Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone Allows Capture of Speech Abnormalities Associated With High Risk of Developing Parkinson’s Disease

Although smartphone technology provides new opportunities for the recording of speech samples in everyday life, its ability to capture prodromal speech impairment in persons with a high risk of developing Parkinson’s disease (PD) has never been investigated. Speech data were acquired through a smartphone as well as a professional microphone with a linear frequency response from 50 participants with a rapid eye movement sleep behavior disorder that are at a high risk of developing PD and related neurodegenerative disorders. Additionally, recordings of 30 newly diagnosed, untreated PD patients and 30 healthy participants were evaluated. Acoustic assessment of 11 speech dimensions representing the key aspects of hypokinetic dysarthria in the early stages of PD was performed. Smartphone allowed the detection of speech abnormalities in participants with a high risk of developing PD. Acoustic measurements related to fundamental frequency variability, duration of pause intervals, and rate of speech timing extracted from spontaneous speech were sufficiently sensitive to significantly separate groups (area under curve of 0.85 between PD and controls) and showed very strong correlation and reliability between the professional microphone and the smartphone. Speech-based biomarkers collected through smartphones may have the potential to revolutionize the diagnostic process in neurodegenerative diseases and improve stratification for future neuroprotective therapy in PD.
Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer's Disease

As members of an increasingly aging society, one of our major priorities is to develop tools to detect the earliest stage of age-related disorders such as Alzheimer's Disease (AD). The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavior data to detect the multimodal symptoms that are often found to be impaired in AD. After gathering longitudinal smart home data for 29 older adults over an average duration of $>$2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing ten behavioral features. Mobility, cognition, and mood were evaluated every six months. Using these data, we created regression models to predict symptoms as measured by the tests and a feature selection analysis was performed. Classification models were built to detect reliable absolute changes in the scores predicting symptoms and SmoteBOOST and wRACOG algorithms were used to overcome class imbalance where needed. Results show that all mobility, cognition, and depression symptoms can be predicted from activity-aware smart home data. Similarly, these data can be effectively used to predict reliable changes in mobility and memory skills. Results also suggest that not all behavioral features contribute equally to the prediction of every symptom. Future work therefore can improve model sensitivity by including additional longitudinal data and by further improving strategies to extract relevant features and address class imbalance. The results presented herein contribute toward the development of an early change detection system based on smart home technology.
Implementing Team Leadership

A Team Leadership Program (TLP) was started in April, 1993. The primary thrust of that effort was to improve the organization's effectiveness through increased teamwork leading to Self-Managed Work Teams. After a year and a half, considerable progress has been achieved. Employee attitudes have shown significant improvement in two departments, and financial returns of over 1,000 percent have been achieved. This paper describes the process and the results with implications for generalization to other companies.
A Multi-Class BCI Based on Somatosensory Imagery

In this paper, we investigated the performance of a multi-class brain-computer interface (BCI). The BCI system is based on the concept of somatosensory attentional orientation (SAO), in which the user shifts and maintains somatosensory attention by imagining the sensation of tactile stimulation of a body part. At the beginning of every trial, a vibration stimulus (200 ms) informed the subjects to prepare for the task. Four SAO tasks were performed following randomly presented cues: SAO of the left hand (SAO-LF), SAO of the right hand (SAO-RT), bilateral SAO (SAO-BI), and SAO suppressed or idle state (SAO-ID). Analysis of the event-related desynchronization and synchronization (ERD/ERS) in the EEG indicated that the four SAO tasks had different somatosensory cortical activation patterns. SAO-LF and SAO-RT exhibited stronger contralateral ERD, whereas bilateral ERD activation was indicative of SAO-BI, and bilateral ERS activation was associated with SAO-ID. By selecting the frequency bands and/or optimal classes, classification accuracy of the system reached 85.2%±11.2% for two classes, 69.5%±16.2% for three classes, and 55.9%±15.8% for four classes. The results validated a multi-class BCI system based on SAO, on a single trial basis. Somatosensory attention to different body parts induces diverse oscillatory dynamics within the somatosensory area of the brain, and the proposed SAO paradigm provided a new approach for a multiple-class BCI that is potentially stimulus independent.
AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications

Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.
Big data from electronic health records for early and late translational cardiovascular research: challenges and potential

Abstract Aims Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. Methods and results We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health. Conclusion High volumes of inherently diverse (‘big’) EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare.
Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning

This paper presents a parametric low-dimensional (LD) representation learning method that allows to reconstruct high-dimensional (HD) input vectors in an unsupervised manner. Under the assumption that the HD data and its LD representation share the same or similar local sparse structure, the proposed method achieves reconstructible dimensionality reduction via jointly learning dictionaries in both the original HD data space and its LD representation space. By regarding the sparse representation as a smooth function with respect to a specific dictionary, we construct an encoding–decoding block for learning LD representations from sparse coefficients of HD data. It is expected that this learning process preserves the desirable structure of HD data in the LD representation space, and simultaneously allows a reliable reconstruction from the LD space back to the original HD space. In addition, the proposed single layer encoding–decoding block can be easily extended to deep learning structures. Numerical experiments on both synthetic data sets and real images show that the proposed method achieves strongly competitive and robust performance in data DR, reconstruction, and synthesis, even on heavily corrupted data. The proposed method can be used as an alternative approach to compressive sensing (CS); however, it can outperform the traditional CS methods in: 1) task-driven learning problems, such as 2-D/3-D data visualization, and 2) data reconstruction at a lower dimensional space.
Índice de tabelas

Fashion is a source of news that stimulates the desire of the individual and a long time ago ceased to be merely a synonym for glamour and frivolity, becoming a powerful social phenomenon and of great economic importance. The Brazilian clothing market has been growing at a rate of 7% per annum and numbers show that women are the biggest consumers of fashion in the country. Women's fashion accounts for 41% of production. Since the male accounts for 35% of the market, children fashion for 18% and the called baby fashion, only 5%. Thus, this study aims to analyze the current scenario of the Brazilian women's fashion retail and raise the important elements for the composition of marketing in this segment, as well as their applications and trends, focusing on the elements just worked today by Brazilian retailers.
Impacts of a School-Wide, Peer-Led Approach to Sexuality Education: A Matched Comparison Group Design.

BACKGROUND Teen Prevention Education Program (PEP) is a school-wide, peer-led comprehensive sexuality education program currently implemented in more than 50 schools across 2 states. Many teen pregnancy prevention researchers and practitioners view peer-led programs as a promising approach for reducing teen pregnancy and associated sexual risk behaviors. However, prior research on the effectiveness of these programs indicates mixed results. METHODS We randomly assigned schools to implement Teen PEP immediately (intervention group) or on a delayed schedule (comparison group) and used propensity score matching to improve the comparability of the study groups. We surveyed students at baseline and about 6 months after the program ended. RESULTS Teen PEP did not significantly impact rates of sexual activity or unprotected sex; however, the program led to improvements in exposure to information about sexual health topics and knowledge of preventing pregnancy and transmission of sexually transmitted infections. CONCLUSIONS Teen PEP succeeded in accomplishing some of its most proximal goals, increasing students' access to information and knowledge. However, we found little evidence that the program affects sexual risk-taking within 6 months of its conclusion. Future research will examine the program's longer-term impacts on sexual risk behaviors.
Microarray Missing Value Imputation: A Regularized Local Learning Method

Microarray experiments on gene expression inevitably generate missing values, which impedes further downstream biological analysis. Therefore, it is key to estimate the missing values accurately. Most of the existing imputation methods tend to suffer from the over-fitting problem. In this study, we propose two regularized local learning methods for microarray missing value imputation. Motivated by the grouping effect of $L_{2}$L2 regularization, after selecting the target gene, we train an $L_{2}$L2 Regularized Local Least Squares imputation model (RLLSimpute_L2) on the target gene and its neighbors to estimate the missing values of the target gene. Furthermore, RLLSimpute_L2 imputes the missing values in an ascending order based on the associated missing rate with each target gene. This contributes to fully utilizing the previously estimated values. Besides $L_{2}$L2, we further explore $L_{1}$L1 regularization and propose an $L_{1}$L1 Regularized Local Least Squares imputation model (RLLSimpute_L1). To evaluate their effectiveness, we conducted extensive experimental studies on six benchmark datasets covering both time series and non-time series cases. Nine state-of-the-art imputation methods are compared with RLLSimpute_L2 and RLLSimpute_L1 in terms of three performance metrics. The comparative experimental results indicate that RLLSimpute_L2 outperforms its competitors by achieving smaller imputation errors and better structure preservation of differentially expressed genes.
Hgf/Met activation mediates resistance to BRAF inhibition in murine anaplastic thyroid cancers

