Miracle-dz/UniFAR-FTUs huggingface dataset:
Bile acid interactions with neurotransmitter transporters

Synthesized in the liver from cholesterol, the bile acids (BAs) primary role is emulsifying fats to facilitate their absorption. BAs can cross the blood-brain barrier (BBB) and be synthesized in the brain. Recent evidence suggests a role for BAs in the gut-brain signaling by modulating the activity of various neuronal receptors and transporters, including the dopamine transporter (DAT). In this study, we investigated the effects of BAs and their relationship with substrates in three transporters of the solute carrier 6 family. The exposure to obeticholic acid (OCA), a semi-synthetic BA, elicits an inward current (IBA) in the DAT, the GABA transporter 1 (GAT1), and the glycine transporter 1 (GlyT1b); this current is proportional to the current generated by the substrate, respective to the transporter. Interestingly, a second consecutive OCA application to the transporter fails to elicit a response. The full displacement of BAs from the transporter occurs only after exposure to a saturating concentration of a substrate. In DAT, perfusion of secondary substrates norepinephrine (NE) and serotonin (5-HT) results in a second OCA current, decreased in amplitude and proportional to their affinity. Moreover, co-application of 5-HT or NE with OCA in DAT, and GABA with OCA in GAT1, did not alter the apparent affinity or the Imax, similar to what was previously reported in DAT in the presence of DA and OCA. The findings support the previous molecular model that suggested the ability of BAs to lock the transporter in an occluded conformation. The physiological significance is that it could possibly avoid the accumulation of small depolarizations in the cells expressing the neurotransmitter transporter. This achieves better transport efficiency in the presence of a saturating concentration of the neurotransmitter and enhances the action of the neurotransmitter on their receptors when they are present at reduced concentrations due to decreased availability of transporters.
Case report: Endoscopic frontal sinus opening surgery for noninflammatory frontal sinus headache: A short case series

Objective The objectives of this study were to analyze rhinogenic headache, i.e., noninflammatory frontal sinus headache, a headache caused by bony obstruction of the frontal sinus drainage channels that receives relatively insufficient attention clinically, and to propose endoscopic frontal sinus opening surgery as a treatment based on the etiology. Study Design Case series. Setting From the data of patients with noninflammatory frontal sinus headache who underwent endoscopic frontal sinus surgery in Hospital of Chengdu University of Traditional Chinese Medicine during 2016–2021, data for three cases with detailed postoperative follow-up data were extracted for case series reports. Methods This report provides detailed information on three patients with noninflammatory frontal sinusitis headache. Treatment options include surgery and rechecking, with the visual analogue scale (VAS) scores of preoperative and postoperative symptoms, CT, and endoscopic images. Three patients had common characteristics: the clinical manifestations were recurrent or persistent with pain and discomfort in the forehead area, but there was no nasal obstruction or runny nose; the paranasal sinus CT revealed no signs of inflammation in the sinuses but suggested bony obstruction of the drainage channel of the frontal sinus. Results All three patients had recovery from headache, nasal mucosal recovery, and patent frontal sinus drainage. The recurrence rate of forehead tightness and discomfort or pain was 0. Conclusion Noninflammatory frontal sinus headache does exist. Endoscopic frontal sinus opening surgery is a feasible treatment modality that can largely or even completely eliminate the stuffy swelling and pain in the forehead. The diagnosis and surgical indications for this disease are based on a combination of anatomical abnormalities and clinical symptoms.
Mining Fuzzy Association Rules: A General Model Based on Genetic Network Programming and its Applications

The initiative of combining association rule mining with fuzzy set theory has been applied frequently in recent years 1–5 . The original idea comes from dealing with quantitative attributes in a database, where discretization of the quantitative values into intervals would lead to under or overestimation of the values that are near the borders. This is called the sharp boundary problem. Fuzzy sets can help us to overcome this problem by allowing different degrees of the membership, not only 1 and 0 treated by traditional methods. Attribute values can thereby be the members of more than one set and therefore give a more realistic view on such data. On the other hand, fuzzy set theory has been shown to be a very useful tool in association rule mining, because the mined rules can be expressed in linguistic terms, which are more natural and understandable for human beings. The linguistic representation is mainly useful when those discovered rules are presented to human experts for study. In this paper, a novel association rule mining approach that integrates the evolutionary optimization technique ‘genetic network programming (GNP)’ and fuzzy set theory has been proposed for mining interesting fuzzy rules from given quantitative data. The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Angioimmunoblastic T-cell lymphoma and correlated neoplasms with T-cell follicular helper phenotype: from molecular mechanisms to therapeutic advances

Angioimmunoblastic T-cell lymphoma (AITL) is the second most frequent subtype of mature T-cell lymphoma (MTCL) in the Western world. It derives from the monoclonal proliferation of T-follicular helper (TFH) cells and is characterized by an exacerbated inflammatory response and immune dysregulation, with predisposition to autoimmunity phenomena and recurrent infections. Its genesis is based on a multistep integrative model, where age-related and initiator mutations involve epigenetic regulatory genes, such as TET-2 and DNMT3A. Subsequently, driver-mutations, such as RhoA G17V and IDH-2 R172K/S promote the expansion of clonal TFH-cells (“second-hit”), that finally begin to secrete cytokines and chemokines, such as IL-6, IL-21, CXCL-13 and VEGF, modulating a network of complex relationships between TFH-cells and a defective tumor microenvironment (TME), characterized by expansion of follicular dendritic cells (FDC), vessels and EBV-positive immunoblasts. This unique pathogenesis leads to peculiar clinical manifestations, generating the so-called “immunodysplastic syndrome”, typical of AITL. Its differential diagnosis is broad, involving viral infections, collagenosis and adverse drug reactions, which led many authors to use the term “many-faced lymphoma” when referring to AITL. Although great advances in its biological knowledge have been obtained in the last two decades, its treatment is still an unmet medical need, with highly reserved clinical outcomes. Outside the setting of clinical trials, AITL patients are still treated with multidrug therapy based on anthracyclines (CHOP-like), followed by up-front consolidation with autologous stem cell transplantation (ASCT). In this setting, the estimated 5-year overall survival (OS) is around 30-40%. New drugs, such as hypomethylating agents (HMAs) and histone deacetylase inhibitors (HDAi), have been used for relapsed/refractory (R/R) disease with promising results. Such agents have their use based on a biological rationale, have significant potential to improve the outcomes of patients with AITL and may represent a paradigm shift in the therapeutic approach to this lymphoma in the near future.
Selective enrichment of the raw milk microbiota in cheese production: Concept of a natural adjunct milk culture

In cheese production, microorganisms are usually added at the beginning of the process as primary starters to drive curd acidification, while secondary microorganisms, with other pro-technological features important for cheese ripening, are added as selected cultures. This research aimed to investigate the possibilities of influencing and selecting the raw milk microbiota using artisanal traditional methods, providing a simple method to produce a natural supplementary culture. We investigated the production of an enriched raw milk whey culture (eRWC), a natural adjunct microbial culture produced from mixing an enriched raw milk (eRM) with a natural whey culture (NWC). The raw milk was enriched by spontaneous fermentation for 21 d at 10°C. Three milk enrichment protocols were tested: heat treatment before incubation, heat treatment plus salt addition, and no treatment. The eRMs were then co-fermented with NWC (ratio of 1:10) at 38°C for 6 h (young eRWC) and 22 h (old eRWC). Microbial diversity during cultures’ preparation was evaluated through the determination of colony forming units on selective growth media, and next-generation sequencing (16S rRNA gene amplicon sequencing). The enrichment step increased the streptococci and lactobacilli but reduced microbial richness and diversity of the eRMs. Although the lactic acid bacteria viable count was not significantly different between the eRWCs, they harbored higher microbial richness and diversity than NWC. Natural adjunct cultures were then tested in cheese making trials, following the microbial development, and assessing the chemical quality of the 120 d ripened cheeses. The use of eRWCs slowed the curd acidification in the first hours of cheese making but the pH 24 h after production settled to equal values for all the cheeses. Although the use of diverse eRWCs contributed to having a richer and more diverse microbiota in the early stages of cheese making, their effect decreased over time during ripening, showing an inferior effect to the raw milk microbiota. Even if more research is needed, the optimization of such a tool could be an alternative to the practice of isolating, geno-pheno-typing, and formulating mixed-defined-strain adjunct cultures that require knowledge and facilities not always available for artisanal cheese makers.
Paget’s disease: a review of the epidemiology, etiology, genetics, and treatment

Paget’s disease of bone (PDB) is the second most prevalent metabolic bone disorder worldwide, with a prevalence rate of 1.5%–8.3%. It is characterized by localized areas of accelerated, disorganized, and excessive bone production and turnover. Typically, PDB develops in the later stages of life, particularly in the late 50s, and affects men more frequently than women. PDB is a complex disease influenced by both genetic and environmental factors. PDB has a complex genetic basis involving multiple genes, with SQSTM1 being the gene most frequently associated with its development. Mutations affecting the UBA domain of SQSTM1 have been detected in both familial and sporadic PDB cases, and these mutations are often associated with severe clinical expression. Germline mutations in other genes such as TNFRSF11A, ZNF687 and PFN1, have also been associated with the development of the disease. Genetic association studies have also uncovered several PDB predisposing risk genes contributing to the disease pathology and severity. Epigenetic modifications of genes involved in bone remodelling and regulation, including RANKL, OPG, HDAC2, DNMT1, and SQSTM1, have been implicated in the development and progression of Paget’s disease of bone, providing insight into the molecular basis of the disease and potential targets for therapeutic intervention. Although PDB has a tendency to cluster within families, the variable severity of the disease across family members, coupled with decreasing incidence rates, indicates that environmental factors may also play a role in the pathophysiology of PDB. The precise nature of these environmental triggers and how they interact with genetic determinants remain poorly understood. Fortunately, majority of PDB patients can achieve long-term remission with an intravenous infusion of aminobisphosphonates, such as zoledronic acid. In this review, we discuss aspects like clinical characteristics, genetic foundation, and latest updates in PDB research.
A Formulation of a Phase-Independent Wave-Activity Flux for Stationary and Migratory Quasigeostrophic Eddies on a Zonally Varying Basic Flow

Abstract A new formulation of an approximate conservation relation of wave-activity pseudomomentum is derived, which is applicable for either stationary or migratory quasigeostrophic (QG) eddies on a zonally varying basic flow. The authors utilize a combination of a quantity A that is proportional to wave enstrophy and another quantity E that is proportional to wave energy. Both A and E are approximately related to the wave-activity pseudomomentum. It is shown for QG eddies on a slowly varying, unforced nonzonal flow that a particular linear combination of A and E, namely, M ≡ (A + E)/2, is independent of the wave phase, even if unaveraged, in the limit of a small-amplitude plane wave. In the same limit, a flux of M is also free from an oscillatory component on a scale of one-half wavelength even without any averaging. It is shown that M is conserved under steady, unforced, and nondissipative conditions and the flux of M is parallel to the local three-dimensional group velocity in the WKB limit. The autho.
A Note on the GMRES Method for Linear Discrete Ill-Posed Problems

In this paper, we are presenting a proposal for new modified algorithms for RRGMRES and AGMRES. It is known that RRGMRES and AGMRES are viable methods for solving linear discrete ill-posed problems. In this paper we have fo- cused on the residual norm and have come-up with two improvements where suc- cessive updates and the stabilization of decreases for the residual norm improve performance respectively. Our numerical experiments confirm that our improved algorithms are effective for linear discrete ill-posed problems. AMS subject classifications: 65F10
Diffuser Optimation at Exhaust System with Catalytic Converter for 110 cc Mopet with Fluid Flow CFD Simulation

Diffuser Optimation at Exhaust System with Catalytic Converter for 110 cc Mopet with Fluid Flow CFD Simulation. CFD simulation used to get behavior of exhaust gas through catalyst, this result will be used to optimize geometry form to perform uniform stream distribution to catalyst, and CFD Simulation will used to analyze backpressure that happened at the model.
Analysis of Data Transfer Mechanism among Wireless Devices in AsthaNet Healthcare Network

AsthaNet Healthcare-as-a-Service (HaaS) was proposed to connect the disconnected communities with basic healthcare services. A portable digital health clinic, combined with wireless connectivity, a collection of network technologies and a smart application packages, can provide an autonomous solution to serve basic healthcare demands to the disconnected rural communities. In this paper, we analyze the data transfer mechanism within the Software Model of AsthaNet HaaS using several data transfer parameters. This ensures the outcomes of stability, survivability and errorless data transfer within the network model, thus ensuring sustainable healthcare solutions in the age of Industry 4.0.
The South China Sea Monsoon Experiment—Boundary Layer Height (SCSMEX-BLH): Experimental Design and Preliminary Results

AbstractKnowing the relationship between local convective precipitation and boundary layer processes is critical for forecasting rainstorms. For the South China Sea area, such a forecast is particularly important during the monsoon season. During such a season, the authors examined the boundary layer features at three sites as part of the South China Sea Monsoon Experiment—Boundary Layer Height (SCSMEX-BLH) experiment. The sites are spread from inland to over sea along a 43.4-km line. Here the authors analyze SCSMEX-BLH data from an intensive observing period that includes a convectively suppressed (inactive) period, a period influenced by a tropical storm, and a convectively active monsoon period. Some preliminary findings include the following: 1) The absorption of shortwave radiation over the sea is the primary driver of the land–sea temperature difference. The difference produces a diurnal variation below 400 m, with a warmer surface layer over the coast at night. 2) In the inactive and storm periods,.
Spray-Dried Plasma Promotes Broiler Chick Growth by Enhancing Immune Surveillance

Simple Summary Over the years, the poultry industry has relied on the use of in-feed antibiotics as a growth-promoting agent and for the prevention of diseases. However, antibiotic use has brought about pathogens that are resistant to antimicrobials. To this end, spray-dried plasma (SDP), an animal blood by-product that is rich in protein-containing lipids, peptides, immunoglobulins, transferrin, and fibrinogen, is being explored as a replacement for in-feed antibiotics in poultry. We evaluated the immunological and biochemical profile of SDP in order to understand how it enhanced performance values when supplemented to a broiler diet. At the end of the four-week study, our findings demonstrated a decrease in the number of heterophils and an increase in immunoglobulin in circulation, with oxidative stress falling in the normal range. Bifidobacteria counts also increased in the SDP-supplemented treatment. This demonstrated that SDP supplementation prevented infection and caused an increase in immunoglobulin concentration required to support intestinal development and gut microbiota modulation. Abstract Spray-dried plasma (SDP) contain a variety of functional proteins that play an immunomodulatory role. To evaluate the potential of SDP to stimulate the immune system, day-old Ross 708 male broiler chicks (200) were allocated randomly to five dietary treatments. Treatment 1 (CX) comprised chicks fed basal unmedicated corn–soybean meal (SBM) without the addition of SDP. Treatment 2 (MX) includes chicks fed unmedicated corn–SBM basal containing Bacitracin methylene disalicylate (BMD) at 0.055 g/kg diet. Treatments 3 (SDP1), 4 (SDP2), and 5 (SDP3) contained chicks given unmedicated corn–SBM basal, into which SDP was included at 10, 20, and 30 g/kg diet, respectively. On d 7, 14, and 21, chicks’ body weight and FCR were calculated. Additionally, leucocyte counts, oxidative status, and IgY concentrations were determined in blood. On d 23, fecal populations of selected indicator bacteria species were determined. Results showed that FCR for SP3 was superior (p < 0.05) to other treatments. Likewise, heterophil numbers decreased in MX and SDP treatments compared to CX. Circulating IgY concentration was higher for SDP dietary treatments (p < 0.05) compared to MX. In conclusion, dietary SDP at 30 g/kg enhanced immune surveillance by increasing circulating IgY levels, maintaining a normal oxidative state, and increasing gut Bifidobacteria, thereby improving chick growth performance.
Vertically Stacked Double-Layer Substrate-Integrated Nonradiative Dielectric Waveguides for THz Applications

Nonradiative dielectric (NRD) waveguides have been studied and exploited to address the leakage issue in dielectric waveguides caused by discontinuities. With the proliferation of substrate integration technologies, planarized substrate-integrated NRD (SINRD) waveguides have emerged, which are found to be more suitable for terahertz (THz) integrated circuits and systems. However, in the making of SINRD waveguides, a process-related conflict may arise between the air-hole perforation of a hosting substrate and its essential residual metallic coating on the perforated region. In this work, a solution to this problem using a multilayer topology is formulated, where the size difference between entrance and exit of the drilled air holes is reduced. Moreover, the exposed metallic coating over both sides of the multilayer SINRD waveguide is well preserved. Therefore, a dense air-hole perforation is allowed when needed. In this letter, a vertically stacked double-layer guiding structure is presented, studied, and experimentally verified, which demonstrates the interesting features of this technique.
Battery-Free Wireless Torque Sensor Powered by RF Energy

Conventional dynamic torque measurement usually requires wires and slip rings for supply and data transmission, which shows many drawbacks in terms of device installation, maintenance, durability, and reliability. We, here, present a wireless torque measurement sensor without a battery, which can achieve a long-term and real-time torque monitoring on either static or dynamic rotation systems. The proposed battery-free wireless torque sensor (BWTS) is nondestructive and low cost. The BWTS is equipped with a Koch–Meander hybrid dipole antenna to receive the radiofrequency (RF) energy emitted from a customized reader. The BWTS can be activated at an input power of −15 dBm ( $32 \mu \text{W}$ ), and the maximum operational distance in the open air is 3 m. We also study the sampling rate of the BWTS on a dynamic shaft and demonstrate it on the road, which to our best knowledge, is the first successful demonstration of fully self-powered and wireless torque monitoring in a real running car. This work indicates the feasibility of wirelessly powering torque sensors using far-field RF energy, introducing a new power solution to wireless and battery-less automotive sensors.
Intelligent Prediction-Intervention approach to Support Students' Success in Web-based Learning Environments: A Case Study in Higher Education

Due to the COVID-19 pandemic, the demand for distance learning has significantly increased in higher education institutions. This type of learning is usually supported by Web-based learning systems such as Massive Open Online Courses (Coursera, edX, etc.) and Learning Management Systems (Moodle, Blackboard-Learn, etc.). However, in this remote context, students often lack feedback and support from educational staff, especially when they face difficulties or challenges. For that reason, this work presents a Prediction-Intervention approach that (a) predicts students who present difficulties during an online learning course, based on two main learning indicators, namely engagement and performance rates, and (b) offers immediate support to students, tailored to the problem they are facing. To predict students’ issues, our approach considers ten machine learning algorithms of different types (standalone, ensemble, and deep learning) which are compared to determine the best performing ones. It has been experimented with a dataset collected from the Blackboard-Learn platform utilized in an engineering school called ESIEE-IT in France during 2021-2022 academic year, showing thus quite promising results.