Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Neural Network Parameters Using Stochastic Fractal Technique to Compensate Kalman Filter for Power System-Tracking-State Estimation

Tracking-state estimation uses previous state vector and recent measurement data to give real-time update on the state of the power system noniteratively during the subsequent time sampling. This paper discusses Kalman filtering enhanced by optimized neural network parameters-based stochastic fractals search technique (KF-MLP-based SFS). Both KF gain (mismodeling error) and measurement noise were replaced by optimized multilayer perceptron (MLP-SFS). This optimized MLP-based SFS could suppress filter divergence and improve the accuracy. The proposed method was used to detect and identify anomalies exhibited in normal operation where loads fluctuate linearly, bad data condition, sudden loss of loads, generators, and transmission lines. The application of the proposed technique (KF-MLP-based SFS) is illustrated on the IEEE 57-bus system. Results of the presented approach are compared to the true state vector (load flow), KF standalone, and KF compensated by radial basis function.
Adaptive Sojourn Time HSMM for Heart Sound Segmentation

Heart sounds are difficult to interpret due to events with very short temporal onset between them (tens of milliseconds) and dominant frequencies that are out of the human audible spectrum. Computer-assisted decision systems may help but they require robust signal processing algorithms. In this paper, we propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. The proposed algorithm infers more suitable sojourn time parameters than those currently suggested by the state of the art, through a maximum likelihood approach. We test our approach over three different datasets, including the publicly available PhysioNet and Pascal datasets. We also release a pediatric dataset composed of 29 heart sounds. In contrast with any other dataset available online, the annotations of the heart sounds in the released dataset contain information about the beginning and the ending of each heart sound event. Annotations were made by two cardiopulmonologists. The proposed algorithm is compared with the current state of the art. The results show a significant increase in segmentation performance, regardless the dataset or the methodology presented. For example, when using the PhysioNet dataset to train and to evaluate the HSMMs, our algorithm achieved average an F-score of ${\text{92}}\%$ compared to ${\text{89}}\%$ achieved by the algorithm described in [D.B. Springer, L. Tarassenko, and G. D. Clifford, “Logistic regressionHSMM-based heart sound segmentation,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822–832, 2016]. In this sense, the proposed approach to adapt sojourn time parameters represents an effective solution for heart sound segmentation problems, even when the training data does not perfectly express the variability of the testing data.
Salient Subsequence Learning for Time Series Clustering

Time series has been a popular research topic over the past decade. Salient subsequences of time series that can benefit the learning task, e.g., classification or clustering, are called shapelets. Shapelet-based time series learning extracts these types of salient subsequences with highly informative features from a time series. Most existing methods for shapelet discovery must scan a large pool of candidate subsequences, which is a time-consuming process. A recent work, [1] , uses regression learning to discover shapelets in a time series; however, it only considers learning shapelets from labeled time series data. This paper proposes an Unsupervised Salient Subsequence Learning (USSL) model that discovers shapelets without the effort of labeling. We developed this new learning function by integrating the strengths of shapelet learning, shapelet regularization, spectral analysis and pseudo-label to simultaneously and automatically learn shapelets to help clustering unlabeled time series better. The optimization model is iteratively solved via a coordinate descent algorithm. Experiments show that our USSL can learn meaningful shapelets, with promising results on real-world and synthetic data that surpass current state-of-the-art unsupervised time series learning methods.
The effects of inulin on the mucosal morphology and immune status of specific pathogen-free chickens1

This study investigated the effects of inulin on mucosal morphology and immune function of specific pathogen-free (SPF) chickens. A total of 200 one-day-old White Leghorns SPF chickens were divided into 5 groups of 4 replicates of 10 chickens each. All SPF chickens were fed a basal diet supplemented with 0, 0.25, 0.5, 1.0, or 2.0% inulin. The mucosal morphology and immune indexes were analyzed on days 7, 14, and 21, respectively. Our results showed that the concentrations of acetate and propionate in the cecum and serum had increased with dietary inulin supplementation on day 21 (P < 0.05). Butyrate could not be detected in the cecal digesta, but was increased in the serum of 1 and 2% groups, as compared with the control group (P < 0.05). The villi height was increased (P < 0.05) and the crypt depth was decreased (P < 0.05) in the duodenum and ileum of SPF chickens fed inulin, as compared with the control group. Also, inulin at a low concentration (0.25 or 0.5%) significantly decreased (P < 0.05) the gene expression of nuclear factor-κB (NF-κB), lipopolysaccharide-induced tumor necrosis factor (LITAF) at 7, 14, and 21 d, and of interleukin-6 (IL-6), and inducible nitric oxide synthase (iNOS) at 7 and 14 d, and increased that of mucin 2 (MUC2) and claudin-1 in the ileum of SPF chickens at 7, 14, and 21 d. High inulin supplementation (2%) significantly increased the gene expression of NF-κB, LITAF, IL-6, iNOS, and Claudin-1 at 14 and 21 d compared to low inulin concentration (0.25 or 0.5%). The results indicated that the effects of inulin on mucosal immune function occurred in a dose-dependent manner. A low concentration (0.25 or 0.5%) of inulin may be beneficial in promoting intestinal immune function.
Novel dual regulators of Pseudomonas aeruginosa essential for productive biofilms and virulence

Gene regulation network in Pseudomonas aeruginosa is complex. With a relatively large genome (6.2 Mb), there is a significant portion of genes that are proven or predicted to be transcriptional regulators. Many of these regulators have been shown to play important roles in biofilm formation and maintenance. In this study, we present a novel transcriptional regulator, PA1226, which modulates biofilm formation and virulence in P. aeruginosa. Mutation in the gene encoding this regulator abolished the ability of P. aeruginosa to produce biofilms in vitro, without any effect on the planktonic growth. This regulator is also essential for the in vivo fitness and pathogenesis in both Drosophila melanogaster and BALB/c mouse lung infection models. Transcriptome analysis revealed that PA1226 regulates many essential virulence genes/pathways, including those involved in alginate, pili, and LPS biosynthesis. Genes/operons directly regulated by PA1226 and potential binding sequences were identified via ChIP‐seq. Attempts to confirm the binding sequences by electrophoretic mobility shift assay led to the discovery of a co‐regulator, PA1413, via co‐immunoprecipitation assay. PA1226 and PA1413 were shown to bind collaboratively to the promoter regions of their regulons. A model is proposed, summarizing our finding on this novel dual‐regulation system.
Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals

Emotion recognition is an important pattern recognition problem that has inspired researchers for several areas. Various data from humans for emotion recognition have been developed, including visual, audio, and physiological signals data. This paper proposes a decision-level weight fusion strategy for emotion recognition in multichannel physiological signals. Firstly, we selected four kinds of physiological signals, including Electroencephalography (EEG), Electrocardiogram (ECG), Respiration Amplitude (RA), and Galvanic Skin Response (GSR). And various analysis domains have been used in physiological emotion features extraction. Secondly, we adopt feedback strategy for weight definition, according to recognition rate of each emotion of each physiological signal based on Support Vector Machine (SVM) classifier independently. Finally, we introduce weight in decision level by linear fusing weight matrix with classification result of each SVM classifier. The experiments on the MAHNOB-HCI database show the highest accuracy. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system based on multichannel data using weight fusion strategy.
The Triad of Risk-Related Behaviors (TriRB): A Three-Dimensional Model of Cyber Risk Taking

Objective: We identify three risk-related behaviors in coping with cyber threats—the exposure to risk a person chooses, use of security features, and responses to security indications. The combinations of behaviors that users choose determine how well they cope with threats and the severity of adverse events they experience. Background: End users’ coping with risks is a major factor in cybersecurity. This behavior results from a combination of risk-related behaviors rather than from a single risk-taking tendency. Method: In two experiments, participants played a Tetris-like game, attempting to maximize their gains, while exogenous occasional attacks could diminish earnings. An alerting system provided indications about possible attacks, and participants could take protective actions to limit the losses from attacks. Results: Variables such as the costs of protective actions, reliability of the alerting system, and attack severity affected the three behaviors differently. Also, users dynamically adjusted each of the three risk-related behaviors after gaining experience with the system. Conclusion: The results demonstrate that users’ risk taking is the complex combination of three behaviors rather than the expression of a general risk-taking tendency. The use of security features, exposure to risk, and responses to security indications reflect long-term strategy, short-term tactical decisions, and immediate maneuvering in coping with risks in dynamic environments. Application: The results have implications for the analysis of cybersecurity-related decisions and actions as well as for the evaluation and design of systems and targeted interventions in other domains.
Examining the relationship and clinical management between traumatic brain injury and pain in military and civilian populations

ABSTRACT In this review, we discuss the comorbidity of traumatic brain injury (TBI) and pain among civilians and military members, the common causes of pain resulting from TBI, and offer insight about the therapeutic management of TBI symptoms and pain. Traumatic brain injury (TBI) is a debilitating health problem and one of the most common post-TBI symptoms is pain, which can contribute to psychological issues such as Post-traumatic stress disorder (PTSD) and depression. Headache pain appears to be the most common type of pain that results from TBI, yet pain can also be more widespread. Managing TBI symptoms and pain simultaneously is difficult because extensive randomized control and clinical studies assessing the effectiveness of therapeutic approaches are lacking. Pharmacological agents such as antidepressants and Triptans and nonpharmacological therapies such as cognitive rehabilitation and physical therapies are commonly used yet it is unknown how effective these therapies are in the long-term. A combination of pharmacological and non-pharmacological therapies is often more effective for managing TBI symptoms and pain than either treatment alone. However, future research is needed to determine the most therapeutic approaches for managing the comorbidity of pain and TBI symptoms in the long term. This review offers suggestions for such future studies.
Absorbable barbed suture device for laparoscopic peritoneal closure after hernia repair via the transabdominal preperitoneal approach: A single‐center experience with 257 cases

The laparoscopic transabdominal preperitoneal approach requires peritoneal closure and technically skilled knotting. We have started to use a barbed running suturing device (V‐Loc 180) without knotting for transabdominal preperitoneal repair of hernias. This study aimed to determine whether using V‐Loc 180 was safe and shortened the time for laparoscopic peritoneal closure.
Neurocognitive disorders and sleep in children with primary ciliary dyskinesia

Primary ciliary dyskinesia (PCD) patients have higher incidence of sleep disordered breathing which lead neurocognitive impairments such as attention‐deficit/hyperactivity disorder (ADHD). It may effect academic performance of children and may cause impairment in emotional relationships. This study aim to evaluate hyperactivity and attention deficiency in PCD patients and investigate the relationship between sleep and hyperactivity and attention deficiency in PCD patients.
Risk Factors and Outcomes Associated With Sepsis After Coronary Artery Bypass and Open Heart Valve Surgeries

Purpose. Sepsis causes significant morbidity and mortality after cardiac surgery and carries a significant burden on health care costs. There is a general association of increased risk of post–cardiac surgery sepsis in patients with postoperative complications. We sought to investigate significant patient and procedural risk factors and outcomes associated with sepsis after cardiac surgery. Materials and Methods. In this retrospective study, we analyzed 531 coronary artery bypass grafting and open heart valve surgery cases that developed postoperative sepsis in the National Surgical Quality Improvement Program database between 2007 and 2014. Patient-based and surgery-based parameters were analyzed for risk factors and outcomes reported in the 30 days postoperatively. The association between sepsis and patient outcomes was assessed in a propensity-matched cohort using univariable logistic regression. Results. Modifiable and nonmodifiable patient characteristics, including age >80, poor preoperative functional status, chronic diseases such as diabetes mellitus, congestive heart failure, chronic kidney disease with serum creatinine ⩾1.5, as well as serum albumin <3.5 and emergent nature of the case were associated with post–cardiac surgery sepsis. Surgical outcomes associated with sepsis included mortality (15.4% vs 4.5%), unplanned intubation (29.8% vs 8.2%), transfusion (53.4% vs 48.4%), acute kidney injury (7.1% vs 1.4%), postoperative dialysis (18.8% vs 3.5%), and return to the operating room (29.8% vs 8.2%). Conclusions. We identified multiple patient and surgical characteristics as well as postoperative outcomes associated with postoperative sepsis development in the high-risk population of patients undergoing cardiac surgery. Early identification of patients who are at high risk for postoperative sepsis can facilitate early treatment interventions.
Feature Selection With  $\ell_{2,1-2}$  Regularization

Feature selection aims to select a subset of features from high-dimensional data according to a predefined selecting criterion. Sparse learning has been proven to be a powerful technique in feature selection. Sparse regularizer, as a key component of sparse learning, has been studied for several years. Although convex regularizers have been used in many works, there are some cases where nonconvex regularizers outperform convex regularizers. To make the process of selecting relevant features more effective, we propose a novel nonconvex sparse metric on matrices as the sparsity regularization in this paper. The new nonconvex regularizer could be written as the difference of the <inline-formula> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> norm and the Frobenius (<inline-formula> <tex-math notation="LaTeX">$\ell _{2,2}$ </tex-math></inline-formula>) norm, which is named the <inline-formula> <tex-math notation="LaTeX">$\ell _{2,1-2}$ </tex-math></inline-formula>. To find the solution of the resulting nonconvex formula, we design an iterative algorithm in the framework of ConCave–Convex Procedure (CCCP) and prove its strong global convergence. An adopted alternating direction method of multipliers is embedded to solve the sequence of convex subproblems in CCCP efficiently. Using the scaled cluster indictors of data points as pseudolabels, we also apply <inline-formula> <tex-math notation="LaTeX">$\ell _{2,1-2}$ </tex-math></inline-formula> to the unsupervised case. To the best of our knowledge, it is the first work considering nonconvex regularization for matrices in the unsupervised learning scenario. Numerical experiments are performed on real-world data sets to demonstrate the effectiveness of the proposed method.
Comparison of Nuclear Matrix and Mitotic Chromosome Scaffold Proteins in Drosophila S2 Cells—Transmission of Hallmarks of Nuclear Organization Through Mitosis*

Chromatin condenses several folds to form mitotic chromosomes during cell division and decondenses post-mitotically to reoccupy their nuclear territory and regain their specific transcriptional profile in a precisely lineage specific manner. This necessitates that the features of nuclear architecture and DNA topology persist through mitosis. We compared the proteome of nuclease and high salt resistant fraction of interphase nucleus known as nuclear matrix (NuMat) and an equivalent biochemical fraction in the mitotic chromosome known as mitotic chromosome scaffold (MiCS). Our study elucidates that as much as 67% of the NuMat proteins are retained in the MiCS indicating that the features of nuclear architecture in interphase nucleus are retained on the mitotic chromosomes. Proteins of the NuMat/MiCS have large dynamic range of MS signal and were detected in sub-femtomolar amounts. Chromatin/RNA binding proteins with hydrolase and helicase activity are highly enriched in NuMat as well as MiCS. Although several transcription factors involved in functioning of interphase nucleus are present exclusively in NuMat, protein components responsible for assembly of membrane-less nuclear bodies are uniquely retained in MiCS. Our study clearly indicates that the features of nuclear architecture, in the structural context of NuMat, are retained in MiCS and possibly play an important role in maintenance of cell lineage specific transcriptional status during cell division and thereby, serve as components of cellular memory.
Complex regional pain syndrome: intradermal injection of phenylephrine evokes pain and hyperalgesia in a subgroup of patients with upregulated &agr;1-adrenoceptors on dermal nerves

Abstract The aim of this study was to determine whether upregulated cutaneous expression of &agr;1-adrenoceptors (&agr;1-AR) is a source of pain in patients with complex regional pain syndrome (CRPS). Immunohistochemistry was used to identify &agr;1-AR on nerve fibres and other targets in the affected and contralateral skin of 90 patients, and in skin samples from 38 pain-free controls. The distribution of &agr;1-AR was compared between patients and controls, and among subgroups of patients defined by CRPS duration, limb temperature asymmetry, and diagnostic subtype (CRPS I vs CRPS II). In addition, &agr;1-AR expression was investigated in relation to pain and pinprick hyperalgesia evoked by intradermal injection of the &agr;1-AR agonist phenylephrine. Expression of &agr;1-AR on nerve bundles in the CRPS-affected limb was greater in patients who reported prolonged pain and pinprick hyperalgesia around the phenylephrine injection site than in patients with transient pain after the injection. In addition, &agr;1-AR expression in nerve bundles was greater in patients with CRPS II than CRPS I, and was greater in acute than more long-standing CRPS. Although less clearly associated with the nociceptive effects of phenylephrine, &agr;1-AR expression was greater on dermal nerve fibres in the painful than contralateral limb. Together, these findings are consistent with nociceptive involvement of cutaneous &agr;1-AR in CRPS. This involvement may be greater in acute than chronic CRPS, and in CRPS II than CRPS I.
Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach

We present here a learning system using the iCub humanoid robot and the SpiNNaker neuromorphic chip to solve the real-world task of object-specific attention. Integrating spiking neural networks with robots introduces considerable complexity for questionable benefit if the objective is simply task performance. But, we suggest, in a cognitive robotics context, where the goal is understanding how to compute, such an approach may yield useful insights to neural architecture as well as learned behavior, especially if dedicated neural hardware is available. Recent advances in cognitive robotics and neuromorphic processing now make such systems possible. Using a scalable, structured, modular approach, we build a spiking neural network where the effects and impact of learning can be predicted and tested, and the network can be scaled or extended to new tasks automatically. We introduce several enhancements to a basic network and show how they can be used to direct performance toward behaviorally relevant goals. Results show that using a simple classical spike-timing-dependent plasticity (STDP) rule on selected connections, we can get the robot (and network) to progress from poor task-specific performance to good performance. Behaviorally relevant STDP appears to contribute strongly to positive learning: “do this” but less to negative learning: “don’t do that. ” In addition, we observe that the effect of structural enhancements tends to be cumulative. The overall system suggests that it is by being able to exploit combinations of effects, rather than any one effect or property in isolation, that spiking networks can achieve compelling, task-relevant behavior.
Odometry-Vision-Based Ground Vehicle Motion Estimation With SE(2)-Constrained SE(3) Poses

This paper focuses on the motion estimation problem of ground vehicles using odometry and monocular visual sensors. While the keyframe-based batch optimization methods become the mainstream approach in mobile vehicle localization and mapping, the keyframe poses are usually represented by SE(3) in vision-based methods or SE(2) in methods based on range scanners. For a ground vehicle, this paper proposes a new SE(2)-constrained SE(3) parameterization of its poses, which can be easily achieved in the batch optimization framework using specially formulated edges. Utilizing such a parameterization of poses, a complete odometry-vision-based motion estimation system is developed. The system is designed in a commonly used structure of graph optimization, providing high modularity and flexibility for further implementation or adaptation. Its superior performance in terms of accuracy on a ground vehicle platform is validated by real-world experiments in industrial indoor environments.