Anaplastic thyroid carcinomas (ATCs) have a high prevalence of BRAF and TP53 mutations. A trial of vemurafenib in nonmelanoma BRAFV600E-mutant cancers showed significant, although short-lived, responses in ATCs, indicating that these virulent tumors remain addicted to BRAF despite their high mutation burden. To explore the mechanisms mediating acquired resistance to BRAF blockade, we generated mice with thyroid-specific deletion of p53 and dox-dependent expression of BRAFV600E, 50% of which developed ATCs after dox treatment. Upon dox withdrawal there was complete regression in all mice, although recurrences were later detected in 85% of animals. The relapsed tumors had elevated MAPK transcriptional output, and retained responses to the MEK/RAF inhibitor CH5126766 in vivo and in vitro. Whole-exome sequencing identified recurrent focal amplifications of chromosome 6, with a minimal region of overlap that included Met. Met-amplified recurrences overexpressed the receptor as well as its ligand Hgf. Growth, signaling, and viability of Met-amplified tumor cells were suppressed in vitro and in vivo by the Met kinase inhibitors PF-04217903 and crizotinib, whereas primary ATCs and Met-diploid relapses were resistant. Hence, recurrences are the rule after BRAF suppression in murine ATCs, most commonly due to activation of HGF/MET signaling, which generates exquisite dependency to MET kinase inhibitors.
Robotic-Assisted Needle Steering Around Anatomical Obstacles Using Notched Steerable Needles

Robotic-assisted needle steering can enhance the accuracy of needle-based interventions. Application of current needle steering techniques are restricted by the limited deflection curvature of needles. Here, a novel steerable needle with improved curvature is developed and used with an online motion planner to steer the needle along curved paths inside tissue. The needle is developed by carving series of small notches on the shaft of a standard needle. The notches decrease the needle flexural stiffness, allowing the needle to follow tightly curved paths with small radius of curvature. In this paper, first, a finite element model of the notched needle deflection in tissue is presented. Next, the model is used to estimate the optimal location for the notches on needle's shaft for achieving a desired curvature. Finally, an ultrasound-guided motion planner for needle steering inside tissue is developed and used to demonstrate the capability of the notched needle in achieving high curvature and maneuvering around obstacles in tissue. We simulated a clinical scenario in brachytherapy, where the target is obstructed by the pubic bone and cannot be reached using regular needles. Experimental results show that the target can be reached using the notched needle with a mean accuracy of 1.2 mm. Thus, the proposed needle enables future research on needle steering toward deeper or more difficult-to-reach targets.
Evaluating Quality of Screen Content Images Via Structural Variation Analysis

With the quick development and popularity of computers, computer-generated signals have drastically invaded into our daily lives. Screen content image is a typical example, since it also includes graphic and textual images as components as compared with natural scene images which have been deeply explored, and thus screen content image has posed novel challenges to current researches, such as compression, transmission, display, quality assessment, and more. In this paper, we focus our attention on evaluating the quality of screen content images based on the analysis of structural variation, which is caused by compression, transmission, and more. We classify structures into global and local structures, which correspond to basic and detailed perceptions of humans, respectively. The characteristics of graphic and textual images, e.g., limited color variations, and the human visual system are taken into consideration. Based on these concerns, we systematically combine the measurements of variations in the above-stated two types of structures to yield the final quality estimation of screen content images. Thorough experiments are conducted on three screen content image quality databases, in which the images are corrupted during capturing, compression, transmission, etc. Results demonstrate the superiority of our proposed quality model as compared with state-of-the-art relevant methods.
Video Magnification Applied in Ultrasound

Goal: This paper describes a method to enhance, visualize, and reveal subtle motion that can be present in medical images. As proposed in vision applications, the principle is to magnify displacement applied, in this case, to cardiovascular tissues (carotid). Methods: In the example presented, ultrasound data were acquired at a high frame rate and two-dimensional motion was estimated, amplified, and reapplied in ultrasound carotid sequences. Results: Video magnification makes fast and complex phenomena of human tissue visible. In fact, not only pulse and reflected wave, but also global radial and longitudinal motion in the example presented are visible with video magnification. Conclusion: Video magnification can be used in medical imaging for subtle motion visualization. One of the many possible applications is direct visualization of a local modification in terms of stiffness of a tissue (due to local necrosis, for instance) from acquisition. Moreover, video magnification can be executed with any type of imaging modality. Significance: Video magnification could be a new tool for physicians to highlight new pathology indicators or for long-term disease monitoring.
Accurate Segmentation and Registration of Skin Lesion Images to Evaluate Lesion Change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolution.
Improve Multichannel Speech Recognition with Temporal and Spatial Information

SUMMARY In this letter, we explored the usage of spatio-temporal information in one uniﬁed framework to improve the performance of multi-channel speech recognition. Generalized cross correlation (GCC) is served as spatial feature compensation, and an attention mechanism across time is embedded within long short-term memory (LSTM) neural networks. Experiments on the AMI meeting corpus show that the proposed method provides a 8.2% relative improvement in word error rate (WER) over the model trained directly on the concatenation of multiple microphone outputs.
Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention

Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e.g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents.