Miracle-dz/UniFAR-FTUs huggingface dataset:
Can perfusion CT unmask postictal stroke mimics?

Objective To study the diagnostic value of volume perfusion CT (VPCT) in patients with transient focal neurologic deficits following and during epileptic seizures, that mimic symptoms of stroke. Methods A retrospective case-control study was performed on 159 patients who presented with a seizure and received an emergency VPCT within the first 3.5 hours of admission, after being misjudged to have an acute stroke. The reference test was a clinical-based, EEG-supported diagnostic algorithm for seizure. Results We included 133 patients: 94 stroke-mimicking cases with postictal focal neurologic deficits (“Todd phenomenon,” n = 67) or ongoing seizure on hospital admission (“ictal patients,” n = 27), and 39 postictal controls without focal neurologic deficits. Patients with Todd phenomenon showed normal perfusion (64%), hypoperfusion (21%), and hyperperfusion (14%) on early VPCT. Ictal patients displayed more hyperperfusion compared to postictal patients (p = 0.015). Test sensitivity of hyperperfusion for ictal patients is 38% (95% confidence interval [CI] 20.7%–57.7%), specificity 86% (95% CI 77.3%–91.7%), positive predictive value is 42% (95% CI 27.5%–58.7%), and the negative predictive value 83% (95% CI 78.6%–86.9%). A cortical distribution was seen in all hyperperfusion scans, compared to a cortico-subcortical pattern in hypoperfusion (p < 0.001). A history of complex focal seizure and age were associated with hyperperfusion (p = 0.046 and 0.038, respectively). Conclusion VPCT can differentiate ictal stroke mimics with hyperperfusion from acute ischemic stroke, but not postictal patients who display perfusion patterns overlapping with ischemic stroke. Classification of evidence This study provides Class IV evidence that VPCT accurately differentiates ictal stroke mimics from acute ischemic stroke.
Chorioamnionitis as a risk factor for retinopathy of prematurity: An updated systematic review and meta-analysis

The role of chorioamnionitis (CA) in the development of retinopathy of prematurity (ROP) is difficult to establish, because CA-exposed and CA-unexposed infants frequently present different baseline characteristics. We performed an updated systematic review and meta-analysis of studies reporting on the association between CA and ROP. We searched PubMed and EMBASE for relevant articles. Studies were included if they examined preterm or very low birth weight (VLBW, <1500g) infants and reported primary data that could be used to measure the association between exposure to CA and the presence of ROP. Of 748 potentially relevant studies, 50 studies met the inclusion criteria (38,986 infants, 9,258 CA cases). Meta-analysis showed a significant positive association between CA and any stage ROP (odds ratio [OR] 1.39, 95% confidence interval [CI] 1.11 to 1.74). CA was also associated with severe (stage ≥3) ROP (OR 1.63, 95% CI 1.41 to 1.89). Exposure to funisitis was associated with a higher risk of ROP than exposure to CA in the absence of funisitis. Additional meta-analyses showed that infants exposed to CA had lower gestational age (GA) and lower birth weight (BW). Meta-regression showed that lower GA and BW in the CA-exposed group was significantly associated with a higher risk of ROP. In conclusion, our study confirms that CA is a risk factor for developing ROP. However, part of the effects of CA on the pathogenesis of ROP may be mediated by the role of CA as an etiological factor for very preterm birth.
Topic Discovery for Streaming Short Texts with CTM

Short texts are prevalent on today’s Web, especially with the emergence of social media. However, how to discover the topics of streaming short texts has become an important task for many content analysis applications. Conventional topic models such as Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) will suffer from sparsity problem when we infer the latent topics from short texts with them. The reason is that they derive topics from document-level word co-occurrence by modeling each document as a mixture of topics. Different from the above idea, Biterm Topic Model (BTM) discovers topics in short texts by directly modeling the generation of word co-occurrence patterns in the whole corpus. But semantic information is lacking for short texts. In this paper, in order to alleviate the sparsity problem, keep the semantic information of documents and get the latent topic information of streaming short texts immediately, we propose a joint topic model for Chinese streaming short texts (CTM) based on the online algorithms of LDA and BTM. Experiments on short texts from Sina Weibo show that our joint topic model can discover more precise topics and carry out more applications. In addition, considering the preprocessing in Chinese text is different from English and errors in extracting key phrases, we use a combined word method to extend the length of short texts and reduce errors in extracting key phrases.
iWalk: Interest-Aware Random Walk for Network Embedding

