Miracle-dz/UniFAR-FTUs huggingface dataset:
Metformin Preserves β-Cell Compensation in Insulin Secretion and Mass Expansion in Prediabetic Nile Rats

Prediabetes is a high-risk condition for type 2 diabetes (T2D). Pancreatic β-cells adapt to impaired glucose regulation in prediabetes by increasing insulin secretion and β-cell mass expansion. In people with prediabetes, metformin has been shown to prevent prediabetes conversion to diabetes. However, emerging evidence indicates that metformin has negative effects on β-cell function and survival. Our previous study established the Nile rat (NR) as a model for prediabetes, recapitulating characteristics of human β-cell compensation in function and mass expansion. In this study, we investigated the action of metformin on β-cells in vivo and in vitro. A 7-week metformin treatment improved glucose tolerance by reducing hepatic glucose output and enhancing insulin secretion. Although high-dose metformin inhibited β-cell glucose-stimulated insulin secretion in vitro, stimulation of β-cell insulin secretion was preserved in metformin-treated NRs via an indirect mechanism. Moreover, β-cells in NRs receiving metformin exhibited increased endoplasmic reticulum (ER) chaperones and alleviated apoptotic unfold protein response (UPR) without changes in the expression of cell identity genes. Additionally, metformin did not suppress β-cell mass compensation or proliferation. Taken together, despite the conflicting role indicated by in vitro studies, administration of metformin does not exert a negative effect on β-cell function or cell mass and, instead, early metformin treatment may help protect β-cells from exhaustion and decompensation.
Ultrasensitive detection of tumor‐specific mutations in saliva of patients with oral cavity squamous cell carcinoma

Oral cavity squamous cell carcinoma (OCSCC) is the most common head and neck malignancy. Although the survival rate of patients with advanced‐stage disease remains approximately 20% to 60%, when detected at an early stage, the survival rate approaches 80%, posing a pressing need for a well validated profiling method to assess patients who have a high risk of developing OCSCC. Tumor DNA detection in saliva may provide a robust biomarker platform that overcomes the limitations of current diagnostic tests. However, there is no routine saliva‐based screening method for patients with OCSCC.
Summary of over Fifty Years with Brain-Computer Interfaces—A Review

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.
PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10−5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.
The mutational landscape and prognostic indicators of pseudomyxoma peritonei originating from the ovary

Pseudomyxoma peritonei (PMP) is a rare disorder with unique pathological and genetic changes. Although several studies have reported the clinical features and mutational changes of PMP that originates from the appendix, few studies on PMP originating from the ovary have been reported due to its extreme rarity. In order to characterize the somatic mutational landscape and to investigate the prognosis predicting factors of ovary‐originating PMP, we examined 830 cases of PMP and identified 16 patients with PMP that originated from the ovary. Whole‐exome sequencing (WES) was performed on 12 cases using formalin‐fixed, paraffin‐embedded (FFPE) tissue samples. We found that 25% (3/12) of the patients carried mutations in cancer driver genes, including TP53, ATM and SETD2, and 16.7% (2/12) of the patients carried mutations in cancer driver genes, including ATRX, EP300, FGFR2, KRAS, NOCR1 and RB1. The MUC16 (58.33%), BSN (41.67%), PCNT (41.67%), PPP2R5A (41.67%), PRSS36 (41.67%), PTPRK (41.67%) and SBF1 (41.67%) genes presented the highest mutational frequencies. The PI3K‐Akt signaling pathway, human papillomavirus infection pathway, cell skeleton, cell adhesion, and extracellular matrix and membrane proteins were the major pathways or functions that were affected. Patients were followed up to 174 months (median: 48.26 months). The 5‐year OS rate for all patients was 71.2% and the median OS was not reached. PTPRK mutations, presurgical CA199 level, completeness of cytoreduction (CCR) and peritoneal cancer index (PCI) were identified as potential predictive factors for patient survival. In conclusion, the mutational landscape for ovary‐originating PMP was revealed and exhibited unique features distinct from appendix‐originating PMP. PTPRK, CA199, CCR and PCI may predict patient survival.
COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning

Today’s societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March–April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government’s 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them.
Energy balance for single or multiple droplet chain s impinging onto a hot slab in the Leidenfrost regime

This work aims at estimating the energy balance of a single droplet chain or multiple droplet chains i mpinging onto a hot wall whose initial temperature is ab ove the Leidenfrost point. The experimental data (t emperature of incoming and outcoming droplets, heat flux at th e wall,…) are required for validating submodels for heat transfer between a hot wall and sprays. Indeed, the se physical submodels are subsequently used in CFD modeling. In the Leidenfrost regime, impinging droplets can experience only three different regimes : perfe ct bouncing, bouncing with a satellite formation and splashing. Modeling heat transfer in these regimes requires th e knowledge of several parameters such as the spreading di ameter, heat transfer at the wall, the thickness of the vapour layer, its temperature as well as the liquid heatin g during the impingement and the evaporation rate. In the splashing regime, data about secondary droplets are also required. Up to now, no experimental data bas e giving all these parameters are available because of the d ifficulty to measure simultaneously all these param eters. In this experimental work, some of these parameters have been measured simultaneously during the interaction o f the droplet with the heated wall while other ones have been estimated by means of an energy balance.
MAGNETIC RESONANCE IN FERROMAGNETIC DILUTED MAGNETIC SEMICONDUCTORS

diluted magnetic semiconductors (e.g. Ga1-xMnxAs, Cd1-xMnxTe, Cd1-xCrxSe). We study the magnetic anisotropy in Cd1-xCrxTe from angular dependence of the resonance field in FMR experiment . The samples were obtained from the melt with nominal concentration x≈0.04. The temperature dependence of the EPR linewidth points on a room Curie temperature.
Fungus-mediated biological approaches towards green synthesis of oxide nanomaterials

A promising avenue of research in materials science is to follow the strategies used by nature to fabricate ornate hierarchical materials. For many ages, organisms have been engaged in on-the-job testing to craft structural and functional materials and have evolved extensively to possibly create the best-known materials. Some of the strategies used by nature may well have practical implications in the world of nanomaterials. Therefore, the efforts to exploit nature’s ingenious work in designing strategies for nanomaterials synthesis has led to biological routes for materials synthesis. This review outlines the biological synthesis of a range of oxide nanomaterials that has hitherto been achieved using fungal biosynthesis routes. A critical overview of the current status and future scope of this field that could potentially lead to the microorganism-mediated commercial, large-scale, environmentally benign, and economically-viable ‘green’ syntheses of oxide nanomaterials is also discussed.
Carbon Nanotubes/Poly(1,2‐diaminobenzene) Nanoporous Composite Film Electrode Prepared by Multipulse Potentiostatic Electropolymerisation and Its Application to Determination of Trace Heavy Metal Ions

A novel method for the fabrication of carbon nanotubes/poly(1,2-diaminobenzene) nanoporous composite based electrode was proposed. By multipulse potentiostatic electropolymerization, the multi-walled carbon nanotubes (MWNTs) and poly(1,2-diaminobenzene) were deposited simultaneously on the electrode surface. Compared with the composite prepared by the traditional potentiodynamic method, the composite synthesized by multipulse potentiostatic method has a unique nanoporous structure, exhibits excellent conductivity and better environmental stability. The surface of the resulting electrode was characterized with scanning electron microscopy (SEM), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The nanoporous composite film modified glassy carbon electrode was also used successfully for the simultaneously voltammetric determination of trace level of Cd2+ and Cu2+ at first-time. Under the optimal conditions, the determination limits of 0.25 and 0.33 ppb for Cd2+ and Cu2+ were obtained, respectively. The calibration graphs were linear in the concentration range of 5–100 ppb. The electrode system provides an excellent platform for ultra sensitive electrochemical sensors for chemical and biological sensing.
Physiological responses of piau (Leporinus friderici, Bloch 1794) to transportation

