Miracle-dz/UniFAR-FTUs huggingface dataset:
Postoperative Pain Trajectories and Pain Chronification—an Empirical Typology of Pain Patients

Background High intensity of acute postsurgical pain is one of the strongest predictors of chronic postsurgical pain (CPSP). We investigated if different types of patients with distinct combinations of initial pain intensity and rate of pain resolution exhibit different risks for increased pain intensity six months after surgery. Methods Data from 174 patients were examined using growth mixture analysis by means of structural equation modeling. Results Three types of patients were distinguished on the basis of acute pain trajectories. The majority of patients (57%) showed an unproblematic pattern of little initial pain on the first postoperative day, combined with further pain resolution over the four subsequent days. There also was a substantial group of patients (30%) who started out with severe pain but exhibited a high rate of pain resolution. Finally, we found a problematic group of patients (13%) who reported high pain intensities throughout all five postoperative measurements, with no signs of pain resolution. Even after controlling for preoperative pain intensity, these patients exhibited significantly higher pain intensities six months after surgery than the remaining patient groups. Conclusions In this study, we demonstrated that there is substantial variation in postsurgical pain trajectories, not only with regard to postsurgical initial pain intensity, but also with regard to individual rates of pain resolution. Successful pain resolution appeared to be a better predictor of absence of increased pain intensities six months after surgery than initial pain immediately after surgery. Hence, attention should be given to appropriate pain treatment in order to minimize the risk of CPSP.
Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning

Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics.
3-D image analysis of abdominal aortic aneurysm

In this paper we propose a technique for 3-D segmentation of abdominal aortic aneurysm (AAA) from computed tomography angiography (CTA) images. Output data (3-D model) form the proposed method can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important in planning of minimally invasive procedure that is for selection of appropriate stent graft device for treatment of AAA. The technique is based on a 3-D deformable model and utilizes the level-set algorithm for implementation of the method. The method performs 3-D segmentation of CTA images and extracts a 3-D model of aortic wall. Once the 3-D model of aortic wall is available it is easy to perform all required measurements for appropriate stent graft selection. The method proposed in this paper uses the level-set algorithm for deformable models, instead of the classical snake algorithm. The main advantage of the level set algorithm is that it enables easy segmentation of complex structures, surpassing most of the drawbacks of the classical approach. We have extended the deformable model to incorporate the a priori knowledge about the shape of the AAA. This helps direct the evolution of the deformable model to correctly segment the aorta. The algorithm has been implemented in IDL and C languages. Experiments have been performed using real patient CTA images and have shown good results.
Modeling the Triggering of Saccades, Microsaccades, and Saccadic Intrusions

When we explore a static visual scene, our eyes move in a sequence of fast eye movements called saccades, which are separated by fixation periods of relative eye stability. Based on uncertain sensory and cognitive inputs, the oculomotor system must decide, at every moment, whether to initiate a saccade or to remain in the fixation state. Even when we attempt to maintain our gaze on a small spot, small saccades, called microsaccades, intrude on fixation once or twice per second. Because microsaccades occur at the boundary of the decision to maintain fixation versus starting a saccade, they offer a unique opportunity to study the mechanisms that control saccadic triggering. Abnormal saccadic intrusions can occur during attempted fixation in patients with neurodegenerative disorders. We have implemented a model of the triggering mechanism of saccades, based on known anatomy and physiology, that successfully simulates the generation of saccades of any size—including microsaccades in healthy observers, and the saccadic intrusions that interrupt attempted fixation in parkinsonian patients. The model suggests that noisy neuronal activity in the superior colliculus controls the state of a mutually inhibitory network in the brain stem formed by burst neurons and omnipause neurons. When the neuronal activity is centered at the rostral pole, the system remains at a state of fixation. When activity is perturbed away from this center, a saccade is triggered. This perturbation can be produced either by the intent to move one’s gaze or by random fluctuations in activity.
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm

We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. The spectral algorithm consists of multiple algorithmic steps, named as "{edges}", to which noise could be injected to obtain differential privacy. We identify \emph{subsets of edges}, named as "{configurations}", such that adding noise to all edges in such a subset guarantees differential privacy of the end-to-end spectral algorithm. We characterize the sensitivity of the edges with respect to the input and thus estimate the amount of noise to be added to each edge for any required privacy level. We then characterize the utility loss for each configuration as a function of injected noise. Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime. We are the first to achieve utility guarantees under the required level of differential privacy for learning in LDA. Overall our method systematically outperforms differentially private variational inference.
Control of the preresonance frequencies range width of the medical electro-magnetic MEMS-scanner at the design stage

