Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Statistical and Mechanistic Mathematical Model Guides Mobile Health Intervention for Chronic Pain

Nearly a quarter of visits to the emergency department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of "big data" sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease (SCD) community. SCD is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study, we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to well predict pain dynamics, given patients reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data-driven recommendations for treating chronic pain.
Cytokine response to the 6‐min walk test in individuals with different degrees of COPD

Physical exercise is a key part of rehabilitation programs in chronic obstructive pulmonary disease (COPD) patients, although it could modulates immune system responses by altering the cytokine profile of such individuals. Furthermore, the degree of severity of COPD could influence the inflammatory response induced by exercise. To evaluate the cytokine profile of individuals with different degrees of COPD in response to a 6‐min walk test (6MWT).
Efficient and secure data forwarding for erasure-code-based cloud storage

Cloud computing is a promising computing paradigm which has drawn extensive attention from both academia and industry. Since that the cloud is very likely to be outside of the trust domain of the users, serious concerns over the confidentiality of the outsourced data are arising. Traditional encryption methods guarantee data confidentiality, but also limit the functionality of the cloud storage as few operations are supported over encrypted data. The main technique contribution of this paper is that we present an efficient data forwarding scheme for the erasure-coded and encrypted cloud, which enforces the cloud not only provide data reliability and confidentiality, but also support the functionality that the encrypted data can be forwarded to another user without being retrieved back. Specifically, we design an all-or-nothing transform based encryption and a variant of ElGamal-based proxy re-encryption algorithms, blending them with the Reed-Solomon erasure code, our scheme is quite more efficient compared with previous studies and only needs to update partial data blocks instead of the whole file for data forwarding. In addition, our scheme also satisfies another practical property that the original data owner can no longer decrypt or forward the re-encrypted data again to other users after a complete data forwarding, which is termed to be “original inaccessibility” in our study. Analysis shows that our scheme is secure and satisfactory. Finally we theoretically and experimentally evaluate the performance of our scheme and the results indicate that our scheme is efficient in the procedure of file dispersal, forward and retrieval.
A mixed-scale dense convolutional neural network for image analysis

Significance Popular neural networks for image-processing problems often contain many different operations, multiple layers of connections, and a large number of trainable parameters, often exceeding several million. They are typically tailored to specific applications, making it difficult to apply a network that is successful in one application to different applications. Here, we introduce a neural network architecture that is less complex than existing networks, is easy to train, and achieves accurate results with relatively few trainable parameters. The network automatically adapts to a specific problem, allowing the same network to be applied to a wide variety of different problems. Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.
Integrating prior knowledge and structure from motion

A new approach for formulating prior knowledge in structure from motion is presented, where the structure is viewed as a 3D stochastic variable, hereby priors are more naturally expressed. It is demonstrated that this formulation is efficient for regularizing structure reconstruction via prior knowledge. Specifically algorithms for imposing priors in the proposed formulation are presented.
Brain Metastases

Approximately 10% of patients with cancer develop brain metastases. Some evidence indicates that as techniques for treating systemic tumors improve, the incidence of brain metastases, sequestered as they are behind the blood-brain barrier, is increasing. Although usually appearing late in the course of the disease, a brain metastasis may cause the initial symptoms, before the primary cancer has been identified. The diagnostic and therapeutic approach depends on the number and location of brain lesions and the stage of the cancer. Patients with brain metastases are rarely cured. However, appropriate treatment can improve both the quality and duration of the patient's life. Treatment must be directed not only at the brain metastasis (definitive care), but also at a multitude of other symptoms that plague patients with brain metastases (supportive care). Judicious selection of pharmacologic agents and nonpharmacologic techniques can effectively treat many serious symptoms in patients with brain metastases, but injudicious selection of pharmacologic agents may have side effects and make the patient's quality of life worse. The authors review some aspects of both definitive and supportive care with particular attention to the side effects of some commonly used pharmacologic agents.
Neural network with saliency based feature selection ability

Convolutional neural network (CNN) is inspired by the biological structure of human visual system (HVS). And there are still mechanisms in HVS that are worthy learn from. We were inspired by the function of feature selection in HVS which is named as visual saliency and proposed Sal-Mask connection and Ada-Sal Network to implement similar function in neural networks. In this paper, we did further research and tried three different improvement schemes on Ada-Sal Network. By visualizing features we illustrated the feature selection ability of Sal-Mask connection helps the neural network to extract features from more subtle details of input images. The experiment results proved that neural network works better with this saliency based feature selection function. We also found that Sal-Mask connection works best on features from the first convolutional connection.
Susceptibility loci for metabolic syndrome and metabolic components identified in Han Chinese: a multi‐stage genome‐wide association study

