Miracle-dz/UniFAR-FTUs huggingface dataset:
Dominant carnivore loss benefits native avian and invasive mammalian scavengers

Scavenging by large carnivores is integral for ecosystem functioning by limiting the build-up of carrion and facilitating widespread energy flows. However, top carnivores have declined across the world, triggering trophic shifts within ecosystems. In this study, we use a natural ‘removal experiment’ of disease-driven decline and island extirpation of native mammalian (marsupial) carnivores to investigate top-down control on utilisation of experimentally placed carcasses by two mesoscavengers – the invasive feral cat and native forest raven. Ravens were the main beneficiary of carnivore loss, scavenging for five times longer in the absence of native mammalian carnivores. Cats scavenged on almost half of all carcasses in the region without dominant native carnivores. This was eight times more than in areas where other carnivores were at high densities. In the absence of native mammalian carnivores, all carcasses persisted in the environment for 3 weeks. Our results reveal the efficiency of carrion consumption by mammalian scavengers. These services are not readily replaced by less-efficient facultative scavengers. This demonstrates the significance of global carnivore conservation and supports novel management approaches, such as rewilding in areas where the natural suite of carnivores is missing.
An unconventional GABAergic circuit differently controls pyramidal neuron activity in two visual cortical areas via endocannabinoids

Perisomatic inhibition of neocortical pyramidal neurons (PNs) coordinates cortical network activity during sensory processing, and it has been mainly attributed to parvalbumin-expressing basket cells (BCs). However, cannabinoid receptor type 1 (CB1)-expressing interneurons also inhibit the perisomatic region of PNs but the connectivity and function of these elusive – yet prominent – neocortical GABAergic cells is unknown. We found that the connectivity pattern of CB1-positive BCs strongly differs between primary and high-order cortical visual areas. Moreover, persistently active CB1 signaling suppresses GABA release from CB1 BCs in the medial secondary visual cortex (V2M), but not in the primary (V1) visual area. Accordingly, in vivo, tonic CB1 signaling is responsible for higher but less coordinated PN activity in V2M than in V1. Our results indicate a differential CB1-mediated mechanism controlling PN activity, and suggest an alternative connectivity schemes of a specific GABAergic circuit in different cortical areas
Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells

Summary Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689–0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC and its local version software were developed to predict potential anti-BC agents. Availability ChemBC webserver is available at http://chembc.idruglab. cn/ and its local version Python software is maintained at a GitHub repository (https://github.com/idruglab/ChemBC). Contact zjqgmc@163.com or lingwang@scut.edu. cn Supplementary information Supplementary data are available at Bioinformatics online.
TTYH family members form tetrameric complexes within the cell membrane

The conserved Tweety homolog (TTYH) family consists of three paralogs in vertebrates, displaying a ubiquitous expression pattern. Although considered as ion channels for almost two decades, recent structural and functional analyses refuted this role. Intriguingly, while all paralogs, studied following detergent solubilization, shared a dimeric stoichiometry, their spatial organization differed. Here, we determined the stoichiometry of intact mouse TTYH (mTTYH) complexes in cells. Using cross-linking and single-molecule fluorescence microscopy, we demonstrated that mTTYH1 and mTTYH3 form tetramers at the plasma membrane. Blue-native PAGE and fluorescence-detection size-exclusion chromatography analyses revealed that detergent solubilization results in the dissolution of tetramers into dimers, suggesting a dimer-of-dimers assembly mode. As cross-linking analysis of the soluble extracellular domains also showed tetrameric stoichiometry, we explored the effect of membrane solubilization and disulfide bridges integrity and established their contribution to tetramer stability. Future studies of the native tetrameric TTYH characterized here may illuminate their long-sought cellular function.
Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population

Background A strong predictor for the development of alcohol use disorders (AUDs) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool for elucidating the genetic basis of behavioral and physiological traits relevant to AUDs; but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations allow for the opportunity to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. Methods We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high density MEGAMuga and GIGAMuga arrays to obtain genotypes ranging from 77,808 – 143,259 SNPs. In addition, we performed RNA sequencing in striatum to map expression QTLs and to identify gene expression-trait correlations. Results We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exist between DNA sequence, gene expression values and ethanol-related phenotypes to prioritize our list of positional candidate genes. Conclusions Our results can be used to identify alleles that contribute to AUDs in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.
Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson’s Disease Using a Wrist-Worn Accelerometer

