Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracellular vesicles and high‐density lipoproteins: Exercise and oestrogen‐responsive small RNA carriers

Decreased systemic estrogen levels (i.e., menopause) affect metabolic health. However, the detailed mechanisms underlying this process remain unclear. Both estrogens and exercise have been shown to improve metabolic health, which may be partly mediated by circulating microRNA (c-miR) signaling. In recent years, extracellular vesicles (EV) have increased interest in the field of tissue crosstalk. However, in many studies on EV-carried miRs, the co-isolation of high-density lipoprotein (HDL) particles with EVs has not been considered, potentially affecting the results. Here, we demonstrate that EV and HDL particles have distinct small RNA (sRNA) content, including both host and nonhost sRNAs. Exercise caused an acute increase in relative miR abundancy in EVs, whereas in HDL particles, it caused an increase in transfer RNA-derived sRNA. Furthermore, we demonstrate that estrogen deficiency caused by menopause blunts acute exercise-induced systemic miR-response in both EV and HDL particles. HIGHLIGHTS Extracellular vesicles and HDL particles have a distinct sRNA content Extracellular vesicles and HDL particles carry both host and nonhost sRNA cargo Estrogen deficiency blunts the c-miR-response induced by acute exercise Exercise responsive miRs in HT users may regulate the choice of energy substrate
Investigating relative contributions to psychiatric disease architecture from sequence elements originating across multiple evolutionary time-scales

It remains an open question when and why psychiatric and other disease evolved in human evolutionary history. We harness large genome-wide association studies and recent studies that have identified human-gained epigenetic marks, selective sweeps, archaic introgression, and accelerated evolution to help tackle this question by assessing the enrichment of heritability in these sequence elements for 41 complex traits. We find that human-gained epigenetic elements in the fetal brain harbor variants affecting skeletal, dermatological, and respiratory traits early and psychiatric traits later in development, that genomic sequences under selection since our divergence with Neanderthals are enriched in heritability for autism, and that regions depleted in ancestry from other hominids contribute more to heritability for psychiatric than other traits. Psychiatric disease may be a concomitant outcome of the evolutionary processes that made us uniquely human. One-Sentence Summary Variants that cause autism and other psychiatric disease today co-locate with sequence elements that have been important during human evolution.
An immunoPET probe to SARS-CoV-2 reveals early infection of the male genital tract in rhesus macaques

The systemic nature of SARS-CoV-2 infection is highly recognized, but poorly characterized. A non-invasive and unbiased method is needed to clarify whole body spatiotemporal dynamics of SARS-CoV-2 infection after transmission. We recently developed a probe based on the anti-SARS-CoV-2 spike antibody CR3022 to study SARS-CoV-2 pathogenesis in vivo. Herein, we describe its use in immunoPET to investigate SARS-CoV-2 infection of three rhesus macaques. Using PET/CT imaging of macaques at different times post-SARS-CoV-2 inoculation, we track the 64Cu-labelled CR3022-F(ab’)2 probe targeting the spike protein of SARS-CoV-2 to study the dynamics of infection within the respiratory tract and uncover novel sites of infection. Using this method, we uncovered differences in lung pathology between infection with the WA1 isolate and the delta variant, which were readily corroborated through computed tomography scans. The 64Cu-CR3022-probe also demonstrated dynamic changes occurring between 1- and 2-weeks post-infection. Remarkably, a robust signal was seen in the male genital tract (MGT) of all three animals studied. Infection of the MGT was validated by immunofluorescence imaging of infected cells in the testicular and penile tissue and severe pathology was observed in the testes of one animal at 2-weeks post-infection. The results presented here underscore the utility of using immunoPET to study the dynamics of SARS-CoV-2 infection to understand its pathogenicity and discover new anatomical sites of viral replication. We provide direct evidence for SARS-CoV-2 infection of the MGT in rhesus macaques revealing the possible pathologic outcomes of viral replication at these sites. Graphic Abstract PET/CT detected SARS-CoV-2 infection of 4 different tissues in the male genital tract illuminates the cause of COVID-19 clinical sequalae of male sexual health and fertility Figure 1. Diagram shows schematic illustration of the male genital tract of the rhesus macaque. Virus icon shows sites of SARS-CoV-2 PET signal. Text highlighting the clinical sequalae associated with each sight of infection is shown in text adjacent to each infection site.
New near-linear time decodable codes closer to the GV bound

