bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selection and enrichment of microbial species with an increased lignocellulolytic phenotype from a native soil microbiome by activity-based probing
Authors: Reichart Nicholas J , Steiger Andrea K , Van Fossen Elise M , McClure Ryan , Overkleeft Herman S , Wright Aaron T 
Published: September 2023

Abstract
Multi-omic analyses can provide information on the potential for activity within a microbial community but often lack specificity to link functions to cell, primarily offer potential for function or rely on annotated databases. Functional assays are necessary for understanding in situ microbial activity to better describe and improve microbiome biology. Targeting enzyme activity through activity-based protein profiling enhances the accuracy of functional studies. Here, we introduce a pipeline of coupling activity-based probing with fluorescence-activated cell sorting, culturing, and downstream activity assays to isolate and examine viable populations of cells expressing a function of interest. We applied our approach to a soil microbiome using two activity-based probes to enrich for communities with elevated activity for lignocellulose-degradation phenotypes as determined by four fluorogenic kinetic assays. Our approach efficiently separated and identified microbial members with heightened activity for glycosyl hydrolases, and by expanding this workflow to various probes for other function, this process can be applied to unique phenotype targets of interest.
Combination Therapies Involving Photodynamic Therapy
Authors: Oluwafemi Oluwatobi , Songca Sandile Phinda 
Published: September 2023

Photodynamic therapy (PDT) is increasingly used among health practitioners in combatting a variety of diseases, most notably various types of cancer, and is often utilised in combination with other therapeutic options to enhance efficacy or reduce adverse reactions. Combination Therapies Involving Photodynamic Therapy reviews the current state of development of PDT and presents the foreseeable advancements of combination approaches to therapeutic strategies.
This book represents a novel approach to the therapeutic applications of five different technologies including the combinations of photodynamic therapy, photothermal hyperthermia therapy, magnetic hyperthermia therapy, chemotherapy, and cold atmospheric pressure plasma therapy. An ideal companion for students, researchers and practitioners in biological sciences, biotechnology, and photobiological and photochemical sciences, as well as medicinal and pharmaceutical chemistry.
A modulized lane‐follower for driverless vehicles using multi‐frame
Authors: Luan Honggang , Gao Yang , Wang Zhenxu , Liu Jinyuan , Wang Shuqi , Zhao Yihui , Wang Junchao 
Published: September 2023

AbstractAs a fundamental function, lane following plays an important role for driverless vehicles. Unfortunately, lane followers generally confront great difficulty in lane line missed situations caused by vague line, shadows etc. However, for most lane line missed situation, clues of the line may be hidden in prior view of it. Consequently, a lane follower called UNL Lane Follower, which contains two deep learning network modules is proposed. The first module is a lane line detection model called UNET_CLB. Here, the sequence of image frames is utilised rather than only the current frame to deal with the missing lane lines. The second module is a lane‐following model called LSTM_DTS, which combines a deep learning attention mechanism (temporal attention network and spatial attention network) with a recurrent neural network. As a result, the proposed UNL Lane Follower produces smoother driving behaviour, especially when a lane line is temporally missed. For better explain ability, the role of each part of the network structure is analysed and explained intuitively. As a modularised network, the UNET_CLB is firstly trained and tested on the TuSimple dataset and CULane dataset. The LSTM_DTS lane follow is then trained and tested on our actual lane following dataset. Finally, the UNL Lane Follower is trained and tested as a whole in a simulation running on Webots, after importing the weight of the two modules trained separately. All testing results showed that the UNL Lane Follower can provide better robustness and accuracy for lane line following mission in the line missed situations.
Null‐space beamforming strategy for secrecy rate balancing of two‐way MIMO relay networks
Authors: Fazeli Samad , Akhlaghi Soroush 
Published: September 2023

AbstractHere, the issue of physical layer security in a two‐way multi‐antenna relay network) is studied. In such a network, two multi‐antenna transceivers attempt to exchange their secret messages via a multi‐antenna relay node in the presence of an eavesdropper. Throughout the first hop, two sources send their messages to the relay node, while an eavesdropper tries to overhear these messages. Then, in the second hop, the relay attempts to send its signal in the null‐space direction of relay‐to‐eavesdropper channel to prevent eavesdropping. The main goal is to obtain effective receive and transmit beamforming vectors at transceivers as well as the beamforming matrix at the relay node to address the secrecy rate balancing problem. The underlying problem is not convex in general. To overcome this issue, the original problem is divided into three subproblems. In the first subproblem the beamforming design at the relay is optimized while in the second subproblem the receive beamforming in a closed form solution is obtained. Finally, in the last subproblem the transmit beamforming is solved using the semi‐definite relaxation and Taylor series expansion. Numerical results represent the effectiveness of the proposed method compared to the previous works in the literature.
Using EtherCAT technology to launch online automated guided vehicle manipulation with unity‐based platform for smart warehouse management
Authors: Nguyen Hung , Nguyen Thanh Phuong , Ngo Ha Quang Thinh 
Published: September 2023

