Miracle-dz/UniFAR-FTUs huggingface dataset:
Common methods in mitochondrial research (Review)

Mitochondrial abnormalities are primarily seen in morphology, structure and function. They can cause damage to organs, including the heart, brain and muscle, by various mechanisms, such as oxidative stress, abnormal energy metabolism, or genetic mutations. Identifying and detecting pathophysiological alterations in mitochondria is the principal means of studying mitochondrial abnormalities. The present study reviewed methods in mitochondrial research and focused on three aspects: Mitochondrial extraction and purification, morphology and structure and function. In addition to classical methods, such as electron microscopy and mitochondrial membrane potential monitoring, newly developed methods, such as mitochondrial ultrastructural determination, mtDNA mutation assays, metabolomics and analyses of regulatory mechanisms, have also been utilized in recent years. These approaches enable the accurate detection of mitochondrial abnormalities and provide guidance for the diagnosis and treatment of related diseases.
Dentin Particulate for Bone Regeneration: An In Vitro Study

The aim of this in vitro study was to investigate the commitment and behavior of dental pulp stem cells (DPSCs) seeded onto two different grafting materials, human dentin particulate (DP) and deproteinized bovine bone matrix (BG), with those cultured in the absence of supplements. Gene expression analyses along with epigenetic and morphological tests were carried out to examine odontogenic and osteogenic differentiation and cell proliferation. Compressive testing of the grafting materials seeded with DPSCs was performed as well. DPSC differentiation into odontoblast-like cells was identified from the upregulation of odontogenic markers (DSPP and MSX) and osteogenic markers (RUNX2, alkaline phosphatase, osteonectin, osteocalcin, collagen type I, bmp2, smad5/8). Epigenetic tests confirmed the presence of miRNAs involved in odontogenic or osteogenic commitment of DPSCs cultured for up to 21 days on DP. Compressive strength values obtained from extracellular matrix (ECM) synthesized by DPSCs showed a trend of being higher when seeded onto DP than onto BG. High expression of VEGF factor, which is related to angiogenesis, and of dentin sialoprotein was observed only in the presence of DP. Morphological analyses confirmed the typical phenotype of adult odontoblasts. In conclusion, the odontogenic and osteogenic commitment of DPSCs and their respective functions can be achieved on DP, which enables exceptional dentin and bone regeneration.
Impact of Whey Protein Edible Coating Containing Fish Gelatin Hydrolysates on Physicochemical, Microbial, and Sensory Properties of Chicken Breast Fillets

This study aims to research the impact of coatings containing whey protein (WP), fish gelatin hydrolysates (FGH), and both compounds together (WP + FGH) on the shelf-life of chicken breast fillets over the course of 16 days of cold storage (4 °C, 4-day intervals), as assessed by their physicochemical, microbiological, and sensory properties. Overall, cooking loss, pH value, total volatile base nitrogen, free fatty acids, peroxide value, and thiobarbituric acid reactive substances increased with storage time in all samples. WP + FGH coated samples had significantly lower variation in all these parameters over the time of storage compared to other coated samples (WP and FGH), while these parameters increased greatly in control (uncoated) samples. WP + FGH coating also resulted in reduced bacterial counts of total mesophilic, aerobic psychrotrophic, and lactic acid bacteria compared to other coated and uncoated samples. The sensory evaluation revealed no differences in the panelists’ overall acceptance at day 0 of storage between samples. The samples were considered “non-acceptable” by day 8 of storage; however, WP + FGH coated samples maintained an overall higher acceptability score for the sensory attributes evaluated by the panelists. Overall, this study shows the potential of WP + FGH coatings for prolonging the shelf-life of chicken breast fillets.
Hypergeometric solutions of the closed eigenvalue problem on Heisenberg Isoperimetric Profiles

