Search

Following the intense studies on topological insulators, significant efforts have recently been devoted to the search for gapless topological systems. These materials not only broaden the topological classification of matter but also provide a condensed matter realization of various relativistic particles and phenomena previously discussed mainly in high energy physics. Weyl semimetals host massless, chiral, low-energy excitations in the bulk electronic band structure, whereas a symmetry protected pair of Weyl fermions gives rise to massless Dirac fermions. We employed scanning tunneling microscopy/spectroscopy to explore the behavior of electronic states both on the surface and in the bulk of topological semimetal phases. By mapping the quasiparticle interference (QPI) and emerging Landau levels at high magnetic field in Dirac semimetals Cd _3 As _2 and Na _3 Bi, we observed extended Dirac-like bulk electronic bands. QPI imaged on Weyl semimetal TaAs demonstrated the predicted momentum dependent delocalization of Fermi arc surface states in the vicinity of the surface-projected Weyl nodes.

Precision magnetometry is fundamental to the development of novel magnetic materials and devices. Recently, the nitrogen-vacancy (NV) center in diamond has emerged as a promising probe for static magnetism in two-dimensional (2D) van der Waals materials, capable of quantitative imaging with nanoscale spatial resolution. However, the dynamic character of magnetism, crucial for understanding the magnetic phase transition and achieving technological applications, has rarely been experimentally accessible in single 2D crystals. Here, we coherently control the NV center’s spin precession to achieve ultrasensitive, quantitative ac susceptometry of a 2D ferromagnet. Combining dc hysteresis with ac susceptibility measurements varying temperature, field, and frequency, we illuminate the formation, mobility, and consolidation of magnetic domain walls in few-layer CrBr_{3}. We show that domain wall mobility is enhanced in ultrathin CrBr_{3}, with minimal decrease for excitation frequencies exceeding hundreds of kilohertz, and is influenced by the domain morphology and local pinning of the flake. Our technique extends NV magnetometry to the multifunctional ac and dc magnetic characterization of wide-ranging spintronic materials at the nanoscale.

Photocurrents are central to understanding and harnessing the interaction of light with matter. Here, we introduce a contact-free method to spatially resolve photocurrent distributions using proximal quantum magnetometers. We interface monolayer MoS_{2} with a near-surface ensemble of nitrogen-vacancy centers in diamond and map the generated photothermal current distribution through its magnetic-field profile. By synchronizing pulsed photoexcitation with dynamical decoupling of the sensor spin, we extend the sensor’s quantum coherence and resolve time-dependent, two-dimensional current densities as small as 20 nA/μm, with a projected sensitivity of 200 nA/(μmsqrt[Hz]). Our spatially resolved measurements reveal that optical excitation can generate micron-sized photocurrent vortices in MoS_{2}, manifesting a photo-Nernst effect exceeding that of gate-tuned graphene at comparable magnetic fields. We further probe the rise time of the photocurrents and show that thermal diffusion determines its spatial variation. These spatiotemporal capabilities establish an optically accessed, local probe for optoelectronic phenomena, ideally suited to the emerging class of two-dimensional materials, for which making contacts is challenging and can alter the intrinsic material properties.

Our systematic review aims to synthesise the evidence on interventions targeting improvement in patient adherence to psychological treatments for common mental disorders. A search was conducted on six electronic databases using search terms under the following concepts: common mental disorders, adherence, psychological treatments and controlled trial study design. Due to the heterogeneity in intervention content and outcomes evaluated in the included studies, a narrative synthesis was conducted. Risk of bias was assessed using the Cochrane Risk of Bias Version 2 tool for randomised controlled trials and the Cochrane ROBINS-I tool for non-randomised controlled trials. The search yielded 23 distinct studies with a total sample size of 2,779 participants. All studies were conducted in high-income or upper-middle-income countries. Interventions to improve patient adherence to psychological treatments included reminders and between-session engagement (e.g., text messages), motivational interviewing, therapy orientation (e.g., expectation-setting) and overcoming structural barriers (e.g., case management). Interventions from 18 out of 23 studies were successful in improving at least one primary adherence outcome of interest (e.g., session attendance). Some studies also reported an improvement in secondary outcomes - six studies reported an improvement in at least one clinical outcome (e.g., depression), and three studies reported improvements in at least one measure of well-being or disability (e.g., days spent in in-patient treatment). By incorporating these interventions into psychological treatment services, therapists can better engage with and support their patients, potentially leading to improved mental health outcomes and overall well-being., Competing Interests: The authors declare that they have no conflict of interest., (© The Author(s) 2024.)

