Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient expression and deep analysis platform of massive traffic video data

Intelligent video surveillance technology has changed the traditional passive reception mode. It can real-time, automatically and intelligently analyze video data to improve its efficiency and reliability. This paper presents a set of efficient expression and deep analysis platform for massive traffic video, which can connect the surveillance cameras from urban transport systems, and perceive the state of people, vehicles, roads and other elements using intelligent visual perception technology, and extract relevant semantic structure information from the non-structured video content and display it in structured text language. Then, its main contents and key technologies are analyzed. Finally, an application case in Guangzhou, China is given. The system can be used for intelligent expression, deep analysis, detection, identification, archiving and management of massive traffic video data, to provide a complete information services for the managers and users.
Origins of PDZ Domain Ligand Specificity

The LAP (leucine-rich repeatand PDZ-containing) family of proteins play a role in maintaining epithelial and neuronal cell size, and mutation of these proteins can have oncogenic consequences. The LAP protein Erbin has been implicated previously in a number of cellular activities by virtue of its PDZ domain-dependent association with the C termini of both ERB-B2 and the p120-catenins. The present work describes the NMR structure of Erbin PDZ in complex with a high affinity peptide ligand and includes a comprehensive energetic analysis of both the ligand and PDZ domain side chains responsible for binding. C-terminal phage display has been used to identify preferred ligands, whereas binding affinity measurements provide precise details of the energetic importance of each ligand side chain to binding. Alanine and homolog scanning mutagenesis (in a combinatorial phage display format) identifies Erbin side chains that make energetically important contacts with the ligand. The structure of a phage-optimized peptide (Ac-TGW−4ETW−1V; IC50 = ∼0.15 μm) in complex with Erbin PDZ provides a structural context to understand the binding energetics. In particular, the very favorable interactions with Trp−1 are not Erbin side chain-mediated (and therefore may be generally applicable to many PDZ domains), whereas the β2-β3 loop provides a binding site for the Trp−4 side chain (specific to Erbin because it has an unusually long loop). These results contribute to a growing appreciation for the importance of at least five ligand C-terminal side chains in determining PDZ domain binding energy and highlight the mechanisms of ligand discrimination among the several hundred PDZ domains present in the human genome.
Human motion recognition using Nonlinear Transient Computation

A novel approach to human motion recognition is proposed that is based on a variation of the Nonlinear Transient Computation Ma- chine (NTCM). The motion data used to train the NTCM comes from point-light display video sequences of a human walking. The NTCM is trained to distinguish between sequences of video frames that depict co- ordinated walking motion from those that depict uncoordinated (random) motion.
Two closely related {4-[(N-substituted amino)(diethoxyphosphoryl)methyl]phenyl}boronic acids.

Organic phosphonic acids and organic phosphonic acid esters have been of much interest due to their applications in the fields of medicine, agriculture and industrial chemistry. Boronic acids can act as synthetic intermediates and building blocks and are used in sensing, protein manipulation, therapeutics, biological labelling and separation. The additional introduction of an aminophosphonic acid group into a boronic acid may give new opportunities for application. To study the structure of such multifunctional compounds, we prepared two new derivatives which can be easily converted to the corresponding phosphonic acids. In the title compounds, {4-[(butylamino)(diethoxyphosphoryl)methyl]phenyl}boronic acid monohydrate, C15H27BNO5P·H2O, (I), and {4-[(diethoxyphosphoryl)(4-nitroanilino)methyl]phenyl}boronic acid, C17H22BN2O7P, (II), three different substituents are attached to a central C-H group, namely 4-boronophenyl, diethoxyphosphoryl and amine. Compound (I) crystallizes as a monohydrate and OB-H.N hydrogen bonds link neighbouring molecules into chains along the [001] direction. The solvent water molecule connects two such chains running in opposite directions. Compound (II) crystallizes as an ansolvate and classical hydrogen bonds result in a layer structure in the (001) plane.
Supervised learning as preference optimization

Learning with preferences is receiving more and more atten- tion in the last few years. The goal in this setting is to learn based on qualitative or quantitative declared preferences between objects of a do- main. In this paper we give a survey of a recent framework for supervised learning based on preference optimization. In fact, many of the broad set of supervised tasks can all be seen as particular instances of this prefer- ence based framework. They include simple binary classication, (single or multi) label multiclass classication, ranking problems, and (ordinal) regression, just to name a few. We show that the proposed general prefer- ence learning model (GPLM), which is based on a large-margin principled approach, gives a exible way to codify cost functions for all the above problems as sets of linear preferences. Examples of how the proposed framework has been eectively used to address a variety of real-world ap- plications are reported clearly showing the exibility and eectiveness of the approach.
A Non-Distributional Approach to Polysemy Detection in WordNet

