Miracle-dz/UniFAR-FTUs huggingface dataset:
ALUminating the Path of Atherosclerosis Progression: Chaos Theory Suggests a Role for Alu Repeats in the Development of Atherosclerotic Vascular Disease

Atherosclerosis (ATH) and coronary artery disease (CAD) are chronic inflammatory diseases with an important genetic background; they derive from the cumulative effect of multiple common risk alleles, most of which are located in genomic noncoding regions. These complex diseases behave as nonlinear dynamical systems that show a high dependence on their initial conditions; thus, long-term predictions of disease progression are unreliable. One likely possibility is that the nonlinear nature of ATH could be dependent on nonlinear correlations in the structure of the human genome. In this review, we show how chaos theory analysis has highlighted genomic regions that have shared specific structural constraints, which could have a role in ATH progression. These regions were shown to be enriched with repetitive sequences of the Alu family, genomic parasites that have colonized the human genome, which show a particular secondary structure and are involved in the regulation of gene expression. Here, we show the impact of Alu elements on the mechanisms that regulate gene expression, especially highlighting the molecular mechanisms via which the Alu elements alter the inflammatory response. We devote special attention to their relationship with the long noncoding RNA (lncRNA); antisense noncoding RNA in the INK4 locus (ANRIL), a risk factor for ATH; their role as microRNA (miRNA) sponges; and their ability to interfere with the regulatory circuitry of the (nuclear factor kappa B) NF-κB response. We aim to characterize ATH as a nonlinear dynamic system, in which small initial alterations in the expression of a number of repetitive elements are somehow amplified to reach phenotypic significance.
Tumor Initiating Cells in Esophageal Squamous Cell Carcinomas Express High Levels of CD44

Background Esophageal Squamous Cell Carcinoma (ESCC) is a major subtype of esophageal cancer causing significant morbility and mortality in Asia. Mechanism of initiation and progression of this disease is unclear. Tumor initiating cells (TICs) are the subpopulation of cells which have the ability to self-renew, as well as, to drive initiation and progression of cancer. Increasing evidence has shown that TICs exist in a variety of tumors. However, the identification and characterization of TICs in esophageal carcinoma has remained elusive. Methodology/Principal Findings to identify TICs in ESCC, ESCC cell lines including two primary cells were used for screening suitable surface marker. Then colony formation assay, drug resistant assay and tumorigenicity assay in immune deficient mice were used to characterize TICs in ESCC. We found that just the CD44 expression correlated with tumorigenicity in ESCC cell lines. And then induced differentiation of ESCC cells by all-trans retinoic acid treatment led to decreased expression of CD44. The FACS isolated cell subpopulations with high CD44 expression showed increased colony formation and drug resistance in vitro, as well as significantly enhanced tumorigenicity in NOD/SICD mice, as compared to the low expressing CD44 ESCC cells. Conclusions/Significance our study has discovered a novel TIC surface marker, CD44, which can be utilized to enrich efficiently the TICs in ESCC. These findings will be useful for further studies of these cells and exploring therapeutic approaches.
Research on Real Time Processing and Intelligent Analysis Technology of Power Big Data

This paper focuses on the research of all kinds of data in the power grid business system, and carries on the research of intelligent analysis technology. It focuses on breaking the technical difficulties of intelligent and efficient analysis and mining, distributed multi-stream real-time processing, developing large-scale stock data for power and high-frequency incremental data efficient analysis system prototype, for wide-area distributed multi-stream real-time computing, from the data quick access to valuable information to solve the problems in the grid business system to improve the overall business performance, to achieve support power Data real-time processing and other technology applications.
Ordinal Pooling Networks: For Preserving Information over Shrinking Feature Maps

In the framework of convolutional neural networks that lie at the heart of deep learning, downsampling is often performed with a max-pooling operation that only retains the element with maximum activation, while completely discarding the information contained in other elements in a pooling region. To address this issue, a novel pooling scheme, Ordinal Pooling Network (OPN), is introduced in this work. OPN rearranges all the elements of a pooling region in a sequence and assigns different weights to these elements based upon their orders in the sequence, where the weights are learned via the gradient-based optimisation. The results of our small-scale experiments on image classification task demonstrate that this scheme leads to a consistent improvement in the accuracy over max-pooling operation. This improvement is expected to increase in deeper networks, where several layers of pooling become necessary.
Prevalence of risk factors for obstructive sleep apnea syndrome in interstate bus drivers.