The application of MEMS scanners in medicine, especially in the methods of treatment, is considered. A review of existing types of scanners has been performed. According to a set of signs, the best performance is determined by electro-magnetic MEMS scanners. To expand the working area of the electro-magnetic scanners, a mathematical model is constructed, an expression found for the resonance frequency, which depends on the design parameters. Conclusions are made regarding the desired changes in design parameters for obtaining the best performance of the scanner.
An Efficient Triangle Enumeration on Parallel and Distributed Frameworks

Triangle enumerating problem is one of fundamental problems of graph data. Although several triangle enumerating algorithms based on MapReduce which is a distributed parallel framework have been proposed, they still suffer from generating a lot of intermediate data. In this paper, we propose an efficient algorithm to enumerate every triangle in massive graph. A wedge consists of a cone vertex and two end-nodes having one pass through the cone vertex. Since a triangle is composed of one edge and one wedge, our algorithm checks the existence of an edge that is incident to two end-nodes in each wedge. We partition vertex set into several partial vertex sets to prevent generating numerous wedges. Our experimental evaluation shows the performance of proposed algorithm is better than competitors.
Cloud Based Face Recognition for Google Glass

Face recognition applications can benefit from the cloud computing as they become widely available and easy to acquire today. There are numerous applications of face recognition in terms of security, assistance, guidance and so on. By performing the face recognition on cloud, we can greatly reduce the processing time and clients will not have to store the big data for the image verification on their local machine (cell phones, pc's etc). Cloud computing increases the processing power and storage with very less cost comparing to the cost of acquiring an equally strong server machine. In this research the plan is to enhance the user experience of augmented display wearing google glass, and for doing that, this system is being proposed in which a person wearing google glass will send an image of a person to cloud server powered by Hadoop (open-source software for reliable, scalable, distributed computing) cloud server will recognize the face from the database already present on server and then response to client device (google glass). Then google glass will display the face details in a form of augmented display to the person wearing them. By moving the face recognition process on cloud, the device will require less processing power, and by having the database on cloud server, multiple clients will no longer require to maintain their local database.
Hyperspectral Unmixing Using a Neural Network Autoencoder

In this paper, we present a deep learning based method for blind hyperspectral unmixing in the form of a neural network autoencoder. We show that the linear mixture model implicitly puts certain architectural constraints on the network, and it effectively performs blind hyperspectral unmixing. Several different architectural configurations of both shallow and deep encoders are evaluated. Also, deep encoders are tested using different activation functions. Furthermore, we investigate the performance of the method using three different objective functions. The proposed method is compared to other benchmark methods using real data and previously established ground truths of several common data sets. Experiments show that the proposed method compares favorably to other commonly used hyperspectral unmixing methods and exhibits robustness to noise. This is especially true when using spectral angle distance as the network’s objective function. Finally, results indicate that a deeper and a more sophisticated encoder does not necessarily give better results.
Should Pharmacies Be Included in Medication Reconciliation? A Report of Recurrent Valproic Acid Toxicity

Including outpatient pharmacies in the medication reconciliation process upon hospital discharge is not commonly performed. This case highlights the consequences of a patient refilling a discontinued prescription for valproic acid (VPA). We present a 32-year old male found unresponsive after ingesting delayed release divalproex sodium. Cerebral edema was visualized on magnetic resonance imaging. Hemodialysis and levo-carnitine treatment led to improved mental status, and VPA was discontinued. The same patient presented with VPA overdose eight months later after he continued to fill an outdated prescription. This case highlights consequences of VPA toxicity; it also demonstrates an opportunity to improve patient safety and high-value care by collaborating with outpatient pharmacies in the medication reconciliation process upon hospital discharge.
The provision of written information and its effect on levels of pain and anxiety during electrodiagnostic studies: A randomised controlled trial

Objective The provision of written information is a low-cost and readily available intervention that has been found to reduce pain and anxiety in a variety of clinical settings. The current study was undertaken to determine if information provision may improve patients’ experience during conventional electrodiagnostic studies. Methods 128 participants were recruited from a tertiary teaching hospital who were referred for electrodiagnostic studies. They were randomized into 2 groups where the intervention group was provided with written information about the electrodiagnostic testing. Patients were invited to complete a questionnaire that included pain and anxiety using a visual analogue scale (VAS) following the testing. All participants underwent nerve conduction studies (NCS) whilst a subset also underwent subsequent needle electromyography (EMG). Results Those who received information had a statistically significant lower perception of anxiety during NCS, whilst only females who received information had a statistically significant lower perception of pain to both NCS and EMG. Conclusions The provision of written information can reduce the degree of pain and anxiety experienced during electrodiagnostic testing. Significance Improving patient comfort and tolerability during electrodiagnostic testing may have practical implications towards more reliable and accurate results obtained from such investigations that may in turn improve patient diagnosis and management.
A Focus on Reward Prediction and the Lateral Habenula: Functional Alterations and the Behavioral Outcomes Induced by Drugs of Abuse