This study evaluated stress indicators of juvenile piau (Leporinus friderici) during and after a 4-hour transportation in order to establish an appropriate transportation protocol for this type of fish. Fish were transported in plastic bags (133.1 g/L) and sampled before loading, during 1, 2, 3 and 4 h and after transportation (2, 6, 12 and 24 h). Blood samples were analyzed for cortisol and glucose levels, hematocrit, hemoglobin level, number and mean corpuscular volume of erythrocytes. Water pH, dissolved oxygen, temperature and ammonia were monitored before, during and after transportation. No mortality was observed through the experiment. Ammonia levels increased throughout transportation, but the low pH values kept NH3 in safe levels for fish. Cortisol levels increased within 4 h of transportation, and returned to control condition 2 h after arrival. Plasma glucose increased within one hour of transportation, reaching peak value within 4 h and returning to initial condition 2 h after arrival. Erythrocyte number and hemoglobin levels showed the lowest levels 2 h after arrival, and mean corpuscular volume increased during transportation, decreasing at 12 and 24 h after arrival. Transporting piau is stressful, but fish recover the initial condition in short time, showing tolerance to the changes in the water quality parameters.
Enhanced production of thermostable lipase from Bacillus cereus ASSCRC-P1 in waste frying oil based medium using statistical experimental design

This study aim to isolate, identify and optimize lipase producing strains using frying oil waste. Nine strains were isolated from an Egyptian soil samples. Among the isolates, a potent bacterial candidate ASSCRC-P1 was found to be the most potent lipase producer strain at 60 °C. Genotypic identification of ASSCRC-P1 showed 94% similarity with Bacillus sp. strains. Phylogenetic tree confirmed that ASSCRC-P1 was nearly similar to Bacillus cereus. Therefore, it was given the name Bacillus cereus ASSCRC-P1 and its 16S rRNA nucleotide has been deposited in the GenBank Data Library under the accession number: KJ531440. A sequential optimization strategy, based on statistical experimental designs, was employed to enhance the lipase production by this strain. A 2-level Plackett–Burman design was applied to differentiate between the bioprocess parameters that significantly influence lipase production followed by Box-Behnken design to optimize the amounts of variables which have the highest positive significant effect on lipase production. Overall more than 2.15-fold improvement in lipase production was achieved due to optimization compared to that obtained using the basal medium.
PROTEIN REQUIREMENT AND NON PROTEIN NITROGEN UTILIZATION 1

Factors governing nitrogen utilization by dairy cattle include the following: (a) Maintenance of ruminal ammonia nitrogen in excess of 5 mg/100 ml rumen fluid has no effect on microbial protein production; (b) Supplemental NPN is not utilized in typical dairy and feedlot beef rations containing, more than 12 to 13% CP (DM basis); © NPN is approximately equal to true protein as a nitrogen source in typical dairy and feedlot rations containing not more than 12 to 13% CP; (d) A scheme based upon metabolizable protein (absorbable protein) for calculating requirements and comparing protein sources in superior to the crude or digestible protein designations. Ultimate expression of the requirement may be in terms of CP for the sake of simplicity; (e) One kg of CP regardless of nitrogen source, equals about . 75 kg metabolizable protein in typical dairy and feedlot beef rations containing not more than 12 to 13% CP. One kg of plant protein (true protein) fed in excess of an amount equivalent to 12 to 13% dietary protein equals about 0.3 kg metabolizable protein; (f) Protein supplementation of lactating cows might better be related to stage of lactation than level of milk production; (g) Lactating cows having above-average lactation Al ability may benefit from dietary protein levels reaching as high as 16 to 18% (DM basis) during the first third of lactation; (h) Cows in the latter two-thirds of lactation appear to require 12.5% dietary protein or less; (I) Plant protein (true protein) should be the supplemental source of nitrogen during the first third of lactation, with NPN providing most, if not all, the supplemental nitrogen during the last two-thirds of lactation.
An Experimental Investigation of Heat Transfer, Transition and Separation on Turbine Blades at Low Reynolds Number and High Turbulence Intensity.

Abstract : The effects of turbulence intensity on the heat transfer distribution, transition and flow separation on a turbine blade was investigated at low Reynolds numbers. Measurements were performed in linear cascades (at both UCDavis and the USAF Academy) at low Reynolds number (67,000 to 144,000) representative of low pressure turbine stages at high altitude. Nominal turbulence intensities of 1% and 10% (generated with biplane lattice grids) were used. The heat transfer was measured with the uniform heat flux (UHF) or heated-coating method. The heated-coating was a gold-film and liquid crystals were used for the surface temperature measurement. A novel laser-tuft surface flow visualization method was also used. For low turbulence levels (1%) the pressure side of the blade exhibited streaks of varying heat transfer possibly associated with Taylor-Gortler vortices. With grid turbulence (10%) these streaks disappeared on the pressure side and the heat transfer nearly doubled. Gird turbulence also increased the heat transfer on the leading edge and suction surface, while advancing the location of boundary layer transition. Good agreement was generally found between the UCDavis and USAFA data. These cascade results compare favorably to those that have been reported with rotation.
Condensation Flow Mechanisms in Microchannels: Basis for Pressure Drop and Heat Transfer Models

This paper presents an overview of the use of flow visualization in micro- and mini-channel geometries for the development of pressure drop and heat transfer models during condensation of refrigerants. Condensation flow mechanisms for round, square, and rectangular tubes with hydraulic diameters in the range of 1–5 mm for 0 < x < 1, and 150 kg/m2-s and 750 kg/m2-s were recorded using unique experimental techniques that permit flow visualization during the condensation process. The effect of channel shape and miniaturization on the flow regime transitions was documented. The flow mechanisms were categorized into four different flow regimes: intermittent flow, wavy flow, annular flow, and dispersed flow. These flow regimes were further subdivided into several flow patterns within each regime. It was observed that the intermittent and annular flow regimes become larger as the tube hydraulic diameter is decreased, and at the expense of the wavy flow regime. These maps and transition lines can be used to predict the flow regime or pattern that will be established for a given mass flux, quality, and tube geometry. These observed flow mechanisms, together with pressure drop measurements, are being used to develop experimentally validated models for pressure drop during condensation in each of these flow regimes for a variety of circular and noncircular channels with 0.4 < Dh < 5 mm. These flow regime-based models yield substantially better pressure drop predictions than the traditionally used correlations that are primarily based on air-water flows for large diameter tubes. Condensation heat transfer coefficients were also measured using a unique thermal amplification technique that simultaneously allows for the accurate measurement of the low heat transfer rates over small increments of refrigerant quality and high heat transfer coefficients characteristic of microchannels. Models for these measured heat transfer coefficients are being developed using the documented flow mechanisms and the corresponding pressure drop models as the basis.
CO2 and CH4 permeation through zeolitic imidazolate framework (ZIF)-8 membrane synthesized via in situ layer-by-layer growth: an experimental and modeling study

In this work, a general model representing the permeation of CO2 and CH4 through Zeolitic Imidazole Framework-8 (ZIF-8) membrane synthesized via in situ layer-by-layer growth under microwave irradiation is developed. The model is formed based on the pressure drop concept in order to predict the intercrystalline properties of the ZIF-8 membrane according to the experimental permeation data of CO2 and CH4. The model combines Knudsen diffusion, viscous flow and generalized Maxwell–Stefan models, which considered the support resistance, gas diffusivity and intercrystalline pores of the membrane layer. The simulated data are fitted well with the experimental gas permeation results and consistent with the physical characterizations, including X-ray diffraction (XRD) and scanning electron microscopy (SEM). Results showed that, layer-by-layer growth managed to reduce the intercrystalline pores present in the ZIF-8 membrane layer, with the approximate pore radius of 2.1 × 10−7 m and porosity of 1.15 × 10−4. However, the presence of the small pores can significantly affect the performance of the ZIF-8 membrane which resulted in CO2/CH4 ideal selectivity of ∼1.