Network embedding plays a key role in network analysis, due to its ability to represent features of network struc- ture in a low-dimensional Euclidean space, making it possible to directly utilize the of f-the-shelf mining techniques in a variety of analysis tasks. Although fruitful research papers on network embedding have sprung up in recent years, most of them neglect an important fact that nodes and edges in real-world networks are of diverse interests especially when the network contains little side information such as labels. To tackle this challenge, we propose a novel iWalk model to learn interest-aware network embedding in an unsupervised fashion. iWalk can automatically assign interest to nodes and edges based on network topology and construct custom paths navigated by assigned interest, then Skip-gram is used to learn network embedding from these paths. Sufficient experiments are conducted on different tasks and three typical datasets, the empirical results demonstrate that our model outperform the stat-of-art methods in most instances.
Expressing Sparse Matrix Computations for Productive Performance on Spatial Architectures

This paper addresses spatial programming of sparse matrix computations for productive performance. The challenge is how to express an irregular computation and its optimizations in a regular way. A sparse matrix has (non-zero) values and a structure. In this paper, we propose to classify the implementations of a computation on a sparse matrix into two categories: (1) structure-driven, or top-down, approach, which traverses the structure with given row and column indices and locates the corresponding values, and (2) values-driven, or bottom-up, approach, which loads and processes the values in parallel streams, and decodes the structure for the values' corresponding row and column indices. On a spatial architecture like FPGAs, the values-driven approach is the norm. We show how to express a sparse matrix computation and its optimizations for a values-driven implementation. A compiler automatically synthesizes a code to decode the structure. In this way, programmers focus on optimizing the processing of the values, using familiar optimizations for dense matrices, while leaving the complex, irregular structure traversal to an automatic compiler. We also attempt to regularize the optimizations of the reduction for a dynamic number of values, which is common in a sparse matrix computation.
Text Dissimilarities Predictions Using Convolutional Neural Networks and Clustering

Text mining is still interesting problem which can be solved using different methods and it can bring many surprising results. Our goal is to analyze text segments of some long text and find segments which have a different stylometry in comparison to the other. We developed two-steps method: (1) clustering of segments, and (2) classification of segments using convolutional neural networks. The method was tested on ten Arabic and ten English long texts. Our new method contributes to the previous results about texts. It classify a text into two classes: reliable or suspicious text and in many cases it confirms the previous evaluation.
Prenatal Development of the Humpback Whale: Growth Rate, Tooth Loss and Skull Shape Changes in an Evolutionary Framework

Extant baleen whales (Mysticeti) share a distinct suite of extreme and unique adaptations to perform bulk filter feeding, such as a long, arched skull, and mandible and the complete loss of adult dentition in favor of baleen plates. However, mysticetes still develop tooth germs during ontogeny. In the fossil record, multiple groups document the transition from ancestral raptorial feeding to filter feeding. Fetal specimens give us an extraordinary opportunity to observe when and how this macroevolutionary transition occurs during gestation. We used iodine‐enhanced and traditional CT scanning to visualize the internal anatomy of five fetuses of humpback whale representing the first two‐thirds of gestation, and we combine these data with previously published reports to provide the first comprehensive qualitative description of the sequence of developmental changes that characterize the skull and dentition. We also use quantitative methods based on 3D landmarks to investigate the shape changes in the fetuses in relation to a juvenile cranial morphology. We found similarities in the ossification patterns of the humpback and other cetaceans (dolphins), but there appear to be major differences when comparing them to terrestrial artiodactyls. As for the tooth germs, this developmental sequence confirms that the tooth‐to‐baleen transition occurs in the last one‐third of gestation. Analysis of cranial shape development revealed a progressive elongation of the rostrum and a resulting posterior movement of the nasals relative to the braincase. Future work will involve acquisition of data from other species to complete our documentation of the teeth‐to‐baleen transition. Anat Rec, 2018. © 2018 American Association for Anatomy.
Words Are Not Temporal Sequences of Characters

Language modeling is a valuable component of generative natural language processing (NLP) tasks, and benefits from explicit representations of the inherent hierarchies in language. We investigate a commonly used architecture that captures the concept that words are built from characters, and modify the word encoding mechanism to use a feed forward neural network rather than a recurrent neural network (RNN). This feed forward architecture facilitates increased performance and a reduction in the number of parameters over models that use common RNN implementations. We investigate whether word representations benelit from position- invariant features in the characters, and lind that fixed-position representations are sufficient.
Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows

Streaming computations are by nature long-running, and their workloads can change in unpredictable ways. This in turn means that maintaining performance may require dynamic scaling of allocated computational resources. Some modern large-scale stream processors allow dynamic scaling but typically leave the difficult task of deciding how much to scale to the user. The process is cumbersome, slow and often inefficient. Where automatic scaling is supported, policies rely on coarse-grained metrics like observed throughput, backpressure, and CPU utilization. As a result, they tend to show incorrect provisioning, oscillations, and long convergence times. We present DS2, an automatic scaling controller for such systems which combines a general performance model of streaming dataflows with lightweight instrumentation to estimate the true processing and output rates of individual dataflow operators. We apply DS2 on Apache Flink and Timely Dataflow and demonstrate its accuracy and fast convergence. When compared to Dhalion, the state-of-the-art technique in Heron, DS2 converges to the optimal, backpressure-free configuration in a single step instead of six.
Accelerating model-based collaborative filtering with item clustering

We present a clustering method designed to complement existing model-based collaborative filtering algorithms. Thanks to clustering, we are able to produce faster recommendations on datasets with a very large number of items. Moreover, we can work with models independent of the number of users, and produce recommendations for new users and in session based environment. We show that our method builds meaningful clusters, and that we can reduce the number of items considered during recommendation by a factor up to 50, with little impact on the accuracy or diversity of the recommendation.
[Correlation of depression and anxiety with social support and quality of life in patients with chronic wounds].

OBJECTIVE To investigate the influential factors for depression and anxiety in patients with chronic wounds, and the relationship between depression and anxiety and social support or quality of life. Methods: We used convenience sampling method to enroll 150 patients with chronic wounds. Self-rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), Short Form 36 Health Survey (SF-36) and Social Support Rating Scale (SSRS) were used to assess the level of depression and anxiety, quality of life, and social support in patients with chronic wounds. Results: The detection rate of depression and anxiety in patients with chronic wounds was 45.3% and 14% respectively. In patients with chronic wounds, age was positively correlated with SDS and SAS scores (P<0.05), while education, eight factors of SF-36, objective support, subjective support, and social support scores of SSRS were negatively correlated with SDS and SAS scores (P<0.05). Further multiple regression analysis showed that the duration of wounds, physical functional score, role physical score and role emotional score of SF-36, objective support score, subjective support score, support utilization score, and social support score of SSRS were the independent influential factors for SDS score (P<0.05). Education, general health score, social functional score, and mental health score of SSRS were the independent factors for SAS score (P<0.05). Conclusion: The lower quality of life and social support of patients with chronic wounds could increase their depression and anxiety. While treating patients with chronic wounds, medical staff should also pay attention to the mutual influence between anxiety and depression, or quality of life and social support.
The polarity-induced force imbalance in Caenorhabditis elegans embryos is caused by asymmetric binding rates of dynein to the cortex

During asymmetric cell division, the molecular motor dynein generates cortical pulling forces that position the spindle to reflect polarity and adequately distribute cell fate determinants. In Caenorhabditis elegans embryos, despite a measured anteroposterior force imbalance, antibody staining failed to reveal dynein enrichment at the posterior cortex, suggesting a transient localization there. Dynein accumulates at the microtubule plus ends, in an EBP-2EB–dependent manner. This accumulation, although not transporting dynein, contributes modestly to cortical forces. Most dyneins may instead diffuse to the cortex. Tracking of cortical dynein revealed two motions: one directed and the other diffusive-like, corresponding to force-generating events. Surprisingly, while dynein is not polarized at the plus ends or in the cytoplasm, diffusive-like tracks were more frequently found at the embryo posterior tip, where the forces are higher. This asymmetry depends on GPR-1/2LGN and LIN-5NuMA, which are enriched there. In csnk-1(RNAi) embryos, the inverse distribution of these proteins coincides with an increased frequency of diffusive-like tracks anteriorly. Importantly, dynein cortical residence time is always symmetric. We propose that the dynein-binding rate at the posterior cortex is increased, causing the polarity-reflecting force imbalance. This mechanism of control supplements the regulation of mitotic progression through the nonpolarized dynein detachment rate.
Rotenone impairs oxidant/antioxidant balance both in brain and intestines in zebrafish