Illicit drug use is a growing concern in India, with a high treatment gap of 73%. Explanatory models can provide valuable insights into the patient's conception of disease and inform help-seeking, treatment, and recovery. Of the studies that examine adolescent drug misuse in India, none have developed a socio-cultural explanatory model. The aim of our study was to develop an explanatory model to better understand the causal beliefs, social context, and self-perception of illicit drug misuse amongst adolescents in India. We conducted semi-structured interviews with 23 adolescents seeking treatment for drug use disorders and 25 healthcare providers recruited across three sites in India. Thematic analysis was used to analyse data. Most adolescent participants reported using multiple drugs, often in combination with cannabis. Frequent usage was reported i.e., daily at multiple times. Causes of initiation and continued use were peer influence, curiosity and pleasure, psychosocial stressors, family conditions, and systemic risk factors (e.g. socioeconomic instability). Drugs were acquired from various sources, including fellow users and pharmacies. Adolescent participants perceived negative impacts of drug use on physical and mental health, family relationships, and everyday functioning. Our findings on common reasons of drug use initiation, importance of peer relationships in continuation of use, impact of use on various aspects of life and the relationship of illicit drug use with socioeconomic status are consistent with previous research done in India on the subject. Understanding how adolescents and caregivers perceive drug use can help inform patient-clinician rapport, improve treatment compliance and understand intervention effectiveness. Such an explanatory model holds crucial implications for shaping interventions and clinical approaches to address adolescent drug use in India., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Biswal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design.

During the final step of the bacteriophage infection cycle, the cytoplasmic membrane of host cells is disrupted by small membrane proteins called holins. The function of holins in cell lysis is carried out by forming a highly ordered structure called lethal lesion, in which the accumulation of holins in the cytoplasmic membrane leads to the sudden opening of a hole in the middle of this oligomer. Previous studies showed that dimerization of holins is a necessary step to induce their higher order assembly. However, the molecular mechanism underlying the holin-mediated lesion formation is not well understood. In order to elucidate the functions of holin, we first computationally constructed a structural model for our testing system: the holin S105 from bacteriophage lambda. All atom molecular dynamic simulations were further applied to refine its structure and study its dynamics as well as interaction in lipid bilayer. Additional simulations on association between two holins provide supportive evidence to the argument that the C-terminal region of holin plays a critical role in regulating the dimerization. In detail, we found that the adhesion of specific nonpolar residues in transmembrane domain 3 (TMD3) in a polar environment serves as the driven force of dimerization. Our study therefore brings insights to the design of binding interfaces between holins, which can be potentially used to modulate the dynamics of lesion formation., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected inmammograms, can be useful riskmarkers associated with the disease. We investigate the feasibility of automated and accurate detection ofBACsinmammograms for risk assessment of coronary artery disease. We develop a 12-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixelwise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists. We evaluate the performance using a set of 840 full-field digital mammograms from 210 cases, using both free-responsereceiveroperatingcharacteristic (FROC) analysis and calcium mass quantification analysis. The FROC analysis shows that the deep learning approach achieves a level of detection similar to the human experts. The calcium mass quantification analysis shows that the inferred calcium mass is close to the ground truth, with a linear regression between them yielding a coefficient of determination of 96.24%. Taken together, these results suggest that deep learning can be used effectively to develop an automated system for BAC detection inmammograms to help identify and assess patients with cardiovascular risks.