Miracle-dz/UniFAR-FTUs huggingface dataset:
Muscle activity detection in pathological, weak and noisy myoelectric signals

The estimation of the on–off timing of the human skeletal muscles during movement is a critical issue in weak and noisy myoelectric signal processing, in motor control studies and in clinical applications. In this study, we used an approach based on the continuous wavelet transform to detect the muscular activity, in muscle with low amplitude and low SNR. The method is based on the calculation of a so-called “manifestation variable” computed as the maximum output of a bank of matched filters at different scales. EMG signals were generated by simulation using software tool based on an EMG mathematical model and different thresholds were applied for estimating the muscle on– off timing in simulated pathological, weak and noisy (several low SNR values were analyzed) myoelectric signals. The true timing of the EMG activity and the estimated timing of the EMG activity were compared by using a relative percentage error criterion. We performed a two-way ANOVA test, with SNR and threshold as factors, to determine possible significant effects on the relative percentage error. Our results showed that this approach shows satisfactory performances especially when proper threshold values are chosen. In particular, despite the estimated timing of the EMG activity approaches the true timing when SNR is higher, the method works well also for very low SNR. Therefore, this approach to estimate the on–off timing of muscles could be used to study pathological, weak and noisy myoelectric signals.
Modeling and System-Level Simulation for Nonideal Conductance Response of Synaptic Devices

This paper presents a new method for modeling the nonideal conductance response (CR) of synaptic devices. Unlike previous studies, which utilize physical device properties for modeling, this paper only uses the measured CR data. This allows the proposed modeling method to be easily applied to various types of synaptic devices without considering the unique physical properties of each device. An efficient piecewise linear approximation method which offers a tradeoff between computational complexity and simulation accuracy of neural networks is also presented to generate a linear device model out of nonlinear CR data. In addition, model parameters, which reflect the nonideal characteristics of the CR such as abrupt and asymmetric conductance changes, conductance variation, and limited conductance dynamic range, are introduced to evaluate the network performances in the presence of the nonidealities. By adjusting the model parameters, the desired CR satisfying the network performance requirements can be derived for device development. A three-layer neural network employing the device model has been designed and trained for the MNIST data set in order to demonstrate the application of the model to system-level simulations and verify the effectiveness of the modeling method.
Measuring the effects of α1‐antitrypsin polymerisation on the structure and biophysical properties of the endoplasmic reticulum

An important function of the endoplasmic reticulum (ER) is to serve as a site of secretory protein folding. When the accumulation of misfolded proteins threatens to disturb luminal homoeostasis, the cell is said to experience ER stress. By contrast, the accumulation of well‐folded proteins inside the ER leads to a distinct form of strain called ER overload. The serpins comprise a large family of proteins whose folding has been studied in great detail. Some mutant serpins misfold to cause ER stress, whereas others fold but then polymerise to cause ER overload. We discuss recent advances in the use of dynamic fluorescence imaging to study these phenomena. We also discuss a new technique that we recently published, rotor‐based organelle viscosity imaging (ROVI), which promises to shed more light on the biophysical features of ER stress and ER overload.
Rigid Registration Method for Medical Volumes with Large Deformations and Missing Data

A rigid registration is a crucial initial step for a correct deformable medical image registration. In this work, we propose rigid registration method resistant to large deformations and missing data. The proposed method is based on the bones segmentation, feature matching and outliers elimination inspired by traditional computer vision approach. The method is compared to other state-of-the-art algorithms, the iterative closest point and intensity-based registration using widely available dataset. The proposed algorithm does not fail into local minima and reconstructs correct deformations for average vector length greater than 150 mm and data overlap ratio less than 50%, where currently applied methods fail. The algorithm is evaluated using angle and magnitude errors between corresponding deformation vectors, Hausdorff distance between bone segmentations and resistance to fail into local minima.
Miniature Triple-Mode Dielectric Resonator Filters

This paper presents a new class of triple mode dielectric resonator filters. These devices use dielectric-loaded cavities with unloaded Q- factor of 3000–5000 and reasonable spurious free window. The proposed structure is less than one quarter of the physical volume of TEM filters of the same Q. A finite element method solver for electromagnetic structures (HFSS) is used to study the main properties of this resonator. Fundamental design rules for a bandpass filter have been presented. A three pole bandpass filter was designed, fabricated and measured to verify the proposed approach.
Epidemiological characteristics of asymptomatic Norovirus infection in a population from oyster (Ostrea rivularis Gould) farms in southern China

