Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic Outcomes in AIDS-Associated Kaposi's Sarcoma Patients on Antiretroviral Therapy Treated with Chemotherapy at Two Tertiary Hospitals in Lusaka, Zambia.

The incidence of HIV-associated Kaposi's sarcoma (KS) remains high in Zambia in the antiretroviral therapy era. The most efficacious treatment regimen for KS has yet to be established. In both developed and developing countries, treatment regimens have had limited efficacy. Late presentation in Africa affects therapeutic outcomes. OBJECTIVE The aim of this study was to determine therapeutic outcomes of epidemic KS patients on combination antiretroviral therapy (cART) after completion of six cycles of Adriamycin, Bleomycin, and Vincristine (ABV) chemotherapy. METHODS This was a descriptive cross-sectional study. Study participants were drawn from a study database of confirmed incident KS patients seen at the Skin Clinic of the University Teaching Hospitals (UTH) during the period between August, 2015 and September, 2016. RESULTS Of the 38 successfully recruited study participants, a complete response was documented in 18 (47%) after 6 cycles of ABV whereas 20 (53%) experienced a partial response. KS recurrence was observed in 8 (44%) of the individuals that experienced an initial complete response. At the time of the study, clinical assessment revealed that KS lesions had completely regressed in 21 (55%) of all the patients. CONCLUSION ABV chemotherapy appears ineffective in long-term resolution of epidemic KS patients on ART. Recurrence rates are high after chemotherapy in patients that experience initially favorable responses to treatment. There is a need to diagnose KS earlier, and to develop more efficacious treatment options in order to reduce recurrence rates for epidemic KS.
Estimation of Graphlet Counts in Massive Networks

Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms; however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually sufficient for many applications. In this paper, we propose an unbiased graphlet estimation framework that is: (a) fast with large speedups compared to the state of the art; (b) parallel with nearly linear speedups; (c) accurate with less than 1% relative error; (d) scalable and space efficient for massive networks with billions of edges; and (e) effective for a variety of real-world settings as well as estimating global and local graphlet statistics (e.g., counts). On 300 networks from 20 domains, we obtain <1% relative error for all graphlets. This is vastly more accurate than the existing methods while using less data. Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.
Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model

Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quantification of retinal abnormalities such as cysts, exudates, retinal layer disorganization, etc. Intra-retinal cysts (IRCs) occur in several macular disorders such as, diabetic macular edema, retinal vascular disorders, age-related macular degeneration, and inflammatory disorders. Automated segmentation of IRCs poses challenges owing to variations in the acquisition system scan intensities, speckle noise, and imaging artifacts. Several segmentation methods have been proposed in the literature for IRC segmentation on vendor-specific OCT images that lack generalizability across imaging systems. In this paper, we propose a fully convolutional network (FCN) model for vendor-independent IRC segmentation. The proposed method counteracts image noise variabilities and trains FCN models on OCT sub-images from the OPTIMA cyst segmentation challenge dataset (with four different vendor-specific images, namely, Cirrus, Nidek, Spectralis, and Topcon). Further, optimal data augmentation and model hyperparametrization are shown to prevent over-fitting for IRC area segmentation. The proposed method is evaluated on the test dataset with a recall/precision rate of 0.66/0.79 across imaging vendors. The Dice correlation coefficient of the proposed method outperforms that of the published algorithms in the OPTIMA cyst segmentation challenge with a Dice rate of 0.71 across the vendors.
Viewpoint Assessment and Recommendation for Photographing Architectures

This paper studies the problem of how to assess the quality of photographing viewpoints and how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from photographs of world famous landmarks that are available on the Internet and their viewpoint quality ranked by online user annotation. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 15 world famous architectures as well as their 3D models from the Internet. Viewpoint recovery for images is carried out through an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users’ viewpoint preferences when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues and perform viewpoint recommendation by learning from both 2D and 3D features using a specifically designed SVM-2K multi-view learner, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. The experiments also demonstrate that our system can be used to recommend viewpoints for rendering textured 3D models of buildings for the use of architectural design, in addition to viewpoint evaluation of photographs and recommendation of viewpoints for photographing architectures in practice.
Exploratory Visual Sequence Mining Based on Pattern-Growth

