Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Ambient Air Pollution on Daily Hospital Visits for Various Respiratory Diseases and the Relevant Medical Expenditures in Shanghai, China

The evidence concerning the acute effects of ambient air pollution on various respiratory diseases was limited in China, and the attributable medical expenditures were largely unknown. From 2013 to 2015, we collected data on the daily visits to the emergency- and outpatient-department for five main respiratory diseases and their medical expenditures in Shanghai, China. We used the overdispersed generalized additive model together with distributed lag models to fit the associations of criteria air pollutants with hospital visits, and used the linear models to fit the associations with medical expenditures. Generally, we observed significant increments in emergency visits (8.81–17.26%) and corresponding expenditures (0.33–25.81%) for pediatric respiratory diseases, upper respiratory infection (URI), and chronic obstructive pulmonary disease (COPD) for an interquartile range increase of air pollutant concentrations over four lag days. As a comparison, there were significant but smaller increments in outpatient visits (1.36–4.52%) and expenditures (1.38–3.18%) for pediatric respiratory diseases and upper respiratory infection (URI). No meaningful changes were observed for asthma and lower respiratory infection. Our study suggested that short-term exposure to outdoor air pollution may induce the occurrences or exacerbation of pediatric respiratory diseases, URI, and COPD, leading to considerable medical expenditures upon the patients.
Constant Factor Optimal Multi-Robot Path Planning in Well-Connected Environments

Fast algorithms for optimal multi-robot path planning are sought after in many real-world applications. Known methods, however, generally do not simultaneously guarantee good solution optimality and fast run time (e.g., polynomial). In this work, we develop a low-polynomial running time algorithm, called SplitAndGroup, that solves the multi-robot path planning problem on grids and grid-like environments and produces constant factor time- and distance-optimal solutions, in expectation. In particular, SplitAndGroup computes solutions with sub-linear makespan. SplitAndGroup is capable of handling cases when the density of robot is extremely high - in a graph-theoretic setting, the algorithm supports cases where all vertices of the underlying graph are occupied by robots. SplitAndGroup attains its desirable properties through a careful combination of divide-and-conquer technique and network flow based methods for routing the robots.
Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.
Engineering and cytosolic delivery of a native regulatory protein and its variants for modulation of ERK2 signaling pathway

The modulation of a cell signaling process using a molecular binder followed by an analysis of the cellular response is crucial for understanding its role in the cellular function and developing pharmaceuticals. Herein, we present the modulation of the ERK2‐mediated signaling pathway through the cytosolic delivery of a native regulatory protein for ERK2, that is, PEA‐15 (phosphoprotein enriched in astrocytes, 15 kDa), and its engineered variants using a bacterial toxin‐based delivery system. Based on biochemical and structural analyses, PEA‐15 variants with different phosphorylation sites and a high affinity for ERK2 were designed. Semi‐rational approach led to about an 830‐fold increase in the binding affinity of PEA‐15, resulting in more effective modulation of the ERK2‐mediated signaling. Our approach enabled an understanding of the cellular function of the ERK2‐mediated signaling process and the effect of PEA‐15 phosphorylation on its action as an ERK2 blocker. We demonstrated the utility and potential of our approach by showing an efficient cytosolic delivery of these PEA‐15 variants and the effective suppression of cell proliferation through the inhibition of the ERK2 function. The present approach can be used broadly for modulating the cell signaling processes and understanding their roles in cellular function, as well as for the development of therapeutics.
126An Optimized, Synthetic DNA Vaccine Encoding the Toxin A and Toxin B Receptor Binding Domains of Clostridium difficile Induces Protective Antibody Responses In Vivo

ABSTRACT Clostridium difficile-associated disease (CDAD) constitutes a large majority of nosocomial diarrhea cases in industrialized nations and is mediated by the effects of two secreted toxins, toxin A (TcdA) and toxin B (TcdB). Patients who develop strong antitoxin antibody responses can clear C. difficile infection and remain disease free. Key toxin-neutralizing epitopes have been found within the carboxy-terminal receptor binding domains (RBDs) of TcdA and TcdB, which has generated interest in developing the RBD as a viable vaccine target. While numerous platforms have been studied, very little data describes the potential of DNA vaccination against CDAD. Therefore, we created highly optimized plasmids encoding the RBDs from TcdA and TcdB in which any putative N-linked glycosylation sites were altered. Mice and nonhuman primates were immunized intramuscularly, followed by in vivo electroporation, and in these animal models, vaccination induced significant levels of both anti-RBD antibodies (blood and stool) and RBD-specific antibody-secreting cells. Further characterization revealed that sera from immunized mice and nonhuman primates could detect RBD protein from transfected cells, as well as neutralize purified toxins in an in vitro cytotoxicity assay. Mice that were immunized with plasmids or given nonhuman-primate sera were protected from a lethal challenge with purified TcdA and/or TcdB. Moreover, immunized mice were significantly protected when challenged with C. difficile spores from homologous (VPI 10463) and heterologous, epidemic (UK1) strains. These data demonstrate the robust immunogenicity and efficacy of a TcdA/B RBD-based DNA vaccine in preclinical models of acute toxin-associated and intragastric, spore-induced colonic disease.
Symbolic Computing in Probabilistic and Stochastic Analysis