OBJECTIVE To determine the prevalence of risk factors for obstructive sleep apnea syndrome in interstate bus drivers. METHODS This study involved 262 professional interstate bus drivers employed by a Brazilian company headquartered in the Federal District. The drivers were evaluated using a questionnaire designed to assess respiratory sleep disorders, the Epworth Sleepiness Scale, test of sustained attention, test of divided attention and anthropometric measurements. RESULTS Body weight was found to be above the ideal in 68% of the drivers evaluated, 34% of which had a neck circumference = 42 cm. During the study period, the drivers reported using tobacco (27%), cola-based soft drinks (55%), alcohol (65%) and coffee (88%). The Epworth Sleepiness Scale score was = 10 points in 28%. Snoring was reported by 36%, sleep apnea by 5%, a sensation of suffocation during sleep by 12%, restless sleep by 29% and drowsiness while driving by 48%. There were 42% who had been involved in transit accidents, 7.6% of which were attributed to hypersomnolence. Those scoring higher than 10 of the Epworth Sleepiness Scale presented lower levels of sustained attention. In addition, a reduction in divided attention was found to correlate with greater daytime sleepiness and larger neck circumference. CONCLUSION The rate of stimulant use found in the group of drivers evaluated is alarming. The high prevalence of daytime sleepiness indicates that attentiveness is reduced in this population.
Differential Responses of Stomata and Photosynthesis to Elevated Temperature in Two Co-occurring Subtropical Forest Tree Species

Global warming could increase leaf transpiration and soil evaporation, which can potentially cause water deficit to plants. As valves, leaf stomata can control plant water loss and carbon gain, particularly under water stress conditions. To investigate the responses of stomata to elevated temperature in Schima superba and Syzygium rehderianum, two co-occurring subtropical forest dominant tree species, functional traits related to gas exchange, stomatal anatomy, and drought resistance were measured under control and warming environment (ca. 2°C higher). We found that leaf water potential at both predawn and midday significantly decreased for the two species grown under warming conditions compared with those grown in the control environment. Warming resulted in significant decrease of stomatal size in S. rehderianum, but had no obvious effect on that of S. superba. By contrast, stomatal density of S. superba significantly decreased under warming conditions, while non-significant change was observed for S. rehderianum. In addition, warming significantly reduced photosynthetic rate, stomatal conductance, and stomatal sensitivity to leaf water potential of S. superba, but had non-significant effects on those of S. rehderianum. Overall, our results demonstrated that, confronting water deficit caused by elevated temperature, the two co-occurring subtropical tree species responded differently through the adjustment of stomatal morphology and photosynthetic function. Consequently, S. rehderianum was able to maintain similar carbon assimilation as under control environment, while S. superba showed a decrease in carbon gain that might bring adverse effect on its dominancy in subtropical forest community under future climate change.
Matrix metalloproteinase substrate recognition, characterization and proteomic discovery

The matrix metalloproteinase (MMP) family of secreted and cell membrane proteases are known to cleave all extracellular matrix proteins and a large number of bioactive molecules. However, the complete role of MMPs in vivo is not fully known. Thus, it is pertinent to identify all substrates of MMPs and to also characterize their interactions with substrates in order to comprehend the molecular mechanisms that lead to proteolysis. Membrane type l-MMP (MT1MMP, MMP-14) and MMP-2 (gelatinase A) are cooperative dynamic components of a cell surface proteolysis apparatus involved in regulating the cellular signaling environment and pericellular collagen homeostasis. Here, we have investigated the structural elements of MT1MMP and MMP-2 involved in the recognition of type I collagen, in order to dissect the enigmatic mechanism of native (triple helical) collagen cleavage by MMPs. Both MT1-MMP and MMP-2 consist of a catalytic domain tethered to a C domain by a linker peptide. MMP-2 contains an additional insert of three fibronectin type II modules within the catalytic domain. We demonstrate that recombinant proteins representing the C domain of MT1-MMP and the fibronectin domain of MMP-2 act in a dominant negative manner to block MT1-MMP and MMP-2 cleavage of type I collagen. These domains alone are also capable of perturbing the native structure of collagen. Together, these results reveal an important divergence in the mode of collagen cleavage between these two enzymes. To probe the in vivo role of MT1-MMP, we developed a rapid proteomic screen for identifying new MT1-MMP substrates in cells using isotope coded affinity tag (ICAT) method of protein quantification. The abundance levels of several extracellular proteins were altered upon overexpression of MT1-MMP, which we took as evidence of proteolytic modification. These potential substrates were confirmed biochemically confirmed and here we report that interleukin (IL)-8, secretory leukocyte protease inhibitor (SLPI), death receptor-6, and connective tissue growth factor (CTGF) are previously undescribed substrates of MT1-MMP. Moreover, the utility and quantitative nature of ICAT as a proteomic screen for protease substrate discovery should make it adaptable for studying the proteolytic function of other protease classes in complex and dynamic biological contexts. T A B L E OF C O N T E N T S Abstract Table of
Doppio: I/O-Aware Performance Analysis, Modeling and Optimization for In-memory Computing Framework

