Miracle-dz/UniFAR-FTUs huggingface dataset:
Grasp Force Estimation from HD-EMG Recordings with Channel Selection Using Elastic Nets: Preliminary Study

Ahstract- The force applied with a prosthetic device is fundamental for the correct handling of objects in daily tasks. However, it is also a factor that normally gets relegated to a secondary plane, as researchers mainly focus on decoding the users intent in terms of movements to be performed. Continuous estimates of the grasp force from the electromyographic (EMG) signals were proposed in the past. As motor actions are preplanned in humans, we hypothesized that it would be possible to decode the intended grasp force from the transient state of the EMG signal. We tested this hypothesis by using features extracted from surface HD-EMG recordings from forearm muscles, classified using artificial neural networks. Data from 6 able-bodied subjects were collected. They were trained and tested at segments of 120 ms with 20 ms overlap, starting 1 s before and ending 0.5 s after the detection of the onset with different subsets of channels. The results obtained showed that the transient phase contains information about the target grasp force, achieving predictions of 2.62 % MVC average absolute errors within 430 ms from the onset of the EMG.
Gastroenterology Ontology Construction Using Synonym Identification and Relation Extraction

Ontology plays an increasingly important role in knowledge management and the semantic Web. However, ontology cannot perform well in realistic diagnosis reasoning unless it contains timely and accurate medical information and its individual items display all attributes of the categories they belong to. In this paper, we present a method that extracts synonyms along with concepts and their relationships for gastroenterology ontology construction. Specifically, we reuse the existing ontology as the basis for ontology completion. In addition, we conduct synonym identification through a combined application of global context features, local context features, and medical-specific features, and incorporate dependency information into deep neural networks for relation extraction. The extracted information is merged for ontology completion. Experimental results demonstrate that the proposed synonym identification and relation extraction method achieves the best performance compared with state-of-the-art methods and also builds a more complete ontology compared with existing gastroenterology disease ontologies. Our results are reproducible, and we will release the source code and ontology of this work after publication: https://github.com/shenyingpku/gastrointestinal_owl
Mandatory medication content in the curricula of six health care personnel types with patient contact in Denmark

Medication use is a complex process involving different types of health care personnel. This study investigated and compared mandatory medication content in the curricula of six types of health care personnel with patient contact. Using content analysis, three independent raters analysed the mandatory medication content for physicians, pharmacists, pharmaconomists, nurses, health care assistants and support workers in the Capital Region of Denmark. Three dimensions were analysed as follows: communication with patients about medication, medication use or pharmacology and medication formulation and production. ECTS credits were totalled for courses analysed to have high or medium content, and inter‐rater reliability was tested with Fleiss’ kappa. The total mandatory medication content for pharmacists was 197.0 ECTS, physicians 136.0 ECTS, pharmaconomists 123.3 ECTS, nurses 52.0 ECTS, health care assistants 17.8 ECTS and support workers 0.0 ECTS. Communication with patients about medication was included to the greatest extent in the educations of pharmaconomists (112.0 ECTS), pharmacists (37.5 ECTS) and physicians (25.0 ECTS). Knowledge about medication use and pharmacology was taught primarily to pharmacists (146.5 ECTS), physicians (123.6 ECTS) and pharmaconomists (89.8 ECTS) and to a lesser extent nurses (52.0 ECTS), health care assistants (17.8 ECTS) and support workers (0.0 ECTS). Medication formulation and production were taught only to pharmacists (93.0 ECTS) and pharmaconomists (25.1 ECTS). Mapping the basic competencies about medication taught to each of the six health care personnel types can lead to a better understanding of how they can complement each other in patient care. The study points to weaknesses in medication curriculum content for health care personnel with the most patient contact.
An Automatic Image Enhancement Method Based on the Improved HCTLS

