Miracle-dz/UniFAR-FTUs huggingface dataset:
Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces.

OBJECT There is a growing interest in the use of recording from the surface of the brain, known as electrocorticography (ECoG), as a practical signal platform for brain-computer interface application. The signal has a combination of high signal quality and long-term stability that may be the ideal intermediate modality for future application. The research paradigm for studying ECoG signals uses patients requiring invasive monitoring for seizure localization. The implanted arrays span cortex areas on the order of centimeters. Currently, it is unknown what level of motor information can be discerned from small regions of human cortex with microscale ECoG recording. METHODS In this study, a patient requiring invasive monitoring for seizure localization underwent concurrent implantation with a 16-microwire array (1-mm electrode spacing) placed over primary motor cortex. Microscale activity was recorded while the patient performed simple contra- and ipsilateral wrist movements that were monitored in parallel with electromyography. Using various statistical methods, linear and nonlinear relationships between these microcortical changes and recorded electromyography activity were defined. RESULTS Small regions of primary motor cortex (< 5 mm) carry sufficient information to separate multiple aspects of motor movements (that is, wrist flexion/extension and ipsilateral/contralateral movements). CONCLUSIONS These findings support the conclusion that small regions of cortex investigated by ECoG recording may provide sufficient information about motor intentions to support brain-computer interface operations in the future. Given the small scale of the cortical region required, the requisite implanted array would be minimally invasive in terms of surgical placement of the electrode array.
Amyotrophic lateral sclerosis-associated mutant SOD1 inhibits anterograde axonal transport of mitochondria by reducing Miro1 levels

Abstract Defective axonal transport is an early neuropathological feature of amyotrophic lateral sclerosis (ALS). We have previously shown that ALS-associated mutations in Cu/Zn superoxide dismutase 1 (SOD1) impair axonal transport of mitochondria in motor neurons isolated from SOD1 G93A transgenic mice and in ALS mutant SOD1 transfected cortical neurons, but the underlying mechanisms remained unresolved. The outer mitochondrial membrane protein mitochondrial Rho GTPase 1 (Miro1) is a master regulator of mitochondrial axonal transport in response to cytosolic calcium (Ca2+) levels ([Ca2+]c) and mitochondrial damage. Ca2+ binding to Miro1 halts mitochondrial transport by modifying its interaction with kinesin-1 whereas mitochondrial damage induces Phosphatase and Tensin Homolog (PTEN)-induced Putative Kinase 1 (PINK1) and Parkin-dependent degradation of Miro1 and consequently stops transport. To identify the mechanism underlying impaired axonal transport of mitochondria in mutant SOD1-related ALS we investigated [Ca2+]c and Miro1 levels in ALS mutant SOD1 expressing neurons. We found that expression of ALS mutant SOD1 reduced the level of endogenous Miro1 but did not affect [Ca2+]c. ALS mutant SOD1 induced reductions in Miro1 levels were Parkin dependent. Moreover, both overexpression of Miro1 and ablation of PINK1 rescued the mitochondrial axonal transport deficit in ALS mutant SOD1-expressing cortical and motor neurons. Together these results provide evidence that ALS mutant SOD1 inhibits axonal transport of mitochondria by inducing PINK1/Parkin-dependent Miro1 degradation.
The effect of rfaD and rfaF of Haemophilus parasuis on lipooligosaccharide induced inflammation by NF-κB/MAPKs signaling in porcine alveolar macrophages

In Haemophilus parasuis, the rfa cluster has been identified as a virulence-associated factor that is involved in lipooligosaccharide (LOS) biosynthesis. In this study, we assessed the roles of rfaD and rfaF genes in H. parasuis SC096 on LOS-induced pro-inflammatory factors and the related signaling pathways in porcine alveolar macrophages (PAMs) by real-time PCR and western blotting. The results showed that the LOSs of both rfaD and rfaF mutants (ΔrfaD-LOS and ΔrfaF-LOS) significantly decreased the mRNA expression of pro-inflammatory factors (IL-1α, IL-1β, IL-6, IL-8 and TNF-α) in PAMs compared with H. parasuis SC096 LOS (WT-LOS). Furthermore, in ΔrfaD-LOS- and ΔrfaF-LOS-treated cells, IκBα degradation was significantly inhibited and levels of phospho-p65 and phospho-p38 were significantly reduced in PAMs. These findings suggested that the rfaD and rfaF genes mediated LOS induction of pro-inflammatory cytokines in PAMs by regulating the NF-κB and MAPKs signaling pathways during H. parasuis infection.
Approximate quaternary addition with the fast carry chains of FPGAs

