Miracle-dz/UniFAR-FTUs huggingface dataset:
Altered Spontaneous Brain Activity Related to Neurologic Dysfunction in Patients With Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) encompasses several diseases affecting the small arteries, arterioles, venules, and capillaries of the brain and refers to several pathological processes and etiologies. Neuroimaging is considered the gold standard for detecting CSVD, which can present diverse features on MRI. Cerebral microbleeds (CMBs) in CSVD have been demonstrated to play a synergistic role in both cerebrovascular and neurodegenerative pathology. Considering previous studies on brain structural abnormalities in CSVD, in the present study, we aimed to explore altered spontaneous brain activity among CSVD patients using amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF) and regional homogeneity (ReHo) methods based on resting-state functional MRI. In this study, we recruited 24 CSVD patients with CMBs (CSVD-c), 42 CSVD patients without CMBs (CSVD-n) and 36 healthy controls from outpatient clinics in Shandong Provincial Hospital affiliated to Shandong First Medical University between September 2018 and June 2019. All subjects underwent 3-T MRI, including blood oxygen level-dependent (BOLD) and susceptibility-weighted imaging (SWI). Anatomic structures were segmented, ALFF/fALFF values were calculated, and ReHo maps were generated. Further statistical analysis was applied to study the difference in ALFF/fALFF/ReHo among the three groups and the association between ALFF/fALFF/ReHo changes in different brain regions and clinical characteristics. Twenty-four CSVD-c patients (age: 67.54 ± 6.00 years, 10 females), 42 CSVD-n patients (age: 66.33 ± 5.25 years, 22 females) and 36 healthy subjects (age: 64.14 ± 8.57 years, 19 females) were evaluated. Compared with controls, the CSVD-c group showed significantly increased ALFF values in the right insula, putamen and left precuneus; decreased fALFF values in the right precentral gyrus and postcentral gyrus; and increased ReHo values in the left precuneus, fusiform gyrus, right supplementary motor area (SMA), and superior frontal gyrus. Notably, the mean ALFF values of the right insula and putamen were not only significantly related to all clinical parameters but also demonstrated the best performance in Receiver Operating Characteristic (ROC) curve analysis. These findings reveal CSVD-c patients have dysfunctions in the default mode network, sensorimotor network and frontoparietal network, which may implicate the underlying neurophysiological mechanisms of intrinsic brain activity. The correlation between altered spontaneous neuronal activity and clinical parameters provides early useful diagnostic biomarkers for CSVD.
Advances of flexible pressure sensors toward artificial intelligence and health care applications

By virtue of their wide applications in personal electronic devices and industrial monitoring, pressure sensors are attractive candidates for promoting the advancement of science and technology in modern society. Flexible pressure sensors based on organic materials, which combine unique advantages of flexibility and low-cost, have emerged as a highly active field due to their promising applications in artificial intelligence systems and wearable health care devices. In this review, we focus on the fundamentals of flexible pressure sensors, and subsequently on several critical concepts for the exploration of functional materials and optimization of sensing devices toward practical applications. Perspectives on self-powered, transparent and implantable pressure sensing devices are also examined to highlight the development directions in this exciting research field.
Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME II)

We have developed a passive and lightweight wearable hand exoskeleton (HandSOME II) that improves range of motion and functional task practice in laboratory testing. For this longitudinal study, we recruited 15 individuals with chronic stroke and asked them to use the device at home for 1.5 h per weekday for 8 weeks. Subjects visited the clinic once per week to report progress and troubleshoot problems. Subjects were then given the HandSOME II for the next 3 months, and asked to continue to use it, but without any scheduled contact with the project team. Clinical evaluations and biomechanical testing was performed before and after the 8 week intervention and at the 3 month followup. EEG measures were taken before and after the 8 weeks of training to examine any recovery associated brain reorganization. Ten subjects completed the study. After 8 weeks of training, functional ability (Action Research Arm Test), flexor tone (Modified Ashworth Test), and real world use of the impaired limb (Motor Activity Log) improved significantly (p < 0.05). Gains in real world use were retained at the 3-month followup (p = 0.005). At both post-training and followup time points, biomechanical testing found significant gains in finger ROM and hand displacement in a reaching task (p < 0.05). Baseline functional connectivity correlated with gains in motor function, while changes in EEG functional connectivity paralleled changes in motor recovery. HandSOME II is a low-cost, home-based intervention that elicits brain plasticity and can improve functional motor outcomes in the chronic stroke population.
Pacing Electrocardiogram Detection With Memory-Based Autoencoder and Metric Learning

Remote ECG diagnosis has been widely used in the clinical ECG workflow. Especially for patients with pacemaker, in the limited information of patient's medical history, doctors need to determine whether the patient is wearing a pacemaker and also diagnose other abnormalities. An automatic detection pacing ECG method can help cardiologists reduce the workload and the rates of misdiagnosis. In this paper, we propose a novel autoencoder framework that can detect the pacing ECG from the remote ECG. First, we design a memory module in the traditional autoencoder. The memory module is to record and query the typical features of the training pacing ECG type. The framework does not directly feed features of the encoder into the decoder but uses the features to retrieve the most relevant items in the memory module. In the training process, the memory items are updated to represent the latent features of the input pacing ECG. In the detection process, the reconstruction data of the decoder is obtained by the fusion features in the memory module. Therefore, the reconstructed data of the decoder tends to be close to the pacing ECG. Meanwhile, we introduce an objective function based on the idea of metric learning. In the context of pacing ECG detection, comparing the error of objective function of the input data and reconstructed data can be used as an indicator of detection. According to the objective function, if the input data does not belong to pacing ECG, the objective function may get a large error. Furthermore, we introduce a new database named the pacing ECG database including 800 patients with a total of 8,000 heartbeats. Experimental results demonstrate that our method achieves an average F1-score of 0.918. To further validate the generalization of the proposed method, we also experiment on a widely used MIT-BIH arrhythmia database.
Neural Changes in Borderline Personality Disorder After Dialectical Behavior Therapy–A Review

The biological component of the biosocial theory of emotion regulation stipulates that borderline personality disorder (BPD) arises from biological vulnerabilities to heightened emotional reactivity. Comprehensive reviews have consistently implicated abnormalities in the amygdala, anterior cingulate cortex, and hippocampus in the neurobiology of BPD. While Dialectical Behavior Therapy (DBT) is the leading evidence-based psychotherapy for the treatment of BPD, there remains a paucity of literature examining changes in the neurobiology of BPD following DBT treatment. Nine studies were identified that examined neurobiological changes in BPD after the completion of DBT. Results indicated that there was significant deactivation of amygdala activity as well as the anterior cingulate cortex in patients with BPD after DBT treatment. As well, several studies found after DBT treatment, BPD patients had a decreased activity in the inferior frontal gyrus in response to arousing stimuli and increased activity in response to inhibitory control. Future research on the neurobiological change after DBT treatment can help clarify biological mechanisms of change in BPD.
Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio and Development of a Nomogram in Breast Cancer Patients

Purpose The objective of this study was to explore the prognostic significance of pretreatment hematologic parameters in predicting disease-free survival (DFS) of breast cancer patients. Materials and Methods The medical records of 440 breast cancer patients in Shandong Cancer Hospital and Institute from 2003 to 2013 were analyzed retrospectively. Through the results of blood routine before treatment, the absolute lymphocyte count (ALC), absolute neutrophil count (ANC), absolute monocyte count (AMC), and absolute platelet count (APC) in peripheral blood were collected. The lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-monocyte ratio (NMR) were calculated. Cox proportional hazard model was used for univariate and multivariate analysis. The DFS was compared using Kaplan–Meier method. The prognostic nomogram of patients with breast cancer was developed. Results The median DFS for all patients was 64.10 months. Univariate analysis showed that the DFS was associated with surgical approach, TNM stage, molecular subtype, neoadjuvant chemotherapy, radiotherapy, and LMR (p < 0.05). TNM stage, molecular subtype, and LMR were independent prognostic factors of breast cancer in multivariate analysis (p < 0.05). According to the Kaplan–Meier survival curve analysis, patients with higher LMR (≥4.85) were associated with longer median DFS (median DFS, 85.83 vs. 60.90, p < 0.001). The proposed nomogram that incorporated LMR, TNM stage, and molecular subtype got a concordance index (c-index) of 0.69 in predicting 5-year DFS. Conclusion In breast cancer patients, higher LMR was associated with longer median DFS and the nomogram including LMR, TNM stage, and molecular subtype could accurately predict the prolonged 5-year DFS of breast cancer patients.
Strategies for Articular Cartilage Repair and Regeneration

Articular cartilage is an avascular tissue, with limited ability to repair and self-renew. Defects in articular cartilage can induce debilitating degenerative joint diseases such as osteoarthritis. Currently, clinical treatments have limited ability to repair, for they often result in the formation of mechanically inferior cartilage. In this review, we discuss the factors that affect cartilage homeostasis and function, and describe the emerging regenerative approaches that are informing the future treatment options.
Liver- and Spleen-Specific Immune Responses in Experimental Leishmania martiniquensis Infection in BALB/c Mice