The lateral habenula (LHb) regulates reward learning and controls the updating of reward-related information. Drugs of abuse have the capacity to hijack the cellular and neurocircuit mechanisms mediating reward learning, forming non-adaptable, compulsive behaviors geared toward obtaining illicit substances. Here, we discuss current findings demonstrating how drugs of abuse alter intrinsic and synaptic LHb neuronal function. Additionally, we discuss evidence for how drug-induced LHb alterations may affect the ability to predict reward, potentially facilitating an addiction-like state. Altogether, we combine ex vivo and in vivo results for an overview of how drugs of abuse alter LHb function and how these functional alterations affect the ability to learn and update behavioral responses to hedonic external stimuli.
Efficient Use of Historical Data for Genomic Selection: A Case Study of Stem Rust Resistance in Wheat

Genomic selection (GS) is a methodology that can improve crop breeding efficiency. To implement GS, a training population (TP) with phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). A key factor impacting prediction accuracy is the relationship between the TP and the SCs. This study used empirical data for quantitative adult plant resistance to stem rust of wheat (Triticum aestivum L.) to investigate the utility of a historical TP (TPH) compared with a population‐specific TP (TPPS), the potential for TPH optimization, and the utility of TPH data when close relative data is available for training. We found that, depending on the population size, a TPPS was 1.5 to 4.4 times more accurate than a TPH, and TPH optimization based on the mean of the generalized coefficient of determination or prediction error variance enabled the selection of subsets that led to significantly higher accuracy than randomly selected subsets. Retaining historical data when data on close relatives were available lead to a 11.9% increase in accuracy, at best, and a 12% decrease in accuracy, at worst, depending on the heritability. We conclude that historical data could be used successfully to initiate a GS program, especially if the dataset is very large and of high heritability. Training population optimization would be useful for the identification of TPH subsets to phenotype additional traits. However, after model updating, discarding historical data may be warranted. More studies are needed to determine if these observations represent general trends.
HDFS memory usage analysis

Due to high use of online activities, there will be lot of data which is getting generated. To handle this data there should be an efficient system which can process the data effectively. One such system is Hadoop Distributed File System. HDFS consists of number of nodes, and one is the master among them while all others are slave nodes. Master node is called as NameNode and slave nodes are called as DataNodes. System will record the activities in the log file. Whatever the transactions are happening at the front end everything will get recorded at backend. We can capture this data and analyze to find out the system behavior in the failure situation. Since we are using the system for large transactions we need to have an idea on usage of the resources in the HDFS such as memory usage. Analyzers can get the stats of the operating system and the other devices separately i.e, for each service we need to have one analyzer to collect the stats. This paper presents the analyzer or shipper for collecting the metrics from operating system, memory and the other installed services.
Tantalum (oxy)nitride based photoanodes for solar-driven water oxidation

Photoelectrochemical (PEC) water splitting is a very promising process to produce hydrogen as a clean energy carrier. To achieve 10% solar to hydrogen (STH) conversion efficiency required for practical applications, the current central task in PEC water splitting is the development of efficient photoelectrodes, particularly photoanodes for water oxidation, used in PEC cells. Tantalum (oxy)nitrides with bandgaps ranging from 2.5 to 1.9 eV, corresponding to theoretical STH efficiencies varying from 9.3% to 20.9%, are considered a class of attractive light adsorbers for use in photoanodes for PEC water oxidation and have attracted much recent research attention. In this review, the recent development of tantalum (oxy)nitride photoanodes is summarized. Special interest is focused on the synthesis methods of tantalum (oxy)nitride films and important approaches for improving PEC conversion efficiency and stability of these films as photoanodes. The future trends of tantalum (oxy)nitride based photoanodes are also discussed.
iSpot: Achieving Predictable Performance for Big Data Analytics with Cloud Transient Servers

