Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant and ACE Inhibitory Activity of Cultivated and Wild Angelica gigas Nakai Extracts Prepared Using Different Extraction Conditions

The purpose of this study was to investigate the biological activities of cultivated Angelica gigas Nakai (CAG) and wild Angelica gigas Nakai (WAG) extracts prepared by extraction with water, 30% ethanol, 60% ethanol, or 90% ethanol. The electron donating ability of the WAG extracts was higher than that of the CAG extracts and 0.1% and 1.0% solutions of the comparative substance, L-ascorbic acid. The superoxide dismutase-like activity of the CAG extracts was higher than that of WAG extracts. Superoxide dismutase-like activity was highest (33.95%) in the CAG water extract. The total polyphenol content was highest in the 60% ethanol extracts of WAG. The nitrite scavenging ability of the CAG and WAG extracts was highest at a pH of 1.2. The tyrosinase inhibitory effect was highest (43.72%) in the water extract of WAG. The angiotensin converting enzyme inhibitory activity was highest (83.84%) in the 60% ethanol extract of WAG. The results of the present study will be useful for understanding the antioxidant and angiotensin-converting enzyme inhibitory activities of Angelica gigas Nakai extracts.
Biomimetic Flexible Sensors and Their Applications in Human Health Detection

Bionic flexible sensors are a new type of biosensor with high sensitivity, selectivity, stability, and reliability to achieve detection in complex natural and physiological environments. They provide efficient, energy-saving and convenient applications in medical monitoring and diagnosis, environmental monitoring, and detection and identification. Combining sensor devices with flexible substrates to imitate flexible structures in living organisms, thus enabling the detection of various physiological signals, has become a hot topic of interest. In the field of human health detection, the application of bionic flexible sensors is flourishing and will evolve into patient-centric diagnosis and treatment in the future of healthcare. In this review, we provide an up-to-date overview of bionic flexible devices for human health detection applications and a comprehensive summary of the research progress and potential of flexible sensors. First, we evaluate the working mechanisms of different classes of bionic flexible sensors, describing the selection and fabrication of bionic flexible materials and their excellent electrochemical properties; then, we introduce some interesting applications for monitoring physical, electrophysiological, chemical, and biological signals according to more segmented health fields (e.g., medical diagnosis, rehabilitation assistance, and sports monitoring). We conclude with a summary of the advantages of current results and the challenges and possible future developments.
Fast Bayesian scan statistics for multivariate event detection and visualization

The multivariate Bayesian scan statistic (MBSS) is a recently proposed, general framework for event detection and characterization in multivariate space–time data. MBSS integrates prior information and observations from multiple data streams in a Bayesian framework, computing the posterior probability of each type of event in each space–time region. MBSS has been shown to have many advantages over previous event detection approaches, including improved timeliness and accuracy of detection, easy interpretation and visualization of results, and the ability to model and accurately differentiate between multiple event types. This work extends the MBSS framework to enable detection and visualization of irregularly shaped clusters in multivariate data, by defining a hierarchical prior over all subsets of locations. While a naive search over the exponentially many subsets would be computationally infeasible, we demonstrate that the total posterior probability that each location has been affected can be efficiently computed, enabling rapid detection and visualization of irregular clusters. We compare the run time and detection power of this ‘Fast Subset Sums’ method to our original MBSS approach (assuming a uniform prior over circular regions) on semi‐synthetic outbreaks injected into real‐world Emergency Department data from Allegheny County, Pennsylvania. We demonstrate substantial improvements in spatial accuracy and timeliness of detection, while maintaining the scalability and fast run time of the original MBSS method. Copyright © 2011 John Wiley & Sons, Ltd.
Clinico-Immunochemical Studies on Airborne Areca catechu L. Pollen, a Probable Risk Factor in Emergency Asthma Hospitalization from Eastern India

