Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Complex Workflows in Bioinformatics: An Interactive Toolkit With GPU Acceleration

Bioinformatics research continues to advance at an increasing scale with the help of techniques such as next-generation sequencing and the availability of tool support to automate bioinformatics processes. With this growth, a large amount of biological data gets accumulated at an unprecedented rate, demanding high-performance and high-throughput computing technologies for processing such datasets. Use of hardware accelerators, such as graphics processing units (GPUs) and distributed computing, accelerates the processing of big data in high-performance computing environments. They enable higher degrees of parallelism to be achieved, thereby increasing the throughput. In this paper, we introduce BioWorkflow, an interactive workflow management system to automate the bioinformatics analyses with the capability of scheduling parallel tasks with the use of GPU-accelerated and distributed computing. This paper describes a case study carried out to evaluate the performance of a complex workflow with branching executed by BioWorkflow. The results indicate the gains of <inline-formula> <tex-math notation="LaTeX">$\times 2.89$ </tex-math></inline-formula> magnitude by utilizing GPUs and gains in speed by average <inline-formula> <tex-math notation="LaTeX">$\times 2.832$ </tex-math></inline-formula> magnitude (over <inline-formula> <tex-math notation="LaTeX">$n = 5$ </tex-math></inline-formula> scenarios) by parallel execution of graph nodes during multiple sequence alignment calculations. Combined speed-ups are achieved <inline-formula> <tex-math notation="LaTeX">$\times 1.71$ </tex-math></inline-formula> times for complex workflows. This confirms the expected higher speed-ups when having parallelism through GPU-acceleration and concurrent execution of workflow nodes than the mainstream sequential workflow execution. The tool also provides a comprehensive user interface with better interactivity for managing complex workflows; a system usability scale score of 82.9 is confirmed high usability for the system.
Multi-Label Co-Training

Multi-label learning aims at assigning a set of appropriate labels to multi-label samples. Although it has been successfully applied in various domains in recent years, most multi-label learning methods require sufficient labeled training samples, because of the large number of possible label sets. Co-training, as an important branch of semi-supervised learning, can leverage unlabeled samples, along with scarce labeled ones, and can potentially help with the large labeled data requirement. However, it is a difficult challenge to combine multi-label learning with co-training. Two distinct issues are associated with the challenge: (i) how to solve the widely-witnessed class-imbalance problem in multi-label learning; and (ii) how to select samples with confidence, and communicate their predicted labels among classifiers for model refinement. To address these issues, we introduce an approach called Multi-Label Co-Training (MLCT). MLCT leverages information concerning the co-occurrence of pairwise labels to address the class-imbalance challenge; it introduces a predictive reliability measure to select samples, and applies label-wise filtering to confidently communicate labels of selected samples among co-training classifiers. MLCT performs favorably against related competitive multi-label learning methods on benchmark datasets and it is also robust to the input parameters.
Arabidopsis Leaf Flatness Is Regulated by PPD2 and NINJA through Repression of CYCLIN D3 Genes1[OPEN]

Down-regulation of the PPD2/NINJA complex results in a convex-shaped primary cell cycle arrest front, upregulation of CYCD3 genes, and plants with dome-shaped leaves. In Arabidopsis (Arabidopsis thaliana), reduced expression of the transcriptional regulator PEAPOD2 (PPD2) results in propeller-like rosettes with enlarged and dome-shaped leaves. However, the molecular and cellular processes underlying this peculiar phenotype remain elusive. Here, we studied the interaction between PPD2 and NOVEL INTERACTOR OF JAZ (NINJA) and demonstrated that ninja loss-of-function plants produce rosettes with dome-shaped leaves similar to those of ppd mutants but without the increase in size. We showed that ninja mutants have a convex-shaped primary cell cycle arrest front, putatively leading to excessive cell division in the central leaf blade region. Furthermore, ppd and ninja mutants have a similar increase in the expression of CYCLIN D3;2 (CYCD3;2), and ectopic overexpression of CYCD3;2 phenocopies the ppd and ninja rosette and leaf shape phenotypes without affecting the size. Our results reveal a pivotal contribution of NINJA in leaf development, in addition to its well-studied function in jasmonate signaling, and imply a new function for D3-type cyclins in, at least partially, uncoupling the size and shape phenotypes of ppd leaves.
Registration Accuracy of Patient-Specific, Three-Dimensional-Printed Prostate Molds for Correlating Pathology With Magnetic Resonance Imaging

