Miracle-dz/UniFAR-FTUs huggingface dataset:
Field-theoretic density estimation for biological sequence space with applications to 5′ splice site diversity and aneuploidy in cancer

Significance Often in computational biology, our data take the form of a set of sequences: for example, a collection of nucleic acid sequences with a shared function. Given such a collection of sequences, how can we estimate the probability distribution that these sequences were drawn from? This problem is important both for identifying other sequences with similar function and because the structure of these probability distributions is often informative about the biophysical and evolutionary processes that generated the observed sequences. Here, we present a method for estimating such probability distributions that generalizes the popular maximum entropy approach. This method is capable of capturing fine details of the distribution in high-density regions where data are plentiful but still behaves conservatively in low-density regions. Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data are plentiful while still maintaining a conservative maximum entropy character in regions of sequence space where data are sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyperparameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5′ splice sites found in the human genome and to understand patterns of chromosomal abnormalities across human cancers.
Comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder in children and adolescents: a systematic review and network meta-analysis

Background Available evidence on the comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder (PTSD) in children and adolescents remains uncertain. Objective We aimed to compare and rank the different types and formats of psychotherapies for PTSD in children and adolescents. Methods We searched eight databases and other international registers up to 31 December 2020. The pairwise meta-analyses and frequentist network meta-analyses estimated pooled standardised mean differences (SMDs) and ORs with random-effects model. Efficacy at post-treatment and follow-up, acceptability, depressive and anxiety symptoms were measured. Findings We included 56 randomised controlled trials with 5327 patients comparing 14 different types of psychotherapies and 3 control conditions. For efficacy, cognitive processing therapy (CPT), behavioural therapy (BT), individual trauma-focused cognitive–behavioural therapy (TF-CBT), eye movement desensitisation and reprocessing, and group TF-CBT were significantly superior to all control conditions at post-treatment and follow-up (SMDs between −2.42 and −0.25). Moreover, CPT, BT and individual TF-CBT were more effective than supportive therapy (SMDs between −1.92 and −0.49). Results for depressive and anxiety symptoms were similar to the findings for the primary outcome. Most of the results were rated as ‘moderate’ to ‘very low’ in terms of confidence of evidence. Conclusions CPT, BT and individual TF-CBT appear to be the best choices of psychotherapy for PTSD in young patients. Other types and different ways of delivering psychological treatment can be alternative options. Clinicians should consider the importance of each outcome and the patients’ preferences in real clinical practice.
Copper oxide (CuO) and manganese oxide (MnO) nanoparticles induced biomass accumulation, antioxidants biosynthesis and abiotic elicitation of bioactive compounds in callus cultures of Ocimum basilicum (Thai basil)

Abstract Nano-elicitation is one among the prioritised strategies considered globally for sustainable and uniform production of industrially important medicinal compounds. Ocimum basilicum (Thai basil), a renowned medicinal species is a reservoir of commercially vital metabolites and proved for its health assuring effects in cancer, diabetes, microbial and cardiovascular diseases. However, its consumption and industrial demand raised intent to divert towards better alternates for ensuring sustainable production of medicinal compounds. Herein, we investigated the comparative potential of metal oxide [copper oxide (CuO) and manganese oxide (MnO)] nanoparticles to elicit the biosynthesis of bioactive metabolites and antioxidative capacity of O.basilicum callus cultures. Results showed that callus grown on MS media supplemented with 10 mg/L CuO-NPs resulted in the highest biomass accumulation (FW: 172.8 g/L, DW: 16.7 g/L), phenolic contents (TPC: 27.5 mg/g DW), and flavonoid contents (TFC: 9.1 mg/g DW) along with antioxidant activities (DPPH: 94%, ABTS: 881 μM TEAC, FRAP: 386 μM TEAC) compared with MnO-NPs and control. Likewise, the Superoxide dismutase (SOD: 1.28 nM/min/mg FW) and Peroxidase (POD: 0.48 nM/min/mg FW) activities were also recorded maximum in CuO-NPs elicited cultures than MnO-NPs and control. Moreover, the HPLC results showed that rosmarinic acid (11.4 mg/g DW), chicoric acid (16.6 mg/g DW), eugenol (0.21 mg/g DW) was found optimum in cultures at 10 mg/L CuO-NPs. Overall, it can be concluded that CuO nanoparticles can be effectively used as a elicitor for biosynthesis of metabolites in callus cultures of O. basilicum (Thai basil). The study is indeed a contribution to the field that will help decoding the mechanism of action of CuO NPs. However, further molecular investigations are needed to fully develop understanding about the metabolic potential of O. bascillicum and scalling up this protocol for bulkup production of bioactive compounds.
Association between sleep characteristics, ideal cardiovascular health, and systemic inflammation, NHANES 2017–2018

