Miracle-dz/UniFAR-FTUs huggingface dataset:
Model and methods to address urban road network problems with disruptions

Disruptions in urban road networks can quickly and significantly reduce the quality of the whole transportation network, and impact urban mobility for light vehicles, public transportation, etc. In this study, we consider both unidirectional and multidirectional road network problems with disruptions and connecting requirements. These problems aim at reconfiguring the urban network in terms of road direction in order to maintain a path among all points of the network (strong connectivity). The former is defined on simple graphs, mainly modeling part of a city such as historical centers, while the latter relies on multigraphs, modeling more general networks. Restoring the network (strong connectivity) after some disruptions may require the modification of the orientation of some streets, that is, arc reversals. Such actions can disturb users' driving habits. Thus, two objectives are considered separately: minimizing the total travel distance and minimizing the number of arc reversals. We define formally both problems and propose two metaheuristics, a biased random key genetic algorithm and an iterated local search. Numerical experiments have been performed on a set of generated instances and on the urban network of Troyes (France).
PCR-Based Strategy for Introducing CRISPR/Cas9 Machinery into Hematopoietic Cell Lines

Simple Summary We used PCR-generated small CRISPR constructs to edit two genes (IDH2 and MYBL2) in hard-to-transfect hemopoietic cells, which are central to the progression of the devastating disease known as acute myeloid leukemia LMA (AML). MYBL2 is a transcription factor; when AML patients show an altered expression of this factor, an adverse prognostic value is involved. IDH2 is particularly interesting because it encodes isocitrate dehydrogenase 2, an enzyme of the citric acid cycle, which, when mutated, produces a different phenotype in AML patients. Hence, our system provides a way to produce CRISPR constructs to easily target genes within mammalian cells, and it provides a model which can be used to study AML mechanisms in vitro. Abstract Acute myeloid leukemia is a complex heterogeneous disease characterized by the clonal expansion of undifferentiated myeloid precursors. Due to the difficulty in the transfection of blood cells, several hematological models have recently been developed with CRISPR/Cas9, using viral vectors. In this study, we developed an alternative strategy in order to generate CRISPR constructs by fusion PCR, which any lab equipped with basic equipment can implement. Our PCR-generated constructs were easily introduced into hard-to-transfect leukemic cells, and their function was dually validated with the addition of MYBL2 and IDH2 genes into HEK293 cells. We then successfully modified the MYBL2 gene and introduced the R172 mutation into the IDH2 gene within NB4 and HL60 cells that constitutively expressed the Cas9 nuclease. The efficiency of mutation introduction with our methodology was similar to that of ribonucleoprotein strategies, and no off-target events were detected. Overall, our strategy represents a valid and intuitive alternative for introducing desired mutations into hard-to-transfect leukemic cells without viral transduction.
Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Simple Summary Machine learning (ML) is subfield of artificial intelligence that enables computers to learn from data and improve their performance without being explicitly programmed by a human. ML has the potential to enhance veterinary medical education by improving learning, teaching, and assessments. This primer introduces ML concepts to veterinary educators and administrators, highlighting their similarities and differences with classical statistics. It then provides a step-by-step example using simulated veterinary student data to address a specific question: which records in the simulated veterinary student data will predict a student passing or failing a specific course. The example demonstrates the use of the Python programming language to create a random forest ML prediction model, a type of ML algorithm which is composed of many decision trees and each of these trees is composed of nodes and leaves. During the creation of the random forest model, we emphasize specific considerations such as managing student records which may have missing information. The results show how decisions made by veterinary educators during ML model creation may impact which type of records are shown to be most important. While this form of ML may prove to be beneficial, transparency in creating ML models is crucial, and further research is needed to establish best practices and guidelines for veterinary medical education ML projects. Abstract Machine learning (ML) offers potential opportunities to enhance the learning, teaching, and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis-driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model, a model composed of decision trees with each decision tree being composed of nodes and leaves. Within each step of the model creation, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.
Selective dopaminergic vulnerability in Parkinson’s disease: new insights into the role of DAT

One of the hallmarks of Parkinson’s disease (PD) is the progressive loss of dopaminergic neurons and associated dopamine depletion. Several mechanisms, previously considered in isolation, have been proposed to contribute to the pathophysiology of dopaminergic degeneration: dopamine oxidation-mediated neurotoxicity, high dopamine transporter (DAT) expression density per neuron, and autophagy-lysosome pathway (ALP) dysfunction. However, the interrelationships among these mechanisms remained unclear. Our recent research bridges this gap, recognizing autophagy as a novel dopamine homeostasis regulator, unifying these concepts. I propose that autophagy modulates dopamine reuptake by selectively degrading DAT. In PD, ALP dysfunction could increase DAT density per neuron, and enhance dopamine reuptake, oxidation, and neurotoxicity, potentially contributing to the progressive loss of dopaminergic neurons. This integrated understanding may provide a more comprehensive view of aspects of PD pathophysiology and opens new avenues for therapeutic interventions.
Actin-Depolymerizing Factor Gene Family Analysis Revealed That CsADF4 Increased the Sensitivity of Sweet Orange to Bacterial Pathogens