Introduction Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefit on symptoms including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. Here, we investigate the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single wrist-accelerometer. Methods As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist-accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the whole one-hour recordings, and medication-state classification analyses were performed on one-minute segments of data. The influence of individual versus group model training, data window length, and total amount of training patients included in group model training on classification was analyzed. Results Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, +/− respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 seconds, and with additional training patient datasets. Conclusion Medication induced fluctuations in bradykinesia can be classified using wrist worn accelerometery at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has important clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
Non-Gaussian, transiently anomalous and ergodic self-diffusion of flexible dumbbells in crowded two-dimensional environments: coupled translational and rotational motions

We employ Langevin-dynamics simulations to unveil non-Brownian and non-Gaussian center-of-mass self-diffusion of massive flexible dumbbell-shaped particles in crowded two-dimensional solutions. We also study the intra-dumbbell dynamics due to the relative motion of the two constituent elastically-coupled disks. Our main focus is on effects of the crowding fraction ϕ and the particle structure on the diffusion characteristics. We evaluate the time-averaged mean-squared displacement (TAMSD), the displacement probability-density function (PDF) and the displacement autocorrelation function (ACF) of the dimers. For the TAMSD at highly crowded conditions of dumbbells, e.g., we observe a transition from the short-time ballistic behavior, via an intermediate subdiffusive regime, to long-time Brownian-like spreading dynamics. The crowded system of dimers exhibits two distinct diffusion regimes distinguished by the scaling exponent of the TAMSD, the dependence of the diffusivity on ϕ, and the features of the displacement-ACF. We attribute these regimes to a crowding-induced transition from a viscous to a viscoelastic diffusion medium upon growing ϕ. We also analyze the relative motion in the dimers, finding that larger ϕ suppress their vibrations and yield strongly non-Gaussian PDFs of rotational displacements. For the diffusion coefficients D(ϕ) of translational and rotational motion of the dumbbells an exponential decay with ϕ for weak and a power-law D(ϕ) ∝ (ϕ – ϕ⋆)2.4 for strong crowding is found. A comparison of simulation results with theoretical predictions for D(ϕ) is discussed and some relevant experimental systems are overviewed.
Aryl hydrocarbon receptor and TGF-β inducible early gene mediate a transcriptional axis modulating immune homeostasis in mosquitoes

Immune homeostasis ensures effective pathogen defense and avoids overactivity, which is achieved through an orchestrated transcriptional network. Here we demonstrate that mosquito AhR and TIEG mediate a transcriptional axis to modulate the immune response. The AhR agonist compromised the immunity with reduced survival upon the challenge with bacterium Serratia fonticola, while the AhR antagonists enhanced the immunity with increased survival. The phenotype of pharmacological immune enhancement was corroborated genetically by the AhR gene silencing. The transcriptome comparison following AhR manipulations highlighted a set of AhR regulated genes, from which transcription factor TIEG, the ortholog of Krüppel-like factor 10, was chosen for further study. TIEG was required for the AhR mediated immune modulation. Silencing TIEG increased survival and reversed the immune suppression mediated by agonist-activated AhR. Among the transcriptomes, there were genes sharing co-expression patterns in the cohorts with AhR manipulation pharmacologically or genetically. Moreover, the mosquitoes with silenced TIEG and AhR shared ~68% altered genes upon infection. Together, the data suggest TIEG is downstream of AhR, acting as a major transcription factor mediating the immune modulation. The TIEG targets include genes involved in sugar metabolism and circadian rhythms, both processes are critical for immune homeostasis. In the naïve mosquitoes, the AhR-TIEG axis prevents the adverse effect of the overactivated IMD pathway created by silencing the inhibitor Caspar. In summary, AhR and TIEG constitute a transcriptional axis that mediates a gene network critical for maintaining immune homeostasis. Significance Immune homeostasis is sustained by various parameters involving different transcriptional regulatory networks. Such knowledge in mosquitoes remains scarce. Here, using AhR manipulation and transcriptome interrogation, we demonstrate that AhR and TIEG (a KLF10 ortholog) constitute a transcriptional axis to mediate immune modulation using an antibacterial immune model in the malaria vector Anopheles gambiae. AhR is a ligand-activated transcription factor that senses environmental signals and transcribes relevant genes to modulate immune responses. TIEG/KLF10, conserved from invertebrates to mammals, mediates various transcriptional networks. Our data show that the AhR-TIEG axis controls the genes involving in sugar sensing and circadian rhythms in the infection context. This finding warrants further study to elucidate the transcriptional control of metabolic and circadian behind immune homeostasis.
Developing New Online Course Accessibility Services for Faculty through Collaboration between Librarians and Campus Departments: A Case Study