AbstractIn Industry 4.0, the innovative concept of a smart warehouse is defined as a progressive development in logistics and operations management. Numerous advanced techniques are recommended for integration, including wearable devices, communication, loading/unloading, optimization, robotics, and automated transportation, among others. Currently, the growing demand for swift cargo distribution, cost‐effective labour utilization, and enhanced productivity stands as an urgent topic for both enterprises and scholars. This paper presents an EtherCAT‐based model of automated guided vehicle (AGV)‐manipulation introduced within the Unity platform for smart warehouse management. The working environment selected for this model is a cross‐docking warehouse with a chessboard layout. Initially, theoretical computations pertaining to mechanical design are established to provide accurate parameters. Subsequently, the structure of the TwinCAT Human–Machine Interface Server and the configuration within the Unity environment are outlined. Following that, the software architecture, encompassing data exchange and a set of motion commands, is meticulously explained. To validate the proposed approach, several laboratory tests are conducted under identical conditions. This research offers several contributions: firstly, it introduces the innovative use of real‐time technology to represent AGV systems; secondly, it elucidates the potential connection between virtual reality and the physical model; and thirdly, it employs fluid motion techniques to illustrate and visualize driving performance. These findings hold significant promise not only for the advancement of smart warehouse management but also for the broader landscape of digital trade.
Computationally efficient angle estimation of bistatic MIMO radar based on multimodal optimization
Authors: Du Yanan , Gao Hongyuan , Liu Yapeng , Sun Rongchen 
Published: September 2023

AbstractIn this letter, a computationally efficient multiple signal classification (MUSIC)‐based evolutionary algorithm for angle estimation of bistatic multiple‐input multiple‐output (MIMO) radar is proposed. The existing MUSIC algorithms require a computationally cumbersome two‐dimensional (2D) peak searching and the performance is highly related to the grid that set, which leads to a conflict between the computational efficiency and estimation performance. To address this difficulty, a multimodal quantum‐inspired salp swarm algorithm, integrating kmeans clustering technique, is proposed to substitute the 2D peak searching to obtain multiple maxima of the MUSIC algorithm. The resulting computationally efficient algorithm obviously reduces the computational complexity of the MUSIC algorithm, avoids grid errors, and further exploits the potential of the MUSIC algorithm. Numerical simulations in various scenarios are carried out to verify the superiority of the method.
Research on prediction method of expressway section traffic flow considering anomaly detection probability
Authors: Wang Zhiyu , Li Linheng , Qu Xu , Mao Peipei , Ran Bin 
Published: September 2023

AbstractReal‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real‐time performance and accuracy due to the post‐processing required to rectify anomaly detection results during data pre‐processing. This paper presents a novel traffic flow prediction model, termed the With‐Anomaly Detection Probability (ADP) Attention‐Bidirectional Long‐Short Term Memory (BiLSTM) model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework. Additionally, the model incorporates an attention mechanism within the long‐short term memory network. Through a comprehensive simulation study utilizing actual measured traffic flow data from the Shanghai‐Chongqing Expressway, the effectiveness of the proposed model is rigorously evaluated. The prediction results show that the model proposed in this paper is a real‐time and accurate short‐time traffic flow prediction model compared with the basic models.
Refined convolution‐based measures for real‐time harmonic distortions estimation in power system dominated by inverter‐based resources
Authors: Kandezy Reza Saeed , Safarishaal Masoud , Hemmati Rasul , Jiang John Ning 
Published: September 2023

AbstractReal‐time estimating harmonic distortion levels near the Point of Interconnections of grid‐connected inverter‐based resources (IBRs) is critical for maintaining the operational reliability and security of the power grid, particularly during transients when IBRs generate excessive harmonics. Traditional methods like Total Harmonic Distortion (THD) are unsuitable for time‐varying waveforms as they suffer from aliasing, spectral leakage, and picket‐fence effects. This paper proposes a novel approach that utilizes convolution‐based measures for estimating harmonic distortion, which offers a more suitable way for ongoing real‐time applications. The first measure uses a 60 Hz sinusoidal function as the convolution kernel, while an enhanced measure employs the Gaussian function as the kernel. Two convolution‐based measures are proposed and compared with THD. The comparison criteria are based on the two significant features describing the variation of harmonics: min/max values and variation curvature of harmonics showing the accurate performance of proposed measures. Moreover, short‐term estimations of harmonic distortion are used to validate the effectiveness of the proposed measures, while long‐term estimations are used to determine the ability of the proposed measures to determine harmonic distortion contributions among power network components. The proposed technique's intrinsic characteristics provide a sampling window with low sensitivity to deviations in the fundamental frequency and signal's stationary condition to prevent aliasing and spectral leakage and rule out the impact of the picket‐fence effect on the harmonic distortion level estimation.
Higher accuracy detection strategy for electroluminescent defects in photovoltaic modules based on improved Yolov5
Authors: Ding Sheng , Chen Tiansheng , Chen Hao , Chen Congyan 
Published: September 2023

