Miracle-dz/UniFAR-FTUs huggingface dataset:
NEK7 Promotes Pancreatic Cancer Progression And Its Expression Is Correlated With Poor Prognosis

The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients is still dismal. Elucidation of associated genomic alteration may provide effective therapeutic strategies for PDAC treatment. NIMA-related protein kinase 7 is widely expressed in various tumors, including breast cancer, colorectal cancer and lung cancer, and promotes the proliferation of liver cancer cells in vitro and in vivo. We investigated the protein expression level of NEK7 in tumor tissues and adjacent normal tissues using immunohistochemistry of 90 patients with PADC. Meanwhile, the RNA expression level of NEK7 was examined using database-based bioinformatic analysis. Correlation and significance of NEK7 expression with patient clinicopathological features and prognosis were examined. Cell proliferation, cell adhesion, migration and invasion capabilities were measured following downregulation of NEK7 expression. 3D tumor organoids of pancreatic cancer were established and splenic xenografted into nude mice, then liver metastatic ability of NEK7 was evaluated in following 4 weeks. We observed NEK7 expression was upregulated in tumor tissues compared to normal tissues at both RNA and protein levels using bioinformatic analysis and immunohistochemistry analysis in PDAC. NEK7 expression was undetectable in normal pancreatic ducts; NEK7 was overexpressed in primary tumor of PDAC; NEK7 expression was highly correlated with advanced T stage, poorly differentiated histological grade invasive ductal carcinoma, and lymphatic invasion. Meanwhile, patients with higher NEK7 expression accompanied by worse survival outcome. Moreover, NEK7 promoted migration, invasion, adhesion, proliferation and liver metastatic ability of pancreatic cancer cells. Taken together, our data indicate that NEK7 promotes pancreatic cancer progression and it may be a potential marker for PDAC prognosis.
Effects of a High-Grain Diet With a Buffering Agent on Milk Protein Synthesis in Lactating Goats

Chinese dairy industries have developed rapidly, providing consumers with high-quality sources of nutrition. However, many problems have also appeared during the development process, especially the low quality of milk. To improve milk quality, a large amount of concentrated feed is usually added to the diet within a certain period of time, which increases the milk production to a certain extent. However, long-term feeding with high-concentration feed can lead to subacute rumen acidosis. Therefore, the present study aimed to determine the effect of adding a buffer on subacute rumen acidosis, and the improvement of milk production and milk quality. We also aimed to study the mechanism of promoting mammary gland lactation. A total of 12 healthy mid-lactating goats were randomly divided into two groups, they were high-grain diet group (Control) and buffering agent group. To understand the effects of high-grain diets with buffers on amino acids in jugular blood and the effects of amino acids on milk protein synthesis, Milk-Testing™ Milkoscan 4000, commercial kits, and high-performance liquid chromatography (HPLC) measurements were integrated with the milk protein rate, the amino acid concentration in jugular venous blood samples, quantitative real-time PCR, comparative proteomics, and western blotting to study differentially expressed proteins and amino acids in mammary gland tissues of goats fed high-grain diets. Feeding lactating goats with buffering agent increased the percentage of milk protein in milk, significantly increased the amino acid content of jugular blood (p < 0.05), and increase the amino acid transporter levels in the mammary gland. Compared with the high-grain group, 2-dimensional electrophoresis technology, matrix-assisted laser desorption/ionization-time of flight/time of flight proteomics analyzer, and western blot analysis further verified that the expression levels of beta casein (CSN2) and lactoferrin (LF) proteins in the mammary glands of lactating goats were higher when fed a high-grain diets and buffers. The mechanism of increased milk protein synthesis was demonstrated to be related to the activation of mammalian target of rapamycin (mTOR) pathway signals.
Marginalizing the Mainstream: How Social Media Privilege Political Information