Abstract Literature has shown that inaccessible content is a barrier to student success and an impediment to student retention. Despite legal obligations for accessible course content, creators of course materials are often unaware of the benefits of improved accessibility and their personal liability. To address these accessibility issues, a partnership was developed between the library and two campus departments to create a formal, campus wide accessibility service that would make all online course content fully accessible on Day 1, through design initiatives rather than having faculty wait for an accommodation request, to foster student success and support faculty course development.
Conformal Design of a High-Performance Antenna for Energy-Autonomous UWB Communication

In view of the need for communication with distributed sensors/items, this paper presents the design of a single-port antenna with dual-mode operation, representing the front-end of a future generation tag acting as a position sensor, with identification and energy harvesting capabilities. An Archimedean spiral covers the lower European Ultra-Wideband (UWB) frequency range for communication/localization purposes, whereas a non-standard dipole operates in the Ultra High Frequency (UHF) band to wirelessly receive the energy. The versatility of the antenna is guaranteed by the inclusion of a High Impedance Surface (HIS) back layer, which is responsible for the low-profile stack-up and the insensitivity to the background material. A conformal design, supported by 3D-printing technology, is pursued to check the versatility of the proposed architecture in view of any application involving its deformation and tracking/powering operations.
Brain-Computer Interface: Advancement and Challenges

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.
Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing

Attempts in digital management of structures are among the most popular topics in the trend of Information of Things (IoT). However, the implementation lags behind. This work recognized that Computer Aided Design (CAD) comprises the core of modern engineering; thus, most digital information can be available if CAD is used not only in design but also for life cycle structural health monitoring (SHM). Based on this concept, the newly designed method utilizes the isogeometric analysis (IGA) tool to include the Distributed Fiber Optic Sensing (DFOS) information by proposing a fiber mesh model. The IGA model can be obtained directly from CAD, and the boundary conditions can be provided directly or indirectly from DFOS in real time and remotely. Hence a practical method of SHM is able to achieve highly efficient and accurate numerical model creation, which can even accommodate non-linear constitutive property of materials. The proposed method was applied to a pipe deformation model as an example. The inverse analysis method is also shown to determine the contact force for loading on the pipe, which shows the potential for many engineering applications.
Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis, psoriasis, or skin lesions. We collected a database of 631 images with healthy skin and different skin pathologies to train and assess all stages of the methodology. The proposed framework starts with the segmentation of the epidermal layer using a DeepLab v3+ model with a pre-trained Xception backbone. We employ transfer learning to train the segmentation model for two purposes: to extract the region of interest for classification and to prepare the skin layer map for classification confidence estimation. For classification, we train five models in different input data modes and data augmentation setups. We also introduce a classification confidence level to evaluate the deep model’s reliability. The measure combines our skin layer map with the heatmap produced by the Grad-CAM technique designed to indicate image regions used by the deep model to make a classification decision. Moreover, we propose a multicriteria model evaluation measure to select the optimal model in terms of classification accuracy, confidence, and test dataset size. The experiments described in the paper show that the DenseNet-201 model fed with the extracted region of interest produces the most reliable and accurate results.
Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.