Miracle-dz/UniFAR-FTUs huggingface dataset:
Injury-induced perivascular niche supports alternative differentiation of adult rodent CNS progenitor cells

Following CNS demyelination, oligodendrocyte progenitor cells (OPCs) are able to differentiate into either remyelinating oligodendrocytes (OLs) or remyelinating Schwann cells (SCs). However, the signals that determine which type of remyelinating cell is generated and the underlying mechanisms involved have not been identified. Here, we show that distinctive microenvironments created in discrete niches within demyelinated white matter determine fate decisions of adult OPCs. By comparative transcriptome profiling we demonstrate that an ectopic, injury-induced perivascular niche is enriched with secreted ligands of the BMP and Wnt signalling pathways, produced by activated OPCs and endothelium, whereas reactive astrocyte within non-vascular area express the dual BMP/Wnt antagonist Sostdc1. The balance of BMP/Wnt signalling network is instructive for OPCs to undertake fate decision shortly after their activation: disruption of the OPCs homeostasis during demyelination results in BMP4 upregulation, which, in the absence of Socstdc1, favours SCs differentiation.
Study on the Application of Machine Learning in Government Service: Take Consumer Protection Service as an Example

The receiving of consumers' complaints is one of the most important work in government service. The classification of complaints for different industries is a bottleneck in the service flow chart, affecting service efficiency, which also leads to low utilization of data information after complaints are accepted. This paper uses machine learning techniques to help government departments solve the difficult issues of receiving and classifying consumers' complaints. In this paper, Naïve Bayesian algorithm and SVM algorithm are introduced to replace the manual classification in the process. In addition, this paper uses text analysis technology and LDA topic model to cluster industry issues. The visualization result helps government departments visually monitor the changes in the consumer environment and hot issues in the industry can be captured promptly and accurately.
Sparsity-Based Space-Time Adaptive Processing for Airborne Radar with Coprime Array and Coprime Pulse Repetition Interval

In this paper, we present a sparsity-based space-time adaptive processing (STAP) algorithm with coprime array and coprime pulse repetition interval (PRI). The considered space-time coprime configuration can significantly save the cost. However, the direct STAP does not exploit the advantage of the large aperture brought by coprime configuration and the recently developed spatial-temporal smoothed-based STAP requires a large number of training snapshots. To solve these issues, we propose a sparsity-based STAP algorithm by using the spacial-temporal sparsity of clutter in virtual domain. Simulation results show that the proposed algorithm can obtain a much higher output signal-to-interference-plus-noise ratio and improve the convergence speed.
Comparison of HAAR and Kekre's Wavelet for Image Denoising

Image restoration is a process of reconstruction of original image from its degraded version. Image denoising is part of restoration when the degradation present is only noise. In this paper a new approach of using Kekre's wavelet for image denoising is proposed and compared with Haar wavelet for hard and soft thresholding using MSE and PSNR. It is found that Kekre's wavelet when used with Wiener filter gives better results than Haar. Haar along with Wiener technique gives better results than Kekre's wavelet + Wiener technique.
Effective Cover Song Identification Based on Skipping Bigrams

So far, few cover song identification systems that utilize index techniques achieve great success. In this paper, we propose a novel approach based on skipping bigrams that could be used for effective index. By applying Vector Quantization, our algorithm encodes signals into code sequences. Then, the bigram histograms of code sequences are used to represent the original recordings and measure their similarities. Through Vector Quantization and skipping bigrams, our model shows great robustness against speed and structure variations in cover songs. Experimental results demonstrate that our model achieves better performance than recent methods and is less computationally demanding.
Fair Carrier Allocation for 5G Integrated Satellite-Terrestrial Backhaul Networks

The anticipated exponential growth in network traffic is posing significant challenges for the implementation of 5G networks. In this context, a major problem is the backhaul network which acts as a bottleneck preventing the efficient flow of ultra-dense and heavy traffic between the end users and the core network. Spectrum scarcity has emerged as the primary problem encountered when trying to accommodate the traffic upsurge. In this paper, we investigate the carrier allocation problem in the context of Integrated Satellite-Terrestrial Backhaul (ISTB) networks. In particular, we consider the satellite component to be integrated with the conventional terrestrial wireless backhaul network thus providing evident benefits in terms of data-offloading. To enhance the overall spectral efficiency of the proposed network, we consider that both terrestrial and satellite segments operate in the Ka band, where the sharing between terrestrial microwave links and satellite communications is already allowed. A novel carrier allocation algorithm based on fairness is proposed, which ensures that all backhaul links are continuously active to satisfy the operator's coverage needs. The problem is NP-hard by definition. As a consequence, we present a two-step sequential carrier allocation strategy specifically tailored to tackle the interference issues emerging from the spectral co-existence. Supporting results based on numerical simulations show that the proposed carrier allocation can provide a 2x improvement in terms of spectral efficiency when compared to benchmark terrestrial-only backhaul networks.
Distance Detection Technique Using Enhancing Inverse Perspective Mapping