The following reports on research undertaken concerning the “misinformation problem” on social media during the run-up to the U.S. presidential elections in 2020. Employing techniques borrowed from data journalism, it develops a form of cross-platform analysis that is attuned to both commensurability as well as platform specificity. It analyses the most engaged-with or top-ranked political content on seven online platforms: TikTok, 4chan, Reddit, Twitter, Facebook, Instagram and Google Web Search. Discussing the extent to which social media platforms marginalize mainstream media and mainstream the fringe, the analyses found that TikTok parodies mainstream media, 4chan and Reddit dismiss it and direct users to alternative influencer networks and extreme YouTube content. Twitter prefers the hyperpartisan over it. Facebook’s “fake news” problem also concerns declining amounts of mainstream media referenced. Instagram has influencers (rather than, say, experts) dominating user engagement. By comparison, Google Web Search buoys the liberal mainstream (and sinks conservative sites), but generally gives special interest sources, as they were termed in the study, the privilege to provide information rather than official sources. The piece concludes with a discussion of source and “platform criticism”, concerning how online platforms are seeking to filter the content that is posted or found there through increasing editorial intervention. These “editorial epistemologies”, applied especially around COVID-19 keywords, are part of an expansion of so-called content moderation to what I call “serious queries”, or keywords that return official information. Other epistemological strategies for editorially moderating the misinformation problem are also treated.
Eco-Friendly Feed Formulation and On-Farm Feed Production as Ways to Reduce the Environmental Impacts of Pig Production Without Consequences on Animal Performance

Animal feeding has a major contribution to the environmental impacts of pig production. One potential way to mitigate such effects is to incorporate an assessment of these impacts in the feed formulation process. The objective of this study was to test the ability of innovative formulation methodologies to reduce the impacts of pig production while also taking into account possible effects on growth performance. We compared three different formulation methodologies: least-cost formulation, in accordance with standard practices on commercial farms; multiobjective (MO) formulation, which considered feed cost and environmental impacts as calculated by life cycle assessment (LCA); and MO formulation, which prioritized locally produced feed ingredients to reduce the impact of transport. Ninety-six pigs were distributed between three experimental groups, with pigs individually weighted and fed using an automatic feeding system from 40 to 115 kg body weight. Based on the experimental results, six categories of impacts were evaluated: climate change (CC), demand in non-renewable energy (NRE), acidification (AC), eutrophication (EU), land occupation (LO), and phosphorus demand (PD), at both feed plant gate and farm gate, with 1 kg of feed and 1 kg of live pig as functional units, respectively. At feed level, MO formulations reduced CC, NRE, AC, and PD impacts but sometimes increased LO and EU impacts. These formulations reduced the proportion of cereals and oil meals into feeds (feed ingredients with high impacts), while the proportion of alternative protein sources, like peas, faba beans, or high-protein agricultural coproducts increased (feed ingredients with low impacts). Overall, animal performance was not affected by the dietary treatment; because of this, the general pattern of results obtained with either MO formulation at farm gate was similar to that obtained at feed level. Thus, MO diet formulation represents an efficient way to reduce the environmental impacts of pig production without compromising animal performance.
Improvement of Diabetes Mellitus After Colorectal Cancer Surgery: A Retrospective Study of Predictive Factors For Type 2 Diabetes Mellitus Remission and Overall Survival

Purpose The purpose of the current study was to evaluate the impact of colorectal cancer (CRC) surgery on type 2 diabetes mellitus (T2DM) and to analyze the change in T2DM on overall survival after CRC surgery. Methods Patients who underwent CRC surgery were retrospectively enrolled from January 2013 to December 2019. The status of T2DM pre- and 1-year after CRC surgery was recorded, and predictive factors for T2DM remission and overall survival were analyzed. Results A total of 296 patients were included in this study. Thirty-eight patients experienced remission of T2DM 1 year after CRC surgery, and the remission rate was 12.8%. Weight loss was significantly higher in the T2DM remission group (p = 0.038), and the T2DM duration was significantly shorter in the T2DM remission group (p = 0.015). In the multivariate logistic regression analysis, higher weight loss (p = 0.046, odds ratio = 1.060, 95% CI = 1.001–1.122) and shorter T2DM duration (p = 0.019, odds ratio = 1007, 95% CI = 1.001–1.014) were predictive factors for remission of T2DM. Furthermore, in multivariate Cox regression analysis, lower TNM stage (p = 0.000, odds ratio = 2.147, 95% CI = 1.474–3.128) and T2DM remission (p = 0.033, odds ratio = 2.999, 95% CI = 1.091–8.243) were the predictive factors for better overall survival. Conclusion Patients with concurrent CRC and T2DM had a 12.8% remission 1 year after CRC surgery. Higher weight loss and shorter T2DM duration contributed to T2DM remission, and patients with T2DM remission could improve in terms of their overall survival.
Gallic Acid Prevents the Oxidative and Endoplasmic Reticulum Stresses in the Hippocampus of Adult-Onset Hypothyroid Rats

