Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.
Assessing Levels and Correlates of Implementation of Evidence-Based Approaches for Colorectal Cancer Screening: A Cross-Sectional Study With Federally Qualified Health Centers

Multiple evidence-based approaches (EBAs) exist to improve colorectal cancer screening in health clinics. The success of these approaches is tied to effective implementation. Therefore, the purpose of this study was to assess the implementation of EBAs for colorectal cancer screening and clinic-level correlates of implementation in federally qualified health centers (FQHCs). We conducted descriptive and cross-sectional analyses using data collected from FQHC clinics across seven states (n = 51). A clinic representative completed electronic surveys about clinic characteristics (e.g., size, patient characteristics, and medical record system characteristics) and the implementation of Community Guide recommended EBAs (e.g., client reminders, small media, and provider assessment and feedback). We used bivariate Spearman correlations to assess clinic-level correlates with implementation outcomes. Most clinics were planning to implement, in the early implementation stages, or inconsistently implementing EBAs. No EBA was fully implemented by more than nine (17.6%) clinics. Clinic size variables were inversely related to implementation levels of one-on-one education; medical record variables were directly related to implementation levels of client and provider reminders as well as provider assessment and feedback; and rapid and timely feedback from clinic leaders was directly associated with implementation levels of four out of six EBAs. Given the varying levels of implementation, clinics need to assess current use of implementation strategies and improve effective program delivery to increase colorectal cancer screening among their patients. In addition, clinics should also consider how their characteristics may support or serve as a barrier to implementation in their respective settings.
Unsupervised learning of semantic representation for documents with the law of total probability

Abstract The semantic information of documents needs to be represented because it is the basis for many applications, such as document summarization, web search, and text analysis. Although many studies have explored this problem by enriching document vectors with the relatedness of the words involved, the performance remains far from satisfactory because the physical boundaries of documents hinder the evaluation of the relatedness between words. To address this problem, we propose an effective approach to further infer the implicit relatedness between words via their common related words. To avoid overestimation of the implicit relatedness, we restrict the inference in terms of the marginal probabilities of the words based on the law of total probability. The proposed method measures the relatedness between words, which is confirmed theoretically and experimentally. Thorough evaluation on real datasets illustrates that significant improvement on document clustering has been achieved with the proposed method compared with state-of-the-art methods.
Feature Preserving Mesh Denoising Based on Graph Spectral Processing

The increasing interest for reliable generation of large scale scenes and objects has facilitated several real-time applications. Although the resolution of the new generation geometry scanners are constantly improving, the output models, are inevitably noisy, requiring sophisticated approaches that remove noise while preserving sharp features. Moreover, we no longer deal exclusively with individual shapes, but with entire scenes resulting in a sequence of 3D surfaces that are affected by noise with different characteristics due to variable environmental factors (e.g., lighting conditions, orientation of the scanning device). In this work, we introduce a novel coarse-to-fine graph spectral processing approach that exploits the fact that the sharp features reside in a low dimensional structure hidden in the noisy 3D dataset. In the coarse step, the mesh is processed in parts, using a model based Bayesian learning method that identifies the noise level in each part and the subspace where the features lie. In the feature-aware fine step, we iteratively smooth face normals and vertices, while preserving geometric features. Extensive evaluation studies carried out under a broad set of complex noise patterns verify the superiority of our approach as compared to the state-of-the-art schemes, in terms of reconstruction quality and computational complexity.
Noninvasive Ultrasonic Neuromodulation in Freely Moving Mice

Neuromodulation is a fundamental method for obtaining basic information about neuronal circuits for use in treatments for neurological and psychiatric disorders. Ultrasound stimulation has become a promising approach for noninvasively inducing neuromodulation in animals and humans. However, the previous investigations were subject to substantial limitations, due to most of them involving anesthetized and fixed small-animal models. Studies of awake and freely moving animals are needed, but the currently used ultrasound devices are too bulky to be applied to a freely moving animal. This study is the first time to design and fabricate a miniature and lightweight head-mounted ultrasound stimulator for inducing neuromodulation in freely moving mice. The main components of the stimulator include a miniature piezoelectric ceramic, a concave epoxy acoustic lens, and housing and connection components. The device was able to induce action potentials recorded in situ and evoke head-turning behaviors by stimulating the primary somatosensory cortex barrel field of the mouse. These findings indicate that the proposed method can be used to induce noninvasive neuromodulation in freely moving mice. This novel method could potentially lead to the application of ultrasonic neuromodulation in more-extensive neuroscience investigations.
A New Approach to Stochastic Stability of Markovian Neural Networks With Generalized Transition Rates