We construct a family of binary codes of relative distance [EQUATION] and rate ε2 · 2−logα(1/ε) for α ≈ [EQUATION] that are decodable, probabilistically, in near-linear time. This improves upon the rate of the state-of-the-art near-linear time decoding near the GV bound due to Jeronimo, Srivastava, and Tulsiani, who gave a randomized decoding of Ta-Shma codes with [EQUATION] [34, 20]. Each code in our family can be constructed in probabilistic polynomial time, or deterministic polynomial time given sufficiently good explicit 3-uniform hypergraphs. Our construction is based on a new graph-based bias amplification method. While previous works start with some base code of relative distance [EQUATION] for ε0 > ε and amplify the distance to [EQUATION] by walking on an expander, or on a carefully tailored product of expanders, we walk over very sparse, highly mixing, hypergraphs. Study of such hypergraphs further offers an avenue toward achieving rate [EQUATION]. For our unique- and list-decoding algorithms, we employ the framework developed in [20].
Trading time and space in catalytic branching programs

An m-catalytic branching program (Girard, Koucký, McKenzie 2015) is a set of m distinct branching programs for f which are permitted to share internal (i.e. non-source non-sink) nodes. While originally introduced as a non-uniform analogue to catalytic space, this also gives a natural notion of amortized non-uniform space complexity for f, namely the smallest value |G|/m for an m-catalytic branching program G for f (Potechin 2017). Potechin (2017) showed that every function f has amortized size O(n), witnessed by an m-catalytic branching program where m = 22n−1. We recreate this result by defining a catalytic algorithm for evaluating polynomials using a large amount of space but O(n) time. This allows us to balance this with previously known algorithms which are efficient with respect to space at the cost of time (Cook, Mertz 2020, 2021). We show that for any ϵ ≥ 2n−1, every function f has an m-catalytic branching program of size Oϵ(mn), where m = 22ϵn. We similarly recreate an improved result due to Robere and Zuiddam (2021), and show that for d ≤ n and ϵ ≥ 2d−1, the same result holds for m = 2(≤nϵd) as long as f is a degree-d polynomial over F2. We also show that for certain classes of functions, m can be reduced to 2poly n while still maintaining linear or quasi-linear amortized size. In the other direction, we bound the necessary length, and by extension the amortized size, of any permutation branching program for an arbitrary function between 3n and 4n − 4.
Transformers for EEG-Based Emotion Recognition: A Hierarchical Spatial Information Learning Model

The spatial information of Electroencephalography (EEG) is essential for emotion recognition model to learn discriminative feature. The convolutional networks and recurrent networks are the conventional choices to learn the complex spatial dependencies through a number of electrodes and brain regions. However, these models have difficulty in capturing long-range dependencies due to the operations of local feature learning. To enhance EEG spatial dependencies capturing and improve the accuracy of emotion recognition, we propose a transformer- based model to hierarchically learn the discriminative spatial information from electrode level to brain-region-level. In the electrode-level spatial learning, the transformer encoders are adopted to integrate information within different brain regions. Next, in view of the different roles of brain regions in the emotion recognition, the self-attention within the transformer could emphasize the contributive brain regions. Hence, in the brain-region-level spatial learning, a transformer encoder is utilized to capture the spatial dependencies among the brain regions. Finally, to validate the effectiveness of the proposed model, the subject-independent experiments are conducted on the DEAP and MAHNOB-HCI database. The experimental results demonstrate that the proposed model achieves outstanding performance in emotion recognition with arousal and valence level. Moreover, the visualization of self-attention indicates that the proposed model could emphasize the discriminative spatial information from pre-frontal lobe, frontal lobe, temporal lobe and parietal lobe.
Freeway traffic accident severity prediction based on multi-dimensional and multi-layer Bayesian network

In order to explore the influence of the characteristic factors of freeway traffic accident on the severity of the accident, this study established the freeway traffic accident severity prediction model influenced by road and environmental factors, explored the influence of road factors and environmental factors on the severity of the accident. Firstly, the XGBoost model was established and SHAP value was introduced to explain the XGBoost model, and the importance ranking of the influence degree of each feature on the target variable was obtained and the global influence of each feature on the target variable is visualized. Then, according to the selected variables and their values, the freeway traffic accident severity prediction model based on Bayesian network was constructed. Finally, based on the data of freeway accidents in Hebei Province, a case study was conducted to predict the severity of accidents by road and environmental factors, and then the severity of accidents under the interaction of different factors was predicted by using the constructed prediction model. The results show that the learning accuracy and prediction accuracy of the model are verified, and the performance of the prediction model is good. According to the analysis results, the corresponding safety management measures can be put forward to reduce the severity of the accident.
Visibility Domain Direction-of-Arrival Optimization for Arbitrary 2-D Arrays

A visibility-modelling based method is introduced to determine the elevation and azimuth angles for a signal received at a quasi-randomly arranged (2-D) array. The method is optimal for the number of antennas when the array is configured such that no antenna baselines are repeated. Simulation shows that our method has comparable accuracy to subspace-based acrlong doa algorithms like acrfull music and measured data shows that it is both more accurate and faster than visibility based methods like acrlong ra all-sky imaging.
Ensuring Safety of Learning-Based Motion Planners Using Control Barrier Functions

