COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms.

BACKGROUND: COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time. RESULTS: In this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination-Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient. CONCLUSION: Our proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript's availability of the data and materials under the declaration section for access.

Monitoring cardiopulmonary resuscitation quality in emergency departments: a national survey in China on current knowledge, attitudes, and practices.

BACKGROUND: To investigate current knowledge, attitudes, and practices for CPR quality control among emergency physicians in Chinese tertiary hospitals. METHODS: Anonymous questionnaires were distributed to physicians in 75 tertiary hospitals in China between January and July 2018. RESULTS: A total of 1405 respondents answered the survey without obvious logical errors. Only 54.4% respondents knew all criteria of high-quality CPR. A total of 91.0% of respondents considered CPR quality monitoring should be used, 72.4% knew the objective method for monitoring, and 63.2% always/often monitored CPR quality during actual resuscitation. The main problems during CPR were related to chest compression: low quality due to fatigue (67.3%), inappropriate depth (57.3%) and rate (54.1%). The use of recommended monitoring methods was reported as follows, ETCO2 was 42.7%, audio-visual feedback devices was 10.1%, coronary perfusion pressure was 17.9%, and invasive arterial pressure was 31.1%. A total of 96.3% of respondents considered it necessary to participate in regular CPR retraining, but 21.4% did not receive any retraining. The ideal retraining interval was considered to be 3 to 6 months, but the actual interval was 6 to 12 months. Only 49.7% of respondents reported that feedback devices were always/often used in CPR training. CONCLUSION: Chinese emergency physicians were very concerned about CPR quality, but they did not fully understand the high-quality criteria and their impact on prognosis. CPR quality monitoring was not a routine procedure during actual resuscitation. The methods recommended in guidelines were rarely used in practice. Many physicians had not received retraining or received retraining at long intervals. Feedback devices were not commonly used in CPR training.

Application of Statistical K-Means Algorithm for University Academic Evaluation.

With the globalization of higher education, academic evaluation is increasingly valued by the scientific and educational circles. Although the number of published papers of academic evaluation methods is increasing, previous research mainly focused on the method of assigning different weights for various indicators, which can be subjective and limited. This paper investigates the evaluation of academic performance by using the statistical K-means (SKM) algorithm to produce clusters. The core idea is mapping the evaluation data from Euclidean space to Riemannian space in which the geometric structure can be used to obtain accurate clustering results. The method can adapt to different indicators and make full use of big data. By using the K-means algorithm based on statistical manifolds, the academic evaluation results of universities can be obtained. Furthermore, through simulation experiments on the top 20 universities of China with the traditional K-means, GMM and SKM algorithms, respectively, we analyze the advantages and disadvantages of different methods. We also test the three algorithms on a UCI ML dataset. The simulation results show the advantages of the SKM algorithm.

University Academic Performance Development Prediction Based on TDA.

With the rapid development of higher education, the evaluation of the academic growth potential of universities has received extensive attention from scholars and educational administrators. Although the number of papers on university academic evaluation is increasing, few scholars have conducted research on the changing trend of university academic performance. Because traditional statistical methods and deep learning techniques have proven to be incapable of handling short time series data well, this paper proposes to adopt topological data analysis (TDA) to extract specified features from short time series data and then construct the model for the prediction of trend of university academic performance. The performance of the proposed method is evaluated by experiments on a real-world university academic performance dataset. By comparing the prediction results given by the Markov chain as well as SVM on the original data and TDA statistics, respectively, we demonstrate that the data generated by TDA methods can help construct very discriminative models and have a great advantage over the traditional models. In addition, this paper gives the prediction results as a reference, which provides a new perspective for the development evaluation of the academic performance of colleges and universities.

The first national survey on practices of neurological prognostication after cardiac arrest in China, still a lot to do.