This paper presents a distance determination technique using an image from the single forward camera. Since too dark or too bright of the image and non-linear relation between height of the object and distance from the camera have effect on the performance of the detection process. Therefore, automatic brightness adjustment and inverse perspective mapping (IMP) is applied in the proposed scheme. In addition, region of interest (ROI) determination is used to decrease the processing time. The experimental results confirm that the proposed technique can detect distance of the object in front of the car where the error is 7.96%.
Unsupervised Image Segmentation by Backpropagation

We investigate the use of convolutional neural networks (CNNs) for unsupervised image segmentation. As in the case of supervised image segmentation, the proposed CNN assigns labels to pixels that denote the cluster to which the pixel belongs. In the unsupervised scenario, however, no training images or ground truth labels of pixels are given beforehand. Therefore, once when a target image is input, we jointly optimize the pixel labels together with feature representations while their parameters are updated by gradient descent. In the proposed approach, we alternately iterate label prediction and network parameter learning to meet the following criteria: (a) pixels of similar features are desired to be assigned the same label, (b) spatially continuous pixels are desired to be assigned the same label, and (c) the number of unique labels is desired to be large. Although these criteria are incompatible, the proposed approach finds a plausible solution of label assignment that balances well the above criteria’ which demonstrates good performance on a benchmark dataset of image segmentation.
Non-Asymptotic Guarantees for Correlation-Aware Support Detection

This paper considers the problem of sparse support recovery in Multiple Measurement Vector (MMV) models, where the support size (K) can exceed the dimension (M) of individual measurement vectors. Existing results in this regime mostly establish asymptotic performance guarantees, where the number of measurement vectors $L\rightarrow\infty$. In this paper, we develop non-asymptotic guarantees (finite $L$), and demonstrate that it is possible to recover supports of size $K= \mathcal{O}(M^{2})$ provided the sparse signals are statistically uncorrelated. In particular, the probability of detecting a wrong support is shown to approach zero exponentially fast in $L$ even when $K > M$, for appropriately designed measurement matrices. Our analysis is based on a simple least squares estimation of signal powers, followed by hard thresholding to detect the support.
Detecting Location-Indicating Phrases in User Utterances for Chat-Oriented Dialogue Systems

This paper establishes a method that detects words or phrases that indicate location in Japanese spoken language for a chat-oriented dialogue system. Although conventional methods for detecting words or phrases focus on named entities (NE)s, humans frequently use non-NE words to signify locations. For example, we can say “I went to that famous tower in Paris” instead of “I went to the Eiffel Tower” if we forget its proper name. Since conventional NE recognizers extract only Paris as a location from the utterance, they cannot correctly understand because the phrase “that famous tower in Paris” denotes the location in this utterance. Such insufﬁcient understanding may allow a system to ask “Where did you go in Paris? ” next, and easily result in dialogue breakdown. To correctly understand location phrases, we focused on conditional random ﬁeld (CRF)-based model as a representative method for NE extraction. Since there is no chat corpus that such location-indicating phrases are annotated, we ﬁrstly created a corpus by annotating location-indicating phrases to actual human-human chat-oriented dialogues. Then, we evaluated with the corpus how the model work. The evaluation shows that human utterances include various location phrases except for NEs. It also shows that a CRF-based model trained a new annotated corpus detects the target phrases with high accuracy.
Sub-optimal Sliding Mode Control on a Cooperative Manipulator using Stereo Vision Machine Feedback

In this paper, a suboptimal sliding mode control method is derived via defining the required gains in common sliding mode control algorithm with the state dependent Riccati equation, and implemented for a nonlinear closed-loop systems. The Lyapunov approach is used to check the stability of the system. Moreover, the mathematical relationship between the three-dimensional reference coordinates and the local coordinates in cameras is presented and to increase the accuracy of the system, the stereo vision algorithm is considered for measuring the position of the robot's end-effector. The vision system is an independent unit consists of two high definition (HD) cameras. The application of this measurement provides the position and orientation of Scout robot's manipulator using the inverse kinematics. The joint angle values could be calculated and then could be used as a feedback in control approach.
Stochastic Dynamical Systems Based Latent Structure Discovery in High-Dimensional Time Series

The brain encodes information by neural spiking activities, which can be described by time series data as spike counts. Latent Variable Models (LVMs) are widely used to study the unknown factors (i.e. the latent states) that are dependent in a network structure to modulate neural spiking activities. Yet, challenges in performing experiments to record on neuronal level commonly results in relatively short and noisy spike count data, which is insufficient to derive latent network structure by existing LVMs. Specifically, it is difficult to set the number of latent states. A small number of latent states may not be able to model the complexities of underlying systems, while a large number of latent states can lead to overfitting. Therefore, based on a specific LVMs called Linear Dynamical System (LDS), we propose a Reduced-Rank Linear Dynamical System (RRLDS) to estimate latent states and retrieve an optimal latent network structure from short, noisy spike count data. This framework estimates the model using Laplace approximation. To further handle count-valued data, we introduce the dispersion-adaptive distribution to accommodate over-/ equal-/ and under-dispersion nature of such data. Results on both simulated and experimental data demonstrate our model can robustly learn latent space from short-length, noisy, count-valued data and significantly improve the prediction performance over the state-of-the-art methods.
Genome Editing and the Problem of Tetraploidy in Cell Modeling of the Genetic Form of Parkinsonism

The prevalent form of familial parkinsonism is caused by mutations in the LRRK2 gene encoding for the mito chondrial protein kinase. In the review, we discuss possible causes of appearance of tetraploid cells in neuronal precursors obtained from induced pluripotent stem cells from patients with the LRRK2 associated form of parkinsonism after genome editing procedure. As LRRK2 protein participates in cell proliferation and maintenance of the nuclear envelope, spindle fibers, and cytoskeleton, mutations in the LRRK2 gene can affect protein functions and lead, via various mechanisms, to the mitotic machinery disintegration and chromosomal aberration. These abnormalities can appear at different stages of fibro blast reprogramming; therefore, editing of the LRRK2 nucleotide sequence should be done during or before the reprogram ming stage. DOI: 10.1134/S0006297918090055
Predictors of Mortality Among Hospitalized Patients With Lower Respiratory Tract Infections in a High HIV Burden Setting

Introduction: Lower respiratory tract infections (LRTIs) are a leading cause of mortality in sub-Saharan Africa. Triaging identifies patients at high risk of death, but laboratory tests proposed for use in severity-of-illness scores are not readily available, limiting their clinical use. Our objective was to determine whether baseline characteristics in hospitalized participants with LRTI predicted increased risk of death. Methods: This was a secondary analysis from the Mulago Inpatient Non-invasive Diagnosis-International HIV-associated Opportunistic Pneumonias (MIND-IHOP) cohort of adults hospitalized with LRTI who underwent standardized investigations and treatment. The primary outcome was all-cause mortality at 2 months. Predictors of mortality were determined using multiple logistic regression. Results: Of 1887 hospitalized participants with LRTI, 372 (19.7%) died. The median participant age was 34.3 years (interquartile range, 28.0–43.3 years), 978 (51.8%) were men, and 1192 (63.2%) were HIV-positive with median CD4 counts of 81 cells/µL (interquartile range, 21–226 cells/µL). Seven hundred eleven (37.7%) participants had a microbiologically confirmed diagnosis. Temperature <35.5°C [adjusted odds ratio (aOR) = 1.77, 95% confidence intervals (CI): 1.20 to 2.60; P = 0.004], heart rate >120/min (aOR = 1.82, 95% CI: 1.37 to 2.43; P < 0.0001), oxygen saturation <90% (aOR = 2.74, 95% CI: 1.97 to 3.81; P < 0.0001), being bed-bound (aOR = 1.88, 95% CI: 1.47 to 2.41; P < 0.0001), and being HIV-positive (aOR = 1.49, 95% CI: 1.14 to 1.94; P = 0.003) were independently associated with mortality at 2 months. Conclusions: Having temperature <35.5°C, heart rate >120/min, hypoxia, being HIV-positive, and bed-bound independently predicts mortality in participants hospitalized with LRTI. These readily available characteristics could be used to triage patients with LRTI in low-income settings. Providing adequate oxygen, adequate intravenous fluids, and early antiretroviral therapy (in people living with HIV/AIDS) may be life-saving in hospitalized patients with LRTI.
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of $21^{\mathrm{s}\mathrm{t}}$ Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.
Region-Object Relevance-Guided Visual Relationship Detection

In this paper, we address the problem of visual relationship detection, which requires joint image and language understanding to predict semantic connections between detected objects. Most of the previous works in this domain have applied visual classiﬁ-cation methods based on extracted visual features to solve this problem. However, each type of relationship has various object combinations and each object pair has diverse interactions; thus, considering all possible relationships is difﬁcult and expensive. In this paper, we mainly propose a region-object relevance-guided framework that identi-ﬁes regions related to the labels of object pairs in images, thereby decreasing the number of possible relationships to be considered for efﬁciency. Furthermore, we construct a semantic space with an exploratory mechanism that weights informative regions as relevant. Our entire network is trained end-to-end using a multi-task loss function for estimating visual relationships. Finally, we evaluate our method on two public large-scale datasets. Our method achieves remarkably high performance levels on both datasets that are better than or comparable to those of state-of-the-art methods under conditions using only relationship annotations from the dataset. In addition, our proposed method facilitates prediction at speeds below 150 ms per image. Thus, our method is well-suited for real-time applications.
STAT3 Knockdown Induces Tumor Formation by MDA-MB-231 Cells