After introducing the sub-Riemannian geometry of the Heisenberg group Hn, n \geq 1, we recall some basics about hypersurfaces endowed with the H-perimeter measure and horizontal Green's formulas. Then, we describe a class of compact closed hypersurfaces of constant horizontal mean curvature called "Isoperimetric Profiles"(they are not CC-balls! ); see Section 2.1. Our main purpose is to study a closed eigenvalue problem on Isoperimetric Profiles, i.e. LHS \phi + {\lambda}\phi = 0, where LHS is a 2nd order horizontal tangential operator analogous to the Laplace-Beltrami operator; see Section 1.5. This is done starting from the radial symmetry of Isoperimetric Profiles with respect to a barycentric axis parallel to the center T of the Lie algebra hn. An interesting feature of radial eigenfunctions is in that they are hypergeometric functions; see Theorem 2.10. Finally, in Section 2.3 we shall begin the study of the general case.
Blood Sugar Changes in Patients with Acute Drug Poisoning

Changes in blood sugar in poisoning can be one of the most important determinants of the outcome of patients with poisoning. Since poisoning is one of the most common and increasing causes of death worldwide and one of the most critical medical emergencies, this study aimed to investigate changes in blood sugar in patients with acute poisoning and how patients' blood sugar can predict the severity and outcome of the disease. The present study was performed on 200 patients with acute drug poisoning referred to the emergency department of Amir Al-Momenin Hospital in Zabol from March 2018 to March 2020. Blood glucose levels of all patients were recorded at the time of admission and every hour to the first 5 hours after admission, and the results were entered in the information form of each patient. Finally, the data were entered into SPSS V22 software and statistically analyzed. The mean age of participants was 23.21 ± 12.80 years, and the minimum and maximum age of patients were 1 year and 77 years, respectively, and only 9.8% of them had a history of diabetes. In this study, the highest rate of intoxication with opioids such as methadone and tramadol was (38%), followed by benzodiazepines (20.5%), NSAIDs (19.5%), and SSRIs (7%) were the most commonly used drugs. The prevalence of hypoglycemia in this study was 3% (6 patients), while no cases of hyperglycemia were reported. In this study, most changes in blood sugar were caused by alcohol poisoning. Also, neuroleptics, NSAIDs, and chemicals had the least changes in blood sugar. However, patients' blood sugar at the beginning of poisoning did not affect patients' prognosis. The present study results showed that changes in blood sugar during treatment during drug intoxication, alcohol, medications(sulfonylurea, glibenclamide), and NSAID are very important, so regular monitoring of blood glucose in intoxication with these cases is essential.
Research on target recognition and tracking in mobile augmented reality assisted maintenance

The recognition and tracking of maintenance targets is the basis of augmented reality assisted maintenance. To address the problems of the current recognition and tracking algorithms, such as high time complexity, low pose tracking accuracy, and high requirements for hardware equipment, this paper studies a light weight maintenance target recognition algorithm based on YOLOv5s, and then the VI ORB‐SLAM algorithm is adopted to track the maintenance target. In addition, the ORB feature extraction and the visual inertial initialization are improved for the VI ORB‐SLAM algorithm. Finally, the combined algorithm is deployed on the mobile phone. Taking the augmented reality assisted vehicle maintenance as an example, it is verified that the proposed approach is practical feasible and effective in the actual assisted maintenance scene.
Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local minimum. Aquila optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutation occur to generate a new population. The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.
PROSES BELAJAR ANAK USIA 0 SAMPAI 12 TAHUN BERDASARKAN KARAKTERISTIK PERKEMBANGANNYA

Anak merupakan individu yang sedang mengalami pertumbuhan dan perkembangan, keduanya terjadi secara bertahap. Pembagian kelompok usia anak menjadi : 0 sampai 6 tahun masa anak usia dini, 7 sampai tahun 12 masa sekolah dasar. Pembelajaran pada anak usia dini befokus pada memerankan perasaan dan nurani, belajar sambil bermain, belajar melaui komunikasi dan interaksi sosial, belajar melaui lingkungan serta belajar memunuhi hasrat dan kebutuhannya. Sedangkan pada usia sekolah dasar anak mulai berfikir secara logis dan konkret namun mereka belum mampu berfikir secara abstrak dikarenaka penalarannya masi terbatas. Mereka hanya mampu menyelesaikan persoalan yang menyakut dengan objek kongkret yang dapat dirasakan oleh panca indera.
Parameter Estimation for Photovoltaic Strings Under Partial Shading Conditions

