Miracle-dz/UniFAR-FTUs huggingface dataset:
Roles of IL-17A and IL-23 in the Pathogenesis of Ulcerative Colitis and Crohn’s Disease

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder, the etiology and pathogenesis of which have been suggested to be influenced by cytokines. Two main clinical types of IBD are recognized, namely ulcerative colitis (UC) and Crohn's disease (CD). The present study examined serum levels of two cytokines (IL-17A and IL-23) in 60 IBD patients (30 UC and 30 CD) and 30 healthy controls. The levels were correlated with age, gender, cigarette-smoking status, disease duration, family history, disease extension, symptoms, extra-intestinal manifestations, and medication. The results depicted that IL-17A level was significantly higher in UC and CD patients compared to control (45.2 ± 23.3 and 47.5 ± 34.4 vs. 15.6 ± 7.5 pg/ml, respectively; p < 0.001). Serum level of IL-23 was similarly increased in UC and CD patients compared to control (64.1± 23.7 and 62.5 ± 27.3 vs. 25.2 ± 11.1 pg/ml, respectively). However, the level of both cytokines showed no significant variation between UC and CD patients (p = 0.713 and 0.777, respectively). Distributing UC and CD patients into subgroups according to some characteristics revealed that IL-17A level was significantly increased in UC male compared to female patients (57.3 ± 18.2 vs. 34.5 ± 22.5 pg/ml; p = 0.005). It was also significantly increased in smoker UC patients compared with non-smoker patients (51.9 ± 19.4 vs. 31.6 ± 25.5 pg/ml; p = 0.022). Smoker CD patients also showed a significantly increased level of IL-23 compared to non-smoker patients (72.7 ± 28.5 vs. 52.2 ± 22.6 pg/ml; p = 0.038). In the case of family history, IL-23 level was significantly decreased in UC patients with a family history of IBD compared to CD patients with a family history (84.5 ± 24.3 vs. 50.4 ± 17.0 pg/ml.; p = 0.042). In conclusion, the present data suggest a role for IL-17A and IL-23 in the etiology and pathogenesis of UC and CD.
Greenhouse trial of green manures on soil properties, chard production and environmental implications

In this trial, legumes dolichos beans (Lablab purpureos L. ex Sweet) and yorimon beans (Vigna unguiculata L. Walp) as green manures were studied. The objectives of the study were to evaluate the ability to capture carbon and fix nitrogen, as well as to measure the effects of its incorporation in the soil, the production of chard (Beta vulgaris var. cicla L.) and to analyze the environmental implications. In the results, L. purpureos produced 1 932 kg of dry matter in 90 days after sowing (869 kg ha of C) and fixed 30 kg of N ha in its tissues, while V. unguiculata produced 2 040 kg of dry matter in 80 days after sowing (918 kg ha of C) and fixed 40 kg of N ha in its tissues. The soil effects were not significant in both AV, compared to the control treatment (without AV). However, both promoted an increase in the weight of chard (40% in L. purpureos and 31% in V. unguiculata). Environmentally, green manures can reduce the carbon footprint by 280 kg ha of CO2 equivalent by dispensing with synthetic nitrogenous chemical fertilizers in the production of chard. Green manures, as a source of nutrients, especially N, could form part of the integral management of the soil in the horticultural production systems of semi-arid zones.
A Survey Paper on Stock Price Prediction using Machine Learning Techniques

--The ability to forecast the stockmarket with a highdegree ofprecisionandaccuracy is important. When making forecasts, most stockbrokers use technical, fundamental, or time series analysis. A comparative study of selective applications concluded that ANNs are widely used in stock price forecasting and have a higher degree of control and relativesimilarityin predictedresult.
IN SILICO STUDIES ON DENGUE AND CHIKUNGUNYA VIRAL PROTEINS WITH SELECTED EMBLICA OFFICINALIS (AMLA) LEAVES CONSTITUENTS

Dengue and chikungunya virus contains seven proteins, which are considered to be the most effective for drug designing. Recent studies have shown that these proteins can effectively cause the inactivation of dengue and chikungunya disease in humans. Phytochemicals present in Emblica offiinalis are found to have anti-bacterial and anti-cancer properties. In our study, the binding efficiency of 4 compounds that is present in the Emblica officinalis with all the fourteen proteins was performed through Insilico methods. By our virtual screening and molecular docking result, we found that the Benzoic acid, 3,4,5-trihydroxyhave highest binding affinity with the proteins and we also predicted the binding site amino acid residues and the type of hydrogen bonding.
w świetle badań ankietowych studentów National Brand and its Implications for the Study of Ukrainian Entrepreneurship of Generation Z in Light of Students Survey Research

The national brand evokes associations and feelings that affect public opinion, social behaviour, including intentions and entrepreneurial behaviour, both at home and abroad. Thus, the formed brand image can stimulate, slow down, and sometimes even block the undertaking of economic initiatives, especially at the “starting point” in a new environment. The aim of the study was to identify and compare the internal and external image of the Ukraine brand, and identify proposals to improve this image, which became the basis for reflection on the place of the national brand in the environment and its potential impact on entrepreneurial intentions and behaviour. The survey method was used in the IGOR KAVETSKYY, PaweŁ czaPliński Uniwersytet Szczeciński, Polska  University of Szczecin, Poland
A Survey on Sentiment Analysis Applied in Opinion Mining

– Sentiment analysis is the process to analysis the people emotions, characteristics and opinion, about the particular product and its problem. This survey paper tackles a comprehensive summary about the methods and its corresponding techniques in various fields. This article mainly focuses on opinion mining techniques such as CBR, Supervised, Unsupervised, Machine learning techniques. We provided complete details about the data set, techniques, and its result such as positive, negative and neutral feedback for each method. The literature survey will give the complete information about the sentiment analysis with the recent research work and its task to identify the opinion mining.
Milanov Dubravka et al.: Pasteurella multocida mastitis in cow

Pasteurella (P.) multocida is a heterogeneous species of Gram-negative bacteria which are common commensals of the upper respiratory system of various mammal and bird species, but are also opportunistic contagious zoonotic pathogens which cause a wide spectre of infections in domestic animals and humans. P. multocida is a rare cause of mastitis in dairy cows. The source of infection mainly remains unknown, mastitis usually is acute, and the therapy by intramammary administration of antibiotics does not lead to satisfactory results. Lethality is possible due to presence of endotoxins in blood. Literature data on P. multocida mastitis in dairy cows is particularly scarce, which is why such a case is described in the current work, with past medical history, clinical findings, laboratory diagnostics and therapeutic approach.
The Decline of the Politically Informed Electorate and the Survival of Political Disagreement within Communication Networks

This article, using the agent-based modeling approach, investigates the conditions under which the society-wide perception of heterogeneity in voter preference increase. The simulation results show that, first, the decline of accessing political news results in a decrease in the number of individuals who perceive heterogeneity voter preference. This result corresponds to a broad concern about the consequence of decreasing TV news viewership and newspaper readership. In contrast to a common expectation that stimulating political discussion during a campaign season cultivates deliberation, the second finding shows that this involvement in
A Numerical Study of the effects of a Corotating Interaction Region on cosmic proton and helium transport

A Corotating Interaction Region (CIR) is formed when the fast solar wind catches up with the slow solar wind. It is known that the intensity of Galactic Cosmic Rays (GCRs) is modulated by CIRs with the GCR intensity suppressed inside the CIR. Previous numerical studies were mainly confined to GCR protons. For this study we utilize a hybrid GCR transport model which incorporates a Magnetohydrodynamic (MHD) simulated solar wind plasma background with a CIR structure. Additionally, adopting appropriate mass, charge and local interstellar spectra, the hybrid transport model is applied to GCR protons and the two helium isotopes. It is found that both proton and total helium studied at 0.3 GV are modulated by the CIR with their fluxes being depressed. However, the modulation for protons and helium is different with helio-longitude, and interestingly the ratio of the proton to helium flux varies with longitude. Similar results are found for the way in which the two helium isotopic fluxes vary.
Leakage Power Reduction in High Speed Domino Logic Circuits in Deep Submicron Technologies for VLSI Applications

- Leakage power and propagation delay are the two major challenges in VLSI design CMOS circuits in small scale technology. This paper proposes a novel technique for designing CMOS domino logic circuits with lector approach and modified inverter for the reduction in leakage power and improved noise performance. NAND and NOR gates are designed with existing and proposed techniques and performances are compared. Designs are tested in 250nm node using TSPICE in CMOS technology at a clock frequency of 5GHz. Simulated results are compared in terms of power consumption and propagation delay. Proposed domino technique has a maximum power reduction from 25% to 80% for different logic circuits and maximum delay reduction from 7% to 20% as compared to the conventional domino logic circuits. Results are proved with TSPICE simulation.
CMAR_A_254596 4253..4260

Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100026, People’s Republic of China Abstract: The Wnt signaling pathway extensively participates in diverse processes such as embryonic development, maintenance of homeostasis and tumor pathogenesis. Dickkopf-1 (DKK1), a Wnt inhibitor, plays a vital role for over the past decades regarding its role in the regulation of several types of cancers. However, studies have shown that DKK1 is expressed differently in cancer and plays a role as a cancer-promoting factor or a tumor suppressor, which is worthy of further exploration. We herein study whether DKK1 is highly expressed in all cancers and plays a crucial role in promoting cancer. Furthermore, we discussed as to which stages of cancer development it plays in. Finally, the present detection methods were introduced and indicated the clinical application of DKK1 in tumor development.
EFFECTIVENESS OF COMPOSED HERBAL EXTRACT IN THE TREATMENT OF GINGIVITIS AND ORAL AND PHARYNGEAL MUCOSA – REVIEW OF STUDIES

Intensive studies on properties of synthetic compounds are simultaneously conducted with studies on the effectiveness and safety of drugs derived from natural compounds. These drugs have been effectively used for years in dentistry, in treatment of inflammatory conditions of the oral cavity and in laryngology, in treatment of pharyngeal inflammatory conditions. The subject of this article is a compilation of studies conducted on medicinal products containing composed ethanolic extract as an active substance, and obtained from: chamomile capitulum (Matricaria recutita L.), oak bark (Quercus spp.), sage leaf (Salvia officinalis L.), arnica herb (Arnica spp.), calamus rhizome (Acorus calamus L.), peppermint herb (Mentha piperita L.), thyme herb (Thymus spp.) Preclinical studies confirmed that a particular tested plant extract exhibits antimicrobial (antibacterial, antifungal and antiprotozoal) as well as anti-inflammatory, immunomodulatory and astringent properties. Under clinical conditions, it was confirmed that drugs containing the analysed extract used in periodontology, in prevention and complex treatment of gingivitis, periodontitis as well as diseases of oral mucosa (but also the throat) of viral, bacterial, fungal and protozoal aetiology significantly improved the effectiveness of therapy, shortened the treatment and improved the patients’ quality of life. The above preparations used in dental surgery contributed to faster tissue regeneration, more quickly relieved pain and swelling after a surgery or difficult dentition. Furthermore, drugs containing the above-mentioned extract can also be successfully used for oral cavity decontamination in the case of various diseases of teeth or the oral and pharyngeal mucosa. The tested composed herbal extract (CHE), applied in the form of a mouthwash and in the form for topical application with benzocaine content (CHEB), is a component of drugs which are great alternatives to widely used synthetic drugs. They are characterised with high effectiveness, comparable to that of synthetic agents, and a higher safety profile. Results of pharmacological and clinical studies justify their use in both prevention and treatment of inflammatory diseases of the gums, periodontium, oral cavity and pharynx.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

The protection of information assets requires interdisciplinary approach and cross-functional capabilities. In recent times, information security and privacy compliance continue to be a complicated task due to increasing regulatory restrictions, changing legislations and public awareness. The newly published information security and privacy standard ISO/IEC 27701:2019 provides support for organisations looking to put in place systems to support compliance with global data privacy requirements. However, there is little known about how does this standard map to other regulatory requirements in different jurisdictions specifically the globally relevant General Data Protection Regulation (GDPR). Hence, this research aims to answer an important research question: whether and how the ISO/IEC 27701:2019 framework represents an opportunity for the GDPR compliance? This research provides a review and mapping of ISO/IEC 27701:2019 and GDPR by using an integrated requirement engineering model as a kernel theory. The results of this research will assist organisations contemplating to meet their compliance needs. It will also help academics and practitioners interested in integrating the ISO/IEC 27701:2019 and GDPR for developing relevant compliance frameworks and tools.
A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia

Emerging diseases caused by coronaviruses of likely bat origin (e.g. SARS, MERS, SADS and COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this "hidden" spillover may help target prevention programs. We derive biologically realistic range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human SARSr-CoV seroprevalence, and antibody duration to estimate that ~400,000 people (median: ~50,000) are infected with SARSr-CoVs annually in South and Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence.
Deeply Buried Nuclei in the Infrared-luminous Galaxies NGC 4418 and Arp 220. I. ALMA Observations at λ = 1.4–0.4 mm and Continuum Analysis

We observed with Atacama Large Millimeter/submillimeter Array three deeply buried nuclei in two galaxies, NGC 4418 and Arp 220, at ∼0. ″2 resolution over a total bandwidth of 67 GHz in f rest = 215–697 GHz. Here we (1) introduce our program, (2) describe our data reduction method for wide-band, high-resolution imaging spectroscopy, (3) analyze in visibilities the compact nuclei with line forests, (4) develop a continuum-based estimation method of dust opacity and gas column density in heavily obscured nuclei, which uses the buried galactic nuclei (BGN) model and is sensitive to log(NH2/cm−2) ∼ 25–26 at λ ∼ 1 mm, and (5) present the continuum data and diagnosis of our targets. The three continuum nuclei have major-axis FWHMs of ∼0. ″1–0. ″3 (20–140 pc) aligned to their rotating nuclear disks of molecular gas. However, each nucleus is described better with two or three concentric components than with a single Gaussian. The innermost cores have sizes of 0. ″05–0. ″10 (8–40 pc), peak brightness temperatures of ∼100–500 K at 350 GHz, and more fractional flux at lower frequencies. The intermediate components correspond to the nuclear disks. They have axial ratios of ≈0.5 and hence inclinations ≳60°. The outermost elements include the bipolar outflow from Arp 220W. We estimate 1 mm dust opacity of τ d,1 mm ≈ 2.2, 1.2, and ≲0.4, respectively, for NGC 4418, Arp 220W, and Arp 220E. The first two correspond to log(NH/cm−2)∼26 for conventional dust-opacity laws, and hence the nuclei are highly Compton thick.
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding

A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Existing methods mainly leverage vanilla soft attention to perform the alignment in a single-step process. However, such single-step attention is insufficient in practice, since complicated relations between inter- and intra-modality are usually obtained through multi-step reasoning. In this paper, we propose an Iterative Alignment Network (IA-Net) for TSG task, which iteratively interacts inter- and intra-modal features within multiple steps for more accurate grounding. Specifically, during the iterative reasoning process, we pad multi-modal features with learnable parameters to alleviate the nowhere-to-attend problem of non-matched frame-word pairs, and enhance the basic co-attention mechanism in a parallel manner. To further calibrate the misaligned attention caused by each reasoning step, we also devise a calibration module following each attention module to refine the alignment knowledge. With such iterative alignment scheme, our IA-Net can robustly capture the fine-grained relations between vision and language domains step-by-step for progressively reasoning the temporal boundaries. Extensive experiments conducted on three challenging benchmarks demonstrate that our proposed model performs better than the state-of-the-arts.
COLLISION AVOIDANCE TECHNOLOGY OF INTELLIGENT VEHICLE SYSTEM BASED ON MULTI-SENSOR FUSION

- In order to reduce the occurrence of traffic accidents, aiming at the shortcomings of using single sensor for target recognition in smart vehicle collision avoidance in the past, such as low perception range and high recognition error, the collision avoidance technology of smart vehicle system based on multi-sensor fusion is deeply studied. Arduino is the main control unit of smart vehicle collision avoidance system, Linux is the development platform, and HC-SR04 ultrasonic ranging module is used. 2 cm to 400 cm non-contact distance sensing is realized, and the detection distance is adjusted by the potentiometer knob in the infrared sensor module; after building the intelligent vehicle motion model, collision avoidance is realized by using the combination mode of ultrasonic single-point obstacle avoidance and infrared double-way cross-obstacle avoidance. In the process of collision avoidance, Mahalanobis distance is introduced to match the observed value of vehicle target sequence, and then the joint probability data association algorithm is applied to fuse. The data of ultrasonic sensor and infrared sensor are used to realize automatic collision avoidance of intelligent vehicle based on multi-sensor fusion. The results show that the collision avoidance technology is not affected by the shape of obstacles and has high collision avoidance rate. It can respond to braking instructions with high efficiency and precision, enhance the collision avoidance performance of intelligent vehicles, and has high accuracy of data fusion. It can effectively realize multi-aspect detection and omni-directional automatic collision avoidance of intelligent vehicles.
A Review of IoT Systems and Machine Learning Techniques in Crop Yield Prediction

— The agricultural sector across the world has always faced challenges in ensuring adequate production amidst changing seasons, weather conditions, arable land, and unpredictable natural disasters. Internet of Things (IoT) and data analytics play an important role in monitoring various parameters affecting the crop yield and using state-of-the-art machine learning techniques to predict the yield based on dependent variables. The IoT systems related to agriculture and the most commonly used components of the system are presented. The trends of various machine learning techniques and the emphasis on certain dependent parameters are identified. The only way forward is to ensure that an affordable and robust solution for crop yield prediction is reaching the farmers at all levels in the agricultural sector.
The Hubble Tension, the M Crisis of Late Time H(z) Deformation Models and the Reconstruction of Quintessence Lagrangians

We present a detailed and pedagogical analysis of recent cosmological data, including CMB, BAO, SnIa and the recent local measurement of H0. We thus obtain constraints on the parameters of these standard dark energy parameterizations, including ΛCDM, and H(z) deformation models such as wCDM (constant equation of state w of dark energy), and the CPL model (corresponding to the evolving dark energy equation-of-state parameter w(z)=w0+waz1+z). The fitted parameters include the dark matter density Ω0m, the SnIa absolute magnitude M, the Hubble constant H0 and the dark energy parameters (e.g., w for wCDM). All models considered lead to a best-fit value of M that is inconsistent with the locally determined value obtained by Cepheid calibrators (M tension). We then use the best-fit dark energy parameters to reconstruct the quintessence Lagrangian that would be able to reproduce these best-fit parameterizations. Due to the derived late phantom behavior of the best-fit dark energy equation-of-state parameter w(z), the reconstructed quintessence models have a negative kinetic term and are therefore plagued with instabilities.
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference

Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly ("women can't park") or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist's motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters' portrayal is centered around appearance, while male figures' focus on intellect.