While polysemy is a form of ambiguity that can complicate natural language processing, it is also a rich lexical resource that yields useful insights into the mapping between words and concepts. WordNet, a comprehensive lexical knowledge-base of English word meanings, is replete with instances of polysemy, but also contains many instances of homonymy, and fails to distinguish between both kinds of ambiguity. We propose in this paper an alternative to the distributional approach for recognizing polysemous sense-pairs in WordNet. Our approach does not rely on the systematicity of regular polysemy to identify the families of words that instantiate a particular metonymic pattern, but seeks instead local ontological evidence for each word, on a case by case basis, before polysemy is hypothesized. We argue that this approach is thus equally adept at recognizing instances of ad-hoc polysemy.
A Data Analytic Framework for Unstructured Text

This paper describes a systematic flow of the unstructured data in industry, collected data, stored data, and the amount of data. Big data uses salable storage index and distributed approach to retrieve required information. Therefore, the paper introduces an unstructured data framework for managing and discovering using the 3Vs of big data: variety, velocity, and volume. Different approaches for managing, collecting, and classification of twitter data, e-mail data and free text are required to manage resources more efficiently, and building software platform around scalable analytics. The development processes in this paper is implemented in Python, build up lexicon and calculated sentiment score. Analyzing twitter data and e-mail data answered many of questions; what are people talking about? , what is the most important? etc. The accuracy of the proposed classifier was 77.78, without stop words and was 78.76 and 79.94 with stop words (25 and 174) respectively. If the stop words are increased, the accuracy will be 87.69. It has been 10% better accuracy between Naïve Bayes and Maximum Entropy classifier. [Hassanin M. Al-Barhamtoshy and Fathy E. Eassa. A Data Analytic Framework for Unstructured Text. Life Sci. J 2014; 11(10):339-350] (ISSN: 1097-8135). http://www.lifesciencesite. com. 48
A Comparative Analysis of Learning Techniques for Cancer Risk Prediction based on Medical Textual Records

In this paper, we compare the performance of a variety of machine learning algorithms, including supervised Na(cid:239)ve Bayes, J48, SVM, Random Tree, Random Forest, and non-supervised KNN for determining the type of cancer a patient is su(cid:27)ering using medical textual records. We train these classi(cid:28)ers on di(cid:27)erent sets of features such as unigrams and bigrams of words, character n -grams using tf-idf weighting scheme and binary feature representation. We evaluated performance of the classi(cid:28)ers in terms of accuracy, precision, recall, and F-measure. The obtained results show that Na(cid:239)ve Bayes and SVM achieve the best performance in this task.
Advances in drug delivery to high grade gliomas

If cancer is hard to be treated, brain cancer is even more, caused by the inability of many effective drugs given systemically to cross the blood brain and blood tumor barriers and reach adequate concentrations at the tumor sites. Effective delivery of drugs to brain cancer tissues is thus a necessary, albeit not sufficient, condition to effectively target the disease. In order to analyze the current status of research on drug delivery to high grade gliomas (HGG‐WHO grades III and IV), the most frequent and aggressive brain cancers, a literature search was conducted in PubMed using the terms: “drug delivery and brain tumor” over the publication year 2015. Currently explored drug delivery techniques for HGG include the convection and permeabilization‐enhanced deliveries, drug‐releasing depots and Ommaya reservoirs. The efficacy/safety ratio widely varies among these techniques and the success of current efforts to increase this ratio widely varies as well.
Green Synthesis of Zinc Oxide Nanoparticles for Enhanced Adsorption of Lead Ions from Aqueous Solutions: Equilibrium, Kinetic and Thermodynamic Studies

