Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Intravenous Lidocaine on Postoperative Recovery of Patients Undergoing Mastectomy: A Double-Blind, Placebo-Controlled Randomized Trial

Background One of the modalities of treatment for breast cancer surgery pain is opioids, and opioids are associated with adverse effects such as itching and postoperative nausea and vomiting (PONV). Intravenous (IV) lidocaine has been shown to reduce opioid consumption and to improve overall postoperative outcomes in abdominal surgery. In this study, we tested the effect of intraoperative IV lidocaine infusion on the quality of postoperative recovery after breast cancer surgery. Methods Seventy-one patients undergoing breast cancer surgery were randomly assigned to receive either placebo (group P; n = 34) or IV lidocaine (group L; n = 37, bolus 1.5 mg/kg at induction, then infusion at 2 mg/kg/h, stopped 2 hours after the end of surgery) in a prospective double-blind design. Intraoperative and postoperative morphine consumption was calculated. Postoperative pain scores, PONV, and fatigue were assessed at 2, 24, and 48 hours after surgery. Duration of postoperative hospital stay was recorded. Results Demographics were the same between the groups. There was no statistically significant difference in intraoperative or postoperative morphine consumption (P = 0.188 and P = 0.758) between groups. Overall pain scores either at rest or activity (P = 0.348 and P = 0.810, respectively), PONV (P = 0.350), fatigue (P = 0.758), or duration of postoperative hospital stay (P = 0.218) were not statistically different. Conclusions Our findings did not show a significant effect of IV lidocaine during breast cancer surgery on opioid consumption, pain score, PONV, or fatigue, indicating that the benefit of this approach does not generalize across all types of surgery.
Increased Y-Chromosome Detection by SRY Duplexing

Objective: Determining fetal sex noninvasively is dependent of a robust assay. We designed a novel SRY assay and combined it with a SRY assay from literature forming a duplex assay with the same fluorescent dye to increase detection of Y-chromosome at low cell-free fetal DNA or chimeric DNA concentrations. Methods: We analyzed 48 plasma samples from pregnant women in gestational weeks 5–35. Fetal sex was determined by DYS14 and SRY. We also compared 2 monoplex SRY assays with a SRY duplex assay at low DNA concentrations. Results: The DYS14 assay was more sensitive than the SRY assay, but less specific at low cell-free fetal DNA concentrations. We obtained a more sensitive duplex SRY assay with the same specificity as the 2 monoplex assays. Conclusion: The SRY duplex assay improves the SRY detection at low DNA concentration.
Collaborative formal modeling of System of Systems

The field of SoS Engineering is still emerging, but it is clear that the interaction between the independent systems as well as the collaboration between the human owners of the systems raise challenges with both technical and human factors. In this paper we present an approach for enabling collaboration between independent system development teams using an Eclipse based tool aimed at creating formal models of SoS. The approach establishes tool-support for collaborative analysis of the development and evolution of system topologies and interfaces by allowing collaboration groups to be established in which developers can exchange selected sub-models of their system models. We show that having a collaborative development environment aimed at the development of formal models for SoS provides an approach for dealing with technical and socio-technical factors in SoS engineering.
2D/3D facial feature extraction

We propose and compare three different automatic landmarking methods for near-frontal faces. The face information is provided as 480x640 gray-level images in addition to the corresponding 3D scene depth information. All three methods follow a coarse-to-fine suite and use the 3D information in an assist role. The first method employs a combination of principal component analysis (PCA) and independent component analysis (ICA) features to analyze the Gabor feature set. The second method uses a subset of DCT coefficients for template-based matching. These two methods employ SVM classifiers with polynomial kernel functions. The third method uses a mixture of factor analyzers to learn Gabor filter outputs. We contrast the localization performance separately with 2D texture and 3D depth information. Although the 3D depth information per se does not perform as well as texture images in landmark localization, the 3D information has still a beneficial role in eliminating the background and the false alarms.
Grasping and Attached Mode in Human-Computer Interaction in the Study of Mouse Substitution