Abstract The main aim is to present recent developments in applications of symbolic computing in probabilistic and stochastic analysis, and this is done using the example of the well-known MAPLE system. The key theoretical methods discussed are (i) analytical derivations, (ii) the classical Monte-Carlo simulation approach, (iii) the stochastic perturbation technique, as well as (iv) some semi-analytical approaches. It is demonstrated in particular how to engage the basic symbolic tools implemented in any system to derive the basic equations for the stochastic perturbation technique and how to make an efficient implementation of the semi-analytical methods using an automatic differentiation and integration provided by the computer algebra program itself. The second important illustration is probabilistic extension of the finite element and finite difference methods coded in MAPLE, showing how to solve boundary value problems with random parameters in the environment of symbolic computing. The response function method belongs to the third group, where interference of classical deterministic software with the non-linear fitting numerical techniques available in various symbolic environments is displayed. We recover in this context the probabilistic structural response in engineering systems and show how to solve partial differential equations including Gaussian randomness in their coefficients.
Neutrophils Are Central to Antibody-Mediated Protection against Genital Chlamydia

ABSTRACT Determining the effector populations involved in humoral protection against genital chlamydia infection is crucial to development of an effective chlamydial vaccine. Antibody has been implicated in protection studies in multiple animal models, and we previously showed that the passive transfer of immune serum alone does not confer immunity in the mouse. Using the Chlamydia muridarum model of genital infection, we demonstrate a protective role for both Chlamydia-specific immunoglobulin G (IgG) and polymorphonuclear neutrophils and show the importance of an antibody/effector cell interaction in mediating humoral immunity. While neutrophils were found to contribute significantly to antibody-mediated protection in vivo, natural killer (NK) cells were dispensable for protective immunity. Furthermore, gamma interferon (IFN-γ)-stimulated primary peritoneal neutrophils (PPNs) killed chlamydiae in vitro in an antibody-dependent manner. The results from this study support the view that an IFN-γ-activated effector cell population cooperates with antibody to protect against genital chlamydia and establish neutrophils as a key effector cell in this response.
Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset ‘UAV_Fire’. A 15-layered self-learning DCNN architecture named ‘Fire_Net’ is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, ‘Fire_Net’ guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified.
Synchronous giant hepatic adenoma in siblings-A case report and brief literature review

ABSTRACT A 47-year-old woman was referred to our department for a hepatic mass. She denied history of hepatitis or alcohol consumption and regular oral contraceptive use, except for the emergency contraceptive pill taken once a decade ago. Hepatitis B surface antigen and anti-hepatitis C antibody were negative, α-fetoprotein was within normal limit. CT scan revealed an enormous mass measuring 26.0×16 5×13 0 cm that almost totally replaced the right hepatic lobe. The neoplasm was completely resected and pathologically diagnosed as hepatic adenoma. Literature review indicates this is the largest hepatic adenoma reported so far. At the same time, clinical examination also revealed a hepatic mass in the patient's 42-year-old brother, which was pathologically confirmed as hepatic adenoma, too. He denied history of anabolic steroid use. Immunohistochemical analysis revealed the subtypes of both tumors as the inflammatory hepatic adenoma. Literature review indicates this is the first report of synchronous hepatic adenomas in siblings.
Motor control of Drosophila feeding behavior

The precise coordination of body parts is essential for survival and behavior of higher organisms. While progress has been made towards the identification of central mechanisms coordinating limb movement, only limited knowledge exists regarding the generation and execution of sequential motor action patterns at the level of individual motoneurons. Here we use Drosophila proboscis extension as a model system for a reaching-like behavior. We first provide a neuroanatomical description of the motoneurons and muscles contributing to proboscis motion. Using genetic targeting in combination with artificial activation and silencing assays we identify the individual motoneurons controlling the five major sequential steps of proboscis extension and retraction. Activity-manipulations during naturally evoked proboscis extension show that orchestration of serial motoneuron activation does not rely on feed-forward mechanisms. Our data support a model in which central command circuits recruit individual motoneurons to generate task-specific proboscis extension sequences. DOI: http://dx.doi. org/10.7554/eLife.19892. 001
Early Left and Right Ventricular Response to Aortic Valve Replacement

