Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of Strengthening the Pedagogic Competence of Islamic Religious Education Teachers in Improving the Quality of Education in Junior High Schools in Jember Regency

Teacher competence is one of the supporters of the successful implementation of education. The purpose of this study will be to explain the competency strengthening model for Islamic religious education teachers carried out by junior high schools in Jember Regency. This research is a qualitative research with a narrative approach. The main data sources in this study consisted of school principals and teachers in the district of Jember totaling 10 people consisting of Islamic religious education teachers and school principals. The selection of participants used purposive sampling technique. Data collection techniques were carried out by in-depth interviews and observations. The analytical technique used in processing the data is by reducing the data, presenting the results and interpreting the data. The results showed that there were three models of strengthening teachers' pedagogic competencies. First, training to equip and strengthen the knowledge, skills and attitudes of teachers in teaching. Second, mentoring is carried out to direct teachers in strengthening skills. Third, the supervision carried out by the principal to measure and provide an assessment of the competence of teachers who have been trained and assisted. The research that has been produced is expected to be the basis for making policies on strengthening competence for teachers in the future. Subsequent more in-depth research is very helpful in adding information about similar research.
Micro-magnetic stimulation of primary visual cortex induces focal and sustained activation of secondary visual cortex

Cortical visual prostheses that aim to restore sight to the blind require the ability to create neural activity in the visual cortex. Electric stimulation delivered via microelectrodes implanted in the primary visual cortex (V1) has been the most common approach, although conventional electrodes may not effectively confine activation to focal regions and thus the acuity they create may be limited. Magnetic stimulation from microcoils confines activation to single cortical columns of V1 and thus may prove to be more effective than conventional microelectrodes, but the ability of microcoils to drive synaptic connections has not been explored. Here, we show that magnetic stimulation of V1 using microcoils induces spatially confined activation in the secondary visual cortex (V2) in mouse brain slices. Single-loop microcoils were fabricated using platinum–iridium flat microwires, and their effectiveness was evaluated using calcium imaging and compared with that of monopolar and bipolar electrodes. Our results show that compared to the electrodes, the microcoils better confined activation to a small region in V1. In addition, they produced more precise and sustained activation in V2. The finding that microcoil-based stimulation propagates to higher visual centres raises the possibility that complex visual perception, e.g. that requiring sustained synaptic inputs, may be achievable. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
Preface to ‘Advanced neurotechnologies: translating innovation for health and well-being’

In the nervous system, huge amounts of neurons constantly induce and transmit electrophysiological signals to communicate between neurons and brain regions. Innovative neurotechnologies have added significantly to neural engineering and have led directly to many fundamental scientific insights into the function of the central and peripheral nervous systems. This issue addressed the novel approaches toward biocompatible neural interfaces, thin and flexible electronics and wireless circuits and systems, which will allow readers to identify the requirements, challenges and future directions related to biointegrated implantable neurotechnologies. Additionally, this special issue aims to report the latest advances and future trends of critical techniques and frameworks in implantable neural devices, which will allow biomedical researchers to identify new opportunities. The theme issue starts with a research article by Lee & Fried [1], which aims to show that innovative magnetic stimulation of the visual cortex (V1) using microcoils induces spatially confined activation in the secondary visual cortex (V2) in mouse brain slices. They demonstrated that, compared with the traditional electrical stimulation, the microcoils-based magnetic stimulation is better for confining the activation to a small region in V1 and produces more precise and sustained
Immersed Lagrangian Floer Theory