Reinforcement learning (RL) has been successfully applied to sequential decision-making problems, e.g., playing computer games or solving robotic tasks in simulations. However, RL methods are not yet ready to be applied to real robotic systems if safety is a major concern. To address this issue, we propose a safety layer based on control barrier functions to ensure the safety for an RL-based motion planner for highway scenarios with a continuous action space. Our method ensures legal safety by following traffic rules. Moreover, we propose a relaxation mechanism so that safety is restored as soon as possible when other vehicles violate traffic rules and render our optimization problem infeasible. We evaluate our approach using a real-world highway dataset and a traffic simulator. Numerical experiments confirm that an agent equipped with our proposed safety layer does not cause any accidents during learning and yet reaches the goal as often as an agent without a safety layer.
Detection of Deep-Morphed Deepfake Images to Make Robust Automatic Facial Recognition Systems

Face Morphing has emerged as a pervasive attack of Facial Recognition Systems. The rapid growth of Generative Adversarial Networks takes it to a complete new level. Deepfake or deep neural network based face morphing, a.k. a deep-morph attack, presents a significant threat to Facial Recognition System. In this paper, we propose a novel Convolutional Neural Network based detection method of deep morphed deepfake images which is suitable for IoT environments in smart cities. A high accuracy of 94.83% has been achieved for the DeepfakeTIMIT HQ dataset. This lightweight and fast network is a natural choice for IoT environments.
Research on Collaborative Filtering Recommendation Algorithm

As a crucial technology in personalized recommendation, collaborative filtering algorithm has been widely used. Today’s collaborative filtering algorithm is mainly divided into two types, user-based and commodity-based. In order to make it clearer to user-based with regard to the differences and application scenarios of the two collaborative filtering recommendation algorithms from the project, this article analyzes the definitions, basic principles, similarity calculation methods, and the advantages and disadvantages of the two algorithms through the method of comparison. Finally, through analysis and comparison, the current problems of these two algorithms and their respective limitations are proved to provide references for future use and improvement of collaborative filtering algorithms.
The Effect of Temperature and Additives on the Dielectric Behavior of Human Whole Blood, Its Different Components, and Cell Suspensions

This investigation involves the study of electrical properties, i.e., conductivity and permittivity of human whole blood and its different components with/without various additives at different temperatures. The open-ended coaxial probe measurement technique was employed to perform the study for the frequency range of 0.3–6.5 GHz. The dielectric behaviors of the freshly received whole blood from five different donors with different anticoagulants and clotting gel were studied at temperatures ranging from 5 °C to 45 °C. The differences in the dielectric behaviors of the whole blood, its different components [packed red blood cells (RBCs), plasma, and serum], and RBC suspensions were also analyzed. The plain whole blood and the whole blood samples incorporated with the clotting gel exhibited lower dielectric behaviors compared to those with the whole blood samples incorporated with the anticoagulants. The analysis of the whole blood, its different components, and RBC suspensions inferred that the packed RBCs exhibited the lowest dielectric constant, loss, and conductivity, and these values were increased for the whole blood followed by serum, plasma, and RBC suspensions, respectively. The dielectric behaviors of the whole blood and its components were observed to be significantly affected by the presence of different additives and also due to the variation in frequencies and temperatures. The modified Debye relaxation model included with the static conductivity term was fitted to the measured permittivity. The dielectric responses of biological samples are very useful and paramount for understanding the specific absorption rate (SAR) and other thermal and nonthermal effects of the radiation.
ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images

Optical coherence tomography (OCT) imaging is a well-known technology for visualizing retinal layers and helps ophthalmologists to detect possible diseases. Accurate and early diagnosis of common retinal diseases can prevent the patients from suffering critical damages to their vision. Computer-aided diagnosis (CAD) systems can significantly assist ophthalmologists in improving their examinations. This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the learning architectures of two robust convolutional models. Spatial resolution is a critical factor in medical images, especially the OCT images that contain tiny essential points. To empower our model, we apply a new post-architecture model to our ensemble model for enhancing spatial resolution learning without increasing computational costs. The introduced post-architecture model can be deployed to any feature extraction model to improve the utilization of the feature map’s spatial values. We have collected two open-source datasets for our experiments to make our models capable of detecting six crucial retinal diseases: Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), Diabetic Retinopathy (DR), Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen alongside the normal cases. Our experiments on two datasets and comparing our model with some other well-known deep convolutional neural networks have proven that our architecture can increase the classification accuracy up to 5%. We hope that our proposed methods create the next step of CAD systems development and help future researches.
Progress toward the Next Generation of Bioreactors for 3D Tissue Engineering