BACKGROUND: The immediate effect of aortic valve replacement (AVR) for aortic stenosis on perioperative myocardial function is unclear. Left ventricular (LV) function may be impaired by cardioplegia-induced myocardial arrest and ischemia-reperfusion injury, especially in patients with LV hypertrophy. Alternatively, LV function may improve when afterload is reduced after AVR. The right ventricle (RV), however, experiences cardioplegic arrest without benefiting from improved loading conditions. Which of these effects on myocardial function dominate in patients undergoing AVR for aortic stenosis has not been thoroughly explored. Our primary objective is thus to characterize the effect of intraoperative events on LV function during AVR using echocardiographic measures of myocardial deformation. Second, we evaluated RV function. METHODS: In this supplementary analysis of 100 patients enrolled in a clinical trial (NCT01187329), 97 patients underwent AVR for aortic stenosis. Of these patients, 95 had a standardized intraoperative transesophageal echocardiographic examination of systolic and diastolic function performed before surgical incision and repeated after chest closure. Echocardiographic images were analyzed off-line for global longitudinal myocardial strain and strain rate using 2D speckle-tracking echocardiography. Myocardial deformation assessed at the beginning of surgery was compared with the end of surgery using paired t tests corrected for multiple comparisons. RESULTS: LV volumes and arterial blood pressure decreased, and heart rate increased at the end of surgery. Echocardiographic images were acceptable for analysis in 72 patients for LV strain, 67 for LV strain rate, and 54 for RV strain and strain rate. In 72 patients with LV strain images, 9 patients required epinephrine, 22 required norepinephrine, and 2 required both at the end of surgery. LV strain did not change at the end of surgery compared with the beginning of surgery (difference: 0.7 [97.6% confidence interval, −0.2 to 1.5]%; P = 0.07), whereas LV systolic strain rate improved (became more negative) (−0.3 [−0.4 to −0.2] s−1; P < 0.001). In contrast, RV systolic strain worsened (became less negative) at the end of surgery (difference: 4.6 [3.1 to 6.0]%; P < 0.001) although RV systolic strain rate was unchanged (0.0 [97.6% confidence interval, −0.1 to 0.1]; P = 0.83). CONCLUSIONS: LV function improved after replacement of a stenotic aortic valve demonstrated by improved longitudinal strain rate. In contrast, RV function, assessed by longitudinal strain, was reduced.
Stability and L 1 × ℓ1-to-L 1 × ℓ1 performance analysis of uncertain impulsive linear positive systems with applications to the interval observation of impulsive and switched systems with constant delays

ABSTRACT Solutions to the interval observation problem for delayed impulsive and switched systems with -performance are provided. The approach is based on first obtaining stability and -to- performance analysis conditions for uncertain linear positive impulsive systems in linear fractional form with norm-bounded uncertainties using a scaled small-gain argument involving time-varying D-scalings. Both range and minimum dwell-time conditions are formulated – the case of constant and maximum dwell-times can be directly obtained as corollaries. The conditions are stated as timer/clock-dependent conditions taking the form of infinite-dimensional linear programmes that can be relaxed into finite-dimensional ones using polynomial optimisation techniques. It is notably shown that under certain conditions, the scalings can be eliminated from the stability conditions to yield equivalent stability conditions on the so-called worst-case system, which is obtained by replacing the uncertainties by the identity matrix. These conditions are then applied to the special case of linear positive systems with delays, where the delays are considered as uncertainties, similarly to as in (Briat (2018a). Stability and performance analysis of linear positive systems with delays using input-output methods. International Journal of Control, 91(7), 1669–1692). As before, under certain conditions, the scalings can be eliminated from the conditions to obtain conditions on the worst-case system, coinciding here with the zero-delay system – a result that is consistent with all the existing ones in the literature on linear positive systems with delays. Finally, the case of switched systems with delays is considered. The approach also encompasses standard continuous-time and discrete-time systems, possibly with delays and the results are flexible enough to be extended to cope with multiple delays, time-varying delays, distributed/neutral delays and any other types of uncertain systems that can be represented as a feedback interconnection of a known system with an uncertainty.
Peg-in-Hole Assembly Based on Two-phase Scheme and F/T Sensor for Dual-arm Robot