Some disabilities caused by sensory malfunction, accident, or congenital disorder could cause people difficulty in grasping the computer mouse. A well-known substitute uses an inertial sensor to monitor the body’s orientation. This paper evaluates the efficiency and effectiveness of different methods of holding the sensor. There are two modes of holding the sensor, the grasping mode and the attached mode; we also evaluate a mouse as a baseline. The attached mode works by placing the sensor on the back of user’s hand. The quantitative and qualitative evaluation procedure is based on ISO/TS 9241 part 411: Ergonomics of human-system interaction standard. The test consists of four levels of difficulty and indicates that the throughput and task completion times were not statistically different between grasping and attached mode. We also found that grasping mode and attached mode did not show significantly statistical differences in comfort and fatigue based on the questionnaires. The results also suggest that the orientation of the hand used in grasping and attached modes is suitable only for lower-impact computer use.
Cetuximab Augments Cytotoxicity with Poly (ADP-Ribose) Polymerase Inhibition in Head and Neck Cancer

Overexpression of the epidermal growth factor receptor (EGFR) is a hallmark of head and neck cancers and confers increased resistance and inferior survival rates. Despite targeted agents against EGFR, such as cetuximab (C225), almost half of treated patients fail this therapy, necessitating novel therapeutic strategies. Poly (ADP-Ribose) polymerase (PARP) inhibitors (PARPi) have gained recent attention due to their unique selectivity in killing tumors with defective DNA repair. In this study, we demonstrate that C225 enhances cytotoxicity with the PARPi ABT-888 in UM-SCC1, UM-SCC6, and FaDu head and neck cancer cells. The mechanism of increased susceptibility to C225 and PARPi involves C225-mediated reduction of non-homologous end-joining (NHEJ)- and homologous recombination (HR)-mediated DNA double strand break (DSB) repair, the subsequent persistence of DNA damage, and activation of the intrinsic apoptotic pathway. By generating a DSB repair deficiency, C225 can render head and neck tumor cells susceptible to PARP inhibition. The combination of C225 and the PARPi ABT-888 can thus be an innovative treatment strategy to potentially improve outcomes in head and neck cancer patients. Furthermore, this strategy may also be feasible for other EGFR overexpressing tumors, including lung and brain cancers.
Learning Feature Hierarchies for Object Recognition

In this thesis we study unsupervised learning algorithms for training feature extractors and building deep learning models. We propose sparse-modeling algorithms as the foundation for unsupervised feature extraction systems. To reduce the cost of the inference process required to obtain the optimal sparse code, we model a feed-forward function that is trained to predict this optimal sparse code. Using an efficient predictor function enables the use of sparse coding in hierarchical models for object recognition. We demonstrate the performance of the developed system on several recognition tasks, including object recognition, handwritten digit classification and pedestrian detection. Robustness to noise or small variations in the input is a very desirable property for a feature extraction algorithm. In order to train locally-invariant feature extractors in an unsupervised manner, we use group sparsity criteria that promote similarity between the dictionary elements within a group. This model produces locally-invariant representations under small perturbations of the input, thus improving the robustness of the features. Many sparse modeling algorithms are trained on small image patches that are the same size as the dictionary elements. This forces the system to learn multiple shifted versions of each dictionary element. However, when used convolutionally over large images to extract features, these models produce very redundant representations. To avoid this problem, we propose convolutional sparse coding algorithms that yield a richer set of dictionary elements, reduce the redundancy of the representation and improve recognition performance.
MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing speed. Binning a typical metagenome assembly takes only a few minutes on a single commodity workstation. We therefore recommend the community adopts MetaBAT 2 for their metagenome binning experiments. MetaBAT 2 is open source software and available at https://bitbucket.org/berkeleylab/metabat.
Aerodynamic analysis of a stator‐augmented linear cascade wind turbine