There is conflicting evidence regarding the associations between sleep deprivation and inflammatory biomarkers indicative of cardiovascular disease risk, such as high‐sensitivity C‐reactive protein (hsCRP). The association between sleep habits and hsCRP was quantified in a nationally representative sample of adults in the United States and mediation by ideal cardiovascular health metrics was explored. A cross‐sectional analysis of cardiovascular disease‐free participants aged 20–79 years from the 2017–2018 National Health and Nutrition Examination Survey was conducted. The primary exposures were self‐reported sleep duration, sleep debt (difference between the average weekday and weekend sleep duration), and ideal cardiovascular health (11–14 points). The primary outcome was hsCRP (high‐risk ≥ 3.0 mg/L). Multivariable robust Poisson models were used to estimate prevalence ratios after multiple imputation. A subgroup analysis of shift workers was also conducted. Of 4027 participants included (mean age 46 years; 52% female; 41% shift workers), the prevalence of sleeping <6 h on weekdays was 9%, with 40% sleeping ≥9 h on weekends. One‐quarter had a high (≥2 h) sleep debt, 82% had poor cardiovascular health, and 34% had high‐risk hsCRP. There were no significant associations between weekday sleep duration or sleep debt with high‐risk hsCRP, even among shift workers. Mediation analysis was not conducted. Ideal cardiovascular health was associated with a lower prevalence of high‐risk hsCRP (prevalence ratios, 0.60, 95% CI, 0.48–0.75). The lack of significant associations suggests a complex interrelationship of hsCRP with factors beyond sleep duration. Examination of populations at highest risk of chronic sleep deprivation could help to elucidate the association with systemic inflammation‐related outcomes.
Advances in spatial transcriptomic data analysis

Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
Process Diagnosis and Aging Effects on a Traditional Millet-Based Drink: Boumkaye

Boumkaye is a traditional fermented millet-based drink (Pennisetum glaucum L.) produced in Casamance (region in Senegal). Unknown and circumcised beverage, it has therapeutic properties due to aqueous creeper extracts from Abrus pulchellus plant and naturally present during the process. The objective of this work was to investigate the fabrication process of a millet-based beverage as an alternative source of nutrients. The diagnosis of beverage production by monitoring its physical and chemical parameters during two months of storage at room temperature was studied. The established manufacturing process, composed by a production of aqueous extracts after maceration of the Abrus pulchellus vines, a preparation of millet slurry, and a fermentation step to obtain the Boumkaye, has several features. The study of aging process showed probable lactic and alcoholic fermentations. Further, the finished Boumkaye was characterized by a high acidity (pH of 3), an alcohol content of 5 g/100g and contains an appreciable amount of polyphenols (54.46 mg/100g).
Low birth weight and birth weight status in Bangladesh: A systematic review and meta-analysis

Abstract The prevalence of low birth weight (LBW) is high in Bangladesh, but no study has collated recent estimates of LBW prevalence from throughout the country. The aim of this meta-analysis was to evaluate the prevalence of LBW and birth weight status in Bangladesh. We searched PubMed, Medline, Ovo and Google Scholar to find published articles in national and international journals from 2000–2020 and reviewed for relevance. Meta-analysis and Q test were performed to estimate the prevalence and heterogeneity of LBW from all included articles. Meta-regression was done to quantify associations with sample size and study year. Stratified analysis was conducted and effect size calculated for differences in LBW prevalence by sex, division and urban/rural area. In total 48 studies with 166,520 births were found and included in this meta-analysis. The pooled prevalence of LBW was 29.1% (95% CI, 28.9–29.3%) in overall, 29.9% (29.7–30.2%) in rural and 15.7% (14.9–16.6%) in urban areas. There was no significant difference in average birth weight between boys and girls (SD, 0.71; 95% CI, −0.43–1.83). Prevalence of LBW was higher in north-east Bangladesh compared to other zones (p<0.05). The pooled prevalence of LBW did not change significantly for last two decades. The prevalence of LBW in Bangladesh remains high. Lack of improvement suggests an urgent need for scaled up maternal and prenatal interventions and services known to reduce LBW. Urban/rural and divisional differences in rates suggest areas of greatest need.
Using Deep Learning to Correlate Reddit Posts with Economic Time Series During the COVID-19 Pandemic

