Miracle-dz/UniFAR-FTUs huggingface dataset:
The Healthy Gluten-Free Diet: Practical Tips to Prevent Metabolic Disorders and Nutritional Deficiencies in Celiac Patients

The gluten-free diet (GFD) is the cornerstone treatment for coeliac disease (CD). However, a healthy GFD is more complex than the only exclusion of gluten-containing foods. Most celiac patients do not receive nutritional advice and tend to consume industrial gluten-free products (GFPs), which often lack fiber, vitamins, and other micronutrients while being rich in saturated fats and refined sugars. This review focuses on the main potential metabolic disorders and nutritional deficiencies in CD patients at diagnosis and dissects the main nutritional and metabolic issues due to a non-balanced GFD. Nutritional tips to achieve an adequate dietary approach in CD are provided. We also compared the main nutritional components of naturally gluten-free cereals (including pseudocereals) to give an exhaustive overview of the possible healthy alternatives to processed GFPs. Clinicians and dietitians should be systematically involved in the diagnosis of CD to monitor the appropriateness of GFD and the patient’s nutritional status over time.
Concordance Between Clinical and Pathological Response Assessment After Neo-Adjuvant Chemotherapy in Patients With Invasive Lobular Carcinoma

Background Neo-adjuvant chemotherapy (NAC) is frequently administered in breast carcinoma patients. The clinical response to NAC guides further treatment. The pathological response is not only an independent prognostic factor, but it also guides further treatment and prognosis. Objectives The aim of our study was to find the degree of concordance between clinical and pathological response assessments after NAC in Invasive lobular Carcinoma (ILC) cases by using World Health Organization (WHO) criteria and different pathological systems, respectively. We also tried to identify any useful parameter of clinical assessment that could better correlate with pathologic assessment and provide a better estimation of residual tumor. Methods This retrospective study was conducted on 26 ILC tumors diagnosed in 24 patients who were treated with NAC followed by surgical resection between January 2009 and December 2020. Medical records and microscopy glass slides were reviewed for clinical and pathological response assessments, respectively. Results The pre-treatment tumor area ranged from 1.8-255 cm2 and the mean±SD was 52.2±66.8 cm2. After NAC, complete clinical response was observed in four (15.3%) cases. The clinically assessed mean tumor area significantly reduced from 52.2±66.8 cm2 to 17.2±22.6 cm2 (p-value<0.001). The pathologically assessed mean tumor area (27.4±24.1 cm2) didn’t differ significantly from the clinically assessed mean tumor area (17.2±22.6 cm2) (p-value=0.114). Pathologically, the majority of the cases showed partial response, and a complete pathological response was achieved in only two (7.7%) cases. The concordance rates between clinical assessment by the WHO method and pathological assessment of the breast using the Sataloff method, Miller-Payne (MP) system, Residual Cancer Burden system, and Chevallier method were 26.7%, 15.8%, 9%, and 3.5%, respectively, with insignificant p-values. Percentage reduction in clinical size and percentage reduction in tumor cellularity differed significantly (p-value=0.038). Conclusion Clinical response assessment provides a less accurate estimation of residual disease, as it shows poor concordance with pathological assessment using different assessment systems/methods.
Some soil biological and chemical properties as affected by biofertilizers and organic ameliorants application on paddy rice

Biofertilizers are compounds that contain microorganisms capable of increasing the nutrient availability to plants and increasing plant growth rate. The purpose of this research is to study the effects of biofertilizers and organic ameliorants on some of soil bio-chemical properties. The pot experiment was conducted at the field of the Faculty of Agriculture, Universitas Padjadjaran Indonesia. The experiment was conducted in a randomized block complete design format consisting of twelve treatments and three replications. The experiment consisted of control, solid biofertilizer (50 kg ha-1), liquid biofertilizer (5 L ha-1), a combination of solid biofertilizers (50 kg ha-1) with organic ameliorants (10t ha-1) (composted straw, biochar and cow manure), a combination of liquid biofertilizer (5 L ha-1) and organic ameliorants (10 t ha-1), and each of ameliorants (10 t ha-1) independently. The results of experiment revealed that the application of solid biofertilizers and organic ameliorants significantly improved some soil biological properties (population of phosphate solubilizing microbes, N-fixing bacteria and phosphatase activity) and increased some soil chemical properties such as total N, available P, organic C and cation exchange capacity.
Can Police Patrols Prevent Pollution? The Limits of Authoritarian Environmental Governance in China