Background: The pollen grain of the Areca catechu L. tree is airborne and allergenic. This study aimed to know the role of this pollen as a source of aeroallergen with effect on emergency asthma hospitalization, to isolate its important allergic fraction and to check its cross-reaction with betel nut. Methods:Areca pollen was monitored with a Burkard sampler. Determination of allergenic activities was studied by in vivo and in vitro analyses. Asthma hospitalization data were collected from two nearby hospitals. The pollen extract was fractionated by a combination of DEAE-Sephadex and Sephacryl S-200 column. The protein components were observed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Cross-reactivity of Areca pollen and betel nut was shown by IgE enzyme-linked immunosorbent assay (ELISA) inhibition. Results: The Areca pollen was perennially airborne. Skin test results of respiratory allergic patients showed 38.6% positivity. The detected aeroallergen spots in particle immunoblotting correlated significantly with airborne pollen count. Areca pollen showed a significant positive correlation with asthma hospitalization. There are 6 IgE-reactive protein components in the whole-pollen extract. IgE-reactive fraction 1 was resolved into 4 subfractions. Subfraction 1a showing IgE reactivity contained 3 protein components, among which 2 of 48 and 118 kDa were IgE reactive. The 48-kDa component was reported to be cross-reactive with other palm pollen types. In IgE ELISA inhibition, the betel nut extract showed 50% inhibition with about 110 ng/ml concentration. Conclusion:A. catechu pollen is a significant contributor to the aeroallergen load in India. Its partially purified IgE-reactive fraction may be useful in therapeutics. The betel nut extract showed remarkable cross-reactivity with Areca pollen.
Development of phospho-specific Rab protein antibodies to monitor in vivo activity of the LRRK2 Parkinson's disease kinase

Mutations that activate the LRRK2 (leucine-rich repeat protein kinase 2) protein kinase predispose to Parkinson's disease, suggesting that LRRK2 inhibitors might have therapeutic benefit. Recent work has revealed that LRRK2 phosphorylates a subgroup of 14 Rab proteins, including Rab10, at a specific residue located at the centre of its effector-binding switch-II motif. In the present study, we analyse the selectivity and sensitivity of polyclonal and monoclonal phospho-specific antibodies raised against nine different LRRK2-phosphorylated Rab proteins (Rab3A/3B/3C/3D, Rab5A/5B/5C, Rab8A/8B, Rab10, Rab12, Rab29[T71], Rab29[S72], Rab35 and Rab43). We identify rabbit monoclonal phospho-specific antibodies (MJFF-pRAB10) that are exquisitely selective for LRRK2-phosphorylated Rab10, detecting endogenous phosphorylated Rab10 in all analysed cell lines and tissues, including human brain cingulate cortex. We demonstrate that the MJFF-pRAB10 antibodies can be deployed to assess enhanced Rab10 phosphorylation resulting from pathogenic (R1441C/G or G2019S) LRRK2 knock-in mutations as well as the impact of LRRK2 inhibitor treatment. We also identify rabbit monoclonal antibodies displaying broad specificity (MJFF-pRAB8) that can be utilised to assess LRRK2-controlled phosphorylation of a range of endogenous Rab proteins, including Rab8A, Rab10 and Rab35. The antibodies described in the present study will help with the assessment of LRRK2 activity and examination of which Rab proteins are phosphorylated in vivo. These antibodies could also be used to assess the impact of LRRK2 inhibitors in future clinical trials.
Quantitative analysis of film coating in a pan coater based on in-line sensor measurements

A method was developed that enables in-line analysis of film coating thickness on tablets during a pan coating operation. Real-time measurements were made using a diffusereflectance near-infrared (NIR) probe positioned inside the pan during the coating operation. Real-time spectra of replicate batches were used for modeling film growth. Univariate analysis provided a simple method for in-line monitoring of the coating process using NIR data. An empirical geometric 2-vector volumetric growth model was developed, which accounts for differential growth on the face and band regions of biconvex tablets. The thickness of the film coat was determined by monitoring the decrease of absorption bands characteristic of a component of the tablet core and monitoring the increase of bands characteristic of a component in the coating material. There was good correlation between values estimated from the NIR data and the measured tablet volumetric growth. In-line measurements allow the coating process to be stopped when a predetermined tablet coating thickness is achieved.
Simulated annealing approach to optimizing the lifetime of sparse time-driven sensor networks

Time-driven sensor networks are devoted to the continuous reporting of ambient data to the base station. In many cases, these data are provided by nodes that have been deployed in a structured manner, either by selecting strategic locations or by adopting some regular sampling pattern. In either case, the resulting inter-node distances may not be small, and thus additional supporting nodes may be necessary. This suggests that the problem could be better addressed from a network planning perspective. In this sense, a particular approach is proposed and, as part of it, the paper focuses on optimizing the lifetime that can be predicted from the network topology, which is assumed to be a static data gathering tree. It is shown that this problem requires the exploration of all possible spanning trees, since the energy consumed by a node depends on its workload, which in turn depends on how this node is connected to its neighborhood. Because this is an NP-hard problem, the use of a heuristic approach is required. Then, an algorithm based on simulated annealing is proposed, which converges asymptotically to the global optimum. This algorithm is tested on different scenarios and its computational complexity is proved to be linearly dependent on the number of nodes.
Leveraging Variational Autoencoders for Parameterized MMSE Estimation