In the present study, ZnO nanoparticles (NPs) were synthesized in zerumbone solution by a green approach and appraised for their ability to absorb Pb(II) ions from aqueous solution. The formation of as-synthesized NPs was established by X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), and UV–visible studies. The XRD and TEM analyses revealed high purity and wurtzite hexagonal structure of ZnO NPs with a mean size of 10.01 ± 2.6 nm. Batch experiments were performed to investigate the impact of process parameters viz. Pb(II) concentration, pH of solution, adsorbent mass, solution temperature, and contact time variations on the removal efficiency of Pb(II). The adsorption isotherm data provided that the adsorption process was mainly monolayer on ZnO NPs. The adsorption process follows pseudo-second-order reaction kinetic. The maximum removal efficiencies were 93% at pH 5. Thermodynamic parameters such as enthalpy change (ΔH0), free energy change (ΔG0), and entropy change (ΔS0) were calculated; the adsorption process was spontaneous and endothermic. The good efficiency of the as-synthesized NPs makes them attractive for applications in water treatment, for removal of heavy metals from aqueous system.
Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
Repairing sulfur vacancies in the MoS2 monolayer by using CO, NO and NO2 molecules

As-grown transition metal dichalcogenides are usually chalcogen deficient and contain a high density of chalcogen vacancies, which are harmful to the electronic properties of these materials. Based on the first-principles calculation, in this study the repairing of the S vacancy in the MoS2 monolayer has been investigated by using CO, NO and NO2 molecules. For CO and NO, the repairing process consists of the first molecule filling the S vacancy and the removing of the extra O atom by the second molecule. However, for NO2, when the molecule fills the S vacancy, it is dissociated directly to form an O-doped MoS2 monolayer. After the repair, the C, N and O-doped MoS2 monolayers can be obtained by the adsorption of CO, NO, and NO2 molecules, respectively. And in particular, the electronic properties of these materials can be significantly improved by N and O doping. Furthermore, according to the calculated energy, the process of S vacancy repairing with CO, NO and NO2 should be easily achieved at room temperature. This study presents a promising strategy for repairing MoS2 nanosheets and improving their electronic properties, which may also apply to other transition metal dichalcogenides.
Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an fmeasure exceeding 97% on the IRIS Dataset. Keywords—Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.
Prognostic value of coronary computed tomographic angiography findings in asymptomatic individuals: a 6-year follow-up from the prospective multicentre international CONFIRM study

Aim The long-term prognostic benefit of coronary computed tomographic angiography (CCTA) findings of coronary artery disease (CAD) in asymptomatic populations is unknown. Methods and results From the prospective multicentre international CONFIRM long-term study, we evaluated asymptomatic subjects without known CAD who underwent both coronary artery calcium scoring (CACS) and CCTA (n = 1226). Coronary computed tomographic angiography findings included the severity of coronary artery stenosis, plaque composition, and coronary segment location. Using the C-statistic and likelihood ratio tests, we evaluated the incremental prognostic utility of CCTA findings over a base model that included a panel of traditional risk factors (RFs) as well as CACS to predict long-term all-cause mortality. During a mean follow-up of 5.9 ± 1.2 years, 78 deaths occurred. Compared with the traditional RF alone (C-statistic 0.64), CCTA findings including coronary stenosis severity, plaque composition, and coronary segment location demonstrated improved incremental prognostic utility beyond traditional RF alone (C-statistics range 0.71-0.73, all P < 0.05; incremental χ2 range 20.7-25.5, all P < 0.001). However, no added prognostic benefit was offered by CCTA findings when added to a base model containing both traditional RF and CACS (C-statistics P > 0.05, for all). Conclusions Coronary computed tomographic angiography improved prognostication of 6-year all-cause mortality beyond a set of conventional RF alone, although, no further incremental value was offered by CCTA when CCTA findings were added to a model incorporating RF and CACS.
Assessing the translational landscape of myogenic differentiation by ribosome profiling

The formation of skeletal muscles is associated with drastic changes in protein requirements known to be safeguarded by tight control of gene transcription and mRNA processing. The contribution of regulation of mRNA translation during myogenesis has not been studied so far. We monitored translation during myogenic differentiation of C2C12 myoblasts, using a simplified protocol for ribosome footprint profiling. Comparison of ribosome footprints to total RNA showed that gene expression is mostly regulated at the transcriptional level. However, a subset of transcripts, enriched for mRNAs encoding for ribosomal proteins, was regulated at the level of translation. Enrichment was also found for specific pathways known to regulate muscle biology. We developed a dedicated pipeline to identify translation initiation sites (TISs) and discovered 5333 unannotated TISs, providing a catalog of upstream and alternative open reading frames used during myogenesis. We identified 298 transcripts with a significant switch in TIS usage during myogenesis, which was not explained by alternative promoter usage, as profiled by DeepCAGE. Also these transcripts were enriched for ribosomal protein genes. This study demonstrates that differential mRNA translation controls protein expression of specific subsets of genes during myogenesis. Experimental protocols, analytical workflows, tools and data are available through public repositories (http://lumc.github.io/ribosome-profiling-analysis-framework/).
microRNA expression signatures and parallels between monocyte subsets and atherosclerotic plaque in humans