Abstract The following paper investigates the prevalence and characteristics of asymptomatic norovirus infection in the population living around oyster farm sites. Two consecutive surveys were conducted from January 2014 to December 2014 and 4549 stool samples were screened during the same time period. The total asymptomatic infection rate was 4.04% (184/4549). Norovirus infection rate was 5.20% in oyster farming population which was significantly higher compared with non-farming population where the infection rate was 3.65% (χ2 = 5.49, P < 0.05). A total of 184 NoV positive samples were identified by real time-quantitative polymerase chain reaction (RT-qPCR) and semi-nested RT-PCR and 136 sequences were obtained. The sequences were clustered into 14 genotypes. GI strains were clustered into six genotypes, including GI.2, GI.3, GI.5, GI.6, GI.8 and GI.9; while GII strains were clustered into GII.2, GII.3, GII.4, GII.5, GII.6, GII.8 and GII.13. GI.9 and GII.17 were the predominant and most prevalent genotypes, respectively. The GII.17 genotype replaced GII.4 becoming the dominant genotype in the oyster farming area in 2014. To sum up, long-term monitoring of asymptomatic infection is crucial for the detection of new variant strains and for identifying outbreaks during the early stage.
HU-Lacking Mutants of Salmonella enterica Enteritidis Are Highly Attenuated and Can Induce Protection in Murine Model of Infection

Salmonella enterica infection is a major public health concern worldwide, particularly when associated with other medical conditions. The serovars Typhimurium and Enteritidis are frequently associated with an invasive illness that primarily affects immunocompromised adults and children with HIV, malaria, or malnutrition. These serovars can also cause infections in a variety of animal hosts, and they are the most common isolates in poultry materials. Here, we described S. Enteritidis mutants, where hupA and hupB genes were deleted, and evaluated their potential use as live-attenuated vaccine candidates. In vitro, the mutants behaved like S. Typhimurium described previously, but there were some particularities in macrophage invasion and survival experiments. The virulence and immunogenicity of the mutant lacking both hupA and hupB (PT4ΔhupAB) were evaluated in a BALB/c mice model. This mutant was highly attenuated and could, therefore, be administrated at doses higher than 109 CFU/treatment, which was sufficient to protect all treated mice challenged with the wild-type parental strain with a single dose. Additionally, the PT4ΔhupAB strain induced production of specific IgG and IgA antibodies against Salmonella and TH1-related cytokines (IFN-γ and TNF-α), indicating that this strain can induce systemic and mucosal protection in the murine model. Additional studies are needed to better understand the mechanisms that lead to attenuation of the double-mutant PT4ΔhupAB and to elucidate the immune response induced by immunization using this strain. However, our data allow us to state that hupAB mutants could be potential candidates to be explore as live-attenuated vaccines.
Transfer Learning Method for Very Deep CNN for Text Classification and Methods for its Evaluation

In recent years, it has become possible to perform text classification with high accuracy by using convolutional neural networks (CNNs). Zhang et al. decomposed words into characters and classified texts using a CNN with relatively deep layers to obtain excellent classification results. However, it is often difficult to prepare a sufficient number of labeled samples for solving real-world text-classification problems. One method for handling this problem is transfer learning, which uses a network tuned for an arbitrary task as the initial network for a target task. While transfer learning is known to be effective for image recognition, for tasks in natural language processing, such as document classification, it has not yet been shown for what types of data and to what extent transfer learning is effective. In this paper, we first introduce a character-level CNN adopting the structure of a residual network to construct a network with deeper layers for Japanese text classification. We then demonstrate that we can improve classification accuracy by performing transfer learning between two particular datasets. Additionally, we propose an approach to evaluate the effectiveness of transfer learning and use it to evaluate our model.
CAPN1 is a novel binding partner and regulator of the tumor suppressor NF1 in melanoma

Neurofibromin 1 (NF1), a tumor suppressor that negatively regulates RAS through its GTPase activity, is highly mutated in various types of sporadic human cancers, including melanoma. However, the binding partners of NF1 and the pathways in which it is involved in melanoma have not been characterized in an in depth manner. Utilizing a mass spectrometry analysis of NF1 binding partners, we revealed Calpain1 (CAPN1), a calcium-dependent neutral cysteine protease, as a novel NF1 binding partner that regulates NF1 degradation in melanoma cells. ShRNA-mediated knockdown of CAPN1 or treatment with a CAPN1 inhibitor stabilizes NF1 protein levels, downregulates AKT signaling and melanoma cell growth. Combination treatment of Calpain inhibitor I with MEKi Trametinib in different melanoma cells is more effective in reducing melanoma cell growth compared to treatment with Trametinib alone, suggesting that this combination may have a therapeutic potential in melanoma. This novel mechanism for regulating NF1 in melanoma provides a molecular basis for targeting CAPN1 in order to stabilize NF1 levels and, in doing so, suppressing Ras activation; this mechanism can be exploited therapeutically in melanoma and other cancers.
Optimising Toward Completed Videos in an Online Video Advertising Exchange