Accurate estimated model parameters are of great significance to the modeling, characterization, and optimization of photovoltaic (PV) systems. The existing parameter estimation algorithms cannot predict parameters of partially shaded PV systems. This paper proposes a low-cost shading pattern identifi-cation algorithm to estimate essential environmental information for partial shading conditions (PSCs). With the help of a comprehensive PV model, the optimal environmental factors and model parameters can be obtained via a proposed exploration-exploitation-Jaya (EE-Jaya) algorithm, which has the capacity to balance the exploration and exploitation of candidate solutions. The proposed method is tested experimentally to evaluate its performance under a variety of test conditions. Results show that it can balance the local and global search and enable the developed model to fit the measured data well, outperforming other state-of-the-art global optimization algorithms in terms of accuracy and reliability.
Achieving Participatory Smart Cities by Making Social Networks Safer

Cases of organised disinformation campaigns on Twitter, including those reported by the social network itself in its Transparency centre, continue unabated. The negative consequences of these attacks in processes of great importance to societies, such as electoral processes or vaccination campaigns, have sparked research into detecting this type of malicious user. State-of-the-art models for bot detection use numerous information collected from profiles, tweets, or network archi-tecture to obtain competitive outcomes. On the other hand, these models allow for post-hoc detection of such users because they rely on fixed training datasets to classify users based on their previous activities. In contrast, we propose a proactive technique that uses user records to predict dangerous attacks before they occur as a measure to make social networks safer, fairer and less biased. For this purpose, our method uses a model that predicts malicious assaults by projecting users' embedding trajectories before completing their actions. We employed a Dynamic Directed Multigraph representation of temporal inter-actions between people in the Twittersphere for the experiments. By comparing them in the same data, our model outperforms state-of-the-art methods by 40.66% in F-score detecting malicious users preemptively. In addition, we propose a model selection study that evaluates the usefulness of several system components.
A Flexible 3D-Printed UHF RFID Tag for Worker-Safety Applications

This paper presents a flexible, 3D-printed, passive UHF RFID tag, specifically designed to be applied on the safety vest of a worker to allow its identification. Usually, conventional in-lay tags, are not easily exploitable for this purpose, because they are particularly sensitive to the proximity of the human tissue, which perturbates their performance. For this reason, a flexible, thick, and low lossy substrate has been realized using a TPE filament suitable for 3D printing so to separate the antenna from the human body, while maintaining an acceptable level of comfort. The conductive parts have been realized using a conductive and adhesive nylon-based fabric from Adafruit™, which has been purposely shaped through a Graphtech ™ CE6000 cutting plotter. This tag has been conceived to be part of a smart tracking system developed within the SMARTGRID project, in collaboration with INAIL (Italian National Institute for the prevention of accidents at work).
Exploring Bias Analysis on Judicial Data using Machine Learning Techniques

The use of data driven automation is not new, but it has gain a lot of attention recently with the wide-spread understanding that it is the solution to all problems in terms of ‘fair’ and ‘non-bias’ classification. This is not different in the law area, where ‘artificial intelligence’ became a ‘magic word’. However, using historic data is a very tricky job which can quite easily propagate discrimination in a very efficient way. Thus, this work is aimed to analyse data from legal proceedings looking for evidence related to the occurrence of bias in the judges' decision-making process, considering mainly the gender or social condition of the convicts. Supervised and unsupervised machine learning techniques, preceded by data analysis and processing procedures, were used to explain and find explicit data behaviour. Our results pointed to the fragility of the techniques to identify biases but suggest the need to improve data pre-processing and the search for more robust classification techniques.
Malnutrition and the Risk of Early Neurological Deterioration in Elderly Patients with Acute Ischemic Stroke