Sequential pattern mining finds applications in numerous diverging fields. Due to the problem's combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user's perspective. Second, as datasets grow, mining large numbers of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a “transparent box” execution model. We propose a novel approach to interactive visual sequence mining that allows the user to guide the execution of a pattern-growth algorithm at suitable points through a powerful visual interface. Our approach (1) introduces the possibility of using local constraints during the mining process, (2) allows stepwise visualization of patterns being mined, and (3) enables the user to steer the mining algorithm towards directions of interest. The use of local constraints significantly improves users’ capability to progressively refine the search space without the need to restart computations. We exemplify our approach using two event sequence datasets; one composed of web page visits and another composed of individuals’ activity sequences.
Robust Activity Recognition for Aging Society

Human activity recognition (HAR) is widely applied to many industrial applications. In the context of Industry 4.0, driven by the same demand of machines’ self-organizing ability, HAR can also be adopted in elderly healthcare. However, HAR should be adaptive to the application scenarios in elderly healthcare. In this paper, we propose a nonintrusive activity recognition method that can be applied to long-term and unobtrusive monitoring for elderlies. The method is robust to obstruction and nontarget object interference. Skeleton sequence is estimated from RGB images. Based on two activity continuity metrics, an interframe matching algorithm is proposed to filter nontarget objects. In order to make full use of spatial-temporal information, we propose a novel activity encoding method based on the interframe joints distances. A convolutional neural network is used to learn the distinguishing features automatically. A specific data augmentation method is designed to avoid the overfitting problem on small-scale datasets. The experiments are performed on two public activity datasets and a newly released noisy activity dataset (NAD). The NAD contains obstruction, nontarget object interference. The experimental results show that the proposed method achieves the state-of-the-art performance while only using one ordinary camera. The proposed method is robust to a realistic environment.
Prediction of Hot Regions in PPIs Based on Improved Local Community Structure Detecting

The hot regions in PPIs are some assembly regions which are composed of the tightly packed HotSpots. The discovery of hot regions helps to understand life activities and has very important value for biological applications. The identification of hot regions is the basis for protein design and cancer prevention. The existing algorithms of predicting hot regions often have some defects, such as low accuracy and unstability. This paper proposes a novel hot region prediction method based on diverse biological characteristics. First, feature evaluation is employed by using an impoved mRMR method. Then, SVM is adopted to create cassification model based on the features selected. In addition, a new clustering algorithm, namely LCSD (Local community structure detecting), is developed to detect and analyze the conformation of hot regions. In the clustering process, the link similarity of protein residues is introduced to handle the boundary nodes. This algorithm can effectively deal with the missing residue nodes and control the local community boundaries. The results indicate that the spatial structure of hot regions can be obtained more effectively, and that our method is more effective than previous methods for precise identification of hot regions.
Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery

We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with state-of-the-art methods by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.
Design and Adaptive Control for an Upper Limb Robotic Exoskeleton in Presence of Input Saturation

This paper addresses the control design for an upper limb exoskeleton in the presence of input saturation. An adaptive controller employing the neural network technology is proposed to approximate the uncertain robotic dynamics. Also, an auxiliary system is designed to deal with the effect of input saturation. Furthermore, we develop both the state feedback and the output feedback control strategies, which effectively estimates the uncertainties online from the measured feedback errors, instead of the model-based control. In addition to the proposed control, a disturbance observer is designed to reject the unknown disturbance online for achieving the trajectory tracking. The method requires a minimal amount of a priori knowledge of system dynamics. Subsequently, the principle of Lyapunov synthesis ensures the stability of the closed-loop system. Finally, the experimental studies are carried out on this robotic exoskeleton.
Tolerance traits related to climate change resilience are independent and polygenic