Metabolic syndrome (MetS), a cluster of metabolic disturbances that increase the risk for cardiovascular disease and diabetes, was because of genetic susceptibility and environmental risk factors. To identify the genetic variants associated with MetS and metabolic components, we conducted a genome‐wide association study followed by replications in totally 12,720 participants from the north, north‐eastern and eastern China. In combined analyses, independent of the top known signal at rs651821 on APOA5, we newly identified a secondary triglyceride‐associated signal at rs180326 on BUD13 (Pcombined = 2.4 × 10−8). Notably, by an integrated analysis of the genotypes and the serum levels of APOA5, BUD13 and triglyceride, we observed that BUD13 was another potential mediator, besides APOA5, of the association between rs651821 and serum triglyceride. rs671 (ALDH2), an east Asian‐specific common variant, was found to be associated with MetS (Pcombined = 9.7 × 10−22) in Han Chinese. The effects of rs671 on metabolic components were more prominent in drinkers than in non‐drinkers. The replicated loci provided information on the genetic basis and mechanisms of MetS and metabolic components in Han Chinese.
Partially Coverage Restoration: An Esthetically Conservative Treatment for a Complex Cavity Restoration

Dental materials have been continuously developing over a long period of time. Every effort has been made to develop the physical and mechanical properties of the materials to mimic the natural tooth substances. However, that has not yet been achieved, making it necessary to apply conservative dental treatments. Conservative treatments are procedures which minimize the amount of defective tooth structure removed while maximizing the remaining amount of intact tooth structure, and defining the margin of the restoration at the supragingival level. In a substantially destructed tooth, the direct restoration, which is the most conservative restoration, has the limitations on the retention or creation of the proper contour of the restoration. The indirectly partial coverage restoration has recently gained in popularity. This treatment preserves the tooth structure while allowing reconstruction of the destructed structure to enable correct and proper tooth functioning and also provides a satisfactory cosmetic appearance. The purpose of this paper is to review and compare the various conservative techniques of partial coverage restorations and the full coverage treatment with crowns.
Molecular Pathways: Targeting Protein Tyrosine Phosphatases in Cancer

The aberrant activation of oncogenic signaling pathways is a universal phenomenon in cancer and drives tumorigenesis and malignant transformation. This abnormal activation of signaling pathways in cancer is due to the altered expression of protein kinases and phosphatases. In response to extracellular signals, protein kinases activate downstream signaling pathways through a series of protein phosphorylation events, ultimately producing a signal response. Protein tyrosine phosphatases (PTP) are a family of enzymes that hydrolytically remove phosphate groups from proteins. Initially, PTPs were shown to act as tumor suppressor genes by terminating signal responses through the dephosphorylation of oncogenic kinases. More recently, it has become clear that several PTPs overexpressed in human cancers do not suppress tumor growth; instead, they positively regulate signaling pathways and promote tumor development and progression. In this review, we discuss both types of PTPs: those that have tumor suppressor activities as well as those that act as oncogenes. We also discuss the potential of PTP inhibitors for cancer therapy. Clin Cancer Res; 23(9); 2136–42. ©2017 AACR.
The influence of diabetes mellitus on periodontal tissues: a histological study.

OBJECTIVE The aim of this study was to investigate histological changes that occur in the periodontium of subjects with type 2 diabetes mellitus without signs of periodontal disease and to establish the influence of this systemic condition upon periodontal structures. MATERIALS AND METHODS Gingival tissue samples were obtained from 12 adult patients with type 2 diabetes mellitus and 10 healthy adults, as control group. The specimens were examined using standard dyes as Hematoxylin and Eosin and PAS-Alcian stain, by a microscope with different magnifications. RESULTS Our results showed that periodontal disease in patients with type 2 diabetes mellitus is characterized by significant inflammation, affecting both epithelial and connective tissues, with degeneration of dermal papilla, increase in number of inflammatory cells, destruction of reticular fibers and accumulation of dense collagen fibers (fibrosis). CONCLUSIONS Within the limits of this study, diabetic subjects presented distortion in periodontal attachment, with changes in both epithelial and connective tissues, when compared to the healthy controls, suggesting that diabetes mellitus has an independent effect on periodontal tissue. This effect is observed in both groups, so that we considered it to be independent of the periodontal condition.
Comprehensive Hygro-Thermo-Vapor Pressure Model for CMOS Image Sensor

A combined effect of moisture diffusion, heat transfer, and hygro-thermo-vapor pressure modeling for pre-mold QFN CMOS Image Sensor (CIS) package has been developed in this study. Hygroscopic swelling properties such as saturation, coefficient of moisture expansion (CME) and activation energy can be extracted through TMA (Thermal Mechanical Analysis) and TGA (Thermal Gravitational Analysis) instruments. Fick’s second law of transient diffusion is solved by using finite element analysis (FEA) to evaluate the overall moisture distributions. With obtained experimental data, a three-dimensional FEA CIS model using the “thermal-wetness” technique is developed to predict the moisture absorption, moisture desorption, temperature distributions, hygro-thermo-vapor pressure mechanical coupled effect and the residual stress distributions at JEDEC pre-conditioning standard JESD22-A120.
A survey on types of machine learning techniques in intrusion prevention systems

