Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Respiratory Muscle Training on Pulmonary Function in Individuals with Spinal Cord Injury: An Updated Meta-analysis

Objective To investigate the pulmonary function responses to respiratory muscle training (RMT) in individuals with tetraplegia and provide a systematic review of the included studies. Methods Computerized retrieval of randomized controlled trials (RCT) in PubMed, Embase, and the Cochrane Library on the improvement of respiratory function in patients with spinal cord injury by RMT was conducted until May 2019. Two researchers independently screened the literature, extracted the data, and evaluated the risk of bias in the included studies. Articles were scored for their methodological quality using the Cochrane Collaboration risk of bias assessment tool. Results Sixteen studies were identified. A significant benefit of RMT was revealed for five outcomes: force vital capacity (FVC, WMD: -0.43, 95% CI -0.84 to -0.03, P = 0.037), vital capacity (VC, WMD: -0.40, 95% CI -0.69 to -0.12, P = 0.037), vital capacity (VC, WMD: -0.40, 95% CI -0.69 to -0.12, P = 0.037), vital capacity (VC, WMD: -0.40, 95% CI -0.69 to -0.12, P = 0.037), vital capacity (VC, WMD: -0.40, 95% CI -0.69 to -0.12, P = 0.037), vital capacity (VC, WMD: -0.40, 95% CI -0.69 to -0.12, Conclusion Our findings demonstrate that RMT can effectively improve spinal cord injury pulmonary function of the patient, which is marked by increasing respiratory strength, function, and endurance. Limited by the quantity and quality of the included studies, the above conclusion needs to be verified by more high-quality studies.
PARAMETRIC EVALUATION OF QUANTUM DOT FLUORESCENCE AND INTERMITTENCE

Recent developments in nano science and technology have revealed the possibility of using Quantum dot fluorescence properties for medical imaging aided diagnostics. Quantum dots being one of the novel nano structure that exhibits wide range of quantum phenomena sprawling across optics, electronics and chemistry. This multi domain characterization makes the understanding and further development of any technology based on these devices quite a challenge that needs to be addressed through cross domain experimental and theoretical study. We present a study of quantum fluorescence property of quantum dots. The emission and absorption rates and energy of the quantum dots are size and environment dependent due to the quantum confinement and discrete energy. We analyze quantum dot emission spectrum response to environmental and structural variations. We consider Bruss energy relation and quantum statistical observations to model the quantum dot fluorescence activity.
Aesthetics outcome of immediately restored single implants placed in extraction sockets in the anterior maxilla: A case series study

Background. Immediate single implant placement and restoration (IIR) is recognized as a novel method and is the main request of many patients. This study was designed to evaluate the aesthetic outcomes of immediately restored single implants placed in extraction sockets in theanterior maxilla. Methods. In this case series study, 18 patients were selected from two private clinics after placing a single-tooth implant in the anterior maxilla. Immediate provisional crowns were delivered on the following day or at most 48 hours later, and guidelines were provided. The Pink Esthetic Score (PES) questionnaire was used at 6- and 12-month follow-ups to assess aesthetic outcomes. Data were analyzed with single t-test and dependent t-test. Results. In general, the results showed that the status of the mesial papilla, distal papilla, curve of the facial soft tissue line, level of the facial peri-implant mucosa and root convexity soft tissue in IIR method were optimal (P<0.05), with total PES means of 9.44±0.783 and 8.58±1.003 after 6 and 12 months, respectively. Also, the results showed a significant difference in PES between the 6-month and 12-month intervals (P<0.05). Conclusion. IIR is a viable method that resulted in optimal aesthetic outcomes based on PES in the short term. Considering its confirmation in this study and previous studies, it is recommended that dentists apply this method.
A Novel Optimization Model for Surface Mount Systems based on Evolutionary and Simulated Annealing Algorithms

— Surface mount technology is an ever increasing research field with the advent of wide number of engineering applications utilizing this technology. The applications cover a wide range varying from electrical motors, electronic circuit technology and chips, and mechanical surface mount devices. The efficiency of the surface mount process is an optimization problem and this research paper investigates the application of genetic based simulated annealing algorithm for producing this optimization. In the method, the problem of finding the optimal route is converted to that of solving an unconstrained and bounded constrained surface mount model for a motor. The well-known simulated annealing model used for a wide range of optimization problems is based on heating a device under study and lowering the temperature to reduce the defects in the model which also leads to reduction of energy. New factors are also added to the equation of optimality utilizing probability to improve its performance, and make it more suitable for solving the route optimization problem in surface mount technology. The simulation result of experiments has shown that the method proposed in this paper is well suited to the route optimization problem in surface mount technology.
Acute ischemic stroke alters the brain’s preference for distinct dynamic connectivity states