Background and Purpose This study aimed to investigate the relationship between malnutrition and early neurological deterioration (END) in elderly patients with acute ischemic stroke in China. Methods We used the registry data in the Third Affiliated Hospital of Nantong University and Nanjing Brain Hospital from June 2019 to January 2021. Malnutrition risk was evaluated by controlling nutritional status score (CONUT), geriatric nutritional risk index (GNRI) and prognostic nutritional index (PNI) score, respectively. END was defined as an increment of at least two points in the total NIHSS score within three days after admission. We evaluated the relationship between malnutrition and END with multivariable logistic regression models and reclassification indexes. Results A total of 732 elderly patients with first-ever acute ischemic stroke were included in the study. 243 patients developed END. 5.7%, 21.4%, 4.6% patients were classified as moderate to severe malnutrition by CONUT, GNRI and PNI, respectively. Malnutrition was associated with the risk of END for CONUT (odds ratio [OR], 1.210; 95% confidence interval [CI] 1.092–1.341; P < 0.001), for GNRI (OR, 0.943; 95% CI, 0.919–0.967; P < 0.001), and for PNI (OR, 0.936; 95% CI, 0.908–0.965; P < 0.001) in multivariable logistic regression models using the back-ward selection method. The discriminative ability was 0.763 (95% CI, 0.727–0.798) for CONUT, 0.769 (95% CI, 0.733–0.805) for GNRI and 0.769 (95% CI, 0.733–0.805) for PNI after adjusting for confounders. Besides, adding malnutrition indexes into models made the prediction of END more accurate. Conclusion Malnutrition was associated with END in elderly Chinese patients with acute ischemic stroke.
Combined thermal and electrical optimization for the sizing of residential microgrids

Many residential building owners are confronted with difficult decisions regarding energy planning towards net zero-emission. This paper presents a methodology to select the optimal combination of equipment and their optimal capacity to meet the electricity and heat needs of a residential microgrid comprising a pre-existing building and its electrical loads while minimizing the total cost and ensuring thermal comfort for the occupants. The methodology first identifies the thermal parame-ters of the building based solely on monitored temperature and heating system activation data. Then, it solves a multi-energy sizing optimization problem that allows the dynamic control of room temperatures. Each component adds a series of physical and technical constraints to the model, formulated as a mixed-integer linear program. The methodology is applied to a real Belgian house, where electrical consumption and room temperatures have been monitored. Results show that the developed methodology can efficiently characterize the thermal behavior of the building and optimally allocate resources between its rooms.
Intracranial In Situ Thermosensitive Hydrogel Delivery of Temozolomide Accomplished by PLGA–PEG–PLGA Triblock Copolymer Blending for GBM Treatment

Glioblastoma (GBM) recurrence after surgical excision has grown to be a formidable obstacle to conquer. In this research, biodegradable thermosensitive triblock copolymer, poly(D, L–lactic acid–co–glycolic acid)–b–poly(ethylene glycol)–b–poly(D, L–lactic acid–co–glycolic acid (PLGA–PEG–PLGA) was utilized as the drug delivery system, loading with micronized temozolomide(micro-TMZ) to form an in situ drug–gel depot inside the resection cavity. The rheology studies revealed the viscoelastic profile of hydrogel under various conditions. To examine the molecular characteristics that affect gelation temperature, 1H–NMR, inverse gated decoupling 13C–NMR, and GPC were utilized. Cryo-SEM and XRD were intended to disclose the appearance of the hydrogel and the micro-TMZ existence state. We worked out how to blend polymers to modify the gelation point (Tgel) and fit the correlation between Tgel and other dependent variables using linear regression. To simulate hydrogel dissolution in cerebrospinal fluid, a membraneless dissolution approach was used. In vitro, micro-TMZ@PLGA–PEG–PLGA hydrogel exhibited Korsmeyer–Peppas and zero–order release kinetics in response to varying drug loading, and in vivo, it suppressed GBM recurrence at an astoundingly high rate. Micro-TMZ@PLGA–PEG–PLGA demonstrates a safer and more effective form of chemotherapy than intraperitoneal TMZ injection, resulting in a spectacular survival rate (40%, n = 10) that is much more than intraperitoneal TMZ injection (22%, n = 9). By proving the viability and efficacy of micro-TMZ@PLGA–PEG–PLGA hydrogel, our research established a novel chemotherapeutic strategy for treating GBM recurrence.
Profiling of Volatile Organic Compounds from Four Plant Growth-Promoting Rhizobacteria by SPME–GC–MS: A Metabolomics Study