The COVID-19 pandemic produced uniquely unstable ﬁnancial conditions that permeated many sectors of the economy, creating complex challenges for existing ﬁnancial models. In this investigation we tackle a subset of these challenges, seeking to determine the relationship between Red-dit posts and actual economic time series during the economically tumultuous year of 2020. More speciﬁcally, we compute correlation between language used in Reddit’s /r/personalfinance forum and initial or continuing unemployment claims in the United States throughout the year. We collect a novel dataset for the task, complete with annotations distinguishing between unemployment-speciﬁc and general employment inquiries. We also train a Convolutional Neural Network (CNN) based deep learning model to distinguish between these categories, achieving a maximum F1 score of 0.857. Finally, we compute Pearson correlation co-efﬁcients between these model predictions and real-world ﬁnancial data relative to a number of reasonable baselines, yielding correlations of up to 0.796 for initial claims and 0.747 for continuing claims.
An Exploration of Automatic Text Summarization of Financial Reports

In the domain of finance, documents tend to be long, averaging at approximately 180 pages. This creates a need for finding efficient ways to use technology to leverage the existence of these huge amounts of textual data. This goes hand in hand with the pressing need to make investment/financial decisions in a fast manner to ensure maximized financial gain. However, exhaustive reading of financial documents such as annual reports is extremely laborious. Hence, automatic summarization methods could simplify this task and provide access to a smaller but informative chunk of a given document. In this paper, we explore several approaches for summarizing the qualitative sections of annual reports using extractive summarization, Natural Language Processing (NLP), machine learning, and deep learning techniques. We investigate multiple approaches by defining two different tasks: a sentence-based summarization task and a section-based summarization one. The latter is tailored to the structure of the annual reports. We evaluate the quality of the summaries using an existing dataset of annual reports published by British firms belonging to the London Stock Exchange and their corresponding manually created summaries built for the 2020 FNS Shared task. Our best model makes use of the power of unsupervised clustering techniques to group sections based on their meaning and achieves a ROUGE-L score of 36%.
Decoupling Pragmatics: Discriminative Decoding for Referring Expression Generation

The shift to neural models in Referring Expression Generation (REG) has enabled more natural set-ups, but at the cost of interpretability. We argue that integrating pragmatic reasoning into the inference of context-agnostic generation models could reconcile traits of traditional and neural REG, as this offers a separation between context-independent, literal information and pragmatic adaptation to context. With this in mind, we apply existing decoding strategies from discriminative image captioning to REG and evaluate them in terms of pragmatic informativity, likelihood to ground-truth annotations and linguistic diversity. Our results show general effectiveness, but a relatively small gain in informativity, raising important questions for REG in general.
Multimodal Behavior Modeling for Socially Interactive Agents

and nonverbal behaviors. They divided the process into smaller problems, which aim to solve correct behavior dynamics and plausible joint conﬁgurations, and to generate diverse and smooth trajectories for nonverbal behaviors. A classiﬁer was used to determine the phase of the behavior, addressing the behavior dynamics. The study found that an adversary loss provides better results than conventional regression losses in mapping the non-deterministic relationship between nonverbal behaviors and speech.
The Study Based On The Deep Learning For Indian Ocean Dipole (IOD) Index Predication

Indian Ocean Dipole (IOD) is a large-scale physical ocean phenomenon occurring in the Indian Ocean basin. IOD is of great help to predict El Niño-Southern Oscillation (ENSO). Predicting the occurrence of IOD is of great significance to the study of climate change and other major marine phenomena. Generally, the occurrence of IOD is judged by calculating the IOD index. In this paper, the Convolutional Long Short-Term Memory(convLSTM) neural network is used to build the model. The purpose is to combine the prior knowledge of physical ocean and Deep Learning to complete the prediction of IOD. The experimental results show that the fitting degree of the predicted IOD index time series to the real IOD index time series is 81.48%, and the fitting degree is 82.84% after adding the wind field data, and the accuracy is improved after adding the wind field data. IOD index line graph can generally fit the real trend of IOD index, which has a profound impact on the study of IOD phenomenon.
Spatial Graph Convolutional and Temporal Involution Network for Skeleton-based Action Recognition