Let (M,w) be a compact symplectic manifold, and L a compact, embedded Lagrangian submanifold in M. Fukaya, Oh, Ohta and Ono construct Lagrangian Floer cohomology for such M,L, yielding groups HF^*(L,b;\Lambda) for one Lagrangian or HF^*((L,b),(L',b');\Lambda) for two, where b,b' are choices of bounding cochains, and exist if and only if L,L' have unobstructed Floer cohomology. These are independent of choices up to canonical isomorphism, and have important invariance properties under Hamiltonian equivalence. Floer cohomology groups are the morphism groups in the derived Fukaya category of (M,w), and so are an essential part of the Homological Mirror Symmetry Conjecture of Kontsevich. The goal of this paper is to extend all this to immersed Lagrangians L in M with immersion i : L --> M, with transverse self-intersections. In the embedded case, Floer cohomology HF^*(L,b;\Lambda) is a modified, 'quantized' version of cohomology H^*(L;\Lambda) over the Novikov ring \Lambda. In our immersed case, HF^*(L,b;\Lambda) turns out to be a quantized version of the sum of H^*(L;\Lambda) with a \Lambda-module spanned by pairs (p,q) for p,q distinct points of L with i(p)=i(q) in M. The theory becomes simpler and more powerful for graded Lagrangians in Calabi-Yau manifolds, when we can work over a smaller Novikov ring \Lambda_{CY}. The proofs involve associating a gapped filtered A-infinity algebra over \Lambda or \Lambda_{CY} to i : L --> M, which is independent of nearly all choices up to canonical homotopy equivalence, and is built using a series of finite approximations called A_{N,0} algebras for N=0,1,2,.
An adaptive voltage‐regulation control strategy of an electric spring based on output current feedback

With the increasing penetration of distributed generation (DG) in power systems, the voltage instability problem of power systems is receiving a great deal of attention. As new power electronics equipment, an electric spring (ES) can effectively restrain the bus voltage fluctuation caused by DG, and in addition achieve the new control paradigm of load demand following power generation. In this article, an adaptive voltage‐regulation control strategy of ES is proposed, in which the proportional integral (PI) postgain changes dynamically according to the real‐time output current of the ES. The simulation results show that, when the proposed control strategy is applied, bus voltage can still be stabilized effectively by the ES under the condition that noncritical loads change. The proposed control strategy brings the adaptive voltage‐regulation capability of the ES, and therefore, it can be in series with multiple noncritical loads reliably and safely, and accordingly the costs can be greatly reduced. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Stochastic Homogenization of Reflected Diffusion Processes

We investigate a functional limit theorem (homogenization) for Reflected Stochastic Differential Equations on a half-plane with stationary coefficients when it is necessary to analyze both the effective Brownian motion and the effective local time. We prove that the limiting process is a reflected non-standard Brownian motion. Beyond the result, this problem is known as a prototype of non-translation invariant problem making the usual method of the "environment as seen from the particle" inefficient.
Flight Muscle Development in Locusta migratoria: Effects of implantation of Corpora Allata on the Attainment of Metabolic Enzyme Activities

The effects of corpora allata (CA) implantation on the development of the dorsolongitudi- nal flight muscles and the wings and on the pigmentation in Locusta migratoria were investi- gated. Fifth-instar female larvae O-24 hr after ecdysis received either one pair of CA taken from adult females or one, two, three, or four pairs of CA taken from young fourth-instar larvae. Implantation of one, two, or (except for a few cases) three pairs of larval CA did not produce any effect. However, after implantation of four pairs of larval CA, 50% of the insects arising from the subsequent molt displayed predominantly larval characteristics regarding pigmentation and wings. This phenomenon was never observed in the locusts implanted with adult CA, since the ecdysis following the implantation always resulted in the production of individuals with predominantly adult characteristics. Implantation of either adult CA or four pairs of larval CA gave rise to an extra larval stage and consequently an extra (sixth) ecdysis. During this extra larval period, flight muscle development appeared to be retarded in comparison with control insects, as was reflected by lower specific activities of enzymes indicative of aerobic metabolic pathways, such as carbohydrate oxidation and, in particular, fatty acid oxidation. Implantation of larval CA or adult CA resulted in essen-tially the same pattern of enzyme activities. The activity of lactate dehydrogenase, which in the control insects reached its maximum value around the fifth (imaginal) ecdysis, was in the CA-implanted locusts at its maximum value at the extra (sixth) ecdysis, which may be indicative of the period of invagination of tracheoblasts into the muscle fibers. The correla-tion between the JH titer and the prevention or inhibition of flight muscle developmental processes is discussed.
Internal Note Higgs cross section at NLO+NLL

