Miracle-dz/UniFAR-FTUs huggingface dataset:
Platelet Function: Meloxicam Intravenous in Whole Blood Samples From Healthy Volunteers

Nonsteroidal anti‐inflammatory drugs (NSAIDs) are effective treatments for pain but may induce bleeding events due to platelet dysfunction associated with inhibition of cyclooxygenase (COX)‐1 impairing thromboxane production. An intravenous nanocrystal formulation of meloxicam, a COX‐2 preferential nonsteroidal anti‐inflammatory drug, is under development for the treatment of moderate to severe pain. This single‐center ex vivo study evaluated the effect of meloxicam intravenous and ketorolac on platelet function in whole blood samples from healthy volunteers. Each whole blood sample was aliquoted to allow analysis using a platelet function analyzer under negative control (untreated), positive control (2 therapeutic ketorolac concentrations), and meloxicam intravenous (1 therapeutic, 3 supratherapeutic concentrations) using both collagen with epinephrine and collagen with adenosine diphosphate reagent cartridges. The platelet function analyzer determines closure time by simulating platelet adhesion and aggregation following vascular injury. The final analysis set included data from 8 subjects. The collagen with adenosine diphosphate analysis (sensitive to thrombocytopathies) showed no significant differences in closure time for meloxicam‐ or ketorolac‐treated samples and untreated control. The collagen with epinephrine analysis (sensitive to aspirin‐induced platelet abnormalities) produced no significant difference in closure time between any meloxicam concentration and untreated control. Ketorolac was associated with significantly longer closure times vs untreated control at both the 2.5‐ and 5‐µg/mL concentrations (P = .003 and .0257, respectively) and vs meloxicam at several concentrations. Similar results were observed when all analyzed samples were included. Meloxicam intravenous had no significant effect on closure times at therapeutic or supratherapeutic concentrations in this ex vivo study.
Effects of Bifidobacterium longum BB536 and Lactobacillus rhamnosus HN001 in IBS patients

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder, which still lacks effective therapy. We aimed to investigate the effects of a novel formulation of Bifidobacterium longum BB536 and Lactobacillus rhamnosus HN001 with vitamin B6 (LBB) on symptoms, intestinal permeability, cultivable bacteria and metabolome in IBS subjects.
Building a pipeline to solicit expert knowledge from the community to aid gene summary curation

Abstract Brief summaries describing the function of each gene’s product(s) are of great value to the research community, especially when interpreting genome-wide studies that reveal changes to hundreds of genes. However, manually writing such summaries, even for a single species, is a daunting task; for example, the Drosophila melanogaster genome contains almost 14 000 protein-coding genes. One solution is to use computational methods to generate summaries, but this often fails to capture the key functions or express them eloquently. Here, we describe how we solicited help from the research community to generate manually written summaries of D. melanogaster gene function. Based on the data within the FlyBase database, we developed a computational pipeline to identify researchers who have worked extensively on each gene. We e-mailed these researchers to ask them to draft a brief summary of the main function(s) of the gene’s product, which we edited for consistency to produce a ‘gene snapshot’. This approach yielded 1800 gene snapshot submissions within a 3-month period. We discuss the general utility of this strategy for other databases that capture data from the research literature. Database URL: https://flybase.org/
Physical virology: From virus self‐assembly to particle mechanics

Abstract Viruses are highly ordered supramolecular complexes that have evolved to propagate by hijacking the host cell's machinery. Although viruses are very diverse, spreading through cells of all kingdoms of life, they share common functions and properties. Next to the general interest in virology, fundamental viral mechanisms are of growing importance in other disciplines such as biomedicine and (bio)nanotechnology. However, in order to optimally make use of viruses and virus‐like particles, for instance as vehicle for targeted drug delivery or as building blocks in electronics, it is essential to understand their basic chemical and physical properties and characteristics. In this context, the number of studies addressing the mechanisms governing viral properties and processes has recently grown drastically. This review summarizes a specific part of these scientific achievements, particularly addressing physical virology approaches aimed to understand the self‐assembly of viruses and the mechanical properties of viral particles. Using a physicochemical perspective, we have focused on fundamental studies providing an overview of the molecular basis governing these key aspects of viral systems. This article is categorized under: Biology‐Inspired Nanomaterials > Protein and Virus‐Based Structures Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
Glutathione and Reactive Oxygen Species Dual-Responsive Block Copolymer Prodrugs for Boosting Tumor Site-Specific Drug Release and Enhanced Antitumor Efficacy.