A heuristic is presented to efficiently synthesize approximate adder trees on Altera and Xilinx FPGAs using their carry chains. The mapper constructs approximate adder trees using an approximate quaternary adder as the fundamental building block. The approximate adder trees are smaller than exact adder trees, allowing more operators to fit into a fixed-area device, trading off arithmetic accuracy for higher throughput.
Chitooligomer-Immobilized Biointerfaces with Micropatterned Geometries for Unidirectional Alignment of Myoblast Cells

Skeletal muscle possesses a robust capacity to regenerate functional architectures with a unidirectional orientation. In this study, we successfully arranged skeletal myoblast (C2C12) cells along micropatterned gold strips on which chitohexaose was deposited via a vectorial chain immobilization approach. Hexa-N-acetyl-d-glucosamine (GlcNAc6) was site-selectively modified at its reducing end with thiosemicarbazide, then immobilized on a gold substrate in striped micropatterns via S–Au chemisorption. Gold micropatterns ranged from 100 to 1000 µm in width. Effects of patterning geometries on C2C12 cell alignment, morphology, and gene expression were investigated. Unidirectional alignment of C2C12 cells having GlcNAc6 receptors was clearly observed along the micropatterns. Decreasing striped pattern width increased cell attachment and proliferation, suggesting that the fixed GlcNAc6 and micropatterns impacted cell function. Possibly, interactions between nonreducing end groups of fixed GlcNAc6 and cell surface receptors initiated cellular alignment. Our technique for mimicking native tissue organization should advance applications in tissue engineering.
An Outlier Detection Technique Based on Spectral Clustering

Spectral clustering receives much attention as a competitive clustering algorithms emerging in recent years, which has achieved excellent efficiency. Outlier detection shows its increasingly high practical value in many application areas such as intrusion detection, fraud detection, discovery of criminal activities in electronic commerce and so on. In this paper, we proposed a new outlier detection method inspired by spectral clustering. Our algorithm combines k-Nearest Neighbor and statistical techniques to acquire and use the information of eigenvalues and eigenvectors of feature space and finally digs the abnormal data as outliers and have achieved the method to directly compute k-neighbors spectral clustering. In this way, it not only effectively reduces the storage cost required for clustering, but also provides important reference values in terms of dimension disaster on the high-dimensional space based on distance and density-based outlier detection. We compare the performance of our method with distance-based outlier detection methods and density-based outlier detection methods. Experimental results show that the algorithm has higher accuracy and better applicability in outlier detection.
Panchromatic and Multispectral Image Fusion Using 2D Nonseparable Lifting Transform

Pansharpening is a fusion technique that merges panchromatic (PAN) images and multispectral (MS) images to create fused products of high spatial and spectral resolutions. A number of pansharpening approaches have been developed based on multiresolution analysis (MRA). This paper proposes a novel pansharpening technique using the 2D nonseparable lifting transform (NLT). It is demonstrated that the lifting structure which consists of predict-update steps can realize the pansharpening task simultaneously. In addition, the NLT possesses the predefined vanishing moments which make the representation sparse and reduce the spectral distortion significantly. Experimental results show that the proposed fusion method has an advantage on retaining spectral information while enhancing the spatial details. And it outperforms other state-of-the-art methods.
Oligotrophic wetland sediments susceptible to shifts in microbiomes and mercury cycling with dissolved organic matter addition

Recent advances have allowed for greater investigation into microbial regulation of mercury toxicity in the environment. In wetlands in particular, dissolved organic matter (DOM) may influence methylmercury (MeHg) production both through chemical interactions and through substrate effects on microbiomes. We conducted microcosm experiments in two disparate wetland environments (oligotrophic unvegetated and high-C vegetated sediments) to examine the impacts of plant leachate and inorganic mercury loadings (20 mg/L HgCl2) on microbiomes and MeHg production in the St. Louis River Estuary. Our research reveals the greater relative capacity for mercury methylation in vegetated over unvegetated sediments. Further, our work shows how mercury cycling in oligotrophic unvegetated sediments may be susceptible to DOM inputs in the St. Louis River Estuary: unvegetated microcosms receiving leachate produced substantially more MeHg than unamended microcosms. We also demonstrate (1) changes in microbiome structure towards Clostridia, (2) metagenomic shifts toward fermentation, and (3) degradation of complex DOM; all of which coincide with elevated net MeHg production in unvegetated microcosms receiving leachate. Together, our work shows the influence of wetland vegetation in controlling MeHg production in the Great Lakes region and provides evidence that this may be due to both enhanced microbial activity as well as differences in microbiome composition.
Semi-supervised multi-organ segmentation via multi-planar co-training