We consider the inclusive production of a Higgs boson in gluon gluon fusion and we study the impact of threshold resummation at next-to-next-to-next-to-leading logarithmic accuracy (NLL) on the recently computed fixed-order prediction at next-to-next-to-next-to-leading order (NLO). In order to facilitate comparisons with other studies, we focus on the rescaled large-mt effective theory (rEFT). We propose a conservative, yet robust way of estimating the perturbative uncertainty from missing higher (fixedor logarithmic-) orders. 1 Threshold resummation(s) In this note we update our results on threshold resummation for Higgs production [1] in view of the recently computed NLO result [2, 3, 4, 5]. We discuss the impact of the resummation on the central value and we suggest a robust way of estimating the theoretical uncertainty. In order to fix the notation, we write the hadron-level cross section for the production of a Higgs boson in hadron-hadron collisions as σ(τ,mH,m 2 t ) = τ ∑
365-xxx Tourism 2020 04ENG.indd

Th is study inspects the dangers of being inauthentic while posing as apostles of authentic, ethnic service providers in tourism. Th e concept of authenticity was adopted to understand how the commodifi cation of cultural features, especially food, of a multiethnic destination infl uences the realness of traditional cuisine. Th e study was conducted in Goa, India, also known as tourist Mecca. Th e research fi ndings demonstrate that tourism acts as a dominant player in creating a transfi gurative replica of tourist's expectations. Th is makes touristifi ed versions of traditional foods, severely infl uencing the integrity of regional cuisines. Th ese results are useful in understanding how inauthentic practices challenge the cultural identity of the destination.
Prevalence and Trends in Gestational Diabetes Mellitus Among Women in the United States, 2006–2017: A Population-Based Study

The prevalence of gestational diabetes mellitus (GDM) has increased with the increasing rate of obesity. However, national data on the prevalence and secular trends of GDM during the past decade in the United States are lacking. This study included 37,357 women aged more than 18 years and who had ever been pregnant from the National Health Interview Survey (NHIS). We examined GDM prevalence in 2006, 2016, and 2017, with age-standardized to the US population in 2000. We found that the prevalence of GDM per 100 people increased from 4.6 (95% CI, 4.1–5.1) in 2006 to 8.2 (95% CI, 7.5–8.9) in 2016 (test for difference; P <0.001), with a relatively increased rate of 78%. Non-Hispanic white women tended to have a lower increase (2.8%) than non-Hispanic black women (3.8%), Hispanic women (4.1%), and women of other race/ethnicity (8.4%). The prevalence of GDM in non-Hispanic white women was higher than that in non-Hispanic black women in 2006 (4.8% vs 3.5%, P = 0.006); such differences became non-significant in 2016 (P = 0.72). Additionally, the increase of GDM from 2006 to 2016 tended to be more evident among women who were overweight (25≤ BMI ≤30 kg/m2), physically inactive, and with family income below the poverty threshold than women in other BMI ranges, with more physical activity, and with higher incomes. The prevalence of GDM per 100 people in 2017 was 8.4 (7.6–9.2), and there was no significant change in the overall and subgroup prevalence compared with 2016. Collectively, in the United States, the prevalence of GDM continuously increased, nearly doubled, from 2006 to 2016, and then leveled off in 2017. The increase appeared more marked among the minority populations and subpopulations with overweight people, insufficient activity, and family incomes below the poverty threshold.
The rs10830963 Polymorphism of the MTNR1B Gene: Association With Abnormal Glucose, Insulin and C-peptide Kinetics