China’s high-profile anti-pollution campaigns have fuelled theories of authoritarian environmental efficiency. In a regime where bureaucrats are sensitive to top-down scrutiny, central campaigns are expected to be powerful tool for reducing pollution. Focusing on China’s nationwide pollution inspections campaign, I assess these claims of authoritarian efficiency. I find that central inspections (or ‘police patrols’) have no discernable impact on air pollution. I argue that inspections were ineffective because environmental enforcement requires a degree of sustained scrutiny that one-off campaigns cannot provide. The deterrent effect of inspections is also undercut by the regime’s ambivalence towards independent courts and unsupervised public participation. These findings suggest that China’s obstacles to pollution enforcement may be greater than anticipated, and theories of authoritarian efficiency overlook gaps in authoritarian state capacity.
Power control scheme for device‐to‐device communication using uplink channel in 5G mm‐Wave network

Mm‐Wave communication ushers next‐gen communication to new heights guaranteeing higher speed and throughput. Enabling device‐to‐device (D2D) communication in next‐gen wireless communication is a Herculean task. This is due to introduction of pathloss attenuation by various environmental factors which deteriorates the signal. This article aims to minimize the effect of interference for quality reception of signals for D2D communication and increase system throughput. Thus, a power control scheme is proposed in underlay mode for uplink channel in mm‐Wave band. Here, D2D communication takes place through two modes namely, general mode and mm‐Wave mode. In presence of large number of proximity D2D users and increasing pathloss attenuation, it switches to mm‐Wave mode for communication. The formulated problem is converted to semiconvex form by expressing it into exponential form in Laplace domain. Our final objective is to maximize the energy efficiency (EE) of the proposed system under certain constraints. To reduce complexity of the problem, feasible regions of transmission power is introduced, that is, least and upper bounds which will ensure that power is maintained within a limit guaranteeing better throughput for D2D users. Enhanced EE and outage probability values also depict better performance of proposed system. Finally, fairness index (FI) shows that the proposed scheme yields better performance for the D2D users to communicate in the mm‐Wave band. A detailed comparison of FI for the proposed scheme is also done with other existing methods to prove the adequate performance of the proposed method. Simulation results also prove the efficacy of the proposed scheme.
Neural-Dynamics Optimization and Repetitive Learning Control for Robotic Leg Prostheses

Rapid development in robotics and bionics makes it possible for robotic leg prostheses to help amputees while imposing challenges on duplicating the motion characteristics of the amputees’ healthy leg. One critical problem in prosthetic control is joint angle drift problem, that is, a repetitive motion trajectory in task space cannot guarantee that the generated motion trajectories in joint space are also repetitive. In order to solve this problem, in this article, we propose neural-dynamics optimization for robotic leg prostheses to generate the repetitive joint trajectories in real time. Our proposed method duplicates the self-selected walking speed of the amputees’ healthy leg. The online motion generation is formulated as a constrained quadratic programming (QP) optimization problem whose objective function adopts the kinetic energy and joint displacement performance criterion. The varying parameter convergent differential neural network is developed as a real-time QP solver which can globally converge to the optimal solution of the constrained QP problem. Then, a repetitive learning controller is designed for robotic leg prostheses to reduce the tracking errors while following the repetitive motion. Through the physical experiments on the developed two-degree-of-freedom robotic leg prosthesis worn on an amputee, the neural-dynamics optimization is substantiated to be a remedy of online motion generation, and the effectiveness of repetitive learning control for reducing the prosthetic tacking errors is verified.
Preparation and preliminary characterization of semi refined kappa carrageenan-based edible film incorporated with cinnamon essential oil

Edibility and biodegradability are the most beneficial properties of edible films and coatings. Carrageenans are natural, water-soluble hydrocolloid composed of a linear chain of sulphated galactans and have high potential of film-forming material. Essential oils have attracted increasing attention in food additive industry since they possess well documented insecticidal, antimicrobial, and antioxidant activities. Cinnamon essential oil, a GRAS material, is amongst the most active essential oils, because of the antimicrobial-antioxidant potential. To the best of our knowledge, there are few report data on the characteristics of carrageenan-essential oil composite films. Therefore, this study aimed at developing semi-refined-kappa-carrageenan based edible films and carrageenan-films containing cinnamon essential oil. This study was divided into two stages. The effect of sorbitol plasticizer’s concentration (0.5%, 1.0%, 1.5 % v/v) on the mechanical and barrier properties of the semi refined kappa carrageena.
THE EFFECTS OF CERTAIN CHEMICAL CHANGES ON THE ADDICTION CHARACTERISTICS OF DRUGS OF THE MORPHINE, CODEINE SERIES

By means of the substitution technique, data were obtained on the potency and duration of physical dependence action of each of ten related drugs of the morphine, codeine series. From comparisons of pairs of drugs, differing from each other in only one structural respect, it would appear that in so far as physical dependence action is concerned: (a) Methylation of the phenolic OH reduces potency and prolongs action, (b) Replacement of the alcoholic OH by H or O increases potency and shortens the action, (c) Spatial shift of the alcoholic OH results in irregular effects, and (d) Saturation of the C-7, 8 double-bond tends to increase both the potency and duration of action. These findings are qualitatively similar to those of Small and Eddy (1, Part I) in so far as the effects of (a) methylation of the phenolic OH and (b) replacement of the alcoholic OH by H or O are concerned, but are qualitatively somewhat dissimilar to their findings on the effects of (c) spatial shift of the alcoholic OH and (d) saturation of C-7, 8 double bond. The dissimilarities can be considered favorable to the assumption of a dissociation of physical dependence action from other morphine effects. None of the types of chemical modification mentioned in this report caused reductions in both duration and intensity of physical dependence action.
COVID-19 Related Acute Hemorrhagic Necrotizing Encephalitis: A Report of Two Cases and Literature Review

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, has proven neurotropism and causes a multitude of neurologic manifestations. Acute hemorrhagic necrotizing encephalitis (AHNE), though rare, can be seen in patients with severe infection and is associated with devastating neurologic outcomes. The true prevalence of this syndrome is unknown due to underrecognition, difficulty in timely acquisition of neuroimaging, and high mortality in this subset of patients escaping detection. It is a distinct clinicoradiological syndrome, with patients suffering from rapidly worsening encephalopathy and coma within the first two weeks of severe illness and hemorrhagic necrotizing parenchymal changes on neuroimaging. The pathophysiology of this syndrome is unclear but hypothesized to occur due to cytokine storm, blood-brain-barrier dysfunction, and direct viral-mediated endotheliopathy. Diagnosis requires a high index of suspicion in patients who have unexplained persistent severe encephalopathy associated with COVID-19 infection. Most patients have elevated systemic inflammatory markers and severe lung disease with hypoxic respiratory failure requiring mechanical ventilation. MRI is the imaging modality of choice, with a distinct neuroimaging pattern. CSF (cerebrospinal fluid) studies have a low yield for viral particle detection with currently available testing. While long-term outcomes are unclear, early immunomodulatory treatment with intravenous immunoglobulin, plasma exchange, and steroids may portend a favorable outcome. We discuss two cases of COVID-19 related AHNE and also include a pertinent literature search of similar cases in PubMed to consolidate the AHNE clinical syndrome, neuroimaging characteristics, management strategies, and reported short-term prognosis.
Origins of Chiral Life in Interstellar Molecular Clouds

The exploring of galactic chemical composition across the the Milky Way, and specifically across the solar neighborhood, provides insights into the chemical evolution of the universe. Since the formation of the first stars some hundred million years after the big bang (BB), heavier elements are synthesized in different stellar production processes at the expense of lighter elements. When the relative abundances of the life-forming elements evaluated for the Last Universal Common Ancestor (LUCA) are compared with the solar neighborhood stellar abundances, a striking similarity occurs. In this study, we tested the hypothesis that in some particular regions and at some particular time, the abundance curve of the first living matter and the universe coincided. Indeed, the best agreement between the two curves was obtained for (4 ± 1)× 109 yr after the BB, indicating the time of the origin of life. All organisms evolved on the Earth independently of place and time are leading to the LUCA and involve chiral molecules such as L amino acids and D sugars in fundamental life processes. The growing evidence from carbonaceous meteorites analysis shows an excess of L-type amino acids and D-type sugars, suggesting that the increase in L-type or D-type molecular chirality is the process that takes place in planetary and stellar forming systems, thus the life emerging from interstellar molecular clouds (IMCs) had to be chiral. Here we propose the spin-polarized proton–proton scattering as a potential physical process that takes place in IMCs environments and could lead to enrichment of L-type amino acids and D-type sugars.
Double‐gate line‐tunneling field‐effect transistor devices for superior analog performance

This paper presents a double‐gate line‐tunneling field‐effect transistor (DGLTFET) device optimized for superior analog performance. DGLTFET has thrice the on currents Ion, at least one order lower off currents I o f f, twice the transconductance gm, at least two orders higher output resistance ro, and at least two orders higher overall intrinsic gain gmro than the equivalent metal‐oxide‐semiconductor field‐effect transistor (MOSFET) having the same width at the same technology node. The proposed device, being a double‐gate (DG) structure, exhibits extremely high vertical fields which ensures that line‐tunneling dominates over the point‐tunneling by several orders. This eliminates the notorious “hump” or the “kink” effects observed in conventional LTFET characteristics, which are detrimental for analog applications. In this work, we have optimized the DGLTFET device by changing critical parameters like the epi‐layer thickness and its doping concentration, which have serious influence on the line‐tunneling behavior. Optimization of the critical parameters for enhanced line‐tunneling leads to improved analog performance parameters like gm and ro, and finally, superior analog circuits. The performance of the DGLTFET was benchmarked with the equivalent MOSFET in fundamental analog VLSI circuits, namely, CS amplifier (both resistive and cascode loads), current mirror (both single‐stage and cascode configurations), and a two‐stage op‐amp. This paper shows that the DGLTFET CS amplifier has a gain‐BW product or unity‐gain BW fT of 15 GHz, while that of MOSFET CS amplifier with the same bias current is 10 GHz. The DGLTFET current mirror has at least three orders of magnitude higher ro than the corresponding MOSFET current mirror. Similarly, the common‐mode rejection ratio (CMRR) of the DGLTFET op‐amp is 57 dB compared to the CMRR of 33.5 dB of the equivalent design in standard 45‐nm complementary metal‐oxide semiconductor (CMOS) technology. The 45‐nm library is the lowest industry‐standard technology node currently available free for academic licenses in the public domain; hence, the benchmarking for validation was carried out at 45 nm. However, the claims/investigations made in this work are also valid for lower technologies, and related results are excluded in this report keeping the manuscript length in mind.
Corrigendum: Hybrid deep learning architecture for general disruption prediction across tokamaks (2021 Nucl. Fusion 61 026007)

In this paper, we present a new deep-learning disruption-prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruption data from the new devices. The explorative data analysis, conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma-state data, with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy for the C-Mod (AUC = 0.801), DIII-D (AUC = 0.947) and EAST (AUC = 0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that a boosted accuracy (AUC = 0.959) is achieved for the EAST predictions by including only 20 disruptive discharges with thousands of non-disruptive discharges from EAST in the training, combined with more than a thousand discharges from DIII-D and C-Mod. The improvement in the predictive ability obtained by combining disruption data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruption data are machine-specific, while disruption data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring in a new device.
THE NEW ADVANCED PROTOTYPE OF AIRBORNE VISUAL CONTROL OF A GROUND ROBOT

Unmanned autonomous robots will be widely used very soon for land use, treatment, and monitoring. The paper addresses the problem of visual navigation of ground robots using a camera positioned at a certain elevation above the confined area. The main idea is that robot’s “eyes” is not located on the robot but are independent autonomous system. As a result, the “eyes” can go up and observe the robot from above. This patented technology of airborne vision-based control of ground robots is already described previously in "International Journal of GEOMATE" and Geomate2019 conference paper (Ehrenfeld et al., 2020). There was described in detail the small toy prototype with very simple algorithms and very shortly the next more complex prototype. In the current paper detailed algorithms for navigation and control of this complex prototype is presented. The system consists of the USB camera on the tower (4 meters) connected to the computer which controls the big ground robot by sending Wi-Fi commands. For navigation (recognition of the ground robot, finding its position and orientation) was used deep learning (artificial neural network). It was demonstrated that this robot can pass two main tests: travelling along some predefined path and random walk inside of some predefined boundary.
Impacts of intercontinental mercury transport on human & ecological health

Both humans and wildlife are adversely affected by exposures to multiple chemical forms of mercury. For all three forms of mercury (divalent mercury — HgII, elemental mercury — Hg0, and methylmercury — MeHg) the severity of health impacts varies with the intensity and duration of exposure (i.e., the dose). Adverse human health effects range from those detectable only with specialized testing protocols to gross, clinically evident abnormalities, as well as death [Clarkson and Magos, 2006]. High levels of MeHg exposure cause a variety of negative health effects in humans and wildlife, including kidney and liver failure, endocrine disruption, reproductive abnormalities, neurodevelopmental delays and compromised cardiovascular health in adults [Clarkson and Magos, 2006; Mergler et al., 2007; Sheuhammer et al., 2007; Tan et al., 2009].
The Effect of Article Characteristics on Citation Number in a Diachronic Dataset of the Biomedical Literature on Chronic Inflammation: An Analysis by Ensemble Machines

Citations are core metrics to gauge the relevance of scientific literature. Identifying features that can predict a high citation count is therefore of primary importance. For the present study, we generated a dataset of 121,640 publications on chronic inflammation from the Scopus database, containing data such as titles, authors, journal, publication date, type of document, type of access and citation count, ranging from 1951 to 2021. Hence we further computed title length, author count, title sentiment score, number of colons, semicolons and question marks in the title and we used these data as predictors in Gradient boosting, Bagging and Random Forest regressors and classifiers. Based on these data, we were able to train these machines, and Gradient Boosting achieved an F1 score of 0.552 on classification. These models agreed that document type, access type and number of authors were the best predicting factors, followed by title length.