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Computer vision has emerged as a powerful tool to elevate behavioral research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics.

Most social species self-organize into dominance hierarchies1,2, which decreases aggression and conserves energy3,4, but it is not clear how individuals know their social rank. We have only begun to learn how the brain represents social rank5-9 and guides behaviour on the basis of this representation. The medial prefrontal cortex (mPFC) is involved in social dominance in rodents7,8 and humans10,11. Yet, precisely how the mPFC encodes relative social rank and which circuits mediate this computation is not known. We developed a social competition assay in which mice compete for rewards, as well as a computer vision tool (AlphaTracker) to track multiple, unmarked animals. A hidden Markov model combined with generalized linear models was able to decode social competition behaviour from mPFC ensemble activity. Population dynamics in the mPFC predicted social rank and competitive success. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus promote dominance behaviour during reward competition. Thus, we reveal a cortico-hypothalamic circuit by which the mPFC exerts top-down modulation of social dominance.

BackgroundIncreasingly, non-English speaking countries use English as the medium of instruction (EMI) to teach academic subjects. This study investigated the challenges and adaptation strategies of teachers and students in an EMI medical education program in China.MethodsData were collected on EMI and non-EMI students' test performances and student and teacher perceptions of the program. Test scores and survey results were analyzed using SPSS. Focus group transcripts and open-ended comments from surveys were examined using thematic coding.ResultsThere were no significant differences in admission and graduation test scores for EMI and non-EMI students. Four challenges with the EMI program were identified: (1) insufficient/inappropriate teaching materials, (2) unsatisfactory teaching, (3) inadequate class interactions, and (4) failure to teach medical humanities. To address these challenges, teachers and students used adaptive strategies, such as the use of alternative textbooks, self-learning skills and Chinese language.ConclusionsEMI programs are difficult to initiate, requiring faculty development and institutional support, and student self and peer group learning strategies to be successful. The adaptive strategies employed by both students and teachers offer insights into how other EMI programs might strengthen their implementation.

Background: Numerous studies have examined whether vitamin D is associated with gestational diabetes mellitus (GDM). Nevertheless, it is still challenging to determine the causality, due to a number of shortcomings in observational research and randomized controlled trials. Objective: Mendelian randomization (MR) with two samples was conducted to investigate the potential causative association between 25-hydroxyvitamin D (25(OH)D), vitamin D binding protein (VDBP) and GDM risk. Methods: Publicly accessible summary data from independent cohorts were used for two-sample MR. For 25(OH)D, we obtained data from UK Biobank, IEU and EBI, then performed a meta-analysis to enhance the statistical power (via METAL); for VDBP, data were obtained from the INTERVAL study; for GDM, data were obtained from FinnGen. The inverse variance weighted (IVW) approach was performed as the main analysis, together with several sensitivity analyses, such as MR–Egger, maximum likelihood, weighted median, and weighted mode. Results: The IVW results revealed a weak negative causal connection between 25(OH)D and GDM risk [OR (95% CI) = 0.71 (0.50, 0.99), p = 0.046]. However, the causal association was unstable according to sensitivity analyses, and Cochran's Q test revealed significant heterogeneity. After removing BMI-related IVs, the causal association between 25(OH)D and GDM disappeared [OR (95% CI) = 0.76 (0.55, 1.06), p = 0.101]. In addition, our study found no proof to support the assumption that VDBP level was related to GDM risk causally [OR (95% CI) = 0.98 (0.93, 1.03), p = 0.408]. Conclusions: According to this study, a weak negative causal association between 25(OH)D and GDM risk was found, while we had little proof to support the link between VDBP and GDM. To further explore whether total or free 25(OH)D levels and GDM are causally related, GWAS data with an emphasis on women of reproductive age and other ethnic groups are required. [ABSTRACT FROM AUTHOR]

While global streamflow reanalysis has been evaluated at different spatial scales to facilitate practical applications, its local performance in the time–frequency domain is yet to be investigated. This paper presents a novel decomposition approach to evaluating streamflow reanalysis by combining wavelet transform with machine learning. Specifically, the time series of streamflow reanalysis and observation are respectively decomposed and then the approximation components of reanalysis are evaluated against those of observed streamflow. Furthermore, the accumulated local effects are derived to showcase the influences of catchment attributes on the performance of streamflow reanalysis at different scales. For streamflow reanalysis generated by the Global Flood Awareness System, a case study is devised based on streamflow observations from the Catchment Attributes and Meteorology for Large-sample Studies. The results highlight that the reanalysis tends to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. The Kling–Gupta efficiency (KGE) values of original time series and approximation components are primarily influenced by precipitation seasonality. High values of KGE tend to be observed in catchments where there is more precipitation in winter, which can be due to low evaporation that results in reasonable simulations of soil moisture and baseflow processes. The longitude, mean precipitation and mean slope also influence the local performance of approximation components. On the other hand, attributes on geology, soils and vegetation appear to play a relatively small part in the performance of approximation components. Overall, this paper provides useful information for practical applications of global streamflow reanalysis. [ABSTRACT FROM AUTHOR]

Conductive hydrogels have been promising candidates for wearable and flexible electronics due to their high flexibility and biocompatibility. However, the previously reported hydrogels with conductivity over 1000 S m−1 usually have poor mechanical properties including low tensile stress (<5 MPa) and toughness (<2 MJ m−3). Here, a liquid metal‐doped polyvinyl alcohol (PVA‐LM) hydrogel is presented, which simultaneously combines ultra‐high conductivity (maximum of 217 895 S m−1) with excellent mechanical properties, including high tensile stress (15.44 MPa), large tensile strain (704%), high toughness (43.02 MJ m−3) and excellent fatigue resistance. Such extremely high conductivity is afforded by self‐sintering behavior of LM at the bottom surface that enables the formation of conductive networks. The formation of polymer crystalline regions and polymer‐tannic acid multiple hydrogen bonds are responsible for the impressive mechanical properties of conductive hydrogels. Particularly, the electric LM filler could be recycled in the robust hydrogel by dissociation of multiple dynamic interactions. Most importantly, wearable electrodes and capacitive sensors are developed utilizing PVA‐LM hydrogel. These devices enable accurate monitoring of bioelectrical signals and human motions, highlighting their immense potential in the realm of soft electronics and wearable technology. [ABSTRACT FROM AUTHOR]