Achieving predictable performance for big data analytics running on cloud transient servers (e.g., EC2 spot instances) is challenging, because the transient server can be revoked by the cloud and the spot price is nontrivial to predict. Undoubtedly, choosing the low-price yet unstable cloud resources can severely degrade the job performance. To tackle this issue, this paper proposes iSpot, a cost-efficient spot instance provisioning framework in the cloud, by focusing on Spark as a representative DAG (Directed Acyclic Graph)-style big analytics workload. Specifically, it identifies the availability zones with stable spot instance resources by devising an accurate LSTM (Long Short-Term Memory)-based price prediction method. iSpot further predicts the performance of Spark stages and jobs by designing a fined-grained performance model using the job profiling and the DAG information of stages. Based on the price prediction and Spark performance model, iSpot is able to provision the spot instances with the cost-efficient instance type (i.e., the instance type that achieves the minimum monetary cost), in order to deliver predictable performance for big data analytics. Extensive prototype experiments on Amazon EC2 demonstrate that iSpot can guarantee the performance of big data analytics while reducing the job budget with cloud transient servers.
Risk for new-onset diabetes mellitus after transplantation: implication of advanced age and immunosuppressive therapy

Background: The impact of the type of calcineurin inhibitor on the development of new-onset diabetes after transplantation (NODAT) has been difficult to assess due to contradictory results in published studies using heterogeneous populations. Aim: We attempted to assess the relative risk for development of NODAT in a population of age-stratified Afro–Caribbean and Hispanic renal-transplant recipients treated with cyclosporin A (CSA) or tacrolimus in an urban academic medical center (n = 284). Patients & methods: Patients, based on age distribution, were divided into group 1 (20–44 years; n = 85); group 2 (45–56 years; n = 110); and group 3 (57–79 years; n = 85). Results: In each age group, tacrolimus-treated patients had significantly higher rates of NODAT (7, 17 and 15%) compared with CSA-treated patients (2, 9 and 7%); p = 0.015. The risk of NODAT was increased in age group 2 (hazard ratio: 4.7; 95% confidence interval: 2.0–11.0; p = 0.01) and group 3 (hazard ratio :3.4; 95% confidence interval: 1.4–8.5; p = 0.01) compared with group 1 and use of tacrolimus compared with CSA (hazard ratio: 2.7; 95% confidence interval: 1.4–5.0; p = 0.01). Race, gender, acute rejection episodes and other demographic factors were insignificant factors between the age groups. Conclusion: We conclude that, in our population, the risk of NODAT increases with age and use of tacrolimus. Discrepancies in assigning risk to immunosuppressive regimens in previous studies may be confounded by the analysis of mixed age groups or predominantly younger patients.
Big Cities transfer learning: An unsupervised multi-view cross-domain classification with misses

Big data has brought many new challenges for machine learning research. In many learning tasks, we have to deal with diverse data from different domains, different representations, different distributions, scale, and density in order to achieve a good performance. With the recent advances in data storage and internet technology, data become more prominent, noisier and more complex which bring new opportunities and challenges into Transfer learning. In Urban computing when inferring knowledge for new or less developed cities we often need to deal with large-scale, multi-view, noisy and incomplete data. This calls for advanced techniques that can make practical use of massive, sparse and noisy data to efficiently transfer knowledge of multiple and diverse datasets (views) from a source domain to a target domain. Such a problem becomes much more challenging in an unsupervised learning setting where we do not dispose any label in the target domain which is not uncommon in my real-world scenarios. To tackle this challenge, in this paper we propose novel unsupervised multi-view transfer with missing data by learning a shared subspace across views from different domains through a latent low-rank transfer. Before performing knowledge transfer our approach learns an enriched representation of the source domain via a novel joint multi-view dictionary learning based on low-rank tensor. We also propose a multi-view co-classifier to predict the label in the target domain. Tailored for big data applications with EM-ADMM based optimization algorithm our method can efficiently perform knowledge transfer from a multi-view source domain to an unlabeled multi-view target domain with a high rate of missing values and noise.
Mesenchymal stromal cell therapy in ischemic stroke

: Stroke is a clinical disease with high incidence, high disability rate, and high mortality. But effective and safe therapy for stroke remains limited. Adult mesenchymal stromal cells (MSCs) perform a variety of therapeutic functions. MSC delivery improves neurological outcomes in ischemic stroke models via neurorestorative and neuroprotective effects such as angiogenic effects, promoting endogenous proliferation, and reducing apoptosis and inflammation. MSC secretome also showed powerful therapeutic effects as a cell-based therapy in animal experiments. Several clinical trials on MSC implantation via different routes have now been completed in patients with stroke. Although challenges such as immunogenicity of allo-MSCs and large-scale production strategies need to be overcome, MSCs can be considered as a promising potential therapy for ischemic stroke.