In conventional Hadoop MapReduce applications, I/O used to play a heavy role in the overall system performance. More recently, a study from the Apache Spark community— state-of-the-art in-memory cluster computing framework— reports that I/O is no longer the bottleneck and has a marginal performance impact on applications like SQL processing. However, we observe that simply replacing HDDs with SSDs in a Spark cluster can have over 10x performance improvement for certain stages in large-scale production-quality genome processing. Therefore, one key question arises: How does I/O quanti- tatively impact the performance of today's big data applications developed using in-memory cluster computing frameworks like Apache Spark? In this paper we select an important yet complex application— the Spark-based Genome Analysis ToolKit (GATK4)—to guide our modeling. We first use different combinations of HDDs and SSDs to measure the I/O impact on GATK4 and change the CPU core number to discover the relation between computation and I/O access. By combining with Spark's underlying implementations, we further analyze the inherent cause of the above observations and build our model based on the analysis. Although we are building upon GATK4, our model maintains generality to other applications. Experimental results show that we can achieve a performance prediction error rate within 10% for typical Spark applications of both iterative and shuffle-heavy algorithms. Finally, we further extend our model to a broader area—that of optimal configuration selection in the public cloud. In Google Cloud, our model enables us to save 38% to 57% of cost for genome sequencing compared with its recommended default configurations. Currently, more and more companies are adopting cloud computing for specific workloads. Our proposed model can have a huge impact on their choices, while also enabling them to significantly reduce their costs.
Driving towards ecotechnologies

Abstract The prospect of using genetic methods to target vector, parasite, and reservoir species offers tremendous potential benefits to public health, but the use of genome editing to alter the shared environment will require special attention to public perception and community governance in order to benefit the world. Public skepticism combined with the media scrutiny of gene drive systems could easily derail unpopular projects entirely, especially given the potential for trade barriers to be raised against countries that employ self-propagating gene drives. Hence, open and community-guided development of thoughtfully chosen applications is not only the most ethical approach, but also the most likely to overcome the economic, social, and diplomatic barriers. Here we review current and past attempts to alter ecosystems using biological methods, identify key determinants of social acceptance, and chart a stepwise path for developers towards safe and widely supported use.
Primary Aldosteronism: The Next Five Years

Abstract The management of primary aldosteronism is widely varied within various published guidelines, with very little in the way of data supporting the choice of one variation over others. Current estimates of prevalence are probably accurate for aldosterone producing adenoma, but fall very short of that for bilateral adrenal hyperplasia. Discovery at the level of basic science has proven illuminating over the past 6 years in terms of unilateral disease and both somatic and germline mutations, with much less focus on the much more common bilateral disease; Attempts at harmonization have begun – for example, criteria for complete/partial/absent cure after adrenalectomy for unilateral disease; again focus on bilateral disease is muted. A number of possibilities are suggested as agenda for active consideration and change, across a wide range of areas – referral patterns, screening, confirmation and lateralization, what will be needed is discussion and agreement, to fill the lacunae within the current guidelines. Those involved will want to change to make such an agenda possible.
A Keyword Extraction Method for Chinese Scientific Abstracts

Keyword extraction plays an essential role for text mining and further semantic analysis. It is a big challenge to extract keywords from short text, especially from short Chinese text. This paper presents a keyword extraction method for Chinese scientific abstracts. Firstly, an abstract is divided into meaningful units of Chinese words. Secondly, after excluding stop words, the TextRank method is adopted to extract keywords with a graph-based ranking model, and generate multi-word candidates by concatenating keywords adjacent in the abstract text. Thirdly, a keyword dictionary and a probability calculation algorithm based on a Chinese corpus are presented to check whether a word sequence is a correct multi-word keyword. To demonstrate the effectiveness of our method, a comparison experiment is conducted to show that our method outperforms the TextRank algorithm and TFIDF algorithm in Chinese multi-word keyword extraction.
Exploratory Analysis of Spark Structured Streaming