In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation. The derived estimator is shown to approximate the minimum mean squared error estimator by utilizing the variational autoencoder as a generative prior for the estimation problem. We propose three estimator variants that differ in their access to ground-truth data during the training and estimation phases. The proposed estimator variant trained solely on noisy observations is particularly noteworthy as it does not require access to ground-truth data during training or estimation. We conduct a rigorous analysis by bounding the difference between the proposed and the minimum mean squared error estimator, connecting the training objective and the resulting estimation performance. Furthermore, the resulting bound reveals that the proposed estimator entails a bias-variance tradeoff, which is well-known in the estimation literature. As an example application, we portray channel estimation, allowing for a structured covariance matrix parameterization and low-complexity implementation. Nevertheless, the proposed framework is not limited to channel estimation but can be applied to a broad class of estimation problems. Extensive numerical simulations first validate the theoretical analysis of the proposed variational autoencoder-based estimators and then demonstrate excellent estimation performance compared to related classical and machine learning-based state-of-the-art estimators.
The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Massive Random Access

The lack of an efficient preamble detection algorithm remains a challenge for solving preamble collision problems in intelligent massive random access (RA) in practical communication scenarios. To solve this problem, we present a novel early preamble detection scheme based on a maximum likelihood estimation (MLE) model at the first step of the grant-based RA procedure. A novel blind normalized Stein variational gradient descent (SVGD)-based detector is proposed to obtain an approximate solution to the MLE model. First, by exploring the relationship between the Hadamard transform and wavelet transform, a new modified Hadamard transform (MHT) is developed to separate high-frequencies from important components using the second-order derivative filter. Next, to eliminate noise and mitigate the vanishing gradients problem in the SVGD-based detectors, the block MHT layer is designed based on the MHT, scaling layer, soft-thresholding layer, inverse MHT and sparsity penalty. Then, the blind normalized SVGD algorithm is derived to perform preamble detection without prior knowledge of noise power and the number of active devices. The experimental results show the proposed block MHT layer outperforms other transform-based methods in terms of computation costs and denoising performance. Furthermore, with the assistance of the block MHT layer, the proposed blind normalized SVGD algorithm achieves a higher preamble detection accuracy and throughput than other state-of-the-art detection methods.
GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning

In the rapidly evolving field of autonomous systems, the safety and reliability of the system components are fundamental requirements. These components are often vulnerable to complex and unforeseen environments, making natural edge-case generation essential for enhancing system resilience. This paper presents GENESIS-RL, a novel framework that leverages system-level safety considerations and reinforcement learning techniques to systematically generate naturalistic edge cases. By simulating challenging conditions that mimic the real-world situations, our framework aims to rigorously test entire system's safety and reliability. Although demonstrated within the autonomous driving application, our methodology is adaptable across diverse autonomous systems. Our experimental validation, conducted on high-fidelity simulator underscores the overall effectiveness of this framework.
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization

This work studies a Reinforcement Learning (RL) problem in which we are given a set of trajectories collected with K baseline policies. Each of these policies can be quite suboptimal in isolation, and have strong performance in complementary parts of the state space. The goal is to learn a policy which performs as well as the best combination of baselines on the entire state space. We propose a simple imitation learning based algorithm, show a sample complexity bound on its accuracy and prove that the the algorithm is minimax optimal by showing a matching lower bound. Further, we apply the algorithm in the setting of machine learning guided compiler optimization to learn policies for inlining programs with the objective of creating a small binary. We demonstrate that we can learn a policy that outperforms an initial policy learned via standard RL through a few iterations of our approach.
A partitioned dipole-antenna shower with improved transverse recoil

The implementation of a new final-state parton-shower algorithm in the Pythia event generator is described. The shower algorithm, dubbed Apollo, combines central aspects of the Vincia antenna shower with the global transverse-recoil scheme of the Alaric framework in order to achieve formal consistency with next-to-leading logarithmic (NLL) resummation. The shower algorithm is constructed in such a way that it facilitates a straightforward combination with fixed-order calculations. As an explicit proof of concept, a general scheme for matrix-element corrections (MECs) and two separate multiplicative next-to-leading order (NLO) matching schemes are outlined. It is argued that both matching schemes retain the logarithmic accuracy of the shower. The improved modelling of radiation is examined by contrasting the new algorithm with existing leading-logarithmic parton showers in Pythia.
A recommender network perspective on the informational value of critics and crowds