The existing skeleton-based action recognition methods, based on graph convolutional networks (GCN), cannot well capture continuous behavior information in the time dimension. As traditional Convolution neural network is difficult to extract timing information and capture long-distance relationship. Relying on stacking a large number of convolution kernels to increase the receptive field and feature diversity, not only increases the amounts of parameters and computational complexity, but also causes a large amount of redundancy in the channel dimensions of the convolution kernel. Therefore, we propose the Spatial Graph Convolutional and Temporal involution network (ST-TI). Firstly, in the spatial dimension, GCN is used to obtain the spatial correlation of a single frame of human skeleton points. Then, in the temporal dimension. Involution operation is used to extract the correlations of skeleton points in different frames. The entire model is composed of 9 layers of SG-TI units. Each SG-TI unit uses residual connection and then uses a fully connected layer to ensure that the output and prediction categories have the same dimensionality. Finally, we feed the output feature to a SoftMax classifier. The effect of the model is verified on two public behavior recognition data sets of kinetics and NTU RGB+D. Experiments show that compared with the benchmark network ST-GCN, the algorithm has reduced the number of parameters by 3.7 times and improved the recognition accuracy by 3%
Precursor: a fast, client-centric and trusted key-value store using RDMA and Intel SGX

As offered by the Intel Software Guard Extensions (SGX), trusted execution enables confidentiality and integrity for off-site deployed services. Thereby, securing key-value stores has received particular attention, as they are a building block for many complex applications to speed-up request processing. Initially, the developers' main design challenge has been to address the performance barriers of SGX. Besides, we identified the integration of a SGX-secured key-value store with recent network technologies, especially RDMA, as an essential emerging requirement. RDMA allows fast direct access to remote memory at high bandwidth. As SGX-protected memory cannot be directly accessed over the network, a fast exchange between the main and trusted memory must be enabled. More importantly, SGX-protected services can be expected to be CPU-bound as a result of the vast number of cryptographic operations required to transfer and store data securely. In this paper, we present Precursor, a new key-value store design that utilizes trusted execution to offer confidentiality and integrity while relying on RDMA for low latency and high bandwidth communication. Precursor offloads cryptographic operations to the client-side to prevent a server-side CPU bottleneck and reduces data movement in and out of the trusted execution environment. Our evaluation shows that Precursor achieves up to 6--8.5 times higher throughput when compared against similar SGX-secured key-value store approaches.
Blockchain Extension for PostgreSQL Data Storage

Blockchain is an emerging technology with the potential to resolve auditing issues. Implementing a new blockchain-related feature implies moving to a platform with another database or duplicating its parts in a blockchain system. Both ways are difficult to migrate and maintain. The alternative is to implement blockchain features within the existing database, including consensus mechanisms and specific data structures for audit needs. The paper describes and evaluates a database extension with blockchain-related structures, leaving consensus beyond the scope. We use an account-based prototype of cryptocurrency as a model example. The proposed extension allows provably checking transaction content and user balance without a full database lookup. The numerical experiments to study the overhead of the proposed extension are provided.
Social Reactions to Socially Interactive Agents and Their Ethical Implications

Citation information: Krämer, N. , and Manzeschke A. , (2021). Social Reactions to Socially Interactive Agents and Their Ethical Implications. In B. Lugrin, C. Pelachaud, D. Traum (Eds.), Handbook on Socially Interactive Agents – 20 Years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics, Volume 1: Methods, Behavior, Cognition (pp. 77103). ACM books. DOI of the final chapter: 10.1145/3477322.3477326 DOI of volume 1 of the handbook: 10.1145/3477322
Exploiting Temperature-Varied Voltage Fingerprints for In-vehicle CAN Intrusion Detection

The Controller Area Network (CAN) is the most commonly used in-vehicle protocol for reliable communication between Electronic Control Units (ECUs). Due to its broadcast nature and absence of message authentication mechanisms, CAN bus is vulnerable to several severe attacks. To combat these defects, many intrusion detection systems (IDSs) that exploit ECU’s inimitable voltage signal have been proposed. However, existing works ignore the impact of temperature changes or only consider a small range of temperature changes. Through experiences, we found that the ECU’s voltage signals will be affected by the temperature, which will cause the state-of-the-art voltage-based IDS to fail. To mitigate this deficiency, we propose a temperature-varied voltage fingerprinting scheme(TVV). TVV collects the voltage signals sent by each ECU at different temperatures, and it constructs a fingerprint database of each ECU. Then TVV performs intrusion detection based on the fingerprint database and the current driving state of the car. We have implemented TVV on both CAN bus prototype and real vehicles at a temperature of 0℃ to 80℃, and results show that our scheme is able to detect the masquerade attack.