Leishmania martiniquensis is a neglected cause of an emerging leishmaniasis in many countries, including France, Germany, Switzerland, the United States of America, Myanmar, and Thailand, with different clinical manifestations ranging from asymptomatic, cutaneous (CL), visceral (VL), and atypically disseminated CL and VL. The persistence of parasites and the recurrence of the disease after treatment are challenges in controlling the disease. To explore efficient prophylaxis and therapy, this study aimed to investigate infection outcome and organ-specific immune responses after inoculation with L. martiniquensis (MHOM/TH/2011/PG; 5 x 106 promastigotes) in BALB/c mice via intravenous and intraperitoneal routes. A quantitative PCR technique, targeting L. martiniquensis ITS1, was primarily established to estimate the parasite burden. We found that the infection in the liver resolved; however, persistent infection was observed in the spleen. Histopathology with Leishmania-specific immunostaining revealed efficient hepatic granuloma formation, while splenic disorganization with parasitized macrophages at different locations was demonstrated. The mRNA expression of Th1 cytokines (IFN-γ, TNF-α, IL-12p40) and iNOS in the liver and spleen was upregulated. In addition, high expression of IL-10 was observed in the spleen in the chronic phase, revealing a significant moderate correlation with the parasite persistence [r(12) = 0.72, P = 0.009]. Further clarification of the mechanisms of persistent infection and experimental infection in immunosuppressed murine models are warranted.
The Importance of Suicide Risk Formulation in Schizophrenia

Suicide is a cause of early mortality in nearly 5% of patients with schizophrenia, and 25–50% of patients with schizophrenia attempt suicide in their lifetime. Evidence points to numerous individual, clinical, social, and psychological risk factors for suicide in patients with schizophrenia. Although recognizing suicidal risk factors in schizophrenia is extremely important in suicidal risk assessment, we have recently witnessed a change in suicide risk management that shifts the focus from suicide risk assessment to suicide risk formulation. Suicide risk formulation is dependent on the data gathered in the suicide risk assessment and assigns a level of suicide risk that is indispensable for the choice of treatment and the management of patients with a high suicidal risk. In this article, we extend the suicide risk formulation model to patients with schizophrenia. Suicide risk formulation results from four different areas that help clinicians collect as much information as possible for the management of suicidal risk. The four distinct judgments comprise risk status (the risk relating to the specific group to which the patient belongs), risk state (the risk for the person compared with his baseline or another reference point in the course of his life), available resources (on whom the person can count during a crisis) and foreseeable events (which can exacerbate the crisis). In schizophrenia, the suicide risk formulation model allows the clinician to evaluate in depth the clinical context of the patient, the patient's own history and patient-specific opportunities for better choosing and applying suicide prevention strategies.
The Linkage Phase of the Polymorphism KCNH2-K897T Influences the Electrophysiological Phenotype in hiPSC Models of LQT2

While rare mutations in ion channel genes are primarily responsible for inherited cardiac arrhythmias, common genetic variants are also an important contributor to the clinical heterogeneity observed among mutation carriers. The common single nucleotide polymorphism (SNP) KCNH2-K897T is associated with QT interval duration, but its influence on the disease phenotype in patients with long QT syndrome type 2 (LQT2) remains unclear. Human induced pluripotent stem cells (hiPSCs), coupled with advances in gene editing technologies, are proving an invaluable tool for modeling cardiac genetic diseases and identifying variants responsible for variability in disease expressivity. In this study, we have used isogenic hiPSC-derived cardiomyocytes (hiPSC-CMs) to establish the functional consequences of having the KCNH2-K897T SNP in cis- or trans-orientation with LQT2-causing missense variants either within the pore-loop domain (KCNH2A561T/WT) or tail region (KCNH2N996I/WT) of the potassium ion channel, human ether-a-go-go-related gene (hERG). When KCNH2-K897T was on the same allele (cis) as the primary mutation, the hERG channel in hiPSC-CMs exhibited faster activation and deactivation kinetics compared to their trans-oriented counterparts. Consistent with this, hiPSC-CMs with KCNH2-K897T in cis orientation had longer action and field potential durations. Furthermore, there was an increased occurrence of arrhythmic events upon pharmacological blocking of hERG. Collectively, these results indicate that the common polymorphism KCNH2-K897T differs in its influence on LQT2-causing KCNH2 mutations depending on whether it is present in cis or trans. This study corroborates hiPSC-CMs as a powerful platform to investigate the modifying effects of common genetic variants on inherited cardiac arrhythmias and aids in unraveling their contribution to the variable expressivity of these diseases.
Multiple target class adaptive wideband radar imaging

We discuss the problem of waveform selection for wideband radar imaging. We show how this problem can be solved in a finite-dimensional setting. We discuss an algorithm for imaging an unknown target from a collection of targets belonging to M classes where M > 2. We present two possible algorithms for simultaneous imaging and classification of unknown targets. We describe how the radar simulations can be done in these cases.
Development of an Analytical Method for Arbitrary Shaped Pattern Synthesis of Planar Arrays