How do the ratings of critics and amateurs compare and how should they be combined? Previous research has produced mixed results about the first question, while the second remains unanswered. We have created a new, unique dataset, with wine ratings from critics and amateurs, and simulated a recommender system using the k-nearest-neighbor algorithm. We then formalized the advice seeking network spanned by that algorithm and studied people's relative influence. We find that critics are more consistent than amateurs, and thus their advice is more predictive than advice from amateurs. Getting advice from both groups can further boost performance. Our network theoretic approach allows us to identify influential critics, talented amateurs, and the information flow between groups. Our results provide evidence about the informational function of critics, while our framework is broadly applicable and can be leveraged to devise good decision strategies and more transparent recommender systems.
ReflectSumm: A Benchmark for Course Reflection Summarization

This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students' reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, %practical tasks with potential implications in the opinion summarization domain in general and the educational domain in particular. The dataset encompasses a diverse range of summarization tasks and includes comprehensive metadata, enabling the exploration of various research questions and supporting different applications. To showcase its utility, we conducted extensive evaluations using multiple state-of-the-art baselines. The results provide benchmarks for facilitating further research in this area.
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation

In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task. How to select informative examples remains an open issue. Previous works on in-context example selection for machine translation (MT) focus on superficial word-level features while ignoring deep syntax-level knowledge. In this paper, we propose a syntax-based in-context example selection method for MT, by computing the syntactic similarity between dependency trees using Polynomial Distance. In addition, we propose an ensemble strategy combining examples selected by both word-level and syntax-level criteria. Experimental results between English and 6 common languages indicate that syntax can effectively enhancing ICL for MT, obtaining the highest COMET scores on 11 out of 12 translation directions.
Expression of Epstein-Barr virus (EBV) transcripts encoding homologues to important human proteins in diverse EBV associated diseases.

AIMS: To examine the expression of Epstein-Barr virus (EBV) transcripts encoding proteins homologous to important human proteins in diverse EBV associated diseases. The proteins were: BHRF1 (homologous to Bcl-2), BDLF2 (homologous to cyclin B1), BARF1 (homologous to intercellular cell adhesion molecule 1 (ICAM-1)), and BCRF1 (viral IL-10 (vIL-10), homologous to human IL-10 (hIL-10)). METHODS: Six cases of oral hairy leukoplakia, seven of Hodgkin's disease, eight of T cell non-Hodgkin's lymphoma, and nine of nasopharyngeal carcinoma were examined at the mRNA level using either the reverse transcriptase polymerase chain reaction (RT-PCR) or nucleic acid sequence based amplification (NASBA). Different primer sets allowed the differentiation by RT-PCR of the latent (Cp/Wp driven) and lytic (Hp driven) transcripts of BHRF1. A specific NASBA reaction was developed for the detection of vIL-10 and BDLF2 transcripts and this was tested initially on cell lines and later on clinical samples. RESULTS: vIL-10 and BDLF2 were expressed almost exclusively in oral hairy leukoplakia, whereas BARF1 transcripts were present in all cases of nasopharyngeal carcinoma, with weak expression in one oral hairy leukoplakia and isolated cases of lymphoid malignancy. Both BHRF1 transcripts were detected across the range of tissues tested, but strong expression of lytic BHRF1 transcripts was seen only in oral hairy leukoplakia. CONCLUSIONS: vIL-10 and BDLF2 transcripts are expressed during productive EBV infection and are unlikely to be important in the pathogenesis of EBV associated malignancies. BARF1 appears to be expressed preferentially during viral latency and is more closely associated with malignant rather than benign epithelial proliferations. The alternative transcripts derived from the BHRF1 open reading frame may have very different roles during latent or productive infection.
Indexing web access-logs for pattern queries

In this paper, we develop a new indexing method for large web access-logs. We are concerned with pattern queries, which advocate the search for access sequences that contain certain query patterns. This kind of queries find applications in processing web-log mining results (e.g., finding typical/atypical access-sequences). The proposed method focuses on scalability to web-logs' sizes. For this reason, we examine the gains due to signature-trees, which can further improve the scalability to very large web-logs. Experimental results illustrate the superiority of the proposed method.
Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and hard to be parallelized while training. In this work, we aim to escape from this inefficient alignment with massive but redundant frames, and instead to directly localize a few action transitions for pseudo segmentation generation, where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation, we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition, to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation, we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed.