Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEW ARTICLE Receptor Mosaics and Their Intramembrane Receptor-Receptor Interactions: Molecular Integration in Transmission and Novel Targets for Drug Development

The concept of intramembrane receptor-receptor interactions and evidence for their existence was introduced by Agnati and Fuxe in 1980/81 suggesting the existence of heteromerization of receptors. In 1982, they proposed the existence of aggregates of multiple receptors in the plasma membrane and coined the term receptor mosaics (RM). In this way, cell signaling becomes a branched process beginning at the level of receptor recognition at the plasma membrane where receptors can directly modify the ligand recognition and signaling capacity of the receptors within a RM. Receptor-receptor interactions in RM are classified as operating either with classical cooperativity, when consisting of homomers or heteromers of similar receptor subtypes having the same transmitter, or non-classical cooperativity, when consisting of heteromers. It has been shown that information processing within a RM depends not only on its receptor composition, but also on the topology and the order of receptor activation determined by the concentrations of the ligands and the receptor properties. The general function of RM has also been demonstrated to depend on allosteric regulators (e.g., homocysteine) of the receptor subtypes present. RM as integrative nodes for receptor-receptor interactions in conjunction with membrane associated proteins may form horizontal molecular networks in the plasma membrane coordinat-ing the activity of multiple effector systems modulating the excitability and gene expression of the cells. The key role of electrostatic epitope-epitope interactions will be discussed for the formation of the RM. These interactions probably represent a general molecular mechanism for receptor-receptor interactions and, without a doubt, indicate a role for phosphorylation-dephosphorylation events in these interactions. The novel therapeutic aspects given by the RMs will be discussed in the frame of molecular neurology and psychiatry and combined drug therapy appears as the future way to go.
Novel Canine Coronavirus Isolated from a Hospitalized Patient With Pneumonia in East Malaysia

Background. During the validation of a highly sensitive panspecies coronavirus (CoV) seminested reverse-transcription poly-merase chain reaction (RT-PCR) assay, we found canine CoV (CCoV) RNA in nasopharyngeal swab samples from 8 of 301 patients (2.5%) hospitalized with pneumonia during 2017–2018 in Sarawak, Malaysia. Most patients were children living in rural areas with frequent exposure to domesticated animals and wildlife. Methods. Specimens were further studied with universal and species-specific CoV and CCoV 1-step RT-PCR assays, and viral isolation was performed in A72 canine cells. Complete genome sequencing was conducted using the Sanger method. Results. Two of 8 specimens contained sufficient amounts of CCoVs as confirmed by less-sensitive single-step RT-PCR assays, and 1 specimen demonstrated cytopathic effects in A72 cells. Complete genome sequencing of the virus causing cytopathic effects identified it as a novel canine-feline recombinant alphacoronavirus (genotype II) that we named CCoV–human pneumonia (HuPn)–2018. Most of the CCoV-HuPn-2018 genome is more closely related to a CCoV TN-449, while its S gene shared significantly higher sequence identity with CCoV-UCD-1 (S1 domain) and a feline CoV WSU 79-1683 (S2 domain). CCoV-HuPn-2018 is unique for a 36-nucleotide (12–amino acid) deletion in the N protein and the presence of full-length and truncated 7b nonstructural protein, which may have clinical relevance. Conclusions. This is the first report of a novel canine-feline recombinant alphacoronavirus isolated from a human patient with pneumonia. If confirmed as a pathogen, it may represent the eighth unique coronavirus known to cause disease in humans. Our findings underscore the public health threat of animal CoVs and a need to conduct better surveillance for them.
The Burgeoning Epidemic of Morbid Obesity in Patients Undergoing Percutaneous Coronary Intervention Insight From the Blue Cross Blue Shield of Cardiovascular Consortium FOCUS ISSUE: CARDIOMETABOLIC RISK

This study sought to examine the prevalence and clinical implications of morbid obesity among patients undergoing percutaneous coronary intervention (PCI). Background Theprevalenceofobesity,andmorbidobesityinparticular,continuestoriserapidlyintheUnitedStates.Obesepatients are at increased risk for cardiac disease and are more likely to need invasive cardiac procedures. There is a paucity of contemporary data on the prevalence and clinical implications of morbid obesity among patients undergoing PCI. Methods We examined the prevalence of morbid obesity (body mass index [BMI] (cid:2) 40 kg/m 2 ) among 227,044 patients undergoing PCI and enrolled in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium registry from 1998 to 2009. Results The proportion of morbidly obese patients undergoing PCI increased from 4.38% in 1998 to 8.36% in 2009. Compared with overweight patients (BMI 25 to 30 kg/m 2 ), these patients had signi ﬁ cantly increased vascular complications (adjusted odds ratio [OR]: 1.31; 95% CI: 1.17 to 1.47; p < 0.0001), contrast-induced nephropathy (adjusted OR: 1.89; 95% CI: 1.70 to 2.11; p < 0.0001), nephropathy requiring dialysis (adjusted OR: 4.08; 95% CI: 2.98 to 5.59; p < 0.0001), and mortality (adjusted OR: 1.63; 95% CI: 1.33 to 2.00; p < 0.0001). Morbid in prevalence among patients PCI and with a higher risk of
Pattern analysis of the user behaviour in a mobile application using unsupervised machine learning

Continuously increasing amount of logged data increases the possibilities of finding new discoveries about the user interaction with the application for which the data is logged. Traces from the data may reveal some specific user behavioural patterns which can discover how to improve the development of the application by showing the ways in which the application is utilized. In this thesis, unsupervised machine learning techniques are used in order to group the users depending on their utilization of SEB Privat Android mobile application. The user interactions in the applications are first extracted, then various data preprocessing techniques are implemented to prepare the data for clustering and finally two clustering algorithms, namely, HDBSCAN and KMedoids are performed to cluster the data. Three types of user behaviour have been found from both K-medoids and HDBSCAN algorithm. There are users that tend to interact more with the application and navigate through its deeper layers, then the ones that consider only a quick check of their account balance or transaction, and finally regular users. Among the resulting features chosen with the help of feature selection methods, 73 % of them are related to user behaviour. The findings can be used by the developers to improve the user interface and overall functionalities of application. The user flow can thus be optimized according to the patterns in which the users tend to navigate through the application.
Results of the O ( α s ) two-loop virtual corrections to B → X s ℓ + ℓ − in the standard model

: We present the results of the O ( α s ) two-loop virtual corrections to the dif-ferential decay width d Γ( B → X s ℓ + ℓ − ) /d ˆ s , where ˆ s is the invariant mass squared of the lepton pair, normalized to m 2 b . Those contributions from gluon bremsstrahlung which are needed to cancel infrared and collinear singularities are also included. Our calculation is restricted to the range 0 . 05 ≤ ˆ s ≤ 0 . 25 where the eﬀects from resonances are small. The new contributions drastically reduce the renormalization scale dependence of existing results for d Γ( B → X s ℓ + ℓ − ) /d ˆ s . The renormalization scale uncertainty of the corresponding branching ratio (restricted to 0 . 05 ≤ ˆ s ≤ 0 . 25) gets reduced from ∼ ± 13% to ∼ ± 6 . 5%.
Time-Based Ensembles for Prediction of Rare Events in News Stream

Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when related social feedback is unavailable. The second strategy, a posteriori, is focused on predicting the popularity of news using related social feedback. However, both strategies present shortcomings related to data availability and time of prediction. To overcome such shortcomings, we propose a hybrid strategy of time-based ensembles using models from both strategies. Using news data from Google News and popularity data from Twitter, we show that the proposed ensembles significantly improve the early and accurate prediction of rare cases of highly popular news. Keywords—Social Media, Regression, Rare Cases, Time-based Ensemble
DNA Sequence Variation Associated With Elevated Fetal

The percentage of 0 y chains in the Hb F of 55 patients and $-thalassemia heterozygotes is generally 40%, but some have 60% to 70% To test the hypothesis that DNA sequence variation 1 58 base pairs 5’ of the 0 y gene is associated with this variation in O,,, values, DNA was analyzed using the restriction endonuclease Xmn I (‘yIVS-Il probe). Xmn I recognizes the sequence from 1 57 to 1 66 only if T is at position 1 58. Individuals from five families had T at 1 58 for 0 y genes in both chromosomes, and the mean value was 69.7% ± 4.6% (SD). For 13 families, individuals with T at 1 58 for the 0 y gene of one chromosome had a value of 60.6% ± 5.7%. With one
Estimating Air Pollution with Regulatory Monitors and Supplementary Data from Low-Cost Sensors and Remotely Sensed Imagery

This thesis has two main objectives: assessing the accuracy of long-term air pollution concentration estimates produced from portable monitoring campaigns, using temporal adjustments; and second, developing spatial models of air pollution concentrations with remotely sensed imagery and machine learning within a land use regression framework. Objective one was achieved by simulating mobile monitoring campaigns and estimating long-term concentrations with multiple temporal adjustments. The results indicated that sample size greatly influenced the accuracy of long-term estimates produced with temporal adjustments and that adjustments which accounted for the log-normal distribution of air pollution observations produced more accurate estimates. Objective two was achieved through the development of annual and seasonal models for NO2 and PM2.5 at three study sites. The results showed that deep learning models were the most accurate however these models could not capture spatial patterns of air pollution from the spectral signature of the land use surrounding monitors.
NO 2 and CO 2 Sensing Properties of LISICON-Based Sensor Operative at Room Temperature

Four types of sensor were fabricated by combining solid-state electrolyte (LISICON or NASICON) disks with sensing electrode materials, such as Au, indium tin oxide (ITO), and auxiliary phase (nitrate or carbonate). Their NO 2 and CO 2 sensing properties were examined under 30% relative humidity (RH) in the temperature range of 30 to 100°C. When the NO 2 sensor was operated in the range of 2 to 7 ppm NO 2 at 30°C, the LISICON sensor attached with ITO-NaNO 2 had the highest sensitivity to NO 2 and the electromotive force (EMF) change was 41.4 mV. At 30°C under 30% RH, the EMF values of the LISICON sensor attached with ITO-NaNO 2 were proportional to the logarithm of NO 2 concentration. Also, when the CO 2 sensor was operated in the range of 250 to 2,500 ppm CO 2 at 30°C, the LISICON sensor attached with ITO-Li 2 CO 3 -BaCO 3 had the highest sensitivity to CO 2 and the EMF change was 20.0 mV. At 30°C under 30% RH, the EMF values of the LISICON sensor attached with ITO-Li
Strategic Management of Intellectual Property: R&D Investment Appraisal Using Dynamic Bayesian Network

: Company executives are under increasing pressure to proactively evaluate the benefits of the huge amounts of investment into intellectual property (IP). The main goal of this paper is to propose a Dynamic Bayesian Network as a tool for modeling the forecast of the distribution of Research and Development (R&D) investment efficiency towards the strategic management of IP. Dynamic Bayesian Network provides a framework for handling the uncertainties and impression in the qualitative and quantitative data that impact the effectiveness and efficiency of investments on R&D. This paper specifies the process of creating the graphical representation using impactful variables, specifying numerical link between the variables and drawing inference from the network.
Accurate and Faster Than Real Time Simulation of Indoor Airflow by Using Fast Fluid Dynamics

The Multizone model is one of the most popular models for simulating indoor energy and airflow; however, its basic problem is that it cannot provide detailed and accurate airflow information. Computational fluid dynamics can be used to obtain detailed airflow information but is restricted due to its high computational cost. Therefore, it is necessary to develop a model which can provide detailed airflow information with reasonable accuracy and computational time. In this study, fast fluid dynamics, which has an unconditionally stable algorithm, is proposed. To investigate this model, four case studies of flow in a lid-driven cavity, flow in a channel, natural and forced flow convention are analyzed, and the results are compared and validated with computational fluid dynamics, experimental data, and analytical solution. The main focus of this study is increasing the computational speed of fast fluid dynamics. For this purpose, the sequence of equations has been modified and suitable numerical methods have been applied to solve each equation. Using the proposed fast fluid dynamics solver, the simulation time of the case studies has decreased between 52 to 94 percent compared to computational fluid dynamics and faster than real time simulation has been achieved on a conventional computer system.
The small EF-hand protein CALML4 functions as a critical myosin light chain within the intermicrovillar adhesion complex

Specialized transporting and sensory epithelial cells employ homologous protocadherin-based adhesion complexes to remodel their apical membrane protrusions into organized functional arrays. Within the intestine, the nutrient-transporting enterocytes utilize the intermicrovillar adhesion complex (IMAC) to assemble their apical microvilli into an ordered brush border. The IMAC bears remarkable homology to the Usher complex, whose disruption results in the sensory disorder type 1 Usher syndrome (USH1). However, the entire complement of proteins that comprise both the IMAC and Usher complex are not yet fully elucidated. influx and exocytosis in sensory hair cells.
Evidence-Based Approach to Fiber Supplements and Clinically Meaningful Health Benefits, Part 2 What to Look for and How to Recommend an Effective Fiber Therapy

Dietary fiber that is intrinsic and intact in fiber-rich foods (eg, fruits, vegetables, legumes, whole grains) is widely recognized to have beneficial effects on health when consumed at recommended levels (25 g/d for adult women, 38 g/d for adult men). Most (90%) of the US population does not consume this level of dietary fiber, averaging only 15 g/d. In an attempt to bridge this ‘‘fiber gap,’’ many consumers are turning to fiber supplements, which are typically isolated from a single source. Fiber supplements cannot be presumed to provide the health benefits that are associatedwith dietary fiber fromwhole foods. Of the fiber supplements on the market today, only a minority possess the physical characteristics that underlie the mechanisms driving clinically meaningful health benefits. In this 2-part series, the first part (previous issue) described the 4 main characteristics of fiber supplements that drive clinical efficacy (solubility, degree/rate of fermentation, viscosity, and gel formation), the 4 clinically meaningful designations that identify which health benefits are associated with specific fibers, and the gel-dependent mechanisms in the small bowel that drive specific health benefits (eg, cholesterol lowering, improved glycemic control). The second part (current issue) of this 2-part series will focus on the effects of fiber supplements in the large bowel, including the 2 mechanisms by which fiber prevents/relieves constipation (insoluble mechanical irritant and soluble gel-dependent water-holding capacity), the gel-dependent mechanism for attenuating diarrhea and normalizing stool form in irritable bowel syndrome, and the combined largebowel/small bowel fiber effects for weight loss/maintenance. The second part will also discuss how processing for marketed products can attenuate efficacy, why fiber supplements can cause gastrointestinal symptoms, and how to avoid symptoms for better long-term compliance. Nutr Today. 2015;50(2):90Y97
Viperin Gene Induction IFN-Mediated Antiviral Effects through IFN Regulatory Factor-1 Bypasses

Viperin is an antiviral protein whose expression is highly upregulated during viral infections via IFN-dependent and/or IFN-independent pathways. We examined the molecular alterations induced by the transcriptional activator IFN regulatory factor (IRF)-1 and found viperin to be among the group of IRF-1 regulated genes. From these data, it was not possible to distinguish genes that are primary targets of IRF-1 and those that are targets of IRF-1–induced proteins, like IFN- b . In this study, we show that IRF-1 directly binds to the murine viperin promoter to the two proximal IRF elements and thereby induces viperin expression. Infection studies with embryonal ﬁbroblasts from different gene knock-out mice demonstrate that IRF-1 is essential, whereas the type I IFN system is dispensable for vesicular stomatitis virus induced viperin gene transcription. Further, IRF-1, but not IFN type I, mediates the induction of viperin transcription after IFN- g treatment. In contrast, IRF-1 is not required for IFN-independent viperin induction by Newcastle disease virus infection and by infection with a vesicular stomatitis virus mutant that is unable to block IFN expression and secretion. We conclude that the IRF-1 mediated type I IFN independent mechanism of enhanced viperin expression provides a redundant mechanism to protect cells from viral infections. This mechanism becomes important when viruses evade innate immunity by antagonizing the induction and function of the IFN system. The Journal of Immunology , 2010, 184: 000–000.
Sudoku as a Constraint Problem

Constraint programming has finally reached the masses, thousands of newspaper readers (especially in the UK) are solving their daily constraint problem. They apply complex propagation schemes with names like “X-Wing” and “Swordfish” to find solutions of a rather simple looking puzzle called Sudoku. Unfortunately, they are not aware that this is constraint programming. In this paper we try to understand the puzzle from a constraint point of view, show models to solve and generate puzzles and give an objective measure of the difficulty of a puzzle instance. This measure seems to correlate well with grades (e.g. easy to hard) that are assigned to problem instances for the general public. We also show how the model can be strengthened with redundant constraints and how these can be implemented using bipartite matching and flow algorithms.
ANTIMICROBIAL ACTIVITY AND PHYTOCHEMICAL SCREENING OF CALLISTEMON CITRINUS EXTRACTS ON URINARY TRACT INFECTIONS

Urinary Tract Infection (UTI) is known to be caused due to microbial invasion of any tissues of the urinary tract. Several microorganisms and fungi like E.coli , Enterobacter aerogenes, Klebsiella pneumonae and Candida albicans have been known to cause Urinary tract infections. The present study involves the use of Callistemon citrinus leaf and flower extract for analyses of its anti microbial activity against such microorganisms. The results showed that the alcoholic leaf extract gave significant zone of inhibition of 2.2 to 3.5 cm against all the bacterial cultures as well as fungi. Candida albicans showed similar inhibition by both alcoholic and aqueous leaf extract.
Feasibility of Laparoscopic Surgery for Large Gastric Gastrointestinal Stromal Tumors: Systematic Review and Meta-analysis

Background: Surgical resection of gastric gastrointestinal stromal tumors (GISTs) via laparoscopy is the gold standard of treatment, but an open laparotomy is the preferred mode of treatment if the patient is unstable. This study aims at comparing the feasibility and safety of laparoscopic versus open resection for gastric GISTs larger than 5 cm. Methods: A systematic search was performed over different medical databases to identify general surgery studies, which studied the outcome of the laparoscopic group versus the open surgery group of large GIST patients. Furthermore, a meta-analysis was performed on blood loss and length of hospital stay (LOS) as primary efficacy outcomes and overall complications and recurrence rates as secondary safety outcomes. Results: Nine studies were identified involving 564 patients, 274 patients in the laparoscopic group, and 290 patients in the open surgery group. Our meta-analysis analysis showed a highly significant decrease in mean blood loss, LOS and complications rate in the laparoscopic group compared to the open surgery group ( p < 0.05), respectively. However, we also found a non-significant difference in the overall recurrence rate ( p > 0.05). Conclusion: To conclude, the laparoscopic approach was associated with decreased length of stay, a trend to less blood loss, and common complications compared with the open surgical approach, but there was no significant difference between both groups according to tumor recurrence.
Edinburgh Research Explorer Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

We present an experiment aimed at improving interpretation robustness of a tutorial dialogue system that relies on detailed semantic interpretation and dynamic natural language feedback generation. We show that we can improve overall interpretation quality by combining the output of a semantic interpreter with that of a statistical classiﬁer trained on the subset of student utterances where semantic interpretation fails. This improves on a previous result which used a similar approach but trained the classiﬁer on a substantially larger data set containing all student utterances. Finally, we discuss how the labels from the statistical classiﬁer can be integrated effectively with the dialogue system’s existing error recovery policies.