The resilience of organisms to climate change through adaptive evolution is dependent on the extent of genetically based variation in key phenotypic traits and the nature of genetic associations between them. For aquatic animals, upper thermal tolerance and hypoxia tolerance are likely to be a important determinants of sensitivity to climate change. To determine the genetic basis of these traits and to detect associations between them, we compared naturally occurring populations of two subspecies of Atlantic killifish, Fundulus heteroclitus, that differ in both thermal and hypoxia tolerance. Multilocus association mapping demonstrated that 47 and 35 single nucleotide polymorphisms (SNPs) explained 43.4% and 51.9% of variation in thermal and hypoxia tolerance, respectively, suggesting that genetic mechanisms underlie a substantial proportion of variation in each trait. However, no explanatory SNPs were shared between traits, and upper thermal tolerance varied approximately linearly with latitude, whereas hypoxia tolerance exhibited a steep phenotypic break across the contact zone between the subspecies. These results suggest that upper thermal tolerance and hypoxia tolerance are neither phenotypically correlated nor genetically associated, and thus that rates of adaptive change in these traits can be independently fine‐tuned by natural selection. This modularity of important traits can underpin the evolvability of organisms to complex future environmental change.
Ad Hoc 802.11-802.15.4 Crosstalk-Based Communication in Practice

802.11-802.15. 4 crosstalk-based communication has been heralded as a potential solution to many interoperability problems posed by low-power wireless devices. Among many issues that can be addressed by this kind of communication, the one that has drawn our attention is configuring motes equipped with 802.15. 4 radios in a convenient way. Such devices have become an intrinsic part of our everyday life, yet they often lack any interfaces suitable for users with no technical background. Consequently, the idea of utilizing an ordinary smartphone as a remote controller for low-power devices seems to be appealing in particular. However, despite the promising results on 802.11-to-802.15. 4 crosstalk, some important elements are missing to realize this vision. Above all, state-of-the-art solutions require access to low-level subsystems of both sides of communication. As we explain, in real-world applications devices involved in the process cannot be always fully controlled. On the other hand, our use-case does not need to address serious difficulties that are typically considered when designing solutions based on the discussed mechanism. This advantage is promising enough to attempt to verify whether less restrictive version of crosstalk-based communication can be deployed in an ad-hoc manner. We answer this question by presenting results of experiments carried out in variety of settings.
Fast Ray-Scene Intersection for Interactive Shadow Rendering with Thousands of Dynamic Lights

We present a method for the fast computation of the intersection between a ray and the geometry of a scene. The scene geometry is simplified with a 2D array of voxelizations computed from different directions, sampling the space of all possible directions. The 2D array of voxelizations is compressed using a vector quantization approach. The ray-scene intersection is approximated using the voxelization whose rows are most closely aligned with the ray. The voxelization row that contains the ray is looked up, the row is truncated to the extent of the ray using bit operations, and a truncated row with non-zero bits indicates that the ray intersects the scene. We support dynamic scenes with rigidly moving objects by building a separate 2D array of voxelizations for each type of object, and by using the same 2D array of voxelizations for all instances of an object type. We support complex dynamic scenes and scenes with deforming geometry by computing and rotating a single voxelization on the fly. We demonstrate the benefits of our method in the context of interactive rendering of scenes with thousands of moving lights, where we compare our method to ray tracing, to conventional shadow mapping, and to imperfect shadow maps.
Self-Triggered Leader-Following Consensus for High-Order Nonlinear Multiagent Systems via Dynamic Output Feedback Control

This paper investigates the event-based leader-following consensus problem for high-order nonlinear multiagent systems whose dynamics are in strict feedback forms and satisfy Lipschitz condition. By using self-triggered control scheme and dynamic output feedback control method in combination, a new class of distributed self-triggered consensus protocols is proposed based only on the relative output measurements of neighboring agents. It is noted that the proposed protocols only require the output information of neighboring agents to be shared and the designed self-triggered algorithm can avoid continuous communication among neighboring agents, thus the communication cost is reduced significantly. Sufficient conditions in terms of matrix inequalities are derived to guarantee the exponential leader-following consensus. The effectiveness of the theoretical results is illustrated through a simulation example.
Controllability Analysis of a Gene Network for Arabidopsis thaliana Reveals Characteristics of Functional Gene Families