AIMS: To investigate current awareness and practices of neurological prognostication in comatose cardiac arrest (CA) patients. METHODS: An anonymous questionnaire was distributed to 1600 emergency physicians in 75 hospitals which were selected randomly from China between January and July 2018. RESULTS: 92.1% respondents fulfilled the survey. The predictive value of brain stem reflex, motor response and myoclonus was confirmed by 63.5%, 44.6% and 31.7% respondents, respectively. Only 30.7% knew that GWR value < 1.1 indicated poor prognosis and only 8.1% know the most commonly used SSEP N20. Status epilepticus, burst suppression and suppression were considered to predict poor outcome by only 35.0%, 27.4% and 20.9% respondents, respectively. Only 46.7% knew NSE and only 24.7% knew S-100. Only a few respondents knew that neurological prognostication should be performed later than 72 hours from CA either in TTM or non-TTM patients. In practice, the most commonly used method was clinical examination (85.4%). Only 67.9% had used brain CT for prognosis and 18.4% for MRI. NSE (39.6%) was a little more widely used than S-100ß (18.0%). However, SSEP (4.4%) and EEG (11.4%) were occasionally performed. CONCLUSIONS: Neurological prognostication in CA survivors had not been well understood and performed by emergency physicians in China. They were more likely to use clinical examination rather than objective tools, especially SSEP and EEG, which also illustrated that multimodal approach was not well performed in practice.

Cross-language differences in the brain network subserving intelligible speech.

How is language processed in the brain by native speakers of different languages? Is there one brain system for all languages or are different languages subserved by different brain systems? The first view emphasizes commonality, whereas the second emphasizes specificity. We investigated the cortical dynamics involved in processing two very diverse languages: a tonal language (Chinese) and a nontonal language (English). We used functional MRI and dynamic causal modeling analysis to compute and compare brain network models exhaustively with all possible connections among nodes of language regions in temporal and frontal cortex and found that the information flow from the posterior to anterior portions of the temporal cortex was commonly shared by Chinese and English speakers during speech comprehension, whereas the inferior frontal gyrus received neural signals from the left posterior portion of the temporal cortex in English speakers and from the bilateral anterior portion of the temporal cortex in Chinese speakers. Our results revealed that, although speech processing is largely carried out in the common left hemisphere classical language areas (Broca's and Wernicke's areas) and anterior temporal cortex, speech comprehension across different language groups depends on how these brain regions interact with each other. Moreover, the right anterior temporal cortex, which is crucial for tone processing, is equally important as its left homolog, the left anterior temporal cortex, in modulating the cortical dynamics in tone language comprehension. The current study pinpoints the importance of the bilateral anterior temporal cortex in language comprehension that is downplayed or even ignored by popular contemporary models of speech comprehension.

Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model.

Social sensors perceive the real world through social media and online web services, which have the advantages of low cost and large coverage over traditional physical sensors. In intelligent transportation researches, sensing and analyzing such social signals provide a new path to monitor, control and optimize transportation systems. However, current research is largely focused on using single channel online social signals to extract and sense traffic information. Clearly, sensing and exploiting multi-channel social signals could effectively provide deeper understanding of traffic incidents. In this paper, we utilize cross-platform online data, i.e., Sina Weibo and News, as multi-channel social signals, then we propose a word2vec-based event fusion (WBEF) model for sensing, detecting, representing, linking and fusing urban traffic incidents. Thus, each traffic incident can be comprehensively described from multiple aspects, and finally the whole picture of unban traffic events can be obtained and visualized. The proposed WBEF architecture was trained by about 1.15 million multi-channel online data from Qingdao (a coastal city in China), and the experiments show our method surpasses the baseline model, achieving an 88.1% F1 score in urban traffic incident detection. The model also demonstrates its effectiveness in the open scenario test.

Predictive Brain Mechanisms in Sound-to-Meaning Mapping during Speech Processing.

