Miracle-dz/UniFAR-FTUs huggingface dataset:
Elevated late-life blood pressure may maintain brain oxygenation and slow amyloid-β accumulation at the expense of cerebral vascular damage

Abstract Hypertension in midlife contributes to cognitive decline and is a modifiable risk factor for dementia. The relationship between late-life hypertension and dementia is less clear. We have investigated the relationship of blood pressure and hypertensive status during late life (after 65 years) to post-mortem markers of Alzheimer’s disease (amyloid-β and tau loads); arteriolosclerosis and cerebral amyloid angiopathy; and to biochemical measures of ante-mortem cerebral oxygenation (the myelin-associated glycoprotein:proteolipid protein-1 ratio, which is reduced in chronically hypoperfused brain tissue, and the level of vascular endothelial growth factor-A, which is upregulated by tissue hypoxia); blood–brain barrier damage (indicated by an increase in parenchymal fibrinogen); and pericyte content (platelet-derived growth factor receptor β, which declines with pericyte loss), in Alzheimer’s disease (n = 75), vascular (n = 20) and mixed dementia (n = 31) cohorts. Systolic and diastolic blood pressure measurements were obtained retrospectively from clinical records. Non-amyloid small vessel disease and cerebral amyloid angiopathy were scored semiquantitatively. Amyloid-β and tau loads were assessed by field fraction measurement in immunolabelled sections of frontal and parietal lobes. Homogenates of frozen tissue from the contralateral frontal and parietal lobes (cortex and white matter) were used to measure markers of vascular function by enzyme-linked immunosorbent assay. Diastolic (but not systolic) blood pressure was associated with the preservation of cerebral oxygenation, correlating positively with the ratio of myelin-associated glycoprotein to proteolipid protein-1 and negatively with vascular endothelial growth factor-A in both the frontal and parietal cortices. Diastolic blood pressure correlated negatively with parenchymal amyloid-β in the parietal cortex. In dementia cases, elevated late-life diastolic blood pressure was associated with more severe arteriolosclerosis and cerebral amyloid angiopathy, and diastolic blood pressure correlated positively with parenchymal fibrinogen, indicating blood–brain barrier breakdown in both regions of the cortex. Systolic blood pressure was related to lower platelet-derived growth factor receptor β in controls in the frontal cortex and in dementia cases in the superficial white matter. We found no association between blood pressure and tau. Our findings demonstrate a complex relationship between late-life blood pressure, disease pathology and vascular function in dementia. We suggest that hypertension helps to reduce cerebral ischaemia (and may slow amyloid-β accumulation) in the face of increasing cerebral vascular resistance, but exacerbates vascular pathology.
1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

—According to estimations made by World Health Organization, heart disease is the largest cause of mortality throughout the globe, and it is safe to assume that diagnosing heart diseases in their earliest stages is very essential. Diagnosis of cardiovascular disease may be carried out by detection of interference in cardiac signals, one of which is called phonocardiography, and it can be accomplished in a number of various ways. Using phonocardiogram (PCG) inputs and deep learning, the researchers aim to develop a classification system for different types of heart illness. The slicing and normalization of the signal served as the first step in the study's signal preprocessing, which was subsequently followed by a wavelet based transformation method that employs mother wavelet analytic morlet. The results of the decomposition are first shown with the use of a scalogram, afterwards, they are utilized as input for the deep CNN. In this investigation, the analyzed PCG signals were separated into categories, denoting normal and pathological heart sounds. The entire utilized data was divided into two categories as training and test data as 80% to 20%. The developed model demonstrates the degree of clinical diagnosis, sensitivity, specificity and AUC-ROC value. As a result, it has been determined that the proposed method was superior to the mother wavelet as well as other classifier approaches. Consequently, we were able to acquire an electronic stethoscope that has a diagnostic accuracy of more than 90% when it comes to identifying cardiac problems. To be more specific, the proposed deep CNN model has an accuracy of 93.25% in identifying aberrant heart sounds and 93.50% in identifying regular heartbeats. In addition, given the fact that an examination may be completed in only 15 seconds, speed is the primary advantage offered by the suggested stethoscope
Some Lower Bounds on Signal Parameter Estimation

New bounds are presented for the maximum accuracy with which parameters of signals imbedded in white noise can be estimated. The bounds are derived by comparing the estimation problem with related optimal detection problems. They are, with few exceptions, independent of the bias and include explicitly the dependence on the a priori interval. The new results are compared with previously known results.
Euclidean lattices for high dimensional indexing and searching

For similarity based searching, multimedia data are represented by one or more numerical vectors: we search the nearest neighbors of the query. Because of the huge number of these data and their high dimension, classical indexing technics are inefficient. The goal of this internship is to study the use of euclidean lattices for database indexing. Lattices have nice properties: they are spatial quantizers, thereby generate a partition of the space and decoding (quantization step) may be done very quickly. Then, we hope to be able to rapidly find a small space region containing data similar to a given query point, without reading all the database.
MULTISCALE SIMULATIONS USING PARTICLES

Summary We outline a multiscale algorithm for the simulation of dense fluids that couples molecular dynamics and continuum fluid dynamics. The coupling between the two models is realized by a common overlap domain and by the alternating Schwarz method. We prefer this method to those based on direct flux exchange, since the accurate estimation of the fluxes requires much more statistics. The boundary conditions on the atomistic region are imposed using stochastic walls to thermalize and confine the molecular dynamics system, and the USHER algorithm to insert new atoms. The forcing of the continuum is achieved using appropriate source terms in the momentum equations. To test the algorithm, we study the flow of liquid argon past a carbon nanotube.
ALGORITMA PENENTUAN UKURAN SAMPEL EKSAK UNTUK DISTRIBUSI NORMAL, DISTRIBUSI POISSON DAN DUA DISTRIBUSI BINOMIAL DALAM MODEL KELUARGA EKSPONENSIAL

Ketika akan melakukan suatu eksperimen dengan menggunakan pendekatan klasik ataupun pendekatan Bayesian, informasi tentang ukuran sampel yang tepat sangatlah penting. Penentuan ukuran sampel ini selain tergantung pada parameter yang diselidiki juga berkaitan dengan biaya yang dibutuhkan untuk survey data. Penentuan ukuran sampel yang tepat akan memberikan kesimpulan dan keputusan yang baik dengan biaya minimal. Tujuan dari penulisan ini adalah membahas masalah penentuan besarnya ukuran sampel untuk distribusi-distribusi dalam model keluarga eksponensial yaitu distribusi Normal, distribusi Poisson dan dua distribusi Binomial dengan menggunakan negatif log normed likelihood .
Phase I Trial of Autologous RNA-electroporated cMET-directed CAR T Cells Administered Intravenously in Patients with Melanoma and Breast Carcinoma

Purpose: Treatments are limited for metastatic melanoma and metastatic triple-negative breast cancer (mTNBC). This pilot phase I trial (NCT03060356) examined the safety and feasibility of intravenous RNA-electroporated chimeric antigen receptor (CAR) T cells targeting the cell-surface antigen cMET. Experimental Design: Metastatic melanoma or mTNBC subjects had at least 30% tumor expression of cMET, measurable disease and progression on prior therapy. Patients received up to six infusions (1 × 10e8 T cells/dose) of CAR T cells without lymphodepleting chemotherapy. Forty-eight percent of prescreened subjects met the cMET expression threshold. Seven (3 metastatic melanoma, 4 mTNBC) were treated. Results: Mean age was 50 years (35–64); median Eastern Cooperative Oncology Group 0 (0–1); median prior lines of chemotherapy/immunotherapy were 4/0 for TNBC and 1/3 for melanoma subjects. Six patients experienced grade 1 or 2 toxicity. Toxicities in at least 1 patient included anemia, fatigue, and malaise. One subject had grade 1 cytokine release syndrome. No grade 3 or higher toxicity, neurotoxicity, or treatment discontinuation occurred. Best response was stable disease in 4 and disease progression in 3 subjects. mRNA signals corresponding to CAR T cells were detected by RT-PCR in all patients’ blood including in 3 subjects on day +1 (no infusion administered on this day). Five subjects underwent postinfusion biopsy with no CAR T-cell signals seen in tumor. Three subjects had paired tumor tissue; IHC showed increases in CD8 and CD3 and decreases in pS6 and Ki67. Conclusions: Intravenous administration of RNA-electroporated cMET-directed CAR T cells is safe and feasible. Significance: Data evaluating CAR T therapy in patients with solid tumors are limited. This pilot clinical trial demonstrates that intravenous cMET-directed CAR T-cell therapy is safe and feasible in patients with metastatic melanoma and metastatic breast cancer, supporting the continued evaluation of cellular therapy for patients with these malignancies.
Modular tensor sparsity preserving projection algorithm for dimension reduction

This paper proposes a modular tensor sparsity preserving projection (MTSPP) algorithm. This algorithm uniformly partitions the high-dimensional matrix data and builds third order tensor data, determines the weight of sparse reconstruction of all samples and applies it to the sparsity preserving projection of the third order tensor. Experiments finally indicate that MTSPP improves the robust performance of the global sparse representation-based dimension reduction algorithm by weighted sparse representation and spatial relationship of characteristics within the module and between modules.
Vitamin D, Cholesterol, and DXA Value Relationship with Bimaxillary Cone Beam CT Values

We evaluated the correlation that Vitamin D (Vit D), cholesterol levels, and T- and Z-scores of dual-energy X-ray absorptiometry (DXA) scans have with cone beam computed tomography values assessed in the anterior and posterior regions of maxillary and mandibular jaws. In total, 187 patients were recruited for this clinical study. Patients’ ages ranged between 45 and 65 years. Patients with valid DXA results, serum Vit D and cholesterol levels, and no evidence of bone disorders in the maxilla or mandibular region were included in the study and grouped in the control (non-osteoporosis) and case (osteoporosis) groups. Patients with a history of medical or dental disease that might complicate the dental implant therapy, chronic alcohol users, and patients who took calcium or Vit D supplements were excluded. The outcome variables assessed in the investigation were Vit D, cholesterol, Z-values, and cone beam computed tomography values. Regarding the case group, a significant (p < 0.05) inverse relationship was observed between Vit D and cholesterol. Although insignificant (p > 0.05), a positive relationship was found between Vit D and the cone beam computed tomography values in all regions of the jaws, except the mandibular posterior region (p < 0.05). Pearson correlation analysis was carried out. Vit D and cholesterol showed a statistically insignificant (p > 0.05) negative association with the cone beam computed tomography values in all regions of the jaws. However, the Z-values were highly correlated with the cone beam computed tomography values in all regions of the jaws (r > 7, p < 0.05). Vit D, cholesterol levels, and Z-values in women and men from young adulthood to middle age (45–65) were related with the cone beam computed tomography values of the jaws.
A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors’ number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.
Probabilistic synthesis of KDP satisfying mutually complementary correctness conditions

On the basis of probabilistic estimates and computer experiments with application of probabilistic algorithms of synthesis of key distribution patterns in a computer network, it is shown that a combination of two well-known correctness conditions KDP, Key Distribution Pattern and HARPS, Hashed Random Preloaded Subset Key Distribution may increase the information rate of the combined scheme HAKDP, Hashed Key Distribution Pattern compared with data rate of schemes based on separate conditions. Keywords—computer network, system key, key distribution patterns, probabilistic synthesis, probabilistic algorithm, probabilistic estimate, key distribution scheme, cryptographic hash function, correctness condition, information rate In this paper, it is shown a possibility of constructing a key distribution patterns which correctness condition is disjunction of correctness conditions of two other schemes. The combined scheme is correct if it satisfies at least one of these conditions. Thies means that these conditions are mutually complementary. At the same time, using probabilistic estimates and computer simulations based on probabilistic algorithms of synthesis of key distribution schemes there is shown a possibility of increasing the information rate of key distribution schemes with combined correctness condition.
EXTERNAL POLYHEDRAL ESTIMATES FOR REACHABLE SETS OF LINEAR DISCRETE-TIME SYSTEMSWITH INTEGRAL BOUNDS ON CONTROLS

The approach for estimating the reachable sets X(k) of the linear discrete-time systems with integral bounds on controls is presented. It is based on considering reachable sets Z(k) in the "extended" phase space and allows to construct estimates for systems without and with state constraints. We construct the external estimates of Z(k) in the form of special polytopes. The specific cross-sections of them provide the parallelepiped-valued estimates of the reachable sets X(k) in the "initial" phase space. The whole families of estimates are introduced. Evolution of estimates is determined by recurrence relations. The families of touching estimates which ensure the exact representations of the reachable sets are described for systems without state constraints. The novel family of estimates for time-invariant systems is introduced. It is efficient for systems without and with small number of state constraints. The results of numerical simulations are presented.
Solving Highly Expensive Optimization Problems via Evolutionary Expected Improvement

Although many methods have been proposed to solve expensive optimization problems (EOPs), they often consume hundreds of function evaluations (FEs) to find the optimal solution, which is unacceptable when facing highly EOPs. To reduce the number of FEs, we incorporate the population distribution into the well-known expected improvement (EI); thus, a new infill criterion called evolutionary EI (EEI) is proposed. In EEI, the covariance matrix adaptation evolution strategy is used to provide the population distribution. Compared with the original EI, EEI focuses more on promising regions provided by the population distribution, thus, reducing the FEs wasted in unpromising regions. By employing EEI as the infill criterion of Bayesian optimization, a new algorithm called EEI-BO is designed. Moreover, we also introduce an extended version of EEI-BO, called EEI-BO+, to handle multitask EOPs. To verify the effectiveness of EEI-BO, it is used to solve 10−, 20−, and 30-D test problems by using only 40, 50, and 60 FEs, respectively. The results show that EEI-BO is able to obtain high-quality solutions by consuming limited FEs. In addition, we apply EEI-BO to deal with the lightweight and crashworthiness design of the side body of an automobile. The results demonstrate that EEI-BO performs well on solving it. Furthermore, the performance of EEI-BO+ is investigated by nine test problems. The results show that it has the capability to solve multitask EOPs with fast convergence speed.
An algorithm for voltage stability assessment of AC/DC power system based on continuation method and interior point theory

