Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Evolutionary Analysis of Putative Non-Specific Herbicide Resistance Genes and Compilation of Core Promoters between Monocots and Dicots

Herbicides are key weed-control tools, but their repeated use across large areas has favored the evolution of herbicide resistance. Although target-site has been the most prevalent and studied type of resistance, non-target-site resistance (NTSR) is increasing. However, the genetic factors involved in NTSR are widely unknown. In this study, four gene groups encoding putative NTSR enzymes, namely, cytochrome-P450, glutathione-S-transferase (GST), uridine 5′-diphospho-glucuronosyltransferase (UDPGT), and nitronate monooxygenase (NMO) were analyzed. The monocot and dicot gene sequences were downloaded from publicly available databases. Phylogenetic trees revealed that most of the CYP450 resistance-related sequences belong to CYP81 (5), and in GST, most of the resistance sequences belonged to GSTU18 (9) and GSTF6 (8) groups. In addition, the study of upstream promoter sequences of these NTSR genes revealed stress-related cis-regulatory motifs, as well as eight transcription factor binding sites (TFBS) were identified. The discovered TFBS were commonly present in both monocots and dicots, and the identified motifs are known to play key roles in countering abiotic stress. Further, we predicted the 3D structure for the resistant CYP450 and GST protein and identified the substrate recognition site through the homology approach. Our description of putative NTSR enzymes may be used to develop innovative weed control techniques to delay the evolution of NTSR.
Antimony Nanoparticles Encapsulated in Self-Supported Organic Carbon with a Polymer Network for High-Performance Lithium-Ion Batteries Anode

Antimony (Sb) demonstrates ascendant reactive activation with lithium ions thanks to its distinctive puckered layer structure. Compared with graphite, Sb can reach a considerable theoretical specific capacity of 660 mAh g−1 by constituting Li3Sb safer reaction potential. Hereupon, with a self-supported organic carbon as a three-dimensional polymer network structure, Sb/carbon (3DPNS-Sb/C) composites were produced through a hydrothermal reaction channel followed by a heat disposal operation. The unique structure shows uniformitarian Sb nanoparticles wrapped in a self-supported organic carbon, alleviating the volume extension of innermost Sb alloying, and conducive to the integrality of the construction. When used as anodes for lithium-ion batteries (LIBs), 3DPNS-Sb/C exhibits a high invertible specific capacity of 511.5 mAh g−1 at a current density of 0.5 A g−1 after 100 cycles and a remarkable rate property of 289.5 mAh g−1 at a current density of 10 A g−1. As anodes, LIBs demonstrate exceptional electrochemical performance.
Total variation-based dense depth from multicamera array

Abstract. Multicamera arrays are increasingly employed in both consumer and industrial applications, and various passive techniques are documented to estimate depth from such camera arrays. Current depth estimation methods provide useful estimations of depth in an imaged scene but are often impractical due to significant computational requirements. This paper presents a framework that generates a high-quality continuous depth map from multicamera array/light-field cameras. The proposed framework utilizes analysis of the local epipolar plane image to initiate the depth estimation process. The estimated depth map is then refined using total variation minimization based on the Fenchel–Rockafellar duality. Evaluation of this method based on a well-known benchmark indicates that the proposed framework performs well in terms of accuracy when compared with the top-ranked depth estimation methods and a baseline algorithm. The test dataset includes both photorealistic and nonphotorealistic scenes. Notably, the computational requirements required to achieve an equivalent accuracy are significantly reduced when compared with the top algorithms. As a consequence, the proposed framework is suitable for deployment in consumer and industrial applications.
A Scalable MAC Framework for Internet of Things Assisted by Machine Learning

The vision of the Internet-of-Things (IoT) networks calls for a large number of power constrained devices communicating with the gateway. To achieve the channel coordination in IoT, IEEE 802.15. 4 standard has been considered as one of the most competitive technologies. However, the length of the Contention Access Period (CAP) of the superframe can hardly adapt to the variation of network traffic, so the performance of IoT is restricted. To resolve the problem, we propose a scalable MAC framework assisted by Machine Learning, called MML. With the implementation of machine learning algorithms such as Neural Network Predictor (NNP), the gateway can detect the number and type of devices from the overlapped signals, as demonstrated in our Universal Software Radio Peripheral (USRP2) testbed. Therefore, MML can dynamically adjust the CAP length based on the knowledge of the number of active devices and a stable throughput can be achieved. Moreover, the throughput of MML is analyzed, which is verified by conducting simulations using network simulator (ns-2.35). The analytical and simulation results demonstrate the superiority of the proposed MML.
WAILS: Watershed Algorithm With Image-Level Supervision for Weakly Supervised Semantic Segmentation