<italic>Objective:</italic> This investigation was performed to evaluate the registration accuracy between magnetic resonance imaging (MRI) and pathology using three-dimensional (3-D) printed molds. <italic>Methods:</italic> Tissue-mimicking prostate phantoms were manufactured with embedded fiducials. The fiducials were used to measure and compare target registration error (TRE) between phantoms that were sliced by hand versus phantoms that were sliced within 3-D-printed molds. Subsequently, ten radical prostatectomy specimens were placed inside molds, scanned with MRI, and then sliced. The <italic>ex vivo</italic> scan was used to assess the true location of whole mount (WM) slides relative to <italic>in vivo</italic> MRI. The TRE between WM and <italic>in vivo</italic> MRI was measured using anatomic landmarks. <italic>Results:</italic> Manually sliced phantoms had a 4.1-mm mean TRE, whereas mold-sliced phantoms had a 1.9-mm mean TRE. Similarly, mold-assisted slicing reduced mean angular misalignment around the left-right (LR) anatomic axis from 10.7° to 4.5°. However, <italic>ex vivo</italic> MRI revealed that excised prostates were misaligned within molds, including a mean 14° rotation about the LR axis. The mean in-plane TRE was 3.3 mm using molds alone and 2.2 mm after registration was corrected with <italic>ex vivo</italic> MRI. <italic>Conclusion:</italic> Patient-specific molds improved accuracy relative to manual slicing techniques in a phantom model. However, the registration accuracy of surgically resected specimens was limited by their imperfect fit within molds. This limitation can be overcome with the addition of <italic>ex vivo</italic> imaging. <italic>Significance:</italic> The accuracy of 3-D-printed molds was characterized, quantifying their utility for facilitating MRI-pathology registration.
Why is change a challenge in acute mental health wards? A cross‐sectional investigation of the relationships between burnout, occupational status and nurses’ perceptions of barriers to change

Abstract Changes in UK psychiatric wards have been difficult to implement. Specific areas of nursing staff resistance remain unclear. Previous healthcare research suggests that burnout is common and that managers’ regard changes more positively than direct care staff. We will therefore examine whether burnout and workforce characteristics influence psychiatric nurses’ perceptions of barriers to change. Psychiatric nurses (N = 125) completed perceptions measures of ‘barriers to change’ (VOCALISE: subscales included ‘powerlessness, confidence and demotivation’); and ‘burnout’ (Maslach Burnout Inventory: subscales included ‘emotional exhaustion, personal accomplishment and depersonalization ‘). Staff characteristics, such as length of employment, occupational status, education, ethnicity, gender and age, were also collected. Correlations between these measures informed random‐effects regression models, which were conducted to predict the barriers to change score and to explore differential effects in the subscales of VOCALISE. Perceptions of barriers to change (VOCALISE) were correlated with burnout (r = 0.39), occupational status (r = −0.18) and age (r = 0.22). Burnout (Coef. β: 10.52; P > 0.001) and occupational status (Coef. β: −4.58; P = 0.05) predicted VOCALISE. Emotional exhaustion (Coef. β: 0.18; P < 0.001) and low personal accomplishment (Coef. β: 0.21; P = 0.001) predicted powerlessness. Emotional exhaustion predicted low motivation regarding changes (Coef. β: 0.11; P = 0.005). Low confidence predicted high levels of depersonalization (Coef β: 0.23; P = 0.01). Direct care staff expressed significantly more powerlessness (Coef. β: −2.60; P = 0.02) and significantly less confidence (Coef. β: −3.07; P = 0.002) than managers. For changes to be successful in psychiatric wards, burnout will need to be addressed. Future change strategies may consider involving direct care staff to improve perceptions of barriers to change.
Representation Learning for Users' Web Browsing Sequences

SUMMARY Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In our work-in-progress paper[1], we introduced an approach that summarizes each sequence of user Web page visits using Paragraph Vector[3], considering users and URLs as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. In this paper, on the basis of analysis of our Web page visit data, we propose Backward PV-DM, which is a modiﬁed version of Paragraph Vector. We show experimental results on two ad-related data sets based on logs from Web services of Yahoo! JAPAN. Our proposed method achieved better results than those of existing vector models.
User Profiling in Elderly Healthcare Services in China: Scalper Detection

Driven by the automation technologies and health informatics of Industry 4.0, hospitals in China have deployed a complete automation system/platform for healthcare services accessing. Without much more Internet knowledge, elderlies usually seek the third-party to assist them to get healthcare services from Web or APPs, it consequently results in an unexpected situation that scalpers could grab all healthcare services booking by unrighteous means in order to resell to elderlies for a much higher price. Moreover, it is hard for physicians to identify the scalpers due to the complexity, ad-hoc, and multiscenario nature of healthcare processes. In this paper, a novel method is proposed for the identification and creation of user groups of scalpers in mobile healthcare services. The approach utilizes and extends state of the art data analysis approaches in the event-logs of the mobile system to identify user groups. Based on the user groups, user profiles are extracted by identifying representative eventcases from hierarchical user-event clusters. A comprehensive evaluation is conducted in a selected test-set from the event-logs of a mobile healthcare APP. The result shows its accuracy and effectiveness in scalper detection in mobile healthcare APP. Further, a complete case study is deployed in a real word hospital to ensure its utility, efficacy, and reliability.
A novel visible-infrared image fusion framework for smart city

Image fusion technology is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Image fusion technology as an efficient way to integrate information from multiple images plays a more and more important role in smart city. The quality of fused image affects the accuracy, efficiency, and robustness of the related applications. Existing sparse representation-based image fusion methods consist of overly complete and redundant dictionaries learning and sparse coding. However, overly complete and redundant dictionary does not consider the discriminative ability of dictionaries that may seriously affect the image fusion. A good dictionary is the key to a successful image fusion technique. To construct a discriminative dictionary, a novel framework that integrates an image-patches clustering and online dictionary learning methods is proposed for visible-infrared image fusion. The comparison experiments with existing solutions are used to validate and demonstrate the effectiveness of the proposed solution for image fusion.
Deep Max-Margin Discriminant Projection

In this paper, a unified Bayesian max-margin discriminant projection framework is proposed, which is able to jointly learn the discriminant feature space and the max-margin classifier with different relationships between the latent representations and observations. We assume that the latent representation follows a normal distribution whose sufficient statistics are functions of the observations. The function can be flexibly realized through either shallow or deep structures. The shallow structure includes linear, nonlinear kernel-based functions, and even the convolutional projection, which can be further trained layerwisely to build a multilayered convolutional feature learning model. To take the advantage of the deep neural networks, especially their highly expressive ability and efficient parameter learning, we integrate Bayesian modeling and the popular neural networks, for example, mltilayer perceptron and convolutional neural network, to build an end-to-end Bayesian deep discriminant projection under the proposed framework, which degenerated into the existing shallow linear or convolutional projection with the single-layer structure. Moreover, efficient scalable inferences for the realizations with different functions are derived to handle large-scale data via a stochastic gradient Markov chain Monte Carlo. Finally, we demonstrate the effectiveness and efficiency of the proposed models by the experiments on real-world data, including four image benchmarks (MNIST, CIFAR-10, STL-10, and SVHN) and one measured radar high-resolution range profile dataset, with the detailed analysis about the parameters and computational complexity.
Unsupervised Learning of 3-D Local Features From Raw Voxels Based on a Novel Permutation Voxelization Strategy

Effective 3-D local features are significant elements for 3-D shape analysis. Existing hand-crafted 3-D local descriptors are effective but usually involve intensive human intervention and prior knowledge, which burdens the subsequent processing procedures. An alternative resorts to the unsupervised learning of features from raw 3-D representations via popular deep learning models. However, this alternative suffers from several significant unresolved issues, such as irregular vertex topology, arbitrary mesh resolution, orientation ambiguity on the 3-D surface, and rigid and slightly nonrigid transformation invariance. To tackle these issues, we propose an unsupervised 3-D local feature learning framework based on a novel permutation voxelization strategy to learn high-level and hierarchical 3-D local features from raw 3-D voxels. Specifically, the proposed strategy first applies a novel voxelization which discretizes each 3-D local region with irregular vertex topology and arbitrary mesh resolution into regular voxels, and then, a novel permutation is applied to permute the voxels to simultaneously eliminate the effect of rotation transformation and orientation ambiguity on the surface. Based on the proposed strategy, the permuted voxels can fully encode the geometry and structure of each local region in regular, sparse, and binary vectors. These voxel vectors are highly suitable for the learning of hierarchical common surface patterns by stacked sparse autoencoder with hierarchical abstraction and sparse constraint. Experiments are conducted on three aspects for evaluating the learned local features: 1) global shape retrieval; 2) partial shape retrieval; and 3) shape correspondence. The experimental results show that the learned local features outperform the other state-of-the-art 3-D shape descriptors.
Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot–Environment Interaction

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.
Hybrid spin-CMOS stochastic spiking neuron for high-speed emulation of In vivo neuron dynamics