A new segment solution for solving load margin of AC/DC power system is proposed in this paper based on continuation method and interior point algorithm.The load increment between the original point and the point when the DC control mode alters,can be solved with continuation method.As the Jacobian and Hessian matrix of AC/DC system is extrapolated from the coupled relationship of AC/DC network,the load margin of AC/DC system after DC control mode is adjusted can be calculated with interior point method.The method characterizes by its theoretical square convergence,convenience of considering restrain of DC system variable and adjusting rationally operation mode.Simulation results show the veracity of the solution.
Stable-inversion based iterative learning control for non-minimum phase plants

In order to deal with the poor tracking effect which occurs when the iterative learning control (ILC) was applied to the non-minimum phase system, an optimal ILC scheme with current feedback was presented for linear non-minimum phase plants based on an optimality criterion and noncausal stable inversion. The convergence of this scheme was analyzed and the utility mode of using the noncausal algorithm was proposed for the practical application.
Relacionando Refactorings e Métricas de Código Fonte - Um Primeiro Passo para Detecção Automática de Oportunidades de Refactoring

Refactoring – melhorar a estrutura interna do software sem modificação no seu comportamento observável – é um mecanismo importante para se evitar a degradação da qualidade do software. Fundamental para tal finalidade é a identificação de trechos do código fonte que apresentam oportunidades de refactoring – comumente chamados de bad smells. Este artigo propõe uma abordagem para auxiliar na detecção de bad smells através de medição de código fonte e seu foco é a apresentação de um primeiro passo no sentido implementar esta abordagem. Para tal finalidade foi realizado um estudo que relaciona métricas, refactorings e bad smells. O estudo é dividido em duas partes. A primeira parte – top-down – é baseada na aplicação analítica do método Meta Pergunta Métrica (MPM ou GQM, em inglês) na definição de métricas para detecção de bad smells. A segunda parte – bottom-up – é um estudo empírico do relacionamento entre métricas conhecidas de código fonte, refactorings e bad smells.
Aerosol retrievals from the multiyear multisatellite AVHRR pathfinder atmosphere (PATMOS) dataset for correcting remotely sensed sea surface temperatures

Abstract Eight-year (1990–98), two-satellite (NOAA-11 and -14), global daily ∼(110 km)2 gridded observations from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmosphere (PATMOS) dataset have been previously merged with the Pathfinder Matchup Database (PFMDB) and used to develop the Phase I aerosol correction for sea surface temperatures (SSTs) from AVHRR. In this study, this unique PATMOS–BUOY matchup dataset (N = 105 831) is used to derive and quality control an advanced set of aerosol parameters to be used in the Phase II algorithm: aerosol optical depths in channels 1 (λ1 = 0.63 μm) and 2 (λ2 = 0.83 μm), τ1 and τ2, and Angstrom exponent α = −ln(τ1/τ2)/ln(λ1/λ2). Inaccurate retrievals at low sun and outliers are removed from the data. PATMOS global, multiyear, multisatellite aerosol properties, derived from cloud-free portions of the (110 km)2 grid, resemble many features previously observed in the space–time-restricted, (8 km)2 resolution Aerosol Observation (AEROBS) operational ret.
A linearly implicit energy-preserving exponential time differencing scheme for the fractional nonlinear Schrödinger equation

In this paper, we present a new method to solve the fractional nonlinear Schrödinger equation. Our approach combines the invariant energy quadratization method with the exponential time differencing method, resulting in a linearly-implicit energy-preserving scheme. To achieve this, we introduce an auxiliary variable to derive an equivalent system with a modified energy conservation law. The proposed scheme uses stabilized exponential time differencing approximations for time integration and Fourier pseudo-spectral discretization in space to obtain a linearly-implicit, fully-discrete scheme. Compared to the original energy-preserving exponential integrator scheme, our approach is more efficient as it does not require nonlinear iterations. Numerical experiments confirm the effectiveness of our scheme in conserving energy and its efficiency in long-time computations.