Image semantic segmentation has great development in many fields, and the lack of fully supervised segmentation labels has always been a major problem in the development of image semantic segmentation. In this paper, we propose a WAILS method to solve this problem. First, the image is coarsely segmented through a weakly supervised network at the image level. Second, to further obtain the shape of the target in the image, the watershed algorithm is used to refine the result of the coarse segmentation. Third, this refined image is used as a label for the first round of training of a fully supervised image semantic segmentation network. At last, the results are refined through the watershed as the label of the second round of fully supervised training and then it iterates. This method achieves the pixel-level semantic segmentation only through image-level labels and watershed pre-segmentation. Our method achieved good performance on the PASCAL 2012 dataset and the COCO dataset, while its segmentation accuracy surpasses all current weakly supervised semantic segmentation models in the category of bird, airplane, sheep, and so on.
Unsupervised Leraning for Sematic Representation of Short Text

Learning generic text embeddings is still a challenge in the field of many natural language modeling applications. For learning generic text embeddings, we propose a new model to learn short text representations that can be used for multiple purposes. The model consists of two convolutional neural networks: one is responsible for extracting the semantic representations of short text which words are normal order, and the other is learning the representations of short text which is in reverse order. We are committed to minimizing the difference between the two representations. What’s more, we assume that the posterior approximation of the semantic representations of short text is Gaussian, we minimize the KL-divergence to map semantic representations into low-dimensional spaces with Gaussian distributions. Based on a Chinese text classification dataset, the experiments show that our model achieves higher scores than baseline systems and learns more semantic representations without supervision.
Portable Vertical Graphene@Au-Based Electrochemical Aptasensing Platform for Point-of-Care Testing of Tau Protein in the Blood

Alzheimer’s disease (AD) is a long-term neurodegenerative disease that poses a serious threat to human life and health. It is very important to develop a portable quantitative device for AD diagnosis and personal healthcare. Herein, we develop a portable electrochemical sensing platform for the point-of-care detection of AD biomarkers in the blood. Such a portable platform integrates nanoAu-modified vertical graphene (VG@Au) into a working electrode, which can significantly improve sensitivity and reduce detection limit due to the large specific surface, excellent electrical conductivity, high stability, and good biocompatibility. The tau protein, as an important factor in the course of AD, is selected as a key AD biomarker. The results show that the linear range of this sensing platform is 0.1 pg/mL to 1 ng/mL, with a detection limit of 0.034 pg/mL (S/N = 3), indicating that this portable sensing platform meets the demand for the detection of the tau protein in the blood. This work offers great potential for AD diagnosis and personal healthcare.
Susceptibility of Two Southern Ocean Phytoplankton Key Species to Iron Limitation and High Light

Although iron (Fe) availability primarily sets the rate of phytoplankton growth and primary and export production in the Southern Ocean, other environmental factors, most significantly light, also affect productivity. As light availability strongly influences phytoplankton species distribution in low Fe-waters, we investigated the combined effects of increasing light (20, 200 and 500 μmol photons m−2 s−1) in conjunction with different Fe (0.4 and 2 nM Fe) availability on the physiology of two ecologically relevant phytoplankton species in the Southern Ocean, Chaetoceros debilis (Bacillariophyceae) and Phaeocystis antarctica (Haptophyceae). Fe-deficient cells of P. antarctica displayed similar high growth rates at all irradiances. In comparison, Fe-deplete C. debilis cells grew much slower under low and medium irradiance and were unable to grow at the highest irradiance. Interestingly, Fe-deficient C. debilis cells were better protected against short-term excessive irradiances than P. antarctica. This tolerance was apparently counteracted by strongly lowered growth and particulate organic carbon production rates of the diatom relative to the prymnesiophyte. Overall, our results show that P. antarctica was the more tolerant species to changes in the availability of Fe and light, providing it a competitive advantage under a high light regime in Fe-deficient waters as projected for the future.
Effects of Footwear on Anterior Cruciate Ligament Forces during Landing in Young Adult Females

Rates of anterior cruciate ligament (ACL) rupture in young people have increased markedly over the past two decades, with females experiencing greater growth in their risk compared to males. In this study, we determined the effects of low- and high-support athletic footwear on ACL loads during a standardized drop–land–lateral jump in 23 late-/post-pubertal females. Each participant performed the task unshod, wearing low- (Zaraca, ASICS) or high- (Kayano, ASICS) support shoes (in random order), and three-dimensional body motions, ground-reaction forces, and surface electromyograms were synchronously acquired. These data were then used in a validated computational model of ACL loading. One-dimensional statistical parametric mapping paired t-tests were used to compare ACL loads between footwear conditions during the stance phase of the task. Participants generated lower ACL forces during push-off when shod (Kayano: 624 N at 71–84% of stance; Zaraca: 616 N at 68–86% of stance) compared to barefoot (770 N and 740 N, respectively). No significant differences in ACL force were observed between the task performed wearing low- compared to high-support shoes. Compared to barefoot, both shoe types significantly lowered push-off phase peak ACL forces, potentially lowering risk of ACL injury during performance of similar tasks in sport and recreation.
Delivery Management System Based on Vehicles Monitoring and a Machine-Learning Mechanism