Background The MTNR1B gene encodes a receptor for melatonin, a hormone regulating biorhythms. Disruptions in biorhythms contribute to the development of type 2 diabetes mellitus (T2DM). Genetic studies suggest that variability in the MTNR1B gene affects T2DM development. Our aim was to compare the distribution of the genetic variant rs10830963 between persons differing in glucose tolerance in a sample of the Czech population (N=1206). We also evaluated possible associations of the polymorphism with insulin sensitivity, beta cell function, with the shape of glucose, insulin and C-peptide trajectories measured 7 times during a 3-hour oral glucose tolerance test (OGTT) and with glucagon response. In a subgroup of 268 volunteers we also evaluated sleep patterns and biorhythm. Results 13 persons were diagnosed with T2DM, 119 had impaired fasting blood glucose (IFG) and/or impaired glucose tolerance (IGT). 1074 participants showed normal results and formed a control group. A higher frequency of minor allele G was found in the IFG/IGT group in comparison with controls. The GG constellation was present in 23% of diabetics, in 17% of IFG/IGT probands and in 11% of controls. Compared to CC and CG genotypes, GG homozygotes showed higher stimulated glycemia levels during the OGTT. Homozygous as well as heterozygous carriers of the G allele showed lower very early phase of insulin and C-peptide secretion with unchanged insulin sensitivity. These differences remained significant after excluding diabetics and the IFG/IGT group from the analysis. No associations of the genotype with the shape of OGTT-based trajectories, with glucagon or with chronobiological patterns were observed. However, the shape of the trajectories differed significantly between men and women. Conclusion In a representative sample of the Czech population, the G allele of the rs10830963 polymorphism is associated with impaired early phase of beta cell function, and this is evident even in healthy individuals.
Chemical Neural Networks Inside Synthetic Cells? A Proposal for Their Realization and Modeling

The exciting sci-tech arena of synthetic biology (SB) provides concepts, tools, and approaches for fundamental revolutions in basic and applied research. SB plays a key role when it is conceived as one of the branches of the “sciences of the artificial” (Cordeschi, 2002; Damiano et al., 2011), together with artificial intelligence (AI) and robotics. In particular, SB contributes to the wetware approaches, which are complementary to the software and hardware ones that characterize the other two most well-known branches. In this perspective, SB offers the unique opportunity of devising novel chemical versions of AI, whose main feature is embodiment, i.e., forms, systems, networks that compute through physical interactions (not based on the abstract representations typical of AI), and that potentially display autonomous adaptive/plastic dynamics (in contrast to mechanical robots). SB, then, can be seen as an experimental platform for unconventional computing, based on (bio)chemicals, organized structures, and reactions. Operations, even when interpreted by observers in terms of logical representations, actually lie in the material domain. As such, operations (reactions, interactions, synthesis and degradation of the operators) and the operators themselves (the molecules performing or subjected to the operations) are truly interwoven, and definitely cancel the distinction between hardware and software, typical of non-chemical machines. In this opinion paper, we aim at sketching a possible implementation of embodied, chemical AI by means of SB tools. In particular, we will focus on bottom-up approaches and on the socalled synthetic (or artificial) cells (SCs or ACs) (Luisi 2002; Salehi-Reyhan et al., 2017; Göpfrich et al., 2018; Guindani et al., 2022), Figure 1A. In the past few years, indeed, the worldwide community of SC practitioners has generated a very relevant momentum, promoted by the onset of numerous consortia and projects (Schwille et al., 2018; Frischmon et al., 2021). The question we would like to deal with is the following: is it possible to devise minimal forms of perceptive chemical AI in SCs? Because of its widespread relevance since the beginning of AI, the system we look at is a chemical perceptron (a chemical neural network), and we will discuss its possible implementation inside SCs. We will first introduce the motif of “phospho-neural networks” (Hellingwerf et al., 1995) and a plan for implanting such networks in SCs, calling for a specific design that would address both experimental feasibility, detailed modeling, and non-trivial behavior. We also suggest that the exploration and the interpretation of chemical networks’ dynamics, especially when they are based on macromolecular elements, is best pursued according to fuzzy logic. Finally, a short comment on the theoretical relevance of these approaches on the more general problem of embodying AI in the chemical domain will complete the paper. Edited by: Ovidiu Radulescu, Université de Montpellier, France
Polarization as a Discriminator of Light-Absorbing Impurities in or Above Snow

This conceptual study presents advanced radiative transfer computations of light polarization originating from a snowpack consisting of nonspherical grains and variable content of light-absorbing impurities, either embedded in the snowpack or (with the same optical properties) lofted above it in the form of atmospheric aerosols. The results highlight the importance of considering shapes other than spherical for the snow grains, which otherwise can lead to non-negligible errors in the retrieval of snow albedo from remote sensing observations. More importantly, it is found that polarimetric measurements provide a means to partition light-absorbing impurities embedded in the snowpack from absorbing aerosols aloft, a task traditionally prohibitive for sensors capable exclusively of measurements of total reflectance. Heritage techniques to obtain snow grain size from shortwave infrared observations of total reflectance are well established, as are those that leverage polarimetric, multiangular observations across the entire optical spectrum to characterize the optical and microphysical properties of atmospheric aerosols. The polarization signatures of near-infrared (e.g., 864 nm) observations carry critical information on snow grain shape. The prospected launch of space-borne polarimeters with proven accuracy, therefore, advocates for the development of data inversion schemes, to boost the accuracy of simultaneous retrievals of atmospheric and surface parameters in the polar and snow-covered regions, critical to climate studies.
Modeling Complex Seasonal Avian Migration: Predictions From the Thermal Environment and Resource Availability

Billions of birds undertake long-distance migration and the complexity of schedules has only recently become clear. Such movements occur as a response to seasonality but the ultimate drivers of these changing distributions remain difficult to study directly. Modeling seasonal distributions based fundamentally on climate and vegetation without parameterizing with empirical data, we focus on the potential role of ambient temperature and available resources in shaping the migratory program. We simulate the complete annual cycle over the Afro-Palearctic region in a round-trip migration model allowing full variation in the extent and timing of movement, and multiple stopovers. The resultant simulated tracks and associated environmental metrics are interrogated: we evaluate the thermal and resource consequences of staying in Europe versus crossing the Sahara, and secondly identify the movement patterns optimizing exposure to green vegetation and local surpluses. There is a distinct thermal gain from crossing the Sahara and the pattern emerging of optimal seasonal vegetation resembles contemporary migration routes regarding Sahara crossing, loop structure and itinerancy. Thus, our first-principle simulations suggest that variations in migration patterns among species are caused by a complex trade-off between risks and rewards of staying versus moving, including innate physiological constraints and the resultant gain of the high-risk Sahara crossing.
Repurposing the Sphingosine-1-Phosphate Receptor Modulator Etrasimod as an Antibacterial Agent Against Gram-Positive Bacteria

New classes of antibiotics are urgently needed in the fight against multidrug-resistant bacteria. Drug repurposing has emerged as an alternative approach to accelerate antimicrobial research and development. In this study, we screened a library of sphingosine-1-phosphate receptor (S1PR) modulators against Staphylococcus aureus and identified five active compounds. Among them, etrasimod (APD334), an investigational drug for the treatment of ulcerative colitis, displayed the best inhibitory activity against S. aureus when growing as free-floating planktonic cells and within biofilms. In follow-up studies, etrasimod showed bactericidal activity and drastic reduction of viable bacteria within 1 h of exposure. It also displayed a potent activity against other Gram-positive bacteria, including penicillin- and methicillin-resistant S. aureus strains, S. epidermidis, and Enterococcus faecalis, with a minimum inhibitory concentration (MIC) ranging from 5 to 10 μM (2.3–4.6 μg/mL). However, no inhibition of viability was observed against Gram-negative bacteria Acinetobacter baumannii, Escherichia coli, and Pseudomonas aeruginosa, showing that etrasimod preferably acts against Gram-positive bacteria. On the other hand, etrasimod was shown to inhibit quorum sensing (QS) signaling in Chromobacterium violaceum, suggesting that it may block the biofilm formation by targeting QS in certain Gram-negative bacteria. Furthermore, etrasimod displayed a synergistic effect with gentamicin against S. aureus, thus showing potential to be used in antibiotic combination therapy. Finally, no in vitro toxicity toward mammalian cells was observed. In conclusion, our study reports for the first time the potential of etrasimod as a repurposed antibacterial compound against Gram-positive bacteria.