Abstract Aim of the study: Rotenone is a commonly used pesticide that inhibits complex I of the mitochondrial electron transport system. Rotenone exposed rats demonstrate many characteristics of Parkinson Disease (PD). Oxidative stress is one of the hallmarks of PD, being the major sources of ROS in the DA neurons. In recent years the strong connection between the intestinal environment and the function of the central nervous system (CNS) has gained widespread popularity. In order to explain the mechanism underlying the GI dysfunction in PD, we aimed to investigate oxidant-antioxidant status in the brain and intestine, as well as locomotor activity, in rotenone exposed zebrafish. Materials and methods: Adult zebrafish were exposed to 2 mg/L rotenone for 30 days. At the end of the experiment, locomotor activity was determined by simple observation. Lipid peroxidation (LPO), nitric oxide (NO) levels, superoxide dismutase (SOD), catalase (CAT) and glutathione-S-transferase (GST) activities were determined in the homogenates. Results: Locomotor activity decreased in the rotenone exposed zebrafish. LPO increased in both brain and intestines whereas NO increased only in the brain. Decreased GST and CAT activities were found in both tissues whereas SOD activity decreased only in the intestines. Conclusion: As a conclusion, the results of our study support the connection between gut and brain axis in rotenone exposed zebrafish by means of oxidative stress and NO for the first time in literature.
Altered hemodynamics contribute to local but not remote functional connectivity disruption due to glioma growth

Glioma growth can cause pervasive changes in the functional connectivity (FC) of brain networks, which has been associated with re-organization of brain functions and development of functional deficits in patients. Mechanisms underlying functional re-organization in brain networks are not understood and efforts to utilize functional imaging for surgical planning, or as a biomarker of functional outcomes are confounded by the heterogeneity in available human data. Here we apply multiple imaging modalities in a well-controlled murine model of glioma with extensive validation using human data to explore mechanisms of FC disruption due to glioma growth. We find gliomas cause both local and distal changes in FC. FC changes in networks proximal to the tumor occur secondary to hemodynamic alterations but surprisingly, remote FC changes are independent of hemodynamic mechanisms. Our data strongly implicate hemodynamic alterations as the main driver of local changes in measurements of FC in patients with glioma.
Robust Locality Preserving Projection Based on Kernel Risk-Sensitive Loss

Traditional locality preserving projection (LPP) is an excellent linear dimensionality reduction method that can preserve the local structure of the data. The objective function of LPP is based on L2-norm criterion, which results in obvious sensitivity to the outliers. In order to solve this problem, researchers proposed some LPP variants based on the L1-norm (LPP-L1) and the maximum correntropy criterion (LPP-MCC). In this paper, we propose a more robust version of LPP, called LPP-KRSL, whose objective function is based on the kernel risk-sensitive loss (KRSL). The objective function can be efficiently solved via a half-quadratic optimization procedure. The experimental results on both synthetic and real-world data demonstrate that LPP-KRSL is more robust and effective than other LPP methods.
Automatic Cluster Labeling Based on Phylogram Analysis

Clustering is one of the main pattern recognition techniques. This technique consists in organizing the elements into groups (clusters) taking into consideration some metric that allows determining the similarity of them. These data sets often describe the elements that compose them through attributes that can assume various values types, requiring efficient methods in the detecting correlations task between complex (or mixed) type data. However, the clustering process does not provide clear information that allows inferring the characteristics of each cluster formed, that is, the clustering process result does not allow the clusters to have their meaning easily understood. Data labeling aims at identifying these characteristics and then allowing full understanding of the resulting clusters. This paper proposes the joint use of unsupervised and supervised Machine Learning methods for data clustering and labeling tasks, respectively. The labeling task consists in identifying the clusters through their most relevant characteristics. The algorithms used are known to be efficient, obtaining satisfactory results in the definitions of the clusters formed in the experiments exposed here.
Towards High-Resolution Face Pose Synthesis

Synthesizing different views of a face image is a challenging task that can potentially help in several computer graphics and computer vision applications. In this work, we present a novel approach to address this task. We leverage the power of Generative Adversarial Networks (GANs) to synthesize face poses in a high-resolution and realistic fashion. We control the rotation of synthesized faces along the three axes of space (roll, pitch, yaw). We start by estimating the pose of each face in the training set and storing a vector containing the rotation angles. Then, we use the images along with the angles to train a conditioned version of a state-of-the-art GAN. Our experiments show image synthesis with a high-realistic finish, plus the absolute control of the pose of synthesized face images.
LSTM-based Flight Trajectory Prediction

Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making.