Spoken language comprehension relies not only on the identification of individual words, but also on the expectations arising from contextual information. A distributed frontotemporal network is known to facilitate the mapping of speech sounds onto their corresponding meanings. However, how prior expectations influence this efficient mapping at the neuroanatomical level, especially in terms of individual words, remains unclear. Using fMRI, we addressed this question in the framework of the dual-stream model by scanning native speakers of Mandarin Chinese, a language highly dependent on context. We found that, within the ventral pathway, the violated expectations elicited stronger activations in the left anterior superior temporal gyrus and the ventral inferior frontal gyrus (IFG) for the phonological-semantic prediction of spoken words. Functional connectivity analysis showed that expectations were mediated by both top-down modulation from the left ventral IFG to the anterior temporal regions and enhanced cross-stream integration through strengthened connections between different subregions of the left IFG. By further investigating the dynamic causality within the dual-stream model, we elucidated how the human brain accomplishes sound-to-meaning mapping for words in a predictive manner. SIGNIFICANCE STATEMENT: In daily communication via spoken language, one of the core processes is understanding the words being used. Effortless and efficient information exchange via speech relies not only on the identification of individual spoken words, but also on the contextual information giving rise to expected meanings. Despite the accumulating evidence for the bottom-up perception of auditory input, it is still not fully understood how the top-down modulation is achieved in the extensive frontotemporal cortical network. Here, we provide a comprehensive description of the neural substrates underlying sound-to-meaning mapping and demonstrate how the dual-stream model functions in the modulation of expectations, allowing for a better understanding of how the human brain accomplishes sound-to-meaning mapping in a predictive manner.

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

BACKGROUND: Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. RESULTS: In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. CONCLUSIONS: The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

Different patterns and development characteristics of processing written logographic characters and alphabetic words: an ALE meta-analysis.

The neural systems for phonological processing of written language have been well identified now, while models based on these neural systems are different for different language systems or age groups. Although each of such models is mostly concordant across different experiments, the results are sensitive to the experiment design and intersubject variability. Activation likelihood estimation (ALE) meta-analysis can quantitatively synthesize the data from multiple studies and minimize the interstudy or intersubject differences. In this study, we performed two ALE meta-analysis experiments: one was to examine the neural activation patterns of the phonological processing of two different types of written languages and the other was to examine the development characteristics of such neural activation patterns based on both alphabetic language and logographic language data. The results of our first meta-analysis experiment were consistent with the meta-analysis which was based on the studies published before 2005. And there were new findings in our second meta-analysis experiment, where both adults and children groups showed great activation in the left frontal lobe, the left superior/middle temporal gyrus, and the bilateral middle/superior occipital gyrus. However, the activation of the left middle/inferior frontal gyrus was found increase with the development, and the activation was found decrease in the following areas: the right claustrum and inferior frontal gyrus, the left inferior/medial frontal gyrus, the left middle/superior temporal gyrus, the right cerebellum, and the bilateral fusiform gyrus. It seems that adults involve more phonological areas, whereas children involve more orthographic areas and semantic areas.

Learning new color names produces rapid increase in gray matter in the intact adult human cortex.

The human brain has been shown to exhibit changes in the volume and density of gray matter as a result of training over periods of several weeks or longer. We show that these changes can be induced much faster by using a training method that is claimed to simulate the rapid learning of word meanings by children. Using whole-brain magnetic resonance imaging (MRI) we show that learning newly defined and named subcategories of the universal categories green and blue in a period of 2 h increases the volume of gray matter in V2/3 of the left visual cortex, a region known to mediate color vision. This pattern of findings demonstrates that the anatomical structure of the adult human brain can change very quickly, specifically during the acquisition of new, named categories. Also, prior behavioral and neuroimaging research has shown that differences between languages in the boundaries of named color categories influence the categorical perception of color, as assessed by judgments of relative similarity, by response time in alternative forced-choice tasks, and by visual search. Moreover, further behavioral studies (visual search) and brain imaging studies have suggested strongly that the categorical effect of language on color processing is left-lateralized, i.e., mediated by activity in the left cerebral hemisphere in adults (hence "lateralized Whorfian" effects). The present results appear to provide a structural basis in the brain for the behavioral and neurophysiologically observed indices of these Whorfian effects on color processing.

Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting.

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention (THA) mechanism represents temporal factors that subsequently fed into feedforward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism (GAM). Moreover, we introduce conditional continuous normalization flow (CCNF) to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.

Small molecule LpxC inhibitors against gram-negative bacteria: Advances and future perspectives.

Uridine diphosphate-3-O-(hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) is a metalloenzyme with zinc ions as cofactors and is a key enzyme in the essential structural outer membrane lipid A synthesis commitment step of gram-negative bacteria. As LpxC is extremely homologous among different Gram-negative bacteria, it is conserved in almost all gram-negative bacteria, which makes LpxC a promising target. LpxC inhibitors have been reported extensively in recent years, such as PF-5081090 and CHIR-090 were found to have broad-spectrum antibiotic activity against P. aeruginosa and E. coli. They are mainly classified into hydroxamate inhibitors and non-hydroxamate inhibitors based on their structure, but no LpxC inhibitors have been marketed due to safety and activity issues. This review, therefore, focuses on small molecule inhibitors of LpxC against gram-negative pathogenic bacteria and covers recent advances in LpxC inhibitors, focusing on their structural optimization process, structure-activity relationships, and future directions, with the aim of providing ideas for the development of LpxC inhibitors and clinical research.

Neural Time-Aware Sequential Recommendation by Jointly Modeling Preference Dynamics and Explicit Feature Couplings.

In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such context-aware user-item interactions in terms of the couplings between the user and item features and sequential user actions on items over time. However, such joint modeling is non-trivial and significantly challenges the existing work on preference modeling, which either only models user-item interactions by latent factorization models but ignores user preference dynamics or only captures sequential user action patterns without involving user/item features and context factors and their coupling and influence on user actions. We propose a neural time-aware recommendation network (TARN) with a temporal context to jointly model 1) stationary user preferences by a feature interaction network and 2) user preference dynamics by a tailored convolutional network. The feature interaction network factorizes the pairwise couplings between non-zero features of users, items, and temporal context by the inner product of their feature embeddings while alleviating data sparsity issues. In the convolutional network, we introduce a convolutional layer with multiple filter widths to capture multi-fold sequential patterns, where an attentive average pooling (AAP) obtains significant and large-span feature combinations. To learn the preference dynamics, a novel temporal action embedding represents user actions by incorporating the embeddings of items and temporal context as the inputs of the convolutional network. The experiments on typical public data sets demonstrate that TARN outperforms state-of-the-art methods and show the necessity and contribution of involving time-aware preference dynamics and explicit user/item feature couplings in modeling and interpreting evolving user preferences.

Back on the scene: Advances and challenges in CD3-related drugs in tumor therapy.

CD3 molecules are mainly distributed on the membrane of mature T cells. They are involved in T cell antigen recognition, signal transduction, and regulation of T cell development. CD3-related monoclonal antibodies (mAbs) are mainly used in the treatment of autoimmune diseases. Nearly half of all bispecific antibodies developed are used in tumor therapy, one of which is CD3 antigen. In this review, we discuss the importance of biological function and the crucial role of CD3 in tumor therapy. We highlight the research status of antibodies and small molecules targeting CD3 to provide guidance for future drug research.

Injury Assessment of Individuals Wounded in the Lushan Earthquake and the Emergency Department Workload: A Corresponding Correlation Study.

OBJECTIVE: In this study, we aimed to evaluate the correlation between the trauma score of individuals wounded in the Lushan earthquake and emergency workload for treatment. We further created a trauma score-emergency workload calculation model. METHODS: We included data from patients wounded in the Lushan earthquake and treated at West China Hospital, Sichuan University. We calculated scores per the following models separately: Revised Trauma Score (RTS), Prehospital Index (PHI), Circulation Respiration Abdominal Movement Speech (CRAMS), Therapeutic Intervention Scoring System (TISS-28), and Nursing Activities Score (NAS). We assessed the association between values for CRAMS, PHI, and RTS and those for TISS-28 and NAS. Subsequently, we built a trauma score-emergency workload calculation model to quantitative workload estimation. RESULTS: Significant correlations were observed for all pairs of trauma scoring models with emergency workload scoring models. TISS-28 score was significantly associated with PHI score and RTS; however, no significant correlation was observed between the TISS-28 score and CRAMS score. CONCLUSIONS: CRAMS, PHI, and RTS were consistent in evaluating the injury condition of wounded individuals; TISS-28 and NAS scores were consistent in evaluating the required treatment workload. Dynamic changes in emergency workload in unit time were closely associated with wounded patient visits.

A structural-functional basis for dyslexia in the cortex of Chinese readers.

Developmental dyslexia is a neurobiologically based disorder that affects approximately 5-17% of school children and is characterized by a severe impairment in reading skill acquisition. For readers of alphabetic (e.g., English) languages, recent neuroimaging studies have demonstrated that dyslexia is associated with weak reading-related activity in left temporoparietal and occipitotemporal regions, and this activity difference may reflect reductions in gray matter volume in these areas. Here, we find different structural and functional abnormalities in dyslexic readers of Chinese, a nonalphabetic language. Compared with normally developing controls, children with impaired reading in logographic Chinese exhibited reduced gray matter volume in a left middle frontal gyrus region previously shown to be important for Chinese reading and writing. Using functional MRI to study language-related activation of cortical regions in dyslexics, we found reduced activation in this same left middle frontal gyrus region in Chinese dyslexics versus controls, and there was a significant correlation between gray matter volume and activation in the language task in this same area. By contrast, Chinese dyslexics did not show functional or structural (i.e., volumetric gray matter) differences from normal subjects in the more posterior brain systems that have been shown to be abnormal in alphabetic-language dyslexics. The results suggest that the structural and functional basis for dyslexia varies between alphabetic and nonalphabetic languages.

Development of an α-amylase reporter system for efficient screening of clones with highly expressed heterologous protein in Hansenula polymorpha.

To check feasibility and effectiveness of the α-amylase reporter system, two vectors were designed and tested using hepatitis B virus surface antigen (HBsAg) and Homo sapiens granulocyte-macrophage colony stimulating factor 2 (hGM-CSF2) as a model. By integrating the vector containing two independent cassettes into the same genome locus, high-producing clones of HBsAg (or hGM-CSF2) were screened using the α-amylase as a reporter. Results show there was a positive correlation (Correlation coefficient, R (2) > 0.95) between the yield of recombinant proteins and the α-amylase activity of corresponding transformants, which was independent of the gene dosage.

PHD2 exerts anti-cancer and anti-inflammatory effects in colon cancer xenografts mice via attenuating NF-κB activity.

Recent studies suggested that prolyl hydroxylase 2 (PHD2) functions as an important regulator in vascular inflammation and Streptococcus pneumonia infection. However, whether PHD2 contributed to tumor progression prompted by intratumoral inflammation remains elusive. In this study, the effects of PHD2 in colon cancer were evaluated, and the underlying molecular mechanisms were investigated. The results showed that overexpressing PHD2 exerted proliferative and migratory inhibition in colon cancer cells. The expression of cell cycle and epithelial-mesenchymal transition (EMT)-associated proteins were changed: CyclinD1, CDK4, N-cadherin, and Vimentin were down-regulated, while E-cadherin was up-regulated in PHD2-overexpressing colon cancer cells. Moreover, in colon cancer xenograft mice, PHD2 overexpression suppressed tumor growth accompanied by decreased Ki67 expression. Importantly, we further demonstrated that overexpressing PHD2 attenuated inflammation in colon cancer xenograft mice through weakening accumulation of myeloid-derived suppressor cells (MDSCs) and M2-like tumor-associated macrophages (TAMs), as well as secretions of pro-inflammatory cytokines including G-CSF, TNF-α, IL-6, IL-8, IL-1ß, and IL-4. Mechanistically, PHD2 overexpression obviously suppressed NF-κB activity through decreasing phosphorylated IκB-α while increasing cytoplasmic NF-κB p65 levels in colon cancer. Our findings support the anti-cancer and anti-inflammatory roles of PHD2 and offer a preclinical proof of tumor progression regulated by cancer cells and inflammation.

Complementarity in Requirements Tracing.

Complementarity between activities reveals that doing any one of them increases the returns to doing the others. In other words, complementarity leads to the synergistic effect that the whole is greater than the sum of its parts. Identifying and exploiting complementarity can benefit many cybernetic activities, where human-machine interactions are inherent and dominant. One such activity is requirements tracing that helps stakeholders to track the status of their goals. Although various kinds of support for human analysts in requirements tracing have been proposed, little is known about the nature of complementarity when different tracing practices are involved. In this paper, we explore the role of complementarity by considering together the tagging-to-trace (T2T) and learning-to-trace (L2T) activities. We present a novel approach to examining which T2T and L2T practices enhance the qualities of each other. Our approach also uncovers the environmental factors which the complementarity is sensitive to. Applying our approach to the logs of 140 analyst-tracing units offers operational insights into the rigorous detection of complementarity and shows the importance of understanding the cybernetic conditions under which the requirements tracing practices may in fact be complementary.