This paper focuses on peg-in-hole assembly based on a two-phase scheme and force/torque sensor (F/T sensor) for a compliant dual-arm robot, the Baxter robot. The coordinated operations of human beings in assembly applications are applied to the behaviors of the robot. A two-phase assembly scheme is proposed to overcome the inaccurate positioning of the compliant dual-arm robot. The position and orientation of assembly pieces are adjusted respectively in an active compliant manner according to the forces and torques derived by a six degrees-of-freedom (6-DOF) F/T sensor. Experiments are conducted to verify the effectiveness and efficiency of the proposed assembly scheme. The performances of the dual-arm robot are consistent with those of human beings in the peg-in-hole assembly process. The peg and hole with 0.5 mm clearance for round pieces and square pieces can be assembled successfully.
Phenotypic and functional characteristics of HLA‐DR+ neutrophils in Brazilians with cutaneous leishmaniasis

The protozoan Leishmania braziliensis causes cutaneous leishmaniasis (CL) in endemic regions. In murine models, neutrophils (PMNs) are recruited to the site of infection soon after parasite inoculation. However, the roles of neutrophils during chronic infection and in human disease remain undefined. We hypothesized that neutrophils help maintain a systemic inflammatory state in subjects with CL. Lesion biopsies from all patients with CL tested contained neutrophils expressing HLA‐DR, a molecule thought to be restricted to professional antigen‐presenting cells. Although CL is a localized disease, a subset of patients with CL also had circulating neutrophils expressing HLA‐DR and the costimulatory molecules CD80, CD86, and CD40. PMNs isolated from a low‐density leukocyte blood fraction (LD‐PMNs) contained a higher percentage of HLA‐DR+ PMNs than did normal‐density PMNs. In vitro coculture experiments suggested LD‐PMNs do not suppress T cell responses, differentiating them from MDSCs. Flow‐sorted HLA‐DR+ PMNs morphologically resembled conventional PMNs, and they exhibited functional properties of PMNs. Compared with conventional PMNs, HLA‐DR+ PMNs showed increased activation, degranulation, DHR123 oxidation, and phagocytic capacity. A few HLA‐DR+ PMNs were observed in healthy subjects, and that proportion could be increased by incubation in either inflammatory cytokines or in plasma from a patient with CL. This was accompanied by an increase in PMN hladrb1 mRNA, suggesting a possible connection between neutrophil “priming” and up‐regulation of HLA‐DR. These data suggest that PMNs that are primed for activation and that also express surface markers of antigen‐presenting cells emerge in the circulation and infected tissue lesions of patients with CL.
Which is a better indicator of bladder outlet obstruction in patients with benign prostatic enlargement--intravesical protrusion of prostate or bladder wall thickness?

OBJECTIVE To determine the correlation of intravesical prostatic protrusion (IPP) and bladder wall thickness (BWT) with clinical and urodynamic parameters, as well as their sensitivity and specificity with regard to bladder outlet obstruction in patients with a benign prostatic enlargement (BPE). MATERIALS AND METHODS 111 patients with lower urinary tract symptoms and confirmed BPE completed the International Prostatic Symptom Score (I-PSS), as well as a transabdominal ultrasound to determine their prostate volume, a grade of IPP and BWT. All the patients were then subjected to the complete urodynamic studies (UDS). RESULTS The IPP showed a good correlation with the prostate volume (r = 0.61) and serum PSA (r = 0.48); p = 0.0000, free uroflowmetry (r = -0.27; p = 0.004), as well as the determinants of urodynamic obstruction: bladder outlet obstruction index-BOOI (r = 0.36; p < 0.0001), and ICS and Schaefer obstruction class nomograms (rho = 0.33 and rho = 0.39, respectively; p < 0.001), while the BWT showed only a statistical correlation with age (r = 0.23; p = 0.02) and serum PSA (r = 0.4; p = 0.0000), regardless of an significant correlation with the IPP (r = 0.45; p = 0.0000). The ANOVA test showed a significant difference between the IPP grades for the observed clinical and urodynamic variables with an increase in significance for IPP>10 mm. The area under the ROC curve in the prediction of obstruction for the IPP is 0.71 (sensitivity 59.6, specificity 81.4), while the AUC for the BWT is 0.61 (sensitivity 64.5, specificity 59.2). The stepwise logistic regression model shows that most significant independent variables for the obstruction are the IPP, Q(max) free and age, with the area under the ROC curve of 0.78 (95% CI 0.695 to 0.856). CONCLUSION The IPP higher than 10 mm as a non-invasive predictor of infravesical obstruction shows good correlations with clinical and urodynamic parameters, while the specificity and PPV against obstruction are significant. Despite a good correlation with IPP, the BWT is only a modest indicator of obstruction.