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger’s inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.
Engineering Deep Representations for Modeling Aesthetic Perception

Many aesthetic models in multimedia and computer vision suffer from two shortcomings: 1) the low descriptiveness and interpretability<xref rid="fn1" ref-type="fn"><sup>1</sup></xref> of the hand-crafted aesthetic criteria (i.e., fail to indicate region-level aesthetics) and 2) the difficulty of engineering aesthetic features adaptively and automatically toward different image sets. To remedy these problems, we develop a deep architecture to learn aesthetically relevant visual attributes from Flickr,<xref rid="fn2" ref-type="fn"><sup>2</sup></xref> which are localized by multiple textual attributes in a weakly supervised setting. More specifically, using a bag-of-words representation of the frequent Flickr image tags, a sparsity-constrained subspace algorithm discovers a compact set of textual attributes (i.e., each textual attribute is a sparse and linear representation of those frequent image tags) for each Flickr image. Then, a weakly supervised learning algorithm projects the textual attributes at image-level to the highly-responsive image patches. These patches indicate where humans look at appealing regions with respect to each textual attribute, which are employed to learn the visual attributes. Psychological and anatomical studies have demonstrated that humans perceive visual concepts in a hierarchical way. Therefore, we normalize these patches and further feed them into a five-layer convolutional neural network to mimic the hierarchy of human perceiving the visual attributes. We apply the learned deep features onto applications like image retargeting, aesthetics ranking, and retrieval. Both subjective and objective experimental results thoroughly demonstrate the superiority of our approach. <fn id="fn1"><label><sup>1</sup></label><p>In this paper, “describing” and “interpretability” means the ability of seeking region-level representation of each mined textual attribute, i.e., a sparse and linear representation of those frequent image tags. </p></fn><fn id="fn2"><label><sup>2</sup></label><p><uri>https://www.flickr. com/</uri></p></fn>
Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore–Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.
Is there association between human development index and tuberculosis mortality risk? Evidence from a spatial analysis study in the south of Brazil

Abstract The goal of this study was to analyse the spatial pattern of tuberculosis (TB) mortality using different approaches, namely: mortality rates (MR), spatial relative risks (RR) and Bayesian rates (Global and Local) and their association with human development index (HDI), Global and its three dimensions: education, longevity and income. An ecological study was developed in Curitiba, Brazil based on data from Mortality Information System (2008–2014). Spatial scan statistics were used to compute RR and identify high-risk clusters. Bivariate Local Indicator of Spatial Associations was used to assess associations. MR ranged between 0 and 25.24/100.000 with a mean (standard deviation) of 1.07 (2.66). Corresponding values for spatial RR were 0–27.46, 1.2 (2.99) and for Bayesian rates (Global and Local) were 0.49–1.66, 0.90 (0.19) and 0–6.59, 0.98 (0.80). High-risk clusters were identified for all variables, except for HDI-income and Global Bayesian rate. Significant negative spatial relations were found between MR and income; between RR and HDI global, longevity and income; and Bayesian rates with all variables. Some areas presented different patterns: low social development/low risk and high risk/high development. These results demonstrate that social development variables should be considered, in mortality due TB.
A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods

This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage compartment from 3-D MR imaging and used principal component analysis (PCA) analysis to process the feature set. Four machine learning methods (artificial neural network (ANN), support vector machine, random forest, and naïve Bayes) were employed to predict the progression of OA, which was measured by the change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on Medial compartment (JSM) grade, and Joint Space Narrowing on Lateral compartment (JSL) grade. To examine the different effects of medial and lateral informative locations, we have divided the 36-D feature set into a 18-D medial feature set and a 18-D lateral feature set and run the experiment on four classifiers separately. Experiment results showed that the medial feature set generated better prediction performance than the lateral feature set, while using the total 36-D feature set generated the best. PCA analysis is helpful in feature space reduction and performance improvement. For KL grade prediction, the best performance was achieved by ANN with AUC = 0.761 and F-measure = 0.714. For JSM grade prediction, the best performance was achieved by random forest with AUC = 0.785 and F-measure = 0.743, while for JSL grade prediction, the best performance was achieved by ANN with AUC = 0.695 and F-measure = 0.796. As experiment results showing that the informative locations on medial compartment provide more distinguishing features than informative locations on the lateral compartment, it could be considered to select more points from the medial compartment while reducing the number of points from the lateral compartment to improve clinical CDI design.
Riociguat prevents hyperoxia-induced lung injury and pulmonary hypertension in neonatal rats without effects on long bone growth