The actin-depolymerizing factor (ADF) gene family regulates changes in actin. However, the entire ADF family in the sweet orange Citrus sinensis has not been systematically identified, and their expressions in different organs and biotic stress have not been determined. In this study, through phylogenetic analysis of the sweet orange ADF gene family, seven CsADFs were found to be highly conserved and sparsely distributed across the four chromosomes. Analysis of the cis-regulatory elements in the promoter region showed that the CsADF gene had the potential to impact the development of sweet oranges under biotic or abiotic stress. Quantitative fluorescence analysis was then performed. Seven CsADFs were differentially expressed against the invasion of Xcc and CLas pathogens. It is worth noting that the expression of CsADF4 was significantly up-regulated at 4 days post-infection. Subcellular localization results showed that CsADF4 was localized in both the nucleus and the cytoplasm. Overexpression of CsADF4 enhanced the sensitivity of sweet orange leaves to Xcc. These results suggest that CsADFs may regulate the interaction of C. sinensis and bacterial pathogens, providing a way to further explore the function and mechanisms of ADF in the sweet orange.
Write-Aware Timestamp Tracking: Effective and Efficient Page Replacement for Modern Hardware

In this paper, we revisit the classical data management problem of page replacement. We propose Write-Aware Timestamp Tracking (WATT), a novel replacement algorithm that is optimized for modern hardware. By explicitly tracking the access history of each cached page, WATT achieves state-of-the-art replacement effectiveness. WATT is also carefully co-designed with modern multi-core CPUs and can be implemented with very low overhead. Finally, WATT allows trading of read versus write I/O operations, which is useful for prolonging flash SSD lifetime.
Wearable Spiral Coils for Joint Flexion Monitoring

This letter presents a wearable inductive resonant wireless link for joint flexion monitoring. A system consisting of four small embroidered spiral coils is experimentally investigated. Data referring to the possibility of using the developed prototype for elbow flexion monitoring are presented and discussed. It is demonstrated that the position of the elbow can be recognized from measurements of the transmission coefficient of the resonant inductive wireless link. The implementation proposed in this work is suitable for the development of a monitoring system that can be easily integrated into garments thus resulting unobtrusive to the user.
ALAT PENGHITUNG JUMLAH LEMBAR KERTAS BERBASIS INTERNET OF THINGS MENGGUNAKAN INFRA RED PADA PT.INDAH KIAT

Pemanfaatan teknologi dalam bidang industri pada era saat ini berpengaruh besar dalam pencapaian efektifitas dan efisiensi kinerja suatu usaha industri, diantaranya seperti pemanfaatan mesin perhitungan kertas secara otomatisasi. Pemanfaatan mesin industri dalam produksi kertas saat ini merupakan hal umum namun dalam segi efisiensi kinerjanya masih memiliki kendala diantaranya dalam proses perhitungan kertas pada seksi finishing. Dalam hal pengembangan teknologi ini maka harus diupayakan adanya sebuah rancangan teknologi pendukung yaitu counter otomatis yang dapat menghitung jumlah lembar kertas secara otomatis. Sistem ini terbuat dari perangkat keras, terdiri dari sensor infra red, lcd 16x2, Buzer, Led, Microcontroler wemos. Sistem ini dibuat dengan bahasa pemrograman C. Cara kerja alat ini ketika kertas berjalan diatas konveyor kemudian kertas tersebut melewati sensor Infra Red maka akan terdeteksi oleh sensor untuk dilakukan penginputan dan sensor akan mengirim hasil inputan ke lcd kemudian LED Indikator akan menyala ketika sensor melakukan penginputan dan Buzer akan mengeluarkan,fungsi Buzer ini sebagai notifikasi ketika sensor bekerja dengan baik. Hasil penginputan jumlah penghitungan kertas tersebut dikirim oleh Microcontroler Wemos secara otomatis melalaui jaringan wifi ke perangkat Device sesuai jumlah permintaan customer. Salah satu fasilitas yang ditawarkan adalah memonitoring penghitungan jumlah lembar kertas dengan memanfaatkan layanan internet Web Ubidots. Web