradication by the host immune response making the clearance of infection very challenging. Furthermore, with the advent of antimicrobial resistance, the need for alternative targets is inevitable. Internalin proteins are crucial to this bacterium as they contribute to bacterial entry to the systemic circulation. In this study, we targeted a highly conserved region of these proteins by an antisense sequence that was covalently conjugated to the cell penetrating peptides (CPP) to overcome the challenging delivery barriers. Then, we evaluated the efficiency of this construct in vitro. We also assessed the antigenicity, cytotoxicity, and probability of apoptosis induction by this construct. The studied CPP-PNA inhibited bacterial growth and suppressed the mRNA expression of internalins in a dose-dependent manner. In addition, at all studied concentrations, CPP-PNA significantly reduced the invasion rate of L. monocytogenes in the examined cell lines. Moreover, different concentrations of CPP-PNA did not have a significant antigenic, cytotoxic, and apoptotic properties compared to the control. These results suggest the effectiveness of CPP-antisense in targeting the mRNAs of internalins for various research, therapeutic and preventive purposes. However, additional research is required to evaluate the potency, safety, and pharmacokinetics of this compound for the prevention and treatment of listeriosis

Quantifying interactions in the water-energy-food nexus: data-driven analysis utilizing a causal inference method

Introduction: There is a pressing need for a holistic approach to optimize water-energy-food (WEF) resources management and to address their interlinkages with other resources due to population growth, socio-economic development, and climate change. However, the structural and spatial extent of the WEF system boundaries cause exponential growth in computational complexity, making exploratory data analysis crucial to obtain insight into the system’s characteristics and focus on critical components. Methods: This study conducts a multiscale investigation of the WEF nexus within the Canadian prairie provinces (Alberta, Saskatchewan, and Manitoba), utilizing causal-correlational analysis and the multispatial Convergence Cross Mapping (mCCM) method. Initially, we employed regression analysis to establish equations, along with their coefficients of determination (R2), to identify patterns among pairs of WEF sectors, gross domestic product (GDP), and greenhouse gas (GHG) emissions. Subsequently, we conducted a causal analysis between correlated pairs using the mCCM method to explore the cause-and-effect relationships between sector pairs within the Canadian prairie provinces; both individually and as a single unit over the period 1990-2020. Results and discussion: Results show that energy and water are the most influential sectors on GHG emissions and GDP in the prairies as a whole. Energy has a stronger influence on GHG compared to water and food sectors, while water has the strongest causal influence on the GDP of Alberta, and food and energy do so for Saskatchewan and Manitoba, respectively. The trade-offs for improving WEF nexus security strongly depend on the scale of the system under investigation, highlighting the need for careful deliberations around boundary judgment for decision-making. This study provides a better understanding of the WEF-GDP-GHG nexus in the Canadian prairies and existing interrelationships among the aforementioned sectors, helping to build more efficient WEF nexus models for further simulation and scenario analysis

The current and future burden of hepatitis B in Switzerland: a modelling study.

Chronic hepatitis B infection (defined as sustained detection of hepatitis B virus [HBV] surface antigen [HBsAg] protein in serum) is a leading cause of cirrhosis, hepatocellular carcinoma and liver-related deaths. A situation analysis carried out by the Swiss Federal Office of Public Health estimated the HBsAg prevalence in Switzerland to be 0.53% (95% CI: 0.32-0.89%) in 2015 (~44,000 cases). A lower prevalence of chronic HBV in the younger generation and the adoption of universal coverage in the first year of life are expected to decrease the burden of HBV; however, a number of people in key populations (including migrants) remain undiagnosed and untreated, and infected individuals remain at risk of progressing to cirrhosis, hepatocellular carcinoma and death. Our primary objective was to examine the current and estimate the future disease burden of HBV in Switzerland and the impact of migration. The secondary objective was to estimate the impact of changing future treatment numbers. A modelling study was performed using an existing, validated model (PRoGReSs Model) applied to the Swiss context. Model inputs were selected through a literature search and expert consensus. Population data from the Federal Statistical Office were used alongside prevalence data from the Polaris Observatory to estimate the number of HBV infections among people born abroad. The PRoGReSs Model was populated with and calibrated to the available data and what-if scenarios were developed to explore the impact of intervention on the future burden of disease. A Monte Carlo simulation was used to estimate 95% uncertainty intervals (95% UIs). In 2020, there were an estimated 50,100 (95% UI: 47,500-55,000) HBsAg+ cases among people born abroad. Among people born in Switzerland, there were approximately 62,700 (UI: 58,900-68,400) total HBV infections (0.72% [UI: 0.68-0.79%] prevalence). Prevalence among infants and children under the age of 5 were both <0.1%. By 2030, prevalence of HBV is expected to decrease, although morbidity and mortality will increase. Increasing diagnosis (90%) and treatment (80% of those eligible) to meet the global health sector strategy on viral hepatitis programme targets could prevent 120 cases of hepatocellular carcinoma and 120 liver-related deaths. Thanks to the historical vaccination programmes and the continued rollout of universal 3-dose coverage in the first year of life, Switzerland is expected to exceed the global health sector strategy targets for the reduction of incidence. While overall prevalence is decreasing, the current diagnosis and treatment levels remain below global health sector strategy targets

High magnetic field transport measurement of charge-ordered Pr0.5_{0.5}Ca0.5_{0.5}MnO3_3 strained thin films

We have investigated the magnetic-field-induced phase transition of charge-ordered (CO) Pr$_{0.5}$Ca$_{0.5}$MnO$_3$ thin films, deposited onto (100)-oriented LaAlO$_3$ and (100)-oriented SrTiO$_3$ substrates using the pulsed laser deposition technique, by measuring the transport properties with magnetic fields up to 22T. The transition to a metallic state is observed on both substrates by application of a critical magnetic field ($H_C>10T$ at 60K). The value of the field required to destroy the charge-ordered insulating state, lower than the bulk compound, depends on both the substrate and the thickness of the film. The difference of the critical magnetic field between the films and the bulk material is explained by the difference of in-plane parameters at low temperature (below the CO transition). Finally, these results confirm that the robustness of the CO state, depends mainly on the stress induced by the difference in the thermal dilatations between the film and the substrate.Comment: 10 pages, 6 figures. To be published in Phys. Rev.

Strong Suppression of Electrical Noise in Bilayer Graphene Nano Devices

Low-frequency 1/f noise is ubiquitous, and dominates the signal-to-noise performance in nanodevices. Here we investigate the noise characteristics of single-layer and bilayer graphene nano-devices, and uncover an unexpected 1/f noise behavior for bilayer devices. Graphene is a single layer of graphite, where carbon atoms form a 2D honeycomb lattice. Despite the similar composition, bilayer graphene (two graphene monolayers stacked in the natural graphite order) is a distinct 2D system with a different band structure and electrical properties. In graphene monolayers, the 1/f noise is found to follow Hooge's empirical relation with a noise parameter comparable to that of bulk semiconductors. However, this 1/f noise is strongly suppressed in bilayer graphene devices, and exhibits an unusual dependence on the carrier density, different from most other materials. The unexpected noise behavior in graphene bilayers is associated with its unique band structure that varies with the charge distribution among the two layers, resulting in an effective screening of potential fluctuations due to external impurity charges. The findings here point to exciting opportunities for graphene bilayers in low-noise applications