Multi-organ segmentation is a critical problem in medical image analysis due to its great value for computer-aided diagnosis, computeraided surgery, and radiation therapy. Although fully-supervised segmentation methods can achieve good performance, they usually require a large amount of 3D data, such as CT scans, with voxel-wised annotations which are usually difficult, expensive, and slow to obtain. By contrast, large unannotated datasets of CT images are available. Inspired by the well-known semi-supervised learning framework co-training, we propose multi-planar co-training (MPCT), to generate more reliable pseudolabels by enforcing consistency among multiple planes, i.e., saggital, coronal, and axial planes, of 3D unlabeled medical data, which play a vital role in our framework. Empirical results show that generating pseudo-labels by the multi-planar fusion rather than a single plane leads to a significant performance gain. We evaluate our approach on a new collected dataset and show that MPCT boosts the performance of a typical segmentation model, fully convolutional networks, by a large margin, when only a small set of labeled 3D data is available, i.e., 77.49% vs. 73.14%.
Variation in the diversity and richness of parasitoid wasps based on sampling effort

Parasitoid wasps are a mega-diverse, ecologically dominant, but poorly studied component of global biodiversity. In order to maximise the efficiency and reduce the cost of their collection, the application of optimal sampling techniques is necessary. Two sites in Auckland, New Zealand were sampled intensively to determine the relationship between sampling effort and observed species richness of parasitoid wasps from the family Ichneumonidae. Twenty traps were deployed at each site at three different times over the austral summer period, resulting in a total sampling effort of 840 Malaise-trap-days. Rarefaction techniques and non-parametric estimators were used to predict species richness and to evaluate the variation and completeness of sampling. Despite an intensive Malaise-trapping regime over the summer period, no asymptote of species richness was reached. At best, sampling captured two-thirds of parasitoid wasp species present. The estimated total number of species present depended on the month of sampling and the statistical estimator used. Consequently, the use of fewer traps would have caught only a small proportion of all species (one trap 7–21%; two traps 13–32%), and many traps contributed little to the overall number of individuals caught. However, variation in the catch of individual Malaise traps was not explained by seasonal turnover of species, vegetation or environmental conditions surrounding the trap, or distance of traps to one another. Overall the results demonstrate that even with an intense sampling effort the community is incompletely sampled. The use of only a few traps and/or for very short periods severely limits the estimates of richness because (i) fewer individuals are caught leading to a greater number of singletons; and (ii) the considerable variation of individual traps means some traps will contribute few or no individuals. Understanding how sampling effort affects the richness and diversity of parasitoid wasps is a useful foundation for future studies.
Aquaporin-2 Promoter Is Synergistically Regulated by Nitric Oxide and Nuclear Factor of Activated T Cells

Background/Aims: We have previously shown that aquaporin-2 (AQP2) is down-regulated in the renal medulla of rats made hypertensive by chronic inhibition of nitric oxide synthase. It has been shown that AQP2 expression is regulated by the calcineurin/nuclear factor of activated T cells (NFATc). Nitric oxide (NO) regulates the activity of NFATc via c-Jun-N-terminal kinase 2 (JNK2). Therefore, we hypothesized that increases in NO enhance NFATc-mediated up-regulation of AQP2 promoter activity. Methods: AQP2 mRNA and protein expression were detected in mouse renal papilla.AQP2 promoter luciferase reporter- and NFAT luciferase reporter-transfected MDCK cells were used to determine AQP2 promoter activity and NFATc activity, respectively. Cells were incubated with classic activators and inhibitors of NFATc and the NO pathway. Results: Our results demonstrate that both Ca2+ and NO have a synergistic effect resulting in an increase in AQP2 mRNA and protein in mouse papilla and activation of the AQP2 promoter in kidney-derived cells. In addition, NO enhances Ca2+-induced NFATc activation. The underlying mechanism involves increased NFATc nuclear import and decreased export via protein kinase G-mediated inhibition of JNK1/2. Conclusions: This is the first study defining novel regulatory roles for NO and NFATc in the control of AQP2, which is an important renal protein.
Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse

Abstract The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided.
LESDroid - A Tool for Detecting Exported Service Leaks of Android Applications

Services are widely used in Android apps. However, services may leak such that they are no longer used but cannot be recycled by the Garbage Collector. Service leaks may cause an app to misbehave, and are vulnerable to malicious external apps when the service is exported or it is accessible through other exported services. In this paper, we present LESDroid for exported service leaks detection. LESDroid automatically generates service instances and workloads (start/stop or bind/unbind of exported services) of the app under test, and applies a designated oracle to the heap snapshot for service leak detection. We evaluated LESDroid using 375 commercial apps, and found 97 leaked services and 98 distinct leak entries in 70 apps.
Comparison of the Expressive Power of Language-Based Access Control Models

This paper compares the expressive power of five language-based access control models. We show that the expressive powers are incomparable between any pair of history-based access control, regular stack inspection and shallow history automata. Based on these results, we introduce an extension of HBAC, of which expressive power exceeds that of regular stack inspection.
Restoration of Simple Lens Captured Images based on Deep Multi-Scale Network

