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ABSTRACTImmune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response.

Abstract Many concepts are defined by their relationships to one another. However, instructors might teach these concepts individually, neglecting their interconnections. For instance, students learning about statistical power might learn how to define alpha and beta, but not how they are related. We report two experiments that examine whether there is a benefit to training subjects on relations among concepts. In Experiment 1, all subjects studied material on statistical hypothesis testing, half were subsequently quizzed on relationships among these concepts, and the other half were quizzed on their individual definitions; quizzing was used to highlight the information that was being trained in each condition (i.e., relations or definitions). Experiment 2 also included a mixed training condition that quizzed both relations and definitions, and a control condition that only included study. Subjects were then tested on both types of questions and on three conceptually related question types. In Experiment 1, subjects trained on relations performed numerically better on relational test questions than subjects trained on definitions (nonsignificant trend), whereas definitional test questions showed the reverse pattern; no performance differences were found between the groups on the other question types. In Experiment 2, relational training benefitted performance on relational test questions and on some question types that were not quizzed, whereas definitional training only benefited performance on test questions on the trained definitions. In contrast, mixed training did not aid learning above and beyond studying. Relational training thus seems to facilitate transfer of learning, whereas definitional training seems to produce training specificity effects.

Methods for estimating heritability, the fraction of variance attributable to genetic factors, assume random mating. Here, the authors show that under assortative mating, when mate choice reflects phenotypic similarity, these methods produce overestimates.

The number of interconnected devices, often referred to as the Internet of Things (IoT), is increasing at a considerable rate. It is inevitable therefore that so too will the energy demand. IoT describes a range of technologies such as sensors, software, smart meters, wearable devices, and communication beacons for the purpose of connecting and exchanging data with other devices and systems over the internet. Often not located near a mains supply power source, these devices may be reliant on primary battery cells. To avoid the need to periodically replace these batteries, it makes sense to integrate the technologies with a photovoltaic (PV) cell to harvest ambient light, so that the technologies can be said to be self-powered. Perovskite solar cells have proven extremely efficient in low-light conditions but in the absence of ambient and low-light testing standards, or even a consensus on what is defined by “ambient light”, it is difficult to estimate the energy yield of a given PV technology in a given scenario. Ambient light harvesting is complex, subject to spectral considerations, and whether the light source is directly incident on the PV cell. Here, we present a realistic scenario-driven method for measuring the energy yield for a given PV technology in various situations in which an IoT device may be found. Furthermore, we show that laboratory-built p-i-n perovskite devices, for many scenarios, produce energy yields close to that of commercial GaAs solar cells. Finally, we demonstrate an IoT device, powered by a mesoporous carbon perovskite solar module and supercapacitor, and operating through several day–night cycles.

Exposure therapy — exposure to a feared stimulus without harmful consequences — can reduce fear responses in many mental disorders. However, such relief is often partial and temporary: fear can return after the therapy has ended. Conditioning research has identified three mechanisms for the return of fear, viz. change in physical context (renewal), the passage of time (spontaneous recovery), and an encounter with the fear-producing unconditioned stimulus (reinstatement). To understand why fear returns and thereby develop more effective therapies, we develop mathematical learning models based on that of Rescorla and Wagner. According to this model, context cues present during extinction become conditioned inhibitors (i.e. safety signals) which prevent total erasure of the threat association. Adding various mechanisms to the model allows it to explain different facets of the return of fear. Among these mechanisms is decay of inhibitory associations, which provides a novel explanation for spontaneous recovery. To make the benefits of exposure robust and permanent, one must minimize the degree to which the extinction context becomes inhibitory in order to maximize unlearning. We simulate several experimental paradigms that reduce the return of fear and explain them according to this principle.

Abstract Background Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 ′ and 5 ′ ends of a gene that enable reinitiation of transcription upon termination. Results Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment. Conclusions Interactions between the 3 ′ and 5 ′ ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.

Noisy gene expression leading to phenotypic variability can help organisms to survive in changing environments. Here, Patange et al. show that noisy expression of a stress response regulator, RpoS, allows E. coli cells to modulate their growth rates to survive future adverse environments.

Introduction: Point-of-care ultrasound (POCUS) education during medical school develops physicians who are properly prepared for the next generation of medicine. The authors present the case of a first-year medical student who self-diagnosed appendicitis using POCUS. Case Report: A 25-year-old, first-year medical student presented to the emergency department with lower abdominal pain. What seemed like a straightforward appendicitis presentation came with a twist; the student brought self-performed ultrasound imaging of his appendix. Conclusion: The student’s ultrasound skill set reflects favorably on the rapid evolution of ultrasound teaching in medical education.

GeoLink has leveraged linked data principles to create a dataset that allows users to seamlessly query and reason over some of the most prominent geoscience metadata repositories in the United States. The GeoLink dataset includes such diverse information as port calls made by oceanographic cruises, physical sample metadata, research project funding and staffing, and authorship of technical reports. The data has been published according to best practices for linked data and is publicly available via a SPARQL Protocol and RDF Query Language (SPARQL) end point that at present contains more than 45 million Resource Description Framework (RDF) triples together with a collection of ontologies and geo-visualization tools. This article describes the geoscience datasets, the modeling and publication process, and current uses of the dataset. The focus is on providing enough detail to enable researchers, application developers and others who wish to leverage the GeoLink data in their own work to do so. The dataset is available at http://hdl.handle.net/1912/9524.

Introduction: Transesophageal echocardiography (TEE) is a well-established method of evaluating cardiac pathology. It has many advantages over transthoracic echocardiography (TTE), including the ability to image the heart during active cardiopulmonary resuscitation. This prospective simulation study aims to evaluate the ability of emergency medicine (EM) residents to learn TEE image acquisition techniques and demonstrate those techniques to identify common pathologic causes of cardiac arrest. Methods: This was a prospective educational cohort study with 40 EM residents from two participating academic medical centers who underwent an educational model and testing protocol. All participants were tested across six cases, including two normals, pericardial tamponade, acute myocardial infarction (MI), ventricular fibrillation (VF), and asystole presented in random order. Primary endpoints were correct identification of the cardiac pathology, if any, and time to sonographic diagnosis. Calculated endpoints included sensitivity, specificity, and positive and negative predictive values for emergency physician (EP)-performed TEE. We calculated a kappa statistic to determine the degree of inter-rater reliability. Results: Forty EM residents completed both the educational module and testing protocol. This resulted in a total of 80 normal TEE studies and 160 pathologic TEE studies. Our calculations for the ability to diagnose life-threatening cardiac pathology by EPs in a high-fidelity TEE simulation resulted in a sensitivity of 98%, specificity of 99%, positive likelihood ratio of 78.0, and negative likelihood ratio of 0.025. The average time to diagnose each objective structured clinical examination case was as follows: normal A in 35 seconds, normal B in 31 seconds, asystole in 13 seconds, tamponade in 14 seconds, acute MI in 22 seconds, and VF in 12 seconds. Inter-rater reliability between participants was extremely high, resulting in a kappa coefficient across all cases of 0.95. Conclusion: EM residents can rapidly perform TEE studies in a simulated cardiac arrest environment with a high degree of precision and accuracy. Performance of TEE studies on human patients in cardiac arrest is the next logical step to determine if our simulation data hold true in clinical practice.