Bronchopulmonary dysplasia (BPD) remains the most common and serious chronic lung disease of premature infants. Severe BPD complicated with pulmonary hypertension (PH) increases the mortality of these infants. Riociguat is an allosteric soluble guanylate cyclase stimulator and is approved by the FDA for treating PH in adults. However, it has not been approved for use in neonates due to concern for adverse effects on long bone growth. To address this concern we investigated if administration of riociguat is beneficial in preventing hyperoxia-induced lung injury and PH without side effects on long bone growth in newborn rats. Newborn rats were randomized to normoxia (21% O2) or hyperoxia (85% O2) exposure groups within 24 hours of birth, and received riociguat or placebo by once daily intraperitoneal injections during continuous normoxia or hyperoxia exposure for 9 days. In the hyperoxia control group, radial alveolar count, mean linear intercept and vascular density were significantly decreased, the pathological hallmarks of BPD, and these were accompanied by an increased inflammatory response. There was also significantly elevated vascular muscularization of peripheral pulmonary vessels, right ventricular systolic pressure and right ventricular hypertrophy indicating PH. However, administration of riociguat significantly decreased lung inflammation, improved alveolar and vascular development, and decreased PH during hyperoxia by inducing cGMP production. Additionally, riociguat did not affect long bone growth or structure. These data indicate that riociguat is beneficial in preventing hyperoxia-induced lung injury and PH without affecting long bone growth and structure and hence, suggests riociguat may be a potential novel agent for preventing BPD and PH in neonates.
A Treatment-Response Index From Wearable Sensors for Quantifying Parkinson's Disease Motor States

The goal of this study was to develop an algorithm that automatically quantifies motor states (off, on, dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from −3 (very Off) to 0 (On) to +3 (very dyskinetic), were used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at prespecified time points after the dose. The participants used wrist sensors containing a three-dimensional accelerometer and gyroscope. Features to quantify the level, variation, and asymmetry of the sensor signals, three-level discrete wavelet transform features, and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in tenfold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS. Values at the end tails of the TRS were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose-effect profiles of the patients. In addition, the TRIS had good test–retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions, the proposed algorithms provided dose-effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD.
Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images

The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen–Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.
NOTCH3 contributes to rhinovirus-induced goblet cell hyperplasia in COPD airway epithelial cells

Rationale Goblet cell hyperplasia (GCH) is one of the cardinal features of chronic obstructive pulmonary disease (COPD) and contributes to airways obstruction. Rhinovirus (RV), which causes acute exacerbations in patients with COPD, also causes prolonged airways obstruction. Previously, we showed that RV enhances mucin gene expression and increases goblet cell number in a COPD mouse model. This study examines whether RV causes sustained GCH in relevant models of COPD. Methods Mucociliary-differentiated COPD and normal airway epithelial cell cultures and mice with normal or COPD phenotype were infected with RV or sham and examined for GCH by immunofluorescence and/or mucin gene expression. In some experiments, RV-infected COPD cells and mice with COPD phenotype were treated with γ-secretase inhibitor or interleukin-13 neutralising antibody and assessed for GCH. To determine the contribution of NOTCH1/3 in RV-induced GCH, COPD cells transduced with NOTCH1/3 shRNA were used. Results RV-infected COPD, but not normal cell cultures, showed sustained GCH and increased mucin genes expression. Microarray analysis indicated increased expression of NOTCH1, NOTCH3 and HEY1 only in RV-infected COPD cells. Blocking NOTCH3, but not NOTCH1, attenuated RV-induced GCH in vitro. Inhibition of NOTCH signalling by γ-secretase inhibitor, but not neutralising antibody to IL-13, abrogated RV-induced GCH and mucin gene expression. Conclusions RV induces sustained GCH via NOTCH3 particularly in COPD cells or mice with COPD phenotype. This may be one of the mechanisms that may contribute to RV-induced prolonged airways obstruction in COPD.
Fixed-Time Leader–Follower Output Feedback Consensus for Second-Order Multiagent Systems

This paper addresses the fixed-time leader–follower consensus problem for second-order multiagent systems without velocity measurement. A new continuous fixed-time distributed observer-based consensus protocol is developed to achieve consensus in a bounded finite time fully independent of initial condition. A rigorous stability proof of the multiagent systems by output feedback control is presented based on the bi-limit homogeneity and the Lyapunov technique. Finally, the efficiency of the proposed methodology is illustrated by numerical simulation.