Summary Small non-coding microRNAs (miRNAs) have emerged to play critical roles in cardiovascular biology. Monocytes critically drive atherosclerotic lesion formation, and can be subdivided into a classical and non-classical subset. Here we scrutinised the miRNA signature of human classical and non-classical monocytes, and compared miRNA expression profiles of atherosclerotic plaques from human carotid arteries and healthy arteries. We identified miRNAs to be differentially regulated with a two-fold or higher difference between classical and non-classical monocyte subsets. Moreover, comparing miRNA expression in atherosclerotic plaques compared to healthy arteries, we observed several miRNAs to be aberrantly expressed, with the majority of miRNAs displaying a two-fold or higher increase in plaques and only few miRNAs being decreased. To elucidate similarities in miRNA signatures between monocyte subsets and atherosclerotic plaque, expression of miRNAs highly abundant in monocytes and plaque tissues were compared. Several miRNAs were found in atherosclerotic plaques but not in healthy vessels or either monocyte subset. However, we could identify miRNAs co-expressed in plaque tissue and classical monocytes (miR-99b, miR-152), or non-classical monocytes (miR-422a), or in both monocytes subsets. We thus unravelled candidate miRNAs, which may facilitate our understanding of monocyte recruitment and fate during atherosclerosis, and may serve as therapeutic targets for treating inflammatory vascular diseases. Note: The editorial process for this article was fully handled by Prof. G. Y. H. Lip, Editor-in-Chief.
Impact of CHO Metabolism on Cell Growth and Protein Production: An Overview of Toxic and Inhibiting Metabolites and Nutrients

For over three decades, Chinese hamster ovary (CHO) cells have been the chosen expression platform for the production of therapeutic proteins with complex post‐translational modifications. However, the metabolism of these cells is far from perfect and optimized, and requires substantial know how and process optimization and monitoring to perform efficiently. One of the main reasons for this is the production and accumulation of toxic and growth‐inhibiting metabolites during culture. Lactate and ammonium are the most known, but many more have been identified. In this review, an overview of metabolites that deplete and accumulate throughout the course of cultivations with toxic and growth inhibitory effects to the cells is presented. Further, an overview of the CHO metabolism with emphasis to metabolic pathways of amino acids, glutathione (GSH), and related compounds which have growth‐inhibiting and/or toxic effect on the cells is provided. Additionally, relevant publications which describe the applications of metabolomics as a powerful tool for revealing which reactions occur in the cell under certain conditions are surveyed and growth‐inhibiting and toxic metabolites are identified. Also, a number of resources that describe the cellular mechanisms of CHO and are available on‐line are presented. Finally, the application of this knowledge for bioprocess and medium development and cell line engineering is discussed.
Predicting calyptrate fly populations from the weather, and probable consequences of climate change

Summary 1. Calyptrate flies include numerous species that are disease vectors and have a high nuisance value, notably Musca domestica . Populations are often associated with livestock farms and domestic waste disposal facilities such as landfill, where the accumulating organic matter provides suitable breeding conditions for a range of species. 2. We examined the relationship between fly numbers and weather conditions using a 4-year data set of weekly fly catches from six sites in southern UK, together with meteorological data. The first 3 years were used to develop predictive models, and these were then used to forecast fly populations in the fourth year. The accuracy of these predictions was assessed by comparison with the actual fly catches for that year. Separate models were developed for M. domestica , Calliphora spp. and all calyptrate flies combined. 3. Predictions based only on humidity, temperature and rainfall were strongly correlated with observed data ( r 2 values ranged from 0·52 to 0·84), suggesting that fly population changes are largely driven by the weather rather than by biotic factors. We can forecast fly populations so that control measures need only be deployed when weather conditions are suitable for a fly outbreak, reducing the need for prophylactic insecticide use. 4. Climate change was simulated using the most recent predictions of future temperature increases. Our models predicted substantial increases in fly populations up to 244% by 2080 compared with current levels, with the greatest increases occurring in the summer months. 5. Synthesis and applications. Models developed use weather data to predict populations of pestiferous flies such as M. domestica , which may prove valuable in integrated control programmes. These models predict substantial increases in fly populations in the future under likely scenarios of climate change. If this occurs we may expect considerable increases in the incidence of fly-borne disease.