Abstract This study proposes to attach stator vanes to PowerWindow, a linear cascade wind turbine, to improve the flow direction in the device. By controlling the angle of attack, the stator vanes increase the acting force and decrease the undesirable force on PowerWindow blades. An analytical model using blade element momentum theory is developed for the new configuration, referred to as stator-augmented PowerWindow. The analytical model has been verified by a computational fluid dynamic simulation. This study shows that the stator vanes are able to minimize/neutralize the undesirable axial force on PowerWindow so that the thrust coefficient decreases from 0.035 in the original model to −0.005 in the stator-augmented one. In addition, by increasing the acting force on the blades, the stator augmentation will simultaneously enhance the coefficient of performance by up to 10%. This study also shows that by using stator vanes to control the angle of attack, unlike in the original PowerWindow, the direction of rotation of the stator-augmented PowerWindow will remain the same regardless of the wind direction, increasing the utility of the device in practice. Abstract This study proposes to attach stator vanes to PowerWindow, a linear cascade wind turbine, to improve the flow direction in the device. By controlling the angle of attack, the stator vanes increase the acting force and decrease the undesirable force on PowerWindow blades. An Analytical model using blade element momentum theory is developed for the new configuration, referred to as stator-augmented PowerWindow. The analytical model has been verified by a computational fluid dynamic simulation. This study shows that the stator vanes are able to minimize/neutralize the undesirable axial force on PowerWindow so that the thrust coefficient decreases from 0.035 in the original model to -0.005 in the stator-augmented one. In addition, by increasing the acting force on the blades, the stator-augmentation will simultaneously enhance the coefficient of performance by up to 10%. This study also shows that by using stator vanes to control the angle of attack, unlike in the original PowerWindow, the direction of rotation of the stator-augmented PowerWindow will remain the same regardless of the wind direction, increasing the utility of the device in practice. of the pitch angle and rotating speed on aerodynamic performance of a counter-rotating wind turbine and showed that the rotational speeds of the wind turbine rotors are strongly dependent on their pitch angles, while both the parameters (pitch angle and rotating speed) significantly affect the aerodynamic performance of the turbine. flow approaching the rear blades. One possible solution to reduce flow detachment from the middle vanes surface is to use symmetric airfoil instead of regular flat vanes.
Quantifying the efficacy of genetic shifting in control of mosquito‐borne diseases

Many of the world's most prevalent diseases are transmitted by animal vectors such as dengue transmitted by mosquitoes. To reduce these vector‐borne diseases, a promising approach is “genetic shifting”: selective breeding of the vectors to be more resistant to pathogens and releasing them to the target populations to reduce their ability to transmit pathogens, that is, lower their vector competence. The efficacy of genetic shifting will depend on possible counterforces such as natural selection against low vector competence. To quantitatively evaluate the potential efficacy of genetic shifting, we developed a series of coupled genetic–demographic models that simulate the changes of vector competence during releases of individuals with low vector competence. We modeled vector competence using different genetic architectures, as a multilocus, one‐locus, or two‐locus trait. Using empirically determined estimates of model parameters, the model predicted a reduction of mean vector competence of at least three standard deviations after 20 releases, one release per generation, and 10% of the size of the target population released each time. Sensitivity analysis suggested that release efficacy depends mostly on the vector competence of the released population, release size, release frequency, and the survivorship of the released individuals, with duration of the release program less important. Natural processes such as density‐dependent survival and immigration from external populations also strongly influence release efficacy. Among different sex‐dependent release strategies, releasing blood‐fed females together with males resulted in the highest release efficacy, as these females mate in captivity and reproduce when released, thus contributing a greater proportion of low‐vector‐competence offspring. Conclusions were generally consistent across three models assuming different genetic architectures of vector competence, suggesting that genetic shifting could generally apply to various vector systems and does not require detailed knowledge of the number of loci contributing to vector competence.
Using Wikipedia as a Resource for Arabic Named Entity Recognition

In this paper we describe a novel approach to delimit named entities and nominal successive mentions in the Arabic Wikipedia. To achieve this a linguistic analysis of named entities and successive mentions in the Arabic Wikipedia, in terms of coverage and complexity, is presented. A supervised machine learning classifier has been used to predict the presence of the named entities in the Arabic Wikipedia by utilising a domain-transferred corpus accompanied by extracted lexical, contextual, morphological and syntactical features. On the top of the classifier, post processing and a mention detection algorithm is developed to efficiently detect the boundary of the named entity phrase and successive mention respectively. The described method has been evaluated on a random sample of Wikipedia texts and achieves 88.62% F-measure of detecting both simple and complex named entity phrases.
Lipopolysaccharide induces acute bursal atrophy in broiler chicks by activating TLR4-MAPK-NF-κB/AP-1 signaling

We investigated the mechanisms that induce atrophy of the chicken bursa of Fabricius (BF) upon lipopolysaccharide (LPS) treatment in young chicks. LPS treatment resulted in ∼36% decrease in bursal weight within 36 h (P < 0.01). Histological analysis showed infiltration of eosinophilic heterophils and nucleated oval shaped RBCs in or near blood vessels of the BF from LPS-treated chicks. Scanning electron micrographs showed severe erosion and breaks in the mucosal membrane at 12 h and complete exuviation of bursal mucosal epithelial cells at 36 h. We observed decreased cell proliferation (low PCNA positivity) and increased apoptosis (high TUNEL and ssDNA positivity) in the BF 12-72 h after LPS treatment. RNA-seq analysis of the BF transcriptome showed 736 differentially expressed genes with most expression changes (637/736) 12 h after LPS treatment. KEGG pathway analysis identified TLR4-MAPK-NF-κB/AP-1 as the key signaling pathway affected in response to LPS stimulation. These findings indicate LPS activates the TLR4-MAPK-NF-κB/AP-1 signaling pathway that mediates acute atrophy of the chicken bursa of Fabricius by inducing inflammation and apoptosis.
Efficacy of ACL injury risk screening methods in identifying high‐risk landing patterns during a sport‐specific task