STAT3 plays a central role in oncogenesis by mediating cell survival, growth, and differentiation. It is constitutively activated in breast cancer. We investigated the role of STAT3 in tumor development by knocking down STAT3 levels in MDA-MB-231 triple negative breast cancer cells using short hairpin RNA. The tumor forming potential of these STAT3-depleted cells was assessed by xenografts in immunocompromised NOD SCID mice. Contrary to its accepted tumor promoting role, we found STAT3 to be a negative regulator of growth in MDA-MB-231- derived tumors. Although similar observations have been made in thyroid carcinoma and lung adenocarcinoma xenograft studies, our novel results showed for the first time that the role of STAT3 in promoting tumorigenesis may be context-specific, and that STAT3 may actually be a negative regulator of certain breast-cancer types. Studies to identify the mechanisms of STAT3’s negative regulatory role may be useful in developing STAT3-based therapeutics.
Effects of Polarized Training on Cardiometabolic Risk Factors in Young Overweight and Obese Women: A Randomized-Controlled Trial

Introduction: Volume and intensity are major variables governing exercise training-mediated beneficial effects in both athletes and patients. Although polarized endurance training optimizes and maximizes physiological gains in highly trained individuals, its cardiometabolic protective-effects have not been established. The purpose of the present single site, randomized-controlled trial was to compare the effects of 12-weeks of high-intensity interval training (HIIT), moderate-intensity continuous training (MICT), and polarized volume training (POL) programs on cardiometabolic risk factors in young overweight and obese women. Materials and Methods: A total of 64 overweight/obese young women (age 23.3 ± 3.8 years, body mass index 33.8 ± 3.8 kg/m2) were randomly assigned to four groups: control group (CTRL), polarized volume training group, moderate-intensity endurance training group, and HIIT group. The cardiorespiratory capacity, glycemic and lipid profiles, whole-body substrate utilization, and body composition were assessed before and after the intervention. Results: After the intervention, VO2peak and power output at VO2peak increased in all exercised-groups (time effect: p < 0.0001). Power output at VT1 was increased only in the POL group compared to the CTRL group (p = 0.019). Relative fold changes in fasting plasma glucose concentrations decreased only in POL group (p = 0.002). Training induced a significant increase in relative fat oxidation in all the groups (time effect: p < 0.001). Relative fat oxidation increased only in the POL group compared to the CTRL group (training effect: p = 0.032). Conclusion: Twelve-weeks of polarized volume training showed overall superior effects on cardiorespiratory fitness, basal glycemic control, and substrate oxidation in comparison to MICT and HIIT training modalities. These data suggest that polarized volume training is an effective non-pharmacological treatment strategy for reducing cardiovascular disease risk factors in young overweight and obese women. The trial is registered at ISRCTN, number ISRCTN34421723.
A Novel Stable Robust Adaptive Impedance Control Scheme for Ankle Prostheses

A stable robust adaptive impedance control strategy is introduced here as a model-based low-level control scheme for active ankle prostheses. The effects of amputee-prosthesis and prosthesis-environment interactions are included in the controller design. An interesting feature of the proposed controller is that only shank and ankle angles and angular velocities, and ground reaction forces are required to implement the control law. In other words, no feedback from amputee-prosthesis interaction forces and moment, global or local positions, and accelerations of amputated place is required. Using a Lyapunov analysis, exponential convergence characteristics of the proposed controller are proven. The results of a simulation study are included at the end of paper to show the effectiveness of the proposed control law.
Available Wrench Set for Planar Mobile Cable-Driven Parallel Robots

Cable-Driven Parallel Robots (CDPRs) have several advantages over conventional parallel manipulators most notably a large workspace. CDPRs whose workspace can be further increased by modification of the geometric architecture are known as Reconfigurable Cable Driven Parallel Robots(RCDPRs). A novel concept of RCDPRs, known as Mobile CDPR (MCDPR) that consists of a CDPR carried by multiple mobile bases, is studied in this paper. The system is capable of autonomously navigating to a desired location then deploying to a standard CDPR. In this paper, we analyze the Static equilibrium (SE) of the mobile bases when the system is fully deployed. In contrast to classical CDPRs we show that the workspace of the MCDPR depends, not only on the tension limits, but on the SE constraints as well. We demonstrate how to construct the Available Wrench Set (AWS) for a planar MCDPR wih a point-mass end-effector using both the convex hull and Hyperplane shifting methods. The obtained results are validated in simulation and on an experimental platform consisting of two mobile bases and a CDPR with four cables.