The computation technology is evolving. The data transmitted and generated using them are growing exponentially. The traffic on these networks requires surveillance. Effective network traffic surveillance, packet analysis and rules to define the traffic flow are in place using various intrusion detection and prevention systems. However, the security issues and concerns are dictating the need to evolve with the novel and ‘human-like’ methods to mitigate them. Normal methods and techniques may prove too tedious, and sometimes will not even render fruitful results. In that case, the techniques that can think, act and mimic human beings assure the possibilities of resolving the concerns where the domain of artificial intelligence plays a critical role. Also, there is a crucial need to design real-time models that can analyze & handle bigger, more complex data; and deliver faster as well as accurate results. Machine learning is a subfield which possesses all of the attributes. Intrusion preventions IDPS's integrate methods that help to prevent intrusive and non-intrusive data packets [2]. According to survey, most of the intrusion prevention methods rely heavily on humans to analyze data and classify into intrusive and non-intrusive networks. When the data to compute is very large, computing methods such as Computational intelligence and its constituent are essential to use as humans have their own shortcomings. Computational intelligence and machine learning are widely used to detect the intrusions, which in turn help in preventing the same. Pattern recognition uses algorithms to handle very large data sets. In this paper, a survey is done that on various machine learning and computational intelligence techniques that envisage using IDPS.
High resolution adaptive arrays based on random processing techniques: frequency hopping modulation

A new architecture for adaptive arrays using frequency hopping modulation is addressed. The resolution of the array and the interference rejection increase substantially applying random processing to the carrier frequency of the signals. The proposed framework is composed of two different stages. The anticipative stage, devoted to minimize the noise and fixed interferences contribution and the GSLC stage which provides cancellation of follower jammers and solves the multiuser collision problem. The developed system requires neither temporal nor spatial reference for its implementation, only the frequency sequence must be known. An adaptive approach has been implemented, allowing a fast convergence to the optimal behavior.
Blind Fake Image Detection Scheme Using SVD

The release of image processing techniques make image modification and fakery easier. Image fakery, here, is defined as a process to copy a region of source image and paste it onto the destination image, with some post processing methods applied, such as boundary smoothing, blurring, etc. to make it natural. The most important characteristic of image fakery is object copy and paste. In order to detect fake images, this letter introduces a blind detection scheme based on singular value decomposition (SVD). Experimental results also show the effectiveness of the proposed scheme.
Brain Drain in Globalization: A General Equilibrium Analysis from the Sending Countries' Perspective

The paper assesses the global effects of brain drain on developing economies and quantifies the relative sizes of various static and dynamic impacts. By constructing a unified generic framework characterized by overlapping generations dynamics and calibrated to real data, this study incorporates many direct impacts of brain drain whose interactions, along with other indirect effects, are endogenously and dynamically generated. Our findings suggest that the short-run impact of brain drain on resident human capital is extremely crucial, as it does not only determine the number of skilled workers available to domestic production, but it also affects the sending economy’s capacity to innovate or to adopt modern technologies. The latter impact plays an important role particularly in a globalized economy where capital investments are made in places with higher production efficiencies ceteris paribus. Hence, in spite of several empirically documented positive feedback effects, those countries with high skilled emigration rates are the most candid victims to brain drain since they are least likely to benefit from the “brain gain” effect, and thus suffering from declines of their resident human capital.
A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones

Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76% and recall of 93.65% for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74%, and is better than both of the several well-known counterparts and commercial products.
Context-based octree coding for point-cloud video

3D and free-viewpoint video has been moving towards solid-scene representation using meshes and point clouds. Point-cloud processing requires much less computation and the points in the cloud are minimally represented by their geometry (3D position) and color. A common point-cloud geometry compression method is the octree representation, which acts on individual frames. We present a lossless inter-frame compression method for pointcloud geometry, by reordering each octree based on previous frames prior to entropy coding. Results show that compared to the octree representation, the proposed solution offers an average rate reduction of 30%, while entropy-coding of the octree yields an average rate reduction of 24%.
The Durability of E-Glass/Vinyl Ester Composite Materials Subjected to Environmental Conditioning and Sustained Loading

A series of experiments has been performed to observe the effects of the combination of sustained load, elevated temperature, and moisture on the durability of an E-glass/vinyl ester composite material. This durability is evaluated in terms of the ultimate tensile strength of the material. The results have confirmed that moisture at elevated temperatures reduces the ultimate tensile strength of the material, as reported in previous studies. In these experiments, a sustained load has been applied to samples during the conditioning process and has been found to further reduce the composite durability. Conditioning also decreases the strain to failure, with a greater decrease overall for samples which were loaded during conditioning.