Screening methods sensitive to movement strategies that increase anterior cruciate ligament (ACL) loads are likely to be effective in identifying athletes at‐risk of ACL injury. Current ACL injury risk screening methods are yet to be evaluated for their ability to identify athletes' who exhibit high‐risk lower limb mechanics during sport‐specific maneuvers associated with ACL injury occurrences. The purpose of this study was to examine the efficacy of two ACL injury risk screening methods in identifying high‐risk lower limb mechanics during a sport‐specific landing task. Thirty‐two female athletes were screened using the Landing Error Scoring System (LESS) and Tuck Jump Assessment. Participants' also completed a sport‐specific landing task, during which three‐dimensional kinematic and kinetic data were collected. One‐dimensional statistical parametric mapping was used to examine the relationships between screening method scores, and the three‐dimensional hip and knee joint rotation and moment data from the sport‐specific landing. Higher LESS scores were associated with reduced knee flexion from 30 to 57 ms after initial contact (P = 0.003) during the sport‐specific landing; however, no additional relationships were found. These findings suggest the LESS and Tuck Jump Assessment may have minimal applicability in identifying athletes' who exhibit high‐risk landing postures in the sport‐specific task examined.
Population Genetic Structure and Diversity of the Invasive Island Apple Snail Pomacea maculata (Perry, 1810) in South Carolina and Georgia

ABSTRACT Population genetics is a valuable tool in understanding various aspects of biological invasions, such as pathways of invasions, predicting invasion success, and determining dispersal capabilities. The island apple snail (Pomacea maculata), which is native to South America, is an invasive gastropod currently established in several southeastern states in the United States, including South Carolina and Georgia. Despite its known negative impacts in invaded regions, knowledge of P. maculata specific to South Carolina is limited, and an in-depth analysis of the population genetic structure of P. maculata in South Carolina and Georgia has not been conducted. The present study aimed to genetically characterize P. maculata individuals from three locations in South Carolina and five locations in Georgia using a suite of 14 microsatellite markers. Population genetic structure was determined using pairwise FST comparisons, isolation by distance analysis, and a likelihood-based approach. Genetic diversity indices, effective population size, inbreeding coefficients, and Garza–Williamson indices, which is the mean ratio of number of alleles to the range in allele size in a population and can be used to detect recent reductions in population size, were all calculated. Significant genetic structure was observed in all sampling locations, apart from the two sites in Kingsland, GA; a weak correlation was found between genetic distance and geographic distance across all the sites; and genetic diversity was low across all loci at the eight sites. Garza–Williamson indices all showed likely recent reductions in population size. These results indicate that the populations in South Carolina and Georgia were probably the result of separate introduction events and that these populations are genetically isolated. The low genetic diversity observed could indicate that these populations are susceptible to eradication efforts and/or stochastic environmental changes.
Flow-and-VM Migration for Optimizing Throughput and Energy in SDN-Based Cloud Datacenter

Minimizing energy consumption and improving performance in data centers are critical to cost-saving for cloud operators, but traditionally, these two optimization objectives are treated separately. Therefore, this paper presents an unified solution combining two strategies, flow migration and VM migration, to maximize throughput and minimize energy, simultaneously. Traffic-aware flow migration (FM) is first incorporated in dynamic reroute (DENDIST), evolving into DENDIST-FM, in a software-defined network (SDN) for improving throughput and avoiding congestion. Second, given energy and topology information, VM migration (ETA-VMM) can help reduce traffic loads and meanwhile save energy. Our experimental result indicates that compared to previous works, the proposed method can improve throughput by 42.5% on average with only 2.2% energy overhead. Accordingly, the unified flow-and-VM migration solution has been proven effective for optimizing throughput and energy in SDN-based cloud data centers.
The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce, Storm and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel. " The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most of the data, in a database or data repository. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The resultant products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.