Remote sensing images often suffer low contrast, and the efficiency and robustness of contrast enhancement for remote sensing images is still a challenge. To meet with the requirements of applications, Liu et al recently proposed a self-adaptive contrast enhancement method (HCTLS) based on the histogram compacting transform (HCT). In this method, some gray levels on which the frequency is smaller than a certain reference, will merged into their adjacent levels for a compact level distribution. However, if the merged levels whose corresponding pixels in some connected regions, local contrast of these connected regions will decrease, even disappear. In this paper, an improved enhancement method (DPHCT) for remote sensing image based on the HCTLS is presented for preserving more the local detail and contrast. Firstly, extracting the connected regions from the enhanced result by HCT where the local contrast is decreased or disappeared. These connected regions are decomposed into the inner regions and the boundary regions adaptively. Then, construct pixel values by using the unified brightness function to maintain the contrast for the connected regions inside. At the same time eliminate stitching lines by using a weighted fusion spliced algorithm to eliminate the problem of borders outstanding in result of intensity roughness. Finally, the image is normalized into [0, 255] by linear stretch. Experimental results indicate that the proposed algorithm not only can enhance the global contrast but also can preserve local contrast and details.
The Relationship between Macular Pigment and Vessel Density in Patients with Type 1 Diabetes Mellitus

Aim: Macular pigment density and microvascular density on optical coherence tomography angiography (OCTA) were measured in a cohort of type 1 diabetes mellitus (T1DM) patients with retinopathy in the attempt to shed light on the pathophysiology of this condition. Methods: Eighty-two consecutive eyes of 59 patients with diabetic retinopathy examined at the Eye Clinic of the University of Naples Federico II from November 2016 to April 2017 were enrolled in this prospective study. Eighty normal eyes of 40 age-matched subjects without diabetes mellitus, without a history of glaucoma or evidence of intraocular surgery, and without retinal pathologic features constituted the control group. All patients and controls underwent a complete ophthalmic examination, best corrected visual acuity evaluation according to the ETDRS visual logMAR scale, measurement of intraocular pressure, OCTA, and evaluation of macular pigment. Results: There were no significant age differences between patients and controls. Both macular pigment measurements and vessel density measured by OCTA were significantly lower in patients than in controls. A moderate correlation was found between vessel density in all ETDRS sectors and macular pigment parameters. Conclusions: There was a reduction in macular pigment and in OCTA vessel density in T1DM patients with retinopathy, which may have prognostic value in determining disease progression.
Recommending GitHub Projects for Developer Onboarding

Open-source platform (e.g., GitHub) creates a tremendous opportunity for developers to learn and build experience. Contribution to open source can be rewarding for developers and advocates the evolutionary progress of the open-source software. However, finding a suitable project to contribute can be intimidating for developers because of the enormous possible choices. Due to various social and technical barriers, developers might fail to contribute successfully. Frequent unsuccessful onboarding hampers not only developers’ individual advancement but also the evolutionary progress of open-source projects. To mitigate developers’ costly efforts for onboarding, we propose a learning-to-rank model, neural network for list-wise ranking (NNLRank), to recommend projects that developers are likely to contribute. NNLRank leverages project features and developers’ experience to recommend projects for onboarding. We develop an efficient approach to optimize the neural network where we leverage a list-wise loss function which intends to minimize the difference between the predicted projects list and the ground-truth list preferred by developers. We evaluate NNLRank with 2044 successful onboarding decisions from GitHub and compare it with three standard learning-to-rank models and a prior onboarding tool. Experimental results show that NNLRank can provide effective and efficient onboarding recommendation to developers, substantially outperforming the previous models.
Long-Horizon Direct Model Predictive Control: Modified Sphere Decoding for Transient Operation

In this paper, we present modifications to the sphere decoder initially introduced in the work of Geyer and Quevedo and modified in the work of Karamanakos et al. that significantly reduce the computation times during transients. The relative position of the unconstrained solution of the integer quadratic program underlying model predictive control (MPC) with respect to the convex hull of the (truncated) lattice of integer points is examined. If it is found that the unconstrained solution does not lie within the convex hull—a phenomenon that is observed mostly during transients—then a projection is performed onto the convex hull. By doing so, a new sphere that guarantees feasibility and includes a significant smaller number of candidate solutions is computed. This reduces the computation time by up to three orders of magnitude when solving the optimization problem at hand. Nonetheless, the reduction of the computational burden comes at a cost of (mild) suboptimal results. The effectiveness of the proposed algorithm is tested with a variable speed drive system consisting of a three-level neutral point clamped voltage source inverter and a medium-voltage induction machine. Based on the presented results, the sphere decoding algorithm with the proposed refinements maintains the very fast transient responses inherent to direct MPC. Moreover, it is observed that the occasional implementation of suboptimal solutions does not lead to a deterioration of the system performance.
A misleading presentation of T cell/histiocyte rich B-cell lymphoma mimicking osteomyelitis