Predictive modelling is used extensively within online programmatic advertising [1], [2]. By predicting online behaviour [3], advertisers and intermediaries can ensure that they are targeting the most profitable users. This has typically been used to predict user clicks in ‘display advertising’, where static images are shown, however in recent years we’ve seen a rise in more complex advert types such as video. Due to the maturity of programmatic video advertising, similar modelling techniques for video events have yet to be demonstrated. In this paper, we outline a deployed system that automatically optimises toward video adverts that people complete (i.e. reach the end of). We discuss the specifics of a cloud based pipeline for data processing which handles 240M records a day in less than 1 hour aggregate wall clock time and provides a 4.4% point increase in completion rate over a two week trial. We provide results demonstrating the success of our system in deployment.
Genetic and Functional Diversity of Bacterial Microbiome in Soils With Long Term Impacts of Petroleum Hydrocarbons

Soil contamination with petroleum, especially in the area of oil wells, is a serious environmental problem. Restoring soil subjected to long-term pollution to its original state is very difficult. Under such conditions, unique bacterial communities develop in the soil that are adapted to the contaminated conditions. Analysis of the structure and function of these microorganisms can be a source of valuable information with regard to bioremediation. The aim of this study was to evaluate structural and functional diversity of the bacterial communities in soils with long-term impacts from petroleum. Samples were taken from the three oldest oil wells at the Crude Oil Mine site in Węglówka, Poland; the oldest was established in 1888. They were collected at 2 distances: (1) within a radius of 0.5 m from the oil wells, representing soil strongly contaminated with petroleum; and (2) 3 m from the oil wells as the controls. The samples were analyzed by 16S rRNA sequencing and the community level physiological profiling (CLPP) method in order to better understand both the genetic and functional structure of soil collected from under oil wells. Significant differences were found in the soil samples with regard to bacterial communities. The soils taken within 0.5 m of the oil wells were characterized by the highest biodiversity indexes. Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria were strongly correlated with biological activity in these soils. Families of Alphaproteobacteria were also dominant, including: Bradyrhizobiaceae, Rhizobiaceae, Rhodobacteraceae, Acetobacteraceae, Hyphomicrobiaceae, and Sphingomonadaceae. The study showed that the long term contamination of soil changes bacterial communities and their metabolic activity. Even so, natural bioremediation leads to the formation of specific groups of bacteria that actively grow at the site of contamination in the soil.
Influence of Urban-Growth Pattern on Air Quality in China: A Study of 338 Cities

Air pollution in China is a serious problem and an inevitable threat to human health. This study evaluated the relationship between air quality and urban growth pattern in China by conducting empirical research involving 338 prefecture-level and above cities. Spatial regression techniques considering spatial autocorrelation were applied to correct the calculation bias. To obtain local and accurate results, a conception of eight economic zones was adopted to delineate cities into different groups and to estimate regression separately. An additional six urban form and socioeconomic indicators served as controlling variables. Significant and positive relationships between the aggregated urban growth pattern index and air pollution were observed in Northeast China, northern coastal China, and Northwest China, indicating that a high degree of urban aggregation is associated with poor air quality. However, a negative parameter was obtained in southern coastal China, showing an opposite association on urban aggregation and air quality. Nonsignificant connections among the other four zones were found. The findings also highlighted that land use mix, population density, and city size exerted varied and significant influence on air quality across eight economic zones. Overall, this study indicated that understanding the quantitative relationships between urban forms and air quality can provide policymakers with alternative ways to improve air quality in rapidly developing China.
Mining Sequential Patterns to Explore Users' Learning Behavior in a Visual Programming App

Visual programming environments combined with problem-solving are useful for promoting students' motivation of learning programming. The purpose of this work is to use a blockly visual programming educational app for university students and analyze their learning behavior patterns. The study uses a sequential pattern mining tool to mine the students' behavioral patterns. The preliminary results showed that the proposed method can find behavior patterns of high-achievement students and low-achievement students.
Dichotomous Dopaminergic Control of Ventral Pallidum Neurons