Acute ischemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for loss of specialized neural tissue and function. Static resting-state functional magnetic resonance imaging (fMRI) studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we utilized dynamic resting-state fMRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting-state fMRI data of 31 acute ischemic stroke patients and 17 age-matched healthy controls. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. Temporal connectivity patterns of stroke patients markedly diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n=18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n=13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased inter-hemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting-state fMRI studies post-stroke.In summary, acute ischemic stroke not only prompted changes in connectivity between distinct functional networks, yet also caused severe aberrations in temporal properties of large-scale network interactions depending on the individual deficit severity. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.
Application of Forward/Reverse Osmosis Hybrid System for Brackish Water Desalination using El-Salam Canal Water, Sinai, Egypt, Part (1): FO Performance

This paper is the first part of two papers focus on the investigation of a forward osmosis – reverse osmosis hybrid process to co-treat brackish water and El-Salam Canal water in Sinai, Egypt. By using this hybrid process, brackish water can be diluted before desalination, hence reducing the energy cost of desalination. Simultaneously, contaminants present in the El-Salam Canal water are prevented from migrating into the product water through the forward and reverse osmosis membranes. The main objective of this part is to investigate the performance of the forward osmosis (FO) system at different draw solution concentrations comparable to the ground water in Sinai. It was found that the FO water fluxes are low and not promising to be economically applied. To improve the FO performance with El-Salam canal water, ammonium bicarbonate was added to water wells to increase the osmotic pressure of draw solution.
APPENDIX 3: LOOKING AHEAD: THE EMERGENCE OF AN ELECTRIC VEHICLE INDUSTRY

One final application of our framework comes from our fieldwork. In addition to considering why value has not migrated in automobiles, we also examined expectations of how value distribution would be affected by the emergence of an electric vehicle (EV) industry that is (slowly) displacing the current dominant design, which relies on gasoline-powered internal combustion engines (ICE). Interestingly, this pending transition is already drawing predictions of a dramatic shift to a horizontal structure and full modularity, not unlike those that were widely floated during the 1990s. Figures A1a and A1b reveal how a prominent consulting firm sees this future industry architecture.
Toward a Positive Medicine: Healing our Healers, From Burnout to Flourishing

It is estimated that between 25-75% of physicians suffer from burnout. Symptoms of emotional exhaustion, depersonalization, and a low sense of personal accomplishment afflict physicians as early as medical school, into residency training, and throughout medical practice, with potential consequences for patient healthcare outcomes, public health, and the costs of our health system. While medical institutions can do more to support physician, trainee, and medical student self-care, physicians cannot wait for institutional change in order to pursue well-being. A construct for physician flourishing is proposed, borrowing from and building off of prior validated constructs of psychological and physical well-being. This proposed model, known as REVAMP, focuses on six elements that comprise physician flourishing. Specific interventions to improve each of these elements are proposed that may be used by the individual practitioner, in formal medical education settings, or by practicing physicians in continuing medical education programs. Waiting to intervene until physicians are burned out and suffering has high costs; proactive approaches such as those suggested within REVAMP can be adopted as early as undergraduate medical school education to help physicians-in-training cultivate optimal wellbeing. Flourishing physicians deliver the highest quality patient care. It is time to help our healers flourish.
A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task’s structure, the received feedback, and the agent’s behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.
Tuning the rheological behavior of colloidal gels through competing interactions

We study colloidal gels formed by competing electrostatic repulsion and short-range attraction by means of extensive numerical simulations under external shear. We show that, upon varying the repulsion strength, the gel structure and its viscoelastic properties can be largely tuned. In particular, the gel fractal dimension can be either increased or decreased with respect to mechanical equilibrium conditions. Unexpectedly, gels with stronger repulsion, despite being mechanically stiffer, are found to be less viscous with respect to purely attractive ones. We provide a microscopic explanation of these findings in terms of the influence of an underlying phase separation. Our results allow for the design of colloidal gels with desired structure and viscoelastic response by means of additional electrostatic interactions, easily controllable in experiments.
Construe: a software solution for the explanation-based interpretation of time series

This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning. The software provides a data model and a set of algorithms to make inference to the best explanation of a time series, resulting in a description in multiple abstraction levels of the processes underlying the time series. As a proof of concept, a comprehensive knowledge base for the electrocardiogram (ECG) domain is provided, so it can be used directly as a tool for ECG analysis. This tool has been successfully validated in several noteworthy problems, such as heartbeat classification or atrial fibrillation detection.
Synthesis of Brain Tumor multi-contrast MR Images for Improved Data Augmentation.

PURPOSE Medical image analysis using deep neural networks has been actively studied. For accurate training of deep neural networks, the learning data should be sufficient and have good quality and generalized characteristics. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients' privacy. In comparison, the medical images of healthy volunteers can be easily acquired. To resolve this data bias problem, the proposed method synthesizes brain tumor images from normal brain images. METHODS Our method can synthesize a huge number of brain tumor multi-contrast MR images from numerous healthy brain multi-contrast MR images and various concentric circles. Because tumors have complex characteristics, the proposed method simplifies them into concentric circles that are easily controllable. Then, it converts the concentric circles into various realistic tumor masks through deep neural networks. The tumor masks are used to synthesize realistic brain tumor images from normal brain images. RESULTS We performed a qualitative and quantitative analysis to assess the usefulness of augmented data from the proposed method. Data augmentation by the proposed method provided significant improvements to tumor segmentation compared with other GAN-based methods. Intuitive experimental results are available online athttps://github.com/KSH0660/BrainTumor CONCLUSIONS: The proposed method can control the grade tumor masks by the concentric circles, and synthesize realistic brain tumor multi-contrast MR images. In terms of data augmentation, the proposed method can successfully synthesize brain tumor images that can be used to train tumor segmentation networks or other deep neural networks.
Supporting Early and Scalable Discovery of Disinformation Websites