The continuously growing online shopping is increasing the number of attended home deliveries. The last-mile delivery plays an important role in online shopping satisfaction and especially for food deliveries. This paper focuses on food delivery retailers and particularly investigates the possibility to enhance deliveries using information and data knowledge. In fact, in addition to optimize and to share delivery routes, delivery vehicles could be monitored in order to always maintain shortest delivery delays. We propose in this paper a delivery management architecture targeting these principles. This system is composed of several core mechanisms that should keep delivery delays to a minimum while maintaining low service times. A proof-of-concept of this delivery management system has been developed using Electric Scooters, smartphones and several algorithms. It demonstrates how this architecture could work in a food delivery scenario.
Molecular Dynamics Study on Behavior of Resist Molecules in UV-Nanoimprint Lithography Filling Process

In this study, we performed molecular dynamics (MD) simulations of the filling process of few-nanometer-wide trenches with various resist materials in ultraviolet nanoimprint lithography (UV-NIL) to identify the main molecular features necessary for a successful filling process. The 2- and 3-nm wide trenches were successfully filled with the resist materials that had (experimentally determined) viscosities less than 10 mPa·s. The resist composed of a three-armed bulky and highly viscous molecule could not fill the trenches. The radius of gyration of this molecule was smaller than half of the distance in which the first peak of its radial distribution function occurred. The available shapes of 1,6-hexanediol diacrylate (HDDA) and tri(propylene glycol) diacrylate (TPGDA), which are linear photopolymers, were compared to reveal that TPGDA is more flexible and adopts more conformations than HDDA. The terminal functional groups of TPGDA can be close due to its flexibility, which would increase the probability of intramolecular crosslinking of the molecule. This simulation result could explain the difference in hardness between the UV-cured HDDA and TPGDA based materials observed by experiments. The findings revealed by our MD simulations provide useful information for selecting and designing resists for fine patterning by UV-NIL.
Gold Nanocone Array with Extensive Electromagnetic Fields for Highly Reproducible Surface-Enhanced Raman Scattering Measurements

Surface-enhanced Raman scattering (SERS) is a technique used to distinguish the constitution of disease-related biomarkers in liquid biopsies, such as exosomes and circulating tumor cells, without any recognition elements. Previous studies using metal nanoparticle aggregates and angular nanostructures have achieved the detection of various biomarkers owing to strong hot spots and electromagnetic (EM) fields by localized surface plasmon resonance (LSPR). Although these SERS platforms enable significant enhancement of Raman signals, they still have some problems with the fabrication reproducibility of platforms in obtaining reproducible SERS signals. Therefore, highly reproducible fabrication of SERS platforms is required. Here, we propose the application of a polymer-based gold (Au) nanocone array (Au NCA), which extensively generates an enhanced EM field near the Au NCA surface by LSPR. This approach was experimentally demonstrated using a 785 nm laser, typically used for SERS measurements, and showed excellent substrate-to-substrate reproducibility (relative standard deviation (RSD) < 6%) using an extremely simple fabrication procedure and very low laser energy. These results proved that a Au NCA can be used as a highly reproducible SERS measurement to distinguish the constitution of biomarkers.
Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation

Unsupervised domain adaptation (UDA) is an emerging learning paradigm that models on unlabeled datasets by leveraging model knowledge built on other labeled datasets, in which the statistical distributions of these datasets are usually not identical. Formally, UDA is to leverage knowledge from a labeled source domain to promote an unlabeled target domain. Although there have been a variety of methods proposed to address the UDA problem, most of them are dedicated to single-source-to-single-target domain, while the works on single-source-to-multitarget domain are relatively rare. Compared to the single-source domain with single-target domain scenario, the UDA from single-source domain to multitarget domain is more challenging since it needs to consider not only the relationships between the source and the target domains but also those among the target domains. To this end, this article proposes a kind of dictionary learning-based unsupervised multitarget domain adaptation method (DL-UMTDA). In DL-UMTDA, a common dictionary is constructed to correlate the single-source and multitarget domains, while individual dictionaries are designed to exploit the private knowledge for the target domains. Through learning the corresponding dictionary representation coefficients in the UDA process, the correlations from the source to the target domains as well as these potential relationships between the target domains can be effectively exploited. In addition, we design an alternating algorithm to solve the DL-UMTDA model with theoretical convergence guarantee. Finally, extensive experiments on benchmark (Office + Caltech) and real datasets (AgeDB, Morph, and CACD) validate the superiority of the proposed method.
Evaluation of the use of GeneXpert MTB/RIF in a zone with high burden of tuberculosis in Thailand

GeneXpert MTB/RIF is a reliable molecular diagnostic tool capable of detecting Mycobacterium tuberculosis (MTB) and identifying genetic determinants of rifampicin (RIF) resistance. This study aimed to assess physicians’ diagnostic decision-making processes for TB based on GeneXpert MTB/RIF results and how this affected the initiation of multidrug resistance (MDR) treatment. This study employed a mixed method: data were collected retrospectively from the medical records of TB patients and in-depth interviews were conducted with healthcare workers in areas with a high TB burden in Thailand. A total of 2,030 complete TB records from 2 patient groups were reviewed, including 1443 suspected cases with negative smear results and 587 with high risk of MDR-TB. GeneXpert MTB/RIF was routinely used to assist the physicians in their decision-making for the diagnosis of pulmonary tuberculosis (PTB) and the initiation of MDR-TB treatment. The physicians used it as a “rule-in test” for all patients with negative chest X-rays (CXR) and smear results, to ensure timely treatment. Approximately one-fourth of the patients with negative CXR/smear and GeneXpert MTB/RIF results were diagnosed with PTB by the physicians, who based their decisions on other evidence, such as clinical symptoms, and did not use GeneXpert MTB/RIF as a “rule-out test. ” GeneXpert MTB/RIF proved effective in early detection within a day, thereby radically shortening the time required to initiate second-line drug treatment. Despite its high sensitivity for detecting PTB and MDR-TB, GeneXpert MTB/RIF had contradictory results (false positive and/or false negative) for 21.8% of cases among patients with negative smear results and 41.1% of cases among patients with high risk of MDR-TB. Therefore, physicians still used the results of other conventional tests in their decision-making process. It is recommended that GeneXpert MTB/RIF should be established at all points of care and be used as the initial test for PTB and MDR-TB diagnosis.
Domestication of plants revisited - Darwin to the present day.

Darwin studied domesticated plants and animals to try to understand the causes of variability. He observed that variation is greatest in the part of the plant most used by humans, but explanations of the causes of this variation had to await the discovery of Mendelian genetics and subsequent advances in the understanding of the structure and mode of action of genes, from the one gene, one enzyme hypothesis to the role of transcriptional regulators. Darwin credited his studies on domesticated plants and animals with demonstrating to him the power of selection. He recognized two forms of human-mediated selection, methodical and unconscious, in addition to natural selection. Selection leaves a signature in the form of reduced diversity in genes that have been the targets of selection and in 'hitch-hiking' genomic regions linked to the target genes. These so-called selective sweeps may serve now to identify genes targeted by selection in early stages of domestication and thus provide a possible guide to crop improvement in future. (C) 2009 The Linnean Society of London, Botanical Journal of the Linnean Society, 2009, 161, 203-212.
Use of Point-of-Care Ultrasound by Intensive Care Paramedics to Assess Respiratory Distress in the Out-of-Hospital Environment: A Pilot Study

Abstract Background Respiratory distress is a common presentation attended by paramedics. Chest auscultation has been shown to have low accuracy for diagnosing respiratory complaints, and this can lead to inaccurate patient assessment and potentially poor patient outcomes. Conversely, lung ultrasound is a relatively simple exam allowing for rapid differentiation of respiratory complaints with comparable accuracy to more advanced imaging modalities. Evidence suggests that lung ultrasound is easy to learn and apply and could be ideal for assessment of respiratory illness by paramedics. Objective This study aimed to explore the utility of out-of-hospital lung ultrasound performed by intensive care paramedics (ICP) for patients with medical causes of respiratory distress, and explore whether the use of lung ultrasound affects the ICP’s clinical impression or management. Methods This was a prospective observational pilot study. After a training program, a sample of ICPs working in metropolitan and regional Victoria, Australia used ultrasound to assess adult patients with respiratory distress and/or dyspnea. ICPs used a handheld point-of-care ultrasound device to scan respiratory patients using a modified protocol, and completed a worksheet with their scan findings. The scans were then reviewed by a subject matter expert for quality and agreement. Results Ninety-five patients were enrolled over the study period. The average image quality score was 2.68/5, and 56% of scans were of interpretable quality. Interrater agreement (between the ICPs and the subject matter expert) was reported using Cohen’s kappa. Moderate overall agreement (0.44) was shown, with the highest reliability reported in A-profile and B-profile (0.49 and 0.57). In 42% of cases performance of the scan affected paramedic clinical impression and/or management. Conclusion ICPs can perform lung ultrasound with moderate accuracy for some respiratory conditions, and the scans may affect clinical impression and management. Future research should focus on enhanced education, expert feedback, and clinical outcomes.