The rhizosphere microbiome is a major determinant of plant health. Plant-beneficial or plant growth-promoting rhizobacteria (PGPR) influence plant growth, plant development and adaptive responses, such as induced resistance/priming. These new eco-friendly choices have highlighted volatile organic compounds (biogenic VOCs) as a potentially inexpensive, effective and efficient substitute for the use of agrochemicals. Secreted bacterial VOCs are low molecular weight lipophilic compounds with a low boiling point and high vapor pressures. As such, they can act as short- or long-distance signals in the rhizosphere, affecting competing microorganisms and impacting plant health. In this study, secreted VOCs from four PGPR strains (Pseudomonas koreensis (N19), Ps. fluorescens (N04), Lysinibacillus sphaericus (T19) and Paenibacillus alvei (T22)) were profiled by solid-phase micro-extraction gas chromatography mass spectrometry (SPME–GC–MS) combined with a multivariate data analysis. Metabolomic profiling with chemometric analyses revealed novel data on the composition of the secreted VOC blends of the four PGPR strains. Of the 121 annotated metabolites, most are known as bioactives which are able to affect metabolism in plant hosts. These VOCs belong to the following classes: alcohols, aldehydes, ketones, alkanes, alkenes, acids, amines, salicylic acid derivatives, pyrazines, furans, sulfides and terpenoids. The results further demonstrated the presence of species-specific and strain-specific VOCs, characterized by either the absence or presence of specific VOCs in the different strains. These molecules could be further investigated as biomarkers for the classification of an organism as a PGPR and selection for agricultural use.
Sur le théorème limite central de Kipnis et Varadhan pour les chaînes réversibles ou normales

On montre par une methode geometrique n'utilisant pas le theoreme spectral que le theoreme limite central pour les chaines de Markov reversibles, etabli par Kipnis et Varadhan, est valide pour la classe plus large des chaines « normales », c'est-a-dire dont l'operateur P de transition verifie P* P = PP*.
Land Use and Land Cover Changes and Prediction Based on Multi-Scenario Simulation: A Case Study of Qishan County, China

Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there is a gap in the scholarly community when it comes to shrinking small cities where the population decreased sharply under the influence of the urban expansion of megacities. Hence, it is necessary to investigate the evolution rule of land use in these regions. This study takes Qishan County in Shanxi Province as the research subject and analyzes the land use change over the last 20 years with remote sensing technology. Comparing the two LUCC models of the CA-Markov Model and the LCM Model, an optimal model is used to predict and simulate land use change under three potential scenarios in 2030. The conclusions are stated as follows: (1) From 2000 to 2020, the cultivated land area increased originally and subsequently decreased, and forest land continued to decrease at a progressively slower speed. In contrast, the urban land area expanded significantly. (2) The comprehensive dynamic change in water land is the most significant, indicating that this is an unstable land resource in the region and more attention should be given to this matter. (3) The scenario of water area protection indicates that the inhibition of the transition of water areas can protect their vulnerable ecological environment without negatively impacting economic development. Furthermore, the ongoing focus on economic development in the region is related to the rapid disappearance of cultivated land, which is not an optimistic perspective for the area’s ecosystem. The results of this study implied land transition features and mechanisms in Qishan County, providing novel insights for decision support for county-level land use planning.