Inspired by computational imaging technology, simple lens can be combined with the image restoration algorithm to achieve satisfactoy imaging quality. Therefore, the conventional complex lens can be replaced with simple lens to reduce the cost and weight. However, images captured by simple-lens cameras suffer from spatial varying blur. An image restoration algorithm is proposed based on a deep multi-scale network for simple lens imaging systems. Image feature are processed and analyzed at multiple resolution levels to improve the learning ability of the network. The experimental and results prove that the image restoration algorithm we proposed can achieve promising restoration results.
How to Detect Performance Changes in Software History: Performance Analysis of Software System Versions

Source code changes can affect the performance of software. Structured knowledge about classes of those changes could guide software developers in avoiding negative changes and improving the performance by positive changes. Neither a comprehensive overview nor a mature method for structured detection of those changes exists for this purpose. We address this research challenge by presenting Performance Analysis of Software Systems (PeASS). PeASS builds up a comprehensive knowledge base of changes affecting the performance of a software by analyzing the version history of a repository using its unit tests. It is based on a method for determining the significant performance changes between two unit tests by measurement and statistical analysis. Furthermore, PeASS uses regression test selection for saving measurement time and root cause isolation method for performance changes analysis. We demonstrate our methodology in the context of Java by analyzing the versions of Apache Commons IO.
Evaluating the impact of clinical librarians on clinical questions during inpatient rounds

Objective The investigation sought to determine the effects of a clinical librarian (CL) on inpatient team clinical questioning quality and quantity, learner self-reported literature searching skills, and use of evidence-based medicine (EBM). Methods Clinical questioning was observed over 50 days of inpatient pediatric and internal medicine attending rounds. A CL was present for 25 days and absent for 25 days. Questioning was compared between groups. Question quality was assessed by a blinded evaluator, who used a rubric adapted from the Fresno Test of Competence in Evidence-Based Medicine. Team members were surveyed to assess perceived impacts of the CL on rounds. Results Rounds with a CL (CLR) were associated with significantly increased median number of questions asked (5 questions CLR vs. 3 NCLR; p<0.01) and answered (3 CLR vs. 2 NCLR; p<0.01) compared to rounds without a CL (NCLR). CLR were also associated with increased mean time spent asking (1.39 minutes CLR vs. 0.52 NCLR; p<0.01) and answering (2.15 minutes CLR vs. 1.05 NCLR; p=0.02) questions. Rounding time per patient was not significantly different between CLR and NCLR. Questions during CLR were 2 times higher in adapted Fresno Test quality than during NCLR (p<0.01). Select participants described how the CL’s presence improved their EBM skills and care decisions. Conclusions Inpatient CLR were associated with more and improved clinical questioning and subjectively perceived to improve clinicians’ EBM skills. CLs may directly affect patient care; further study is required to assess this. CLs on inpatient rounds may be an effective means for clinicians to learn and use EBM skills.
Autocalib: automatic traffic camera calibration at scale

Emerging smart cities are typically equipped with thousands of outdoor cameras. However, these cameras are typically not calibrated, i.e., information such as their precise mounting height and orientation is not available. Calibrating these cameras allows measurement of real-world distances from the video, thereby, enabling a wide range of novel applications such as identifying speeding vehicles, city road planning, etc. Unfortunately, robust camera calibration is a manual process today and is not scalable. In this paper, we propose AutoCalib, a system for scalable, automatic calibration of traffic cameras. AutoCalib exploits deep learning to extract selected key-point features from car images in the video and uses a novel filtering and aggregation algorithm to automatically produce a robust estimate of the camera calibration parameters from just hundreds of samples. We have implemented AutoCalib as a service on Azure that takes in a video segment and outputs the camera calibration parameters. Using video from real-world traffic cameras, we show that AutoCalib is able to estimate real-world distances with an error of less than 12%.
An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange

Abstract Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatility estimates. The second one is to contribute to extend the very scarce empirical research on VaR estimation in emerging financial markets. Methods/Approach: Using the daily returns of the Macedonian stock exchange index-MBI 10, we have tested the performance of the symmetric GARCH (1,1) and the GARCH-M model as well as of the asymmetric EGARCH (1,1) model, the GARCH-GJR model and the APARCH (1,1) model with different residual distributions. Results: The most adequate GARCH family models for estimating volatility in the Macedonian stock market are the asymmetric EGARCH model with Student’s t-distribution, the EGARCH model with normal distribution and the GARCH-GJR model. Conclusion: The econometric estimation of VaR is related to the chosen GARCH model. The obtained findings bear important implications regarding VaR estimation in turbulent times that have to be addressed by investors in emerging capital markets