Online disinformation is a serious and growing sociotechnical problem that threatens the integrity of public discourse, democratic governance, and commerce. The internet has made it easier than ever to spread false information, and academic research is just beginning to comprehend the consequences. In response to this growing problem, online services have established processes to counter disinformation. These processes predominantly rely on costly and painstaking manual analysis, however, often responding to disinformation long after it has spread. We design, develop, and evaluate a new approach for proactively discovering disinformation websites. Our approach is inspired by the information security literature on identifying malware distribution, phishing, and scam websites using distinctive non-perceptual infrastructure characteristics. We show that automated identification with similar features can effectively support human judgments for early and scalable discovery of disinformation websites. Our system significantly exceeds the state of the art in detecting disinformation websites, and we present the first reported real-time evaluation of automation-supported disinformation discovery. We also demonstrate, as a proof of concept, how our approach could be easily operationalized in ordinary consumer web browsers.
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions

High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future activity of his or her neighbors. While past work has focused on estimating the underlying network structure based solely on the times at which events occur on each node of the network, this paper examines the more nuanced problem of estimating context-dependent networks that reflect how features associated with an event (such as the content of a social media post) modulate the strength of influences among nodes. Specifically, we leverage ideas from compositional time series and regularization methods in machine learning to conduct network estimation for high-dimensional marked point processes. Two models and corresponding estimators are considered in detail: an autoregressive multinomial model suited to categorical marks and a logistic-normal model suited to marks with mixed membership in different categories. Importantly, the logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories. We provide theoretical guarantees for both estimators, which we validate by simulations and a synthetic data-generating model. We further validate our methods through two real data examples and demonstrate the advantages and disadvantages of both approaches.
Quantal diffusion approach for multinucleon transfer processes in the 
Ni58,64+Pb208
 reactions: Toward the production of unknown neutron-rich nuclei

Background: In recent years, substantial efforts have been made for the study of multinucleon transfer reactions at energies around the Coulomb barrier both experimentally and theoretically, aiming at the production of unknown neutron-rich heavy nuclei. It is crucial to provide reliable theoretical predictions based on microscopic theories with sufficient predictive power. Purpose: This paper aims to clarify the applicability of the quantal diffusion approach based on the stochastic mean-field (SMF) theory for multinucleon transfer processes. Isotope production cross sections are evaluated for the reactions of $^{64}$Ni+$^{208}$Pb at $E_{c.m. }$=268 MeV and $^{58}$Ni+$^{208}$Pb at $E_{c.m. }$=270 MeV and are compared with available experimental data. Methods: Three-dimensional time-dependent Hartree-Fock (TDHF) calculations are carried out for a range of initial orbital angular momenta with Skyrme SLy4d functional. Quantal diffusion equations, derived based on the SMF theory, for variances and covariance of neutron and proton numbers of reaction products are solved, with microscopic drift and diffusion coefficients obtained from the time evolution of occupied single-particle orbitals in TDHF. Secondary deexcitation processes, both particle evaporation and fission, are simulated by a statistical model, GEMINI++. Results: We find that the quantal diffusion approach reproduces the measured cross sections for abundant reaction products, including both projectile-like and target-like fragments, as well as fragments of transfer-induced fission. The results underline the importance of beyond mean-field effects in describing multinucleon transfer processes. We emphasize that the quantal diffusion description does not involve any adjustable parameters, once an energy density functional is given. Possible future directions are discussed. (Shortened due to the word limit)
The Differences between Analytical and Numerical Ignition Curve and its possible effects on Hydrodynamics of Ignited He-C-O Mixture

In this paper, we use large reaction networks to find ignition conditions of single-zone nuclear fuel with compositions typical of white dwarf (WD) matter. The necessary but, possibly not sufficient condition for initiation of detonation is that the nuclear burning proceeds on smaller timescale than the sound-crossing timescale. Under typical white dwarf thermodynamic conditions, the nuclear timescale depends sensitively on the chosen nuclear reaction network, and widely-used analytical formulae are not sufficient to accurately determine unstable ignition conditions the answer to which lies in the fully compressible turbulent simulations. We model the nuclear reactions in the numerical simulation using the astrophysical code TORCH. We report the differences introduced because of the choice of the size of the nuclear reaction network. Our findings have implications for numerical results obtained using the widely-used 19-species network in multidimensional simulations of WD of Chandrasekhar and sub-Chandrasekhar masses. We present a sufficiently accurate general criterion in temperature-density ($T-\rho$) plane for a given initial fuel to ignite in an unstable fashion.