AbstractAt present, the domestic photovoltaic (PV) industry is developing rapidly. In order to improve the production efficiency of PV cells, a fast and accurate automatic detection model of PV modules’ defects that can be applied in the production line is essential. In this paper, based on the characteristics of significant differences in PV module defect size and a large number of fine defects, an improved defect detection algorithm based on Yolov5 is proposed. Thirteen mainstream defects are divided into two categories according to size, and a series‐connected detection network is constructed for a two‐stage detection. In order to better detect fine defects, this paper proposes the TR‐ResNet module, a residual module composed of the self‐attention, based on the self‐attention mechanism, to replace some fully convolutional residual modules (CNN‐ResNet module) in the Yolov5 backbone network. After testing, the Precision, Recall and mAP of the model are greatly improved and gained 0.904, 0.845, and 0.840. Moreover, the model performs well in stability detection, which can adapt to different production environments and quality requirements. The present study may make the detection work more efficient and improve productivity.
Decision‐dependent distributionally robust integrated generation, transmission, and storage expansion planning: An enhanced Benders decomposition approach
Authors: Qiu Lu , Dan Yangqing , Li Xukun , Cao Ye 
Published: September 2023

AbstractIntegrated generation, transmission, and storage expansion planning (IGT&SP) is the cornerstone to realize low‐carbon transition considering security constraints in the long run. A novel IGT&SP planning scheme is proposed to balance the planning cost, i.e. renewable energy sources (RESs) and energy storage systems, based on the distributionally ambiguity sets. A novel decision‐dependent ambiguity set is proposed to capture the relation between the uncertainties of RES output and long‐term planning. A two‐stage risk‐averse distributionally robust optimization is formulated, where the RESs, energy storage systems, and transmission line expansion are optimized in the first stage and a unit commitment problem is proposed in the second‐stage optimization to assess the performance of the expanded system. This problem is reformulated into a two‐stage optimization problem with complete mixed‐integer recourse, where the state variable is binary. A novel enhanced Benders decomposition algorithm is proposed to solve the IGT&SEP efficiently, where the cutting planes are generated by a primal‐dual relaxation of the recourse problem. Simulations are conducted on the modified IEEE‐30 test system and modified IEEE‐118 test system. Compared with adjustable robust optimization and L1‐norm Wasserstein distance‐based distributionally robust optimization, numerical results verify the effectiveness of the proposed IGT&SP, together with the solution algorithm.
Complete Hydatidiform Mole with a Coexisting Viable Male Fetus Detected by Cell-Free DNA
Authors: Lemieux Mackenzie , Kus Lauren , Stewart Kali , He Mai , Rowe Jackson , Brady Matthew , Bligard Katherine , Lawlor Megan , Kelly Jeannie 
Published: September 2023

AbstractComplete hydatidiform mole with coexisting fetus (CHMCF) is rare, and diagnosis is challenging due to limited data. Here, we present the case of a patient with noninvasive prenatal test (NIPT) resulting in “likely molar pregnancy” in the second trimester. Subsequent ultrasound confirmed a cystic appearing portion of the placenta. At 22 weeks, the patient delivered a demised fetus and two placentas. Pathology was consistent with CHMCF. This case is the first to show primary detection of a CHMCF with single-nucleotide polymorphism (SNP)-based NIPT prior to ultrasound identification. Our case suggests the use of SNP-based NIPT as an alternative noninvasive method to guide shared decision-making and clinical management for patients with this diagnosis.
Nourishing social solidarity in exchanging gifts: a study on social exchange in Shanghai communities during COVID-19 lockdown
Authors: Zhou Youjia , Dong Chen 
Published: September 2023