Thyroid hormone is essential for hippocampal redox environment and neuronal viability in adulthood, where its deficiency causes hypothyroidism related to oxidative and endoplasmic reticulum stresses in the hippocampus, resulting in neuronal death. One option of treatment is antioxidants; however, they must be transported across the blood-brain barrier. Gallic acid is a polyphenol that meets these criteria. Thus, this study aimed to prove that the neuroprotective mechanism of GA is associated with the prevention of oxidative and endoplasmic reticulum stresses in the hippocampus of adult-onset hypothyroid rats. Male Wistar rats were divided into euthyroid (n = 20) and hypothyroid groups (n = 20). Thyroidectomy with parathyroid gland reimplementation caused hypothyroidism. Each group was subdivided into two: vehicle and 50 mg/kg/d of gallic acid. 3 weeks after thyroidectomy, six animals of each group were euthanized, and the hippocampus was dissected to evaluate oxidative and endoplasmic reticulum stress markers. The rest of the animals were euthanized after 4 weeks of treatment for histological analysis of the hippocampus. The results showed that hypothyroidism increased lipid peroxidation, reactive oxygen species, and nitrites; it also increased endoplasmic reticulum stress by activating the inositol-requiring enzyme-1α (IRE1α) pathway, the protein kinase RNA-like endoplasmic reticulum kinase (PERK) and activated transcription factor 6α (ATF6α) pathways associated with a proapoptotic state that culminates in hippocampal neuronal damage. Meanwhile, the hypothyroid rat treated with gallic acid reduced oxidative stress and increased endoplasmic reticulum-associated degradation (ERAD) through IRE1α and ATF6. Also, the gallic acid treatment prevented the Bax/BCl2 ratio from increasing and the overexpression of p53 and caspase 12. This treatment in hypothyroid animals was associated with the neuronal protection observed in the hippocampus. In conclusion, gallic acid prevents hypothyroidism-induced hippocampal damage associated with oxidative and endoplasmic reticulum stresses.
Taxifolin, an Inhibitor of Sortase A, Interferes With the Adhesion of Methicillin-Resistant Staphylococcal aureus

The evolution and spread of methicillin-resistant Staphylococcus aureus (MRSA) poses a significant hidden risk to human public health. The majority of antibiotics used clinically have become mostly ineffective, and so the development of novel anti-infection strategies is urgently required. Since Staphylococcus aureus (S. aureus) cysteine transpeptidase sortase A (SrtA) mediates the surface-anchoring of proteins to its surface, compounds that inhibit SrtA are considered potential antivirulence treatments. Herein, we report on the efficacy of the potent SrtA inhibitor taxifolin (Tax), a flavonoid compound isolated from Chinese herbs. It was able to reversibly block the activity of SrtA with an IC50 of 24.53 ± 0.42 μM. Tax did not display toxicity toward mammalian cells or S. aureus at a concentration of 200 μM. In addition, Tax attenuated the virulence-related phenotype of SrtA in vitro by decreasing the adherence of S. aureus, reducing the formation of a biofilm, and anchoring of S. aureus protein A on its cell wall. The mechanism of the SrtA-Tax interaction was determined using a localized surface plasmon resonance assay. Subsequent mechanistic studies confirmed that Asp-170 and Gln-172 were the principal sites on SrtA with which it binds to Tax. Importantly, in vivo experiments demonstrated that Tax protects mice against pneumonia induced by lethal doses of MRSA, significantly improving their survival rate and reducing the number of viable S. aureus in the lung tissue. The present study indicates that Tax is a useful pioneer compound for the development of novel agents against S. aureus infections.
Detection of Defective Speech Using Convolutional Neural Networks

This paper presents an algorithm for detecting a pathological voice. It is shown that the convolutional neural network effectively extracts features from the spectrograms of voice recordings and diagnoses voice disorders. The deep belief convolutional network helps to initialize weights and makes the system more reliable. The effect of the size of convolutional network filters on each layer on the system performance is also studied.
Scheduling Replica Voting in Fixed-Priority Real-Time Systems