A remarkable hall mark of cancer cells is heterogeneous co-existence of overproduced intracellular glutathione (GSH) and high level of reactive oxygen species (ROS) compared with normal cells, which have been frequently used as the stimuli to trigger drug release from the nanocarriers. Most of stimuli-responsive delivery vehicles have been designed to respond to only one redox stimulus (e.g. GSH or ROS). Herein, we develop a GSH and ROS dual-responsive amphiphilic diblock copolymer prodrug (BCP) (GR-BCP) consisting of poly(ethylene glycol) (PEG) and camptothecin (CPT)-conjugated poly(methacrylate) in the side chains via thioether bonds. In comparison, GSH or ROS single-responsive BCPs (G-BCPs or R-BCPs) were prepared, where CPT drugs were linked by disulfide and thioketal bonds, respectively. The three BCPs can form well-defined spherical micellar nanoparticles in aqueous solution with the diameter ~ 50 nm. As compared with G-BCP and R-BCP, GR-BCP realized the highest cytotoxicity against HeLa cells with the half-inhibitory concentration (IC50) of 6.3 μM, which is much lower than 17.8 μM for G-BCP and 28.9 μM for R-BCP. Moreover, for in vivo antitumor performance, G-BCP, R-BCP, and GR-BCP showed similar efficiency in blood circulation and tumor accumulation after intravenous injection. However, GR-BCP realized the most efficient tumor suppression with low side effects. Our findings demonstrate that intracellular GSH and ROS dual-responsive BCPs show more efficient responsive drug release inside tumor cells for boosting antitumor efficacy as compared with GSH or ROS single-responsive BCPs, which provides novel strategies for redox-responsive BCPs design.
Human Alveolar Echinococcosis, Croatia

Alveolar echinococcosis is a parasitic disease caused by the tapeworm larval stage of Echinococcus multilocularis. This zoonotic disease has not been known to occur in Croatia. We report a confirmed case of human alveolar echinococcosis in a patient in Croatia who had never visited a known E. multilocularis–endemic area.
Intervention Effect of Dexmedetomidine on Inflammatory Response Following Traumatic Brain Injury in Rats

Objective To investigate the neuroprotective effect of alpha 2-adrenergic agonist (dexmedetomidine,DEX) on traumatic brain injury (TBI) in rats.Methods Sixty male Sprague-Dawley (SD) rats were randomly divided into normal control group,sham operation group,TBI group,and DEX group,each of which was subgrouped at 6,24,and 72 hours.Parietal brain contusion was produced by a free-falling weight on the exposed dura of the left parietal lobe.Neurone specific enolase (NSE) activity in the injured brain tissue and the concentrations of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) were detected using ELISA.Results As compared with TBI group,the NSE concentration of brain tissue and the serum TNF-α level in DEX group were significantly decreased respectively at the 6,24,and 72 hours after injury (P0.05,P0.05),and the serum concentration of IL-6 in DEX group was significantly decreased at the 6 and 72 hours after injury (P0.05). Conclusions Dexmedetomidine can dramatically decrease the serum levels of TNF-α and IL-6,which may be part of the neuroprotective mechanism at an early phase following TBI.
Early Adalimumab and Anti‐Adalimumab Antibody Levels for Prediction of Primary Nonresponse in Ankylosing Spondylitis Patients

This study aimed at exploring the concentration‐effect relationship of adalimumab and early adalimumab and anti‐adalimumab antibody (AAA) levels in predicting primary nonresponse in a real‐world pilot cohort of patients with ankylosing spondylitis. Thirty‐one patients were included. The Ankylosing Spondylitis Disease Activity Score improved with increasing adalimumab trough level at week 12 and reached a major improvement with levels between 8 and 12 μg/mL. Moreover, weeks 4 and 2 adalimumab levels below 4.28 and 3.37 μg/mL were predictive of primary nonresponse (area under the curve (AUC) = 0.89, 0.88; P = 0.0003, P = 0.034, respectively). Week 4 AAA signal‐to‐noise levels were significantly higher among primary nonresponders, and the cutoff for primary nonresponse prediction was above 5.31 (AUC = 0.81; P = 0.004). Adalimumab trough levels in a range of 8–12 μg/mL are optimum to reach major improvement, and lower adalimumab with higher AAA levels at the early stage (week 4) predict primary nonresponse by supporting proactive monitoring to optimize adalimumab therapy.
Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery

Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.
Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.
S-Subgradient Projection Methods with S-Subdifferential Functions for Nonconvex Split Feasibility Problems

In this paper, the original C Q algorithm, the relaxed C Q algorithm, the gradient projection method ( G P M ) algorithm, and the subgradient projection method ( S P M ) algorithm for the convex split feasibility problem are reviewed, and a renewed S P M algorithm with S-subdifferential functions to solve nonconvex split feasibility problems in finite dimensional spaces is suggested. The weak convergence theorem is established.
Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.
Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.
An exploration of effective fuzzing for side‐channel cache leakage

Adversaries can compute the secret information of a program, such as the key for encryption routines, from side channels in the light of timing‐based and access‐based CPU cache behaviours. As a result, it is crucial to understand whether a program is vulnerable to side‐channel cache leakage or not. Yet how we can find out such a vulnerability in a program remains a problem. In this paper, we revisit this problem and contemplate a test‐generation methodology, which, in both timing‐based and access‐based dimensions, systematically discovers the cache side‐channel leakage of an arbitrary software program. At the core of our test‐generation framework is an algorithm that explores the program's input space and adapts at runtime according to observed cache performance in the executed tests. We have implemented our test generator for timing‐based and access‐based attack tests and evaluated it with open‐source subject programs, including ones from OPENSSL and Linux GDK libraries. Our extensive evaluation effectively discloses the vulnerabilities of these real‐world software to both timing‐based and access‐based cache attacks. We also empirically show that our test generator achieves higher and comparable effectiveness, respectively, in simulations and real hardware platforms with regard to revealing cache side‐channel leakage than do state‐of‐the‐art fuzz testing tools.
Adaptive Markov‐based approach for dynamic virtual machine consolidation in cloud data centers with quality‐of‐service constraints

Dynamic virtual machine (VM) consolidation is one of the emerging technologies that has been considered for low‐cost computing in cloud data centers. Quality‐of‐service (QoS) assurance is one of the challenging issues in the VM consolidation problem since it is directly affected by the increase of resource utilization due to the consolidations. In this paper, we take advantage of Markov chain models to propose a novel approach for VM consolidation that can be used to explicitly set a desired level of QoS constraint in a data center to ensure the QoS goals while improving system utilization. For this purpose, an energy‐efficient and QoS‐aware best fit decreasing algorithm for VM placement is proposed, which considers QoS objective when determining the location of a migrating VM. This algorithm employs an online transition matrix estimator method to deal with the nonstationary nature of real workload data. We also propose new policies for detecting overloaded and underloaded hosts. The performance of our proposed algorithms is evaluated through simulations. The results show that the proposed VM consolidation algorithms in this paper outperforms the benchmark algorithms in terms of energy consumption, service‐level agreement violations, and other cost factors.
Time Delay Evaluation on the Water-Leaving Irradiance Retrieved from Empirical Models and Satellite Imagery

Temporal delays and spatial randomness between ground-based data and satellite overpass involve important deviations between the empirical model output and real data; these are factors poorly considered in the model calibration. The inorganic matter-generated turbidity in Lake Chapala (Mexico) was taken as a study case to expose the influence of such factors. Ground-based data from this study and historical records were used as references. We take advantage of the at-surface reflectance from Landsat-8, sun-glint corrections, a reduced NIR-band range, and null organic matter incidence in these wavelengths to diminish the physical phenomena-related radiometric artifacts; leaving the spatio-temporal relationships as the principal factor inducing the model uncertainty. Non-linear correlations were assessed to calibrate the best empirical model; none of them presented a strong relationship (<73%), including that based on hourly delays. This last model had the best predictability only for the summer-fall season, explaining 71% of the turbidity variation in 2016, and 59% in 2017, with RMSEs < 24%. The instantaneous turbidity maps depicted the hydrodynamic complexity of the lake, highlighting a strong component of spatial randomness associated with the temporal delays. Reasonably, robust empirical models will be developed if several dates and sampling-sites are synchronized with more satellite overpasses.
Responses of Water Use Efficiency to Drought in Southwest China

Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000–2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg−1H2O) > shrublands (2.00 gC kg−1H2O) > croplands (1.76 gC kg−1H2O) > grasslands (1.04 gC kg−1H2O). During the period 2000–2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management.