AbstractDuring the COVID-19 lockdown in Shanghai, we found many social exchanges within communities. In this study, we focus on exploring the driving forces behind social exchanges and their impacts on the social solidarity of local communities. Twenty-eight residents of separate communities are interviewed, and grounded theory is applied to have the motivation for social exchanges coded and analyzed. We find that reciprocal and generalized exchanges, rather than negotiated exchanges, occur in most communities. Among these exchanges, the willingness to share is stimulated by the traditional cultural norms taking place in online social groups. Residents will be propelled to carry on exchanges through sentiments cultivated within and to develop the micro social order through interactions in the media space. Social exchanges bring social solidarity to communities and develop it into a noticeable form consisting of functional, emotional, and communal aspects.
Isolated Nocturnal Hypertension in People Living With Human Immunodeficiency Virus: A Cross-Sectional Study
Authors: Zanuzzi Matias G , López Silvina M , Cattaneo Máximo J , Pérez-Maure María A , Lahiri Cecile D , Romero Cesar A 
Published: September 2023

Abstract

BACKGROUND
Nocturnal hypertension (NH) is a potent cardiovascular risk factor described frequently in people with HIV (PWH). Isolated NH (INH) is less well reported in PWH because of the need for ambulatory blood pressure monitoring (ABPM) in office normotensive patients. We aim to document the prevalence of NH and INH and the clinical factors associated with these phenotypes.

METHODS
Cross-sectional study from an HIV program in Argentina. Office and ABPM measurements, as well as clinical and laboratory exploration, were performed. We defined INH as NH with daytime normotension in patients with office normotension.

RESULTS
We obtained ABPM in 66 PWH, 60% male, aged 44.7 (IQR 27–69) years; 87% receiving antiretroviral therapy, and 86.2% virologically suppressed. ABPM-based hypertension prevalence was 54.7% (95% CI: 42.5–66.3). The prevalence of NH was 48.5% (32/66), while the INH prevalence was 19.7% (95% CI: 11.7–30.9). No differences were found regarding sex, HIV viral load, CD4+ T lymphocytes count, or years of infection between normotensive and INH patients. Multiple linear regression model adjusted for sex and age determined that body mass index (β = 0.93, P &lt; 0.01), plasma uric acid (β = 0.25, P = 0.04), plasma potassium (β = −10.1, P = 0.01), and high-sensitivity C-reactive protein (hs-CRP) (β = 0.78, P = 0.02) independently predicted nocturnal systolic blood pressure (BP) in PWH. In a multiple logistic regression model adjusted for age and sex, the presence of sedentariness, plasma potassium &lt;4 mEq/L, BMI, and hs-CRP levels were predictors of INH.

CONCLUSION
INH is highly prevalent in PWH. Metabolic and inflammatory markers predict nocturnal SBP in PWH.
Sex biases and the scarcity of sex metadata in global herpetology collections
Authors: Wainwright Tara , Trevena Morwenna , Alewijnse Sarah R , Campbell Patrick D , Jones Marc E H , Streicher Jeffrey W , Cooper Natalie 
Published: September 2023

Abstract
Natural history specimens are a widely used and valuable resource for conservation, ecology, and evolutionary biology. One might assume that these collections are representative of natural populations, but recent work has suggested that many collections have disproportionately more male than female specimens. Here, we investigate sex ratios in &gt; 5 000 000 amphibian and reptile specimen records from global natural history collections. We found a slight bias towards males in amphibians (39% females) and reptiles (47% females), but this varied among orders and families. Body size, sexual size dimorphism, and year of collection had little effect. Strikingly however, &gt; 95% of herpetology specimen records had no sex data associated with them at all, even from recent collections. This lack of sex data substantially limits the utility of herpetological museum collections in many ways. We propose that enhanced efforts to train taxonomic specialists and support their careers would unlock the potential of sex-based research using museum collections and their associated public databases.
BCEDB: a linear B-cell epitopes database for SARS-CoV-2
Authors: Tai Chengzheng , Li Hongjun , Zhang Jing 
Published: September 2023

Abstract
The 2019 Novel Coronavirus (SARS-CoV-2) has infected millions of people worldwide and caused millions of deaths. The virus has gone numerous mutations to replicate faster, which can overwhelm the immune system of the host. Linear B-cell epitopes are becoming promising in prevention of various deadly infectious diseases, breaking the general idea of their low immunogenicity and partial protection. However, there is still no public repository to host the linear B-cell epitopes for facilitating the development vaccines against SARS-CoV-2. Therefore, we developed BCEDB, a linear B-cell epitopes database specifically designed for hosting, exploring and visualizing linear B-cell epitopes and their features. The database provides a comprehensive repository of computationally predicted linear B-cell epitopes from Spike protein; a systematic annotation of epitopes including sequence, antigenicity score, genomic locations of epitopes, mutations in different virus lineages, mutation sites on the 3D structure of Spike protein and a genome browser to visualize them in an interactive manner. It represents a valuable resource for peptide-based vaccine development.
Database URL: http://www.oncoimmunobank.cn/bcedbindex