The ventral pallidum (VP) is crucially involved in reward processing. Dopaminergic afferents reach the VP from the ventral tegmental area (VTA). Recent in vivo studies suggest dopamine application increase the firing in the VP. However, little is known about the cellular effects of dopamine within the VP. We aimed to address this paucity of data using brain slices containing the VP and multi-electrode array recordings. Dopamine significantly affected firing in 86% of spontaneously active VP neurons. Among the affected neurons, 84% were excited, while 16% were inhibited. The selective D1-like receptor agonist SKF81297 also had modulatory effects on the majority of VP neurons, but its effects were universally excitatory. On the other hand, the D2-like receptor agonist quinpirole had modulatory effects on 87% of VP neurons studied. It caused significant inhibitory effects in 33% of the cases and excitatory effects in the remaining 67%. The effects of D1-like receptor activation were presynaptic as blocking synaptic transmission with low Ca2+ abolished the effects of SKF81297 application. Furthermore, SKF81297 effects were abolished by blocking ionotropic glutamate receptors, suggesting that D1-like receptors boost glutamate release, which in turn excites VP neurons through postsynaptic glutamate receptors. Effects caused by D2-like receptor activation were found to involve pre and postsynaptic mechanisms, as low Ca2+ abolished the excitatory effects of quinpirole but not the inhibitory ones. Increases in firing frequency (ff) to quinpirole application were abolished by a group 2/3 mGluR antagonist, suggesting that D2-like receptors cause presynaptic inhibition of glutamate release, resulting in reduced postsynaptic activation of inhibitory mGluRs. Conversely, the inhibitory effects of quinpirole persisted in low Ca2+ and therefore can be attributed to postsynaptic D2-like receptor activation. VP neurons excited by dopamine had shorter spike half-widths and are excited by D1-like receptors (presynaptically) and by D2-like receptors (postsynaptically). VP neurons inhibited by dopamine have longer spike half-widths and while D1-like receptor activation has a presynaptic excitatory influence on them, D2-like receptor activation has a postsynaptic inhibitory effect that prevails, on balance. These data provide novel insights into the cellular mechanisms by which dopamine controls information processing within the VP.
Epidemiological Features of Spinal Cord Injury in China: A Systematic Review

Background: Spinal cord injury (SCI) is a severe condition that disrupts patients' physiological, mental, and social well-being state and exerts great financial burden on patients, their families and social healthcare system. This review intends to compile studies regarding epidemiological features of SCI in China. Methods: Searches were conducted on PubMed, EMBASE, Web of Science and Cochrane Library for relevant studies published through January, 2018. Studies reported methodological and epidemiological data were collected by two authors independently. Results: Seventeen studies met the inclusion criteria. Two studies reported incidence of SCI that is 60.6 in Beijing (2002) and 23.7 in Tianjin (2004–2008). All studies showed male had a larger percentage in SCI compared to female except Taiwan (2000–2003). The average male and female ratio was 3–4:1 in China and the highest male and female ratio was 5.74: 1 in Tianjin (2004–2007). Farmers, laborers and unemployed people accounted for more than half of the SCI patients in China. Fall was the primary causation with exception of Heilongjiang (2009–2013), Beijing (2001–2010), and Taiwan (2002–2003), where motor vehicle collision (MCVs) was the leading causation. Pulmonary infection, urinary tract infection and bedsore were common complications, accounting for approximately 70% of SCI patients in China. Conclusion: This review shows that epidemiological features of SCI are various in different regions in China and prevention should be implemented by regions. The number of patients with SCI result from fall and MCVs may become a main public health problem because population aging and economic developing in China. However, because all included studies were retrospective and lacking a register system in China, some data were incomplete and some cases may be left out, so the conclusion may not be generalizable to the other regions.
PACO: Signal Restoration via PAtch COnsensus

Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments (typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output. In most cases where pach overlapping occurs, the final value of those samples that are estimated by more than one patch is resolved by averaging those estimates; this includes many recent image processing algorithms. In other cases, typically frequency-based restoration methods, the average is implicitly weighted by some window function such as Hanning, Blackman, etc. which is applied prior to the Fourier/DCT transform in order to avoid Gibbs oscillations in the processed patches. Such averaging may incidentally help in covering up artifacts in the restoration process, but more often will simply degrade the overall result, posing an upper limit to the size of the patches that can be used. In order to avoid such drawbacks, we propose a new methodology where the different estimates of any given sample are forced to be identical. We show that, together, these consensus constraints constitute a non-empty convex feasible set, provide a general formulation of the resulting constrained optimization problem which can be applied to a wide variety of signal restoration tasks, and propose an efficient algorithm for finding the corresponding solutions. Finally, we describe in detail the application of the proposed methodology to three different signal processing problems, in some cases surpassing the state of the art by a significant margin.
The Turtleback Diagram for Conditional Probability

We elaborate on an alternative representation of conditional probability to the usual tree diagram. We term the representation “turtleback diagram” for its resemblance to the pattern on turtle shells. Adopting the set theoretic view of events and the sample space, the turtleback diagram uses elements from Venn diagrams—set intersection, complement and partition—for conditioning, with the additional notion that the area of a set indicates probability whereas the ratio of areas for conditional probability. Once parts of the diagram are drawn and properly labeled, the calculation of conditional probability involves only simple arithmetic on the area of relevant sets. We discuss turtleback diagrams in relation to other visual representations of conditional probability, and detail several scenarios in which turtleback diagrams prove useful. By the equivalence of recursive space partition and the tree, the turtleback diagram is seen to be equally expressive as the tree diagram for abstract concepts. We also provide empirical data on the use of turtleback diagrams with undergraduate students in elementary statistics or probability courses.