In practice, it is often necessary to design an array that will yield desired radiation pattern. For this purpose, several time-consuming algorithms are introduced in the literature. In this paper, an analytical method is presented to synthesize the radiation pattern of planar and ring arrays. In this method, two new parameters are defined to reconstruct the array factor and simplify the calculation complexity. To accomplish this, we use the double integral to generate two distinct Sinc functions from a bivariate function utilizing the sampling theory notion. This stage generates a set of linear equations that, when solved, yields the complex excitation coefficients. The proposed method is verified by presenting several practical examples. Also, the performance of the method is compared with that of other approaches. The results show that the proposed method is a good candidate for synthesizing a prescribed pattern of planar arrays.
Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network

In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.
Concept Note on Presumptive Treatment for Malaria for Consideration by the E 8 Technical Committee May 2016 Current Malaria Elimination Strategies

All malaria endemic countries record cases in which patients (usually symptomatic) passively present and get treated at health facilities. While this identification and surveillance of cases is very important for surveillance, it is restricted to symptomatic treatment-seeking individuals, and thus has little impact on malaria transmission. Where transmission of malaria is very low, strategies for elimination include finding and treating individual infections. In order to have an impact on malaria transmission, it is necessary to identify and clear parasites in all infected individuals both those who are symptomatic and treatment seeking, as well as those who are asymptomatic and do not present to health facilities. It has been shown that even in low transmission settings, a large proportion, if not the majority of infections, are asymptomatic. In order target the asymptomatic parasite pool and thereby reduce transmission in low endemicity settings, active case detection (ACD) is recommended by WHO for the purpose of malaria elimination, and it is increasingly adopted by malaria control programs in low transmission countries. Various forms of ACD have had a long history in most E8 countries.
ECG SIGNAL CLASSIFICATION FOR ARRHYTHMIA DETECTION USING DEA AND ELM

The electrical mobility of the human heart is represented by the Electrocardiogram signals. In the modern years the automatic classification of ECG signals has great significance as the cardiologists and the technicians take the decision on heart disease based on automatic classification. The goal of the work is to optimize the total number of hidden neurons by using DEA ( Differential Evolution algorithm ) and ELM (Extreme Learning Machine) algorithm, which makes the classification of the ECG signals with high rate of accuracy. The extreme learning machine is an application of single feed-forward Neural network whose performance depends on weight, activation function and threshold value of the modeled data. The Pan-Tompkins technique were used here to obtain different characteristics of the ECG data, mainly works with the QRS complex of the ECG signals. The ventricular depolarization is maintained by QRS complex where PR period, ST period, QT period are used to analyze the characteristics properties of the ECG signals. A series of filters are applied here to remove the background noise from the ECG signals which makes quick heart depolarization to get adaptive thresholds to identify the peaks of the signal. The ECG samples were simulated with ELM and then the DEA algorithm was used to optimize the problem for better classification of the ECG data. The performance of the classification was measured and it was compared with other related works. In this work the total accuracy was 96.027% where different numbers of hidden neurons were taken (maximum up to 160 ) which was tested with conventional ELM
Spektri odgovora za seizmičku procjenu zgrada

Opisane su metoda postupnog guranja (Pushover Analysis) i metoda spektra kapaciteta (Capacity Spectrum Method) koje se primjenjuju u seizmickoj procjeni postojecih i novih zgrada. Nelinearnim statickim proracunom dobiva se krivulja kapaciteta konstrukcije, a zahtjev potresa predstavljen je spektrima ubrzanja. Obje krivulje su u formatu spektralno ubrzanje - spektralni pomak. Usporedba kapaciteta konstrukcije i zahtjeva potresa daje podatke o mogucem odgovoru konstrukcije na potres. Key words building, seismic evaluation, response spectrum, nonlinear analysis, acceleration spectrum, displacement M. Hrasnica Preliminary note
A DDITIONAL AUTHENTICATION OF PRIVILEGED USERS BY THE GEOMETRY OF THEIR FACE IN INFORMATION SYSTEMS USING SINGLE SIGN-ON TECHNOLOGY

Streszczenie: Artykuł przedstawia możliwość dodatkowego uwierzytelnienia uprzywilejowanych użytkowników systemów informatycznych przy użyciu technologii pojedynczego logowania. W tym celu proponuje się wykorzystanie technologii biometrycznej do rozpoznawania użytkowników po geometrii ich twarzy. Rozważane są etapy systemu uwierzytelniania opartego na proponowanej technologii biometrycznej. Na podstawie analizy wyników eksperymentów; określono celowość zastosowania tej technologii biometrycznej oraz określono warunki jej skuteczności w stagnacji.