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Fig. 1. A triphasic bone scan scintigraphy showed increased late-phase activity in the frontal and parietal bones, right hip joint, right femoral condyle, right proximal, distal epiphyses of the tibia, and right tarsal bones. BLOOD RESEARCH Volume 53ᆞNumber 3ᆞSeptember 2018
Dishevelled enables casein kinase 1–mediated phosphorylation of Frizzled 6 required for cell membrane localization

Frizzleds (FZDs) are receptors for secreted lipoglycoproteins of the Wingless/Int-1 (WNT) family, initiating an important signal transduction network in multicellular organisms. FZDs are G protein–coupled receptors (GPCRs), which are well known to be regulated by phosphorylation, leading to specific downstream signaling or receptor desensitization. The role and underlying mechanisms of FZD phosphorylation remain largely unexplored. Here, we investigated the phosphorylation of human FZD6. Using MS analysis and a phospho-state– and -site–specific antibody, we found that Ser-648, located in the FZD6 C terminus, is efficiently phosphorylated by casein kinase 1 ϵ (CK1ϵ) and that this phosphorylation requires the scaffolding protein Dishevelled (DVL). In an overexpression system, DVL1, -2, and -3 promoted CK1ϵ-mediated FZD6 phosphorylation on Ser-648. This DVL activity required an intact DEP domain and FZD-mediated recruitment of this domain to the cell membrane. Substitution of the CK1ϵ-targeted phosphomotif reduced FZD6 surface expression, suggesting that Ser-648 phosphorylation controls membrane trafficking of FZD6. Phospho-Ser-648 FZD6 immunoreactivity in human fallopian tube epithelium was predominantly apical, associated with cilia in a subset of epithelial cells, compared with the total FZD6 protein expression, suggesting that FZD6 phosphorylation contributes to asymmetric localization of receptor function within the cell and to epithelial polarity. Given the key role of FZD6 in planar cell polarity, our results raise the possibility that asymmetric phosphorylation of FZD6 rather than asymmetric protein distribution accounts for polarized receptor signaling.
From single-molecule spectroscopy to super-resolution imaging of the neuron: a review

For more than 20 years, single-molecule spectroscopy has been providing invaluable insights into nature at the molecular level. The field has received a powerful boost with the development of the technique into super-resolution imaging methods, ca. 10 years ago, which overcome the limitations imposed by optical diffraction. Today, single molecule super-resolution imaging is routinely used in the study of macromolecular function and structure in the cell. Concomitantly, computational methods have been developed that provide information on numbers and positions of molecules at the nanometer-scale. In this overview, we outline the technical developments that have led to the emergence of localization microscopy techniques from single-molecule spectroscopy. We then provide a comprehensive review on the application of the technique in the field of neuroscience research.
Bandwidth selection for kernel density estimation using Fourier domain constraints

Kernel density estimation (KDE) is widely-used for non-parametric estimation of an underlying density from data. The performance of KDE is mainly dependent on the bandwidth parameter of the kernel. This study presents an alternative method of estimating the bandwidth by incorporating sparsity priors in the Fourier transform domain. By using cross-validation (CV) together with an l 1 constraint, the proposed method significantly reduces the under-smoothing effect of traditional CV methods. A solution for all free parameters in the minimisation is proposed, such that the algorithm does not need any additional parameter tuning. Simulation results indicate that the new approach is able to outperform classical and more recent approaches over a set of distributions of interest.
EAR: ECC-aided refresh reduction through 2-D zero compression

Continuous DRAM scaling and integration is making refresh operations not scalable because more rows have to be refreshed in the same refresh interval. Thus, refresh operations are expected to consume more power in future DRAMs. To alleviate this problem, we propose a compression-based refresh-reducing DRAM architecture. To exploit prevalent zero and small memory values, we devise a novel 2-D ZERO compression scheme to increase compression coverage significantly with simple hardware support. 2-D ZERO compression can achieve 77% compression coverage compared to 50% of the conventional zero-value compression. The freed space of memory blocks obtained by 2-D zero compression is exploited to store ECC bits, which can correct bit-errors that occur when the refresh interval is lengthened to reduce refresh operations. Experimental results show that more than 98% of refresh operations can be removed and overall DRAM power consumption is reduced by 9%.
Shape selection partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter for extended target tracking

The Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter is a promising approach for tracking an unknown number of extended targets. However, it does not achieve satisfactory performance if targets in different sizes are spatially close and manoeuvring because the partitioning methods are sensitive to manoeuvres. To solve this problem, the authors propose the shape selection partitioning (SSP) measurement partitioning algorithm. The proposed algorithm first calculates potential centres and shapes of targets. It then combines each centre with different shapes to divide measurements into subcells. Accordingly, some candidate partitions can be obtained. Finally, it selects the most likely candidate partition and outputs the corresponding subcells. Simulation results show that the application of SSP to the GIW-PHD filter can achieve better performance when targets are spatially close and manoeuvring, which leads to a lower optimal subpattern assignment distance and a higher accuracy of the sum of weights.
Nearly lethal suicide attempt — implications for research and prevention

Approximately 400 people die in Ireland each year as a result of suicide. Males account for 80% of cases and 40% of these are aged 30 years and under. In fact, suicide is now the leading cause of death for Irish men aged 15 to 34 years. The aetiology of suicidal behaviour is complex and multifactorial, being associated with factors such as psychiatric illness (most notably depression), alcohol and substance use disorders, employment status, marital status and stressful life events. The importance of suicide as a public health problem in Ireland is highlighted by documents and initiatives such as Suicide in Ireland, A National Study,' the Report of National Task Force on Suicide and the important work being undertaken by organisations such as The Irish Association of Suicidology and Aware. "
Design & Performance Analysis of Low Power 1-bit Full Adder at 90 nm node using PTL Logic

This paper explores a new full-adder (FA) cell which works in low power. Here the FA architecture is designed using pass transistor logic (PTL) style with 14 transistors and implemented using Cadence Virtuous Tools in 90 nm technology node. The investigation has been carried out through anatomizing the FA cell into sub modules. The different design metrics are compared with the existing design. The result shows an average power and delay of 1.494 μW and 0.003612 ns respectively, which is less compared with the existing FA.
Distortion-Free Golden-Hadamard Codebook Design for MISO Systems

In this letter, a novel Golden-Hadamard codebook (GHC) scheme is proposed to improve the performance of the traditional precoded Alamouti coding for multiple-input and single-output systems. Although the traditional discrete Fourier transform codebook (DFTC) performs satisfactorily with Alamouti coding and offers numerous benefits for the Rayleigh fading channel, this scheme inherently generates huge codeword distortion, which leads to a lower minimum chordal distance (MCD). Furthermore, the uncertain format of all prior versions of codebooks results in poorer minimum determinant (MD) values. Hence, the proposed GHC scheme successfully deals with the issues of traditional DFTC to achieve a better codebook format that completely overcomes both MCD and MD problems. The effectiveness of the proposed GHC scheme is confirmed, in terms of bit-error-rate through Monte Carlo simulations.
Computing Len for Exploring the Historical People's Social Network

A typical social research topic is to figure out the influential people's relationship and its weights. It is very tedious for social scientists to solve those problems by studying massive literature. Digital humanities bring a new way to a social subject. In this paper, we propose a framework for social scientists to find out ancient figures'power and their camp. The core of our framework consists of signed graph model and novel group partition algorithm. We validate and verify our solution by China Biographical Database Project (CBDB) dataset. The analytic results on a case study demonstrate the effectiveness of our framework, which gets information that consists with the literature's facts and social scientists' viewpoints.
SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.
Investigation of the Liquid Loading Effect on the Flexible Acoustic Wave Devices for in-Liquid Sensing Applications

Flexible electronics has many unique qualities such as bendable, lightweight and low-cost fabrication, and are particularly suitable for biosensor applications. Biosensors based on acoustic wave devices feature high resolution, digital output, and wireless operation. In this paper, we address the important issue when the flexible acoustic wave devices are used in liquid for sensing applications. A two-port resonator structure is proposed to implement the flexible acoustic wave device. Electrical measurements were conducted for the same device under various environments, and the liquid loading induced lowering of the quality factor are analyzed.