Based on structural controllability of complex networks and a constructed gene network with 9,241 nodes for Arabidopsis thaliana, we classified nodes into five categories via their roles in control or node deletion, including indispensable, neutral, dispensable, driver, and critical driver nodes. The indispensable nodes can increase the number of drivers after deletion, which are never drivers or critical drivers. About 10 percent of nodes are indispensable. However, more than 60 percent of nodes are neutral ones. More than 62 percent of nodes are drivers, which indicates the gene network is very difficult to be fully controlled. Gene Ontology (GO) enrichment analysis reveals that different sets of nodes have preferred biological functions and processes. The indispensable nodes are significantly enriched as essential genes, drought responsive and abscisic acid (ABA) independent genes, transcriptional factors (TFs), core cell cycle genes, and ABA and Gibberellin (GA) related genes. The critical drivers are enriched as receptor kinase-like genes, while shorted in WRKY TFs and functional genes that are enriched in the indispensable nodes. Robustness analysis based on node and edge additions, edge rewiring indicate the obtained conclusions are robust to network perturbations. Our investigations clarify control roles of some gene families and provide potential implications for identifying functional genes in other plant species, such as drought responsive genes and TFs.
Distributed Graph Hashing

Recently, hashing-based approximate nearest neighbors search has attracted considerable attention, especially in big data applications, due to its low computation cost and fast retrieval speed. In the literature, most of the existing hashing algorithms are centralized. However, in many large-scale applications, the data are often stored or collected in a distributed manner. In this situation, the centralized hashing methods are not suitable for learning hash functions. In this paper, we consider the distributed learning to hash problem. We propose a novel distributed graph hashing model for learning efficient hash functions based on the data distributed across multiple agents over network. The graph hashing model involves a graph matrix, which contains the similarity information in the original space. We show that the graph matrix in the proposed distributed hashing model can be decomposed into multiple local graph matrices, and each local graph matrix can be constructed by a specific agent independently, with moderate communication and computation cost. Then, the whole objective function of the distributed hashing model can be represented by the sum of local objective functions of multiple agents, and the hashing problem can be formulated as a nonconvex constrained distributed optimization problem. For tractability, we transform the nonconvex constrained distributed optimization problem into an equivalent bi-convex distributed optimization problem. Then we propose two algorithms based on the idea of alternating direction method of multipliers to solve this problem in a distributed manner. We show that the proposed two algorithms have moderate communication and computational complexities, and both of them are scalable. Experiments on benchmark datasets are given to demonstrate the effectiveness of the proposed methods
Regional Intervention of Stroke Care to Increase Thrombolytic Therapy for Acute Ischemic Stroke: The Southeast Texas Experience

Background and Purpose— The aims of this study were to investigate the effect of an intervention to unblind data on r-tPA (recombinant tissue-type plasminogen activator) administration and sharing data with chief executive officers of participating hospitals, on r-tPA administration rates postintervention and on potential healthcare cost savings implemented at 26 Southeast Texas Regional Advisory Council hospitals. Methods— Retrospective analysis of prospective data on thrombolytic therapy from 26 Southeast Texas Regional Advisory Council hospitals, collected between April 2014 and June 2016. The control (blinded) period (Q2-2014 to Q2-2015) was followed by unblinding (Q3-2015). Results— Intervention was associated with 21.1% increase in r-tPA administration rates, with 38.5% increase in r-tPA administration with door-to-needle time ⩽60 minutes. An absolute increase in r-tPA administration of 2.1% was seen with an average lifetime cost savings of $3.6 million. Conclusions— Transparent regional data sharing was associated with improved r-tPA administration and healthcare cost savings.