The spintronic stochastic spiking neuron (S3N) developed herein realises biologically mimetic stochastic spiking characteristics observed within in vivo cortical neurons, while operating several orders of magnitude more rapidly and exhibiting a favourable energy profile. This work leverages a novel probabilistic spintronic switching element device that provides thermally-driven and current-controlled tunable stochasticity in a compact, low-energy, and high-speed package. In order to close the loop, the authors utilise a second-order complementary metal-oxide-semiconductor (CMOS) synapse with variable weight control that accumulates incoming spikes into second-order transient current signals, which resemble the excitatory post-synaptic potentials found in biological neurons, and can be used to drive post-synaptic S3Ns. Simulation program with integrated circuit emphasis (SPICE) simulation results indicate that the equivalent of 1 s of in vivo neuronal spiking characteristics can be generated on the order of nanoseconds, enabling the feasibility of extremely rapid emulation of in vivo neuronal behaviours for future statistical models of cortical information processing. Their results also indicate that the S3N can generate spikes on the order of ten picoseconds while dissipating only 0.6-9.6 μW, depending on the spiking rate. Additionally, they demonstrate that an S3N can implement perceptron functionality, such as AND-gate- and OR-gate-based logic processing, and provide future extensions of the work to more advanced stochastic neuromorphic architectures.
Approximate capacity of symmetric two-pair one-way relay channel with overhearing links

: The capacity region of the symmetric two-pair one-way relay channel with overhearing links (OLs) is studied. First, the problem is explored in the linear shift deterministic channel model and the capacity region of the deterministic channel model is characterised. The upper bound is proven tightly by authors' designed transmit strategy. With the gains obtained from the deterministic network, the transmit strategy for this relay channel in Gaussian setup is proposed. According to the strength of OLs, this strategy applies the corresponding superposition of nested lattice codes and random Gaussian codes at the source nodes and successive interference cancellation at all receive nodes. The achievable rate of this scheme is analysed according to the quality of these OLs. It is shown that this scheme achieves the single user rate to within 1.15 bits/s/Hz of the upper bound for all channel gains.
Beyond Urban Versus Rural

the Democrats’ urban vote share in 2008 and 2012, and Clinton maintained it in 2016. Next on the continuum from Democratic to Republican counties are the suburbs of these large metropolitan areas where both Obama in 2012 and Clinton earned slightly less than a majority (49.6 and 47.8 percent), respectively. The voting patterns in smaller urban areas are similar to those in the larger metropolitan areas (Figure 2). In 2012, Obama received nearly half of the vote in the urban cores of smaller metropolitan areas. Clinton received slightly less. As with the larger metropolitan areas, the suburbs of smaller urban areas are more strongly Republican. It is at this point along the rural–urban continuum that the contrast between earlier elections and 2016 is evident. Here the gap between Democratic support in 2012 and 2016 widened—to Clinton’s detriment. Many of these small suburban counties contain the sparsely settled urban periphery that is more rural than urban. Defining 2016 as the tale of two Americas—one urban, one rural—hinders a nuanced understanding of the country’s political geography.
Reorganization of cortical oscillatory dynamics underlying disinhibition in frontotemporal dementia

See Meder and Siebner (doi:10.1093/brain/awy195) for a scientific commentary on this article. Impulsivity and disinhibition are major symptoms of frontotemporal dementia, with limited treatment options. Using magnetoencephalography, Hughes et al. characterise the neural oscillatory dynamics of response inhibition, revealing impaired beta-band desynchronization and disrupted cross-frequency coupling in patients. This cortical oscillatory signature of disease represents a potential pharmacological target for symptomatic treatment.
RF Channel Modeling for Implant-to-Implant Communication and Implant to Subcutaneous Implant Communication for Future Leadless Cardiac Pacemakers

Propagation of radio-frequency signals inside human body is demanding to analyze as it is a highly complex medium consisting of different frequency-dependent lossy materials of varying thickness. Moreover, experimental analyses are also unfeasible because that requires probes to be placed inside a human body to collect the signals. This paper focuses on in-body to in-body implant communication for future multinodal capsule-like leadless cardiac pacemaker technology. The frequency range of 0.3–3 GHz is analyzed using very detailed numerical simulations of digital human models. The results show that the Industrial, Scientific, and Medical radio band of the frequency range of 2.4–2.5 GHz is optimal, having the least attenuation of signals considering the size constraints of the implant antenna. Furthermore, the placement of an additional subcutaneous implant transceiver is studied. The analysis shows that the abdominal wall is the optimal position for the placement of the implant compared to shoulder and lateral side of the body. This result is further validated by an in vivo experiment on an adult pig. The other novelty of the study is the investigation of the channel behavior based on ventricular blood volume of the heart to find out the appropriate timing of the transmission of signals between the implants. The results show that the attenuation of the signal increases with the increase in blood volume inside the heart.