In the Big Data era, stream processing has become a common requirement for many data-intensive applications. This has lead to many advances in the development and adaption of large scale streaming systems. Spark and Flink have become a popular choice for many developers as they combine both batch and streaming capabilities in a single system. However, introducing the Spark Structured Streaming in version 2.0 opened up completely new features for SparkSQL, which are alternatively only available in Apache Calcite. This work focuses on the new Spark Structured Streaming and analyses it by diving into its internal functionalities. With the help of a micro-benchmark consisting of streaming queries, we perform initial experiments evaluating the technology. Our results show that Spark Structured Streaming is able to run multiple queries successfully in parallel on data with changing velocity and volume sizes.
Optimal-channel Selection Algorithms in Mental Tasks based Brain-computer Interface

Brain computer interface (BCI) for healthy people is a growing field. Minimizing the number of electroencephalography (EEG) channels is a key technological advantage for the application of BCI, which would make the system more mobile, easier to setup and long-time use in the real life. In this paper, to decrease the number of channels, multi-channel common spatial pattern (MCSP) algorithm is used to extract the features with two mental tasks (i.e., mental arithmetic and spatial imagery), and support vector machine (SVM) is used to classify the tasks performed. In detail, the separability value of each individual channel is computed based on between/within-group variance and a modified entropy criterion to evaluate its contribution to classification performance. The optimal channels are chosen based on the separability ranking. The performance of proposed methods is compared with recursive channel elimination and genetic algorithm. The results demonstrate that the EEG signals have different trends between the two mental tasks with highest brain activity in left central-parietal and parietal lobes, and the separability values allow reduction of number of electrodes from 15 to 4 and 10 while the classification accuracy reaches 80% and 90% respectively. Therefore, the optimal-channel algorithms can reduce the number of channels and improve the performance of the mental tasks based BCI.
Is the rising cost of education uniform across all of Australia's Capital Cities?

This paper compares and contrasts the aggregate cost of education in Australia with the cost of education in each of its eight capital cities surveyed in the Consumer Price Index. It appears that education is becoming a relatively more expensive item among Australian households with rising substantial differences across various geographical areas. Over the last three decades on average the Australian economy witnessed an overall annual inflation rate of 4.2 per cent, whereas the growth of education cost was 7.3 per cent per annum. It is interesting to note that the rising cost of education was not the same across all capital cities. This paper shows that in Adelaide, Brisbane and Sydney the cost of education grew more than Darwin, Canberra and Melbourne. Our results clearly indicate that the introduction of the Higher Education Contribution Scheme (HECS) in 1989, the Goods and Services Tax (GST) in 2000 and the Reserve Bank of Australia’s (RBA) inflation-targeting policy launched in 1993 each have significantly contributed to changes in the real cost of education over our sample period (1982q1-2009q4).
Lactobacilli Are Prominent Members of the Microbiota Involved in the Ruminal Digestion of Barley and Corn

The chemical composition of barley grain can vary among barley varieties (Fibar, Xena, McGwire, and Hilose) and result in different digestion efficiencies in the rumen. It is not known if compositional differences in barley can affect the microbiota involved in the ruminal digestion of barley. The objective of this study was to characterize the in situ rumen degradability and microbiota of four barley grain varieties and to compare these to corn. Three ruminally cannulated heifers were fed a low (60% barley silage, 37% barley grain, and 3% supplement) or high grain (37% barley silage, 60% barley grain, and 3% supplement) diet. One set of bags was used to estimate dry matter (DM), starch and crude protein (CP) degradability. A second set was used to extract DNA from the adherent microbiota and visualize grain after incubation using scanning electron microscopy (SEM). DNA was subjected to amplicon 16S rRNA gene sequencing followed by analysis using QIIME. In the low grain diet, McGwire had the highest effective degradability (ED) of DM (P < 0.01). The ED of starch was highest (P < 0.01) for Fibar, McGwire, and Xena, but the ED of CP was not affected by variety. For the high grain diet, Xena and McGwire had the highest ED of DM (P < 0.01). The ED of starch was highest (P < 0.01) for Xena and Fibar. The ED of protein was highest (P < 0.01) for Xena and McGwire. Although the microbiota did not differ among barley varieties, they did differ from corn and with incubation time. Lactobacilli were dominant members of the mature biofilms associated with corn and barley and were accompanied by a notable increase in the lactic acid utilizing genera, Megasphaera. As none of the cattle exhibited subclinical or clinical acidosis during the study, our results suggest that lactobacilli play a more prominent role in routine starch digestion than presently surmised.
Oral Psoriasis: An Overlooked Enigma

Although cutaneous psoriasis is common, the existence of its manifestations in the oral cavity has been questioned. The definitive diagnosis of oral psoriasis can be challenging due to the variability of presentations, and overlapping clinical and histological features with a number of other conditions as well as the lack of consensus. We review oral psoriasis, noting its variable clinical appearance, delineate the differential diagnosis, and discuss management strategies.