Reliability and safety are mandatory requirements for safety-critical embedded systems. The design of a fault-tolerant system is required in many fields (e.g., railway, automotive, avionics) and redundancy helps in achieving this goal. Redundant systems typically leverage voting techniques applied to the outputs produced by tasks to detect and even tolerate failures. This paper studies the integration of distributed voting protocols in fixed-priority real-time systems from a scheduling perspective. It analyzes two scheduling strategies for implementing voting. One is attractive and friendly for software developers and based on suspending the task execution until the replica provides the data to be voted. The other one is inspired by the Logical Execution Time (LET) paradigm and requires introducing additional tasks in the system to accomplish votingrelated activities. Queuing and delays introduced by inter-replica communication interfaces are also analyzed. Experimental results are finally presented to compare the two strategies, showing that LETinspired voting is much more predictable and hence more suitable than the other strategy for fixed-priority real-time systems. 2012 ACM Subject Classification Computer systems organization → Dependable and fault-tolerant systems and networks; Computer systems organization → Real-time systems
Database-Less Extraction of Event Logs from Redo Logs

Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.
Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data

Recently we observe a significant increase in the amount of easily accessible data on transport and mobility. This data is mostly massive streams of high velocity, magnitude, and heterogeneity, which represent a flow of goods, shipments and the movements of fleet. It is therefore necessary to develop a scalable framework and apply tools capable of handling these streams. In the paper we propose an approach for the selection of software for stream processing solutions that may be used in the transportation domain. We provide an overview of potential stream processing technologies, followed by the method for choosing the selected software for real-time analysis of data streams coming from objects in motion. We have selected two solutions: Apache Spark Streaming and Apache Flink, and benchmarked them on a real-world task. We identified the caveats and challenges when it comes to implementation of the solution in practice.
Disorders and Permutations

The additive x-disorder of a permutation is the sum of the absolute differences of all pairs of consecutive elements. We show that the additive x-disorder of a permutation of S(n), n ≥ 2, ranges from n − 1 to ⌊ n2/2 ⌋ − 1, and we give a complete characterization of permutations having extreme such values. Moreover, for any positive integers n and d such that n ≥ 2 and n − 1 ≤ d ≤ ⌊ n2/2 ⌋ − 1, we propose a linear-time algorithm to compute a permutation π ∈ S(n) with additive x-disorder d. 2012 ACM Subject Classification Mathematics of computing → Permutations and combinations
Mobile Accessibility Guidelines Adoption under the Perspective of Developers and Designers

Mobile applications these days offer a myriad of services and possibilities to users. But what could be ease of usage for part of the population, could also be the exclusion of another part with disabilities. Studies show that even large mobile projects have limitation in providing accessibility appropriately, even when implementing basic items. This work presents a mixed-method study with developers and user interface (UI) designers of mobile applications, to understand more about the dissemination and usage of well-established mobile accessibility guidelines. Our preliminary results show that UI designers have a better perception than developers on that topic.
Forecasting the Spread of COVID-19 Pandemic with Prophet

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally. © 2021 Lavoisier. All rights reserved.
Discriminative correlation filters in robot vision

In less than ten years, deep neural networks have evolved into all­encompassing tools inmultiple areas of science and engineering, due to their almost unreasonable effectiveness in modeling complex real­world relationships. In computer vision in particular, they have taken tasks such as object recognition, that were previously considered very difficult, and transformed them into everyday practical tools. However, neural networks have to be trained with supercomputers on massive datasets for hours or days, and this limits their ability adjust to changing conditions. This thesis explores discriminative correlation filters, originally intended for tracking large ob­ jects in video, so­called visual object tracking. Unlike neural networks, these filters are small and can be quickly adapted to changes, with minimal data and computing power. At the same time, they can take advantage of the computing infrastructure developed for neural networks and operate within them. The main contributions in this thesis demonstrate the versatility and adaptability of correlation filters for various problems, while complementing the capabilities of deep neural networks. In the first problem, it is shown that when adopted to track small regions and points, they outper­ form the widely used Lucas­Kanade method, both in terms of robustness and precision. In the second problem, the correlation filters take on a completely new task. Here, they are used to tell different places apart, in a 16 by 16 square kilometer region of ocean near land. Given only a horizon profile ­ the coast line silhouette of islands and islets as seen from an ocean vessel ­ it is demonstrated that discriminative correlation filters can effectively distinguish between locations. In the third problem, it is shown how correlation filters can be applied to video object segmen­ tation. This is the task of classifying individual pixels as belonging either to a target or the back­ ground, given a segmentation mask provided with the first video frame as the only guidance. It is also shown that discriminative correlation filters and deep neural networks complement each other; where the neural network processes the input video in a content­agnostic way, the fil­ ters adapt to specific target objects. The joint function is a real­time video object segmentation method. Finally, the segmentation method is extended beyond binary target/background classification to additionally consider distracting objects. This addresses the fundamental difficulty of coping with objects of similar appearance.