This research is part of an interdisciplinary project to achieve composite tissue transplantation through novel technology that induces the growth of thick, immunocompatible, tissue implants that are compatible with the patient's immune system. While the common approach to growing cells in vitro utilizes scaffolds embedded in perfusion bioreactors, state of the art methods still suffer from many limitations such as failing to accommodate the dynamical aspect where different stages of development lead to changes in the material properties. The aim of this work is to introduce a new generation of perfusion bioreactors not bound by current limitations. This is achieved by developing noninvasive feedback visualization techniques, new scaffold-patterning technology, new approach to bioreactors design, and control and optimization methods. These novel tools presented herein will contribute to the development of stable vascular networks that are functionally competent to sustain physiological flow and permeate tissue grafts.
AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement

Machine learning (ML) project deployments often have long lead times and may face delays or failures due to lack of data, poor data quality, and data drift. To address these problems, we introduce AgileML, a novel machine learning product development lifecycle where the end consumer and development team work collaboratively through an iterative process of development. We use AgileML to develop a commercial spend classification service and demonstrate that the earliest alpha deployment can offer users significant commercial value. User-testing with a professional spend analyst demonstrates that the system can lead to a five-fold increase in classification speed.
Construction of a Novel Ferroptosis-Related Gene Signature for Predicting Survival of Patients With Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most diagnosed subtype of lung cancer; ferroptosis is widely involved in the pathological cell death associated with various cancers, including lung cancer. However, the comprehensive relationship between ferroptosis and LUAD is little known in molecular levels until now. In the present study, 513 LUAD patients could be aggregated into three clusters by consensus clustering based on RNA sequencing data of 291 ferroptosis-related genes (FRGs) in The Cancer Genome Atlas (TCGA) database; cluster2 had significant survival advantage compared to the other two clusters. A novel prognostic model of 8 differential FRGs was constructed to effectively divide LUAD patients into high- or low-risk group according to the risk scores by the Cox and LASSO regression analyses. The overall survival of LUAD patients in the high-risk group was significantly worse in the TCGA and GEO cohorts. Moreover, patients with radiation therapy or high clinical stage had obviously higher risk scores. We validated the differential mRNA and protein expression of four FRGs in paired tumor and normal samples from our clinical cohort. Our study constructed a novel FRG signature to predict the prognosis of LUAD patients, which might provide a new prognostic tool and potential therapeutic targets for LUAD.
Comparison of Two Donor Liver Procurement Methods for Treatment of Pediatric Acute Liver Failure

Background To evaluate the difference and efficacy of two donor liver procurement methods for treatment of pediatric acute liver failure (PALF) by living donor liver transplantation (LDLT). Methods A total of 17 patients (12 men, 5 women) with PALF who underwent LDLT in our hospital between October 2016 and October 2020, and prognostic efficacy of donors and recipients using two donor liver procurement methods were analyzed. Results The donors and recipients were both divided into laparoscopic (7 cases) and open (10 cases) donor liver procurement groups. In the recipients, two deaths occurred in the laparoscopic group and one in the open group, and there were three postoperative complications in the laparoscopic group and six in the open group. The cumulative 1-year and 3-year survival rates in the laparoscopic group and the open group were 80.0% and 85.7% separately. There was no difference in the postoperative survival and complications rates between the two groups. In the donors, the operation time, postoperative hospital stay, and blood loss of the laparoscopic group was significantly reduced compared with the open group (P ≤ 0.01). No death or serious complication occurred in either donor group. Conclusion Laparoscopic donor liver procurement is worth recommending than open donor liver procurement for treatment of PALF combined with LDLT in qualified transplant centers.
Force-Generation by the Trans-Envelope Tol-Pal System

The Tol-Pal system spans the cell envelope of Gram-negative bacteria, transducing the potential energy of the proton motive force (PMF) into dissociation of the TolB-Pal complex at the outer membrane (OM), freeing the lipoprotein Pal to bind the cell wall. The primary physiological role of Tol-Pal is to maintain OM integrity during cell division through accumulation of Pal molecules at division septa. How the protein complex couples the PMF at the inner membrane into work at the OM is unknown. The effectiveness of this trans-envelope energy transduction system is underscored by the fact that bacteriocins and bacteriophages co-opt Tol-Pal as part of their import/infection mechanisms. Mechanistic understanding of this process has been hindered by a lack of structural data for the inner membrane TolQ-TolR stator, of its complexes with peptidoglycan (PG) and TolA, and of how these elements combined power events at the OM. Recent studies on the homologous stators of Ton and Mot provide a starting point for understanding how Tol-Pal works. Here, we combine ab initio protein modeling with previous structural data on sub-complexes of Tol-Pal as well as mutagenesis, crosslinking, co-conservation analysis and functional data. Through this composite pooling of in silico, in vitro, and in vivo data, we propose a mechanism for force generation in which PMF-driven rotary motion within the stator drives conformational transitions within a long TolA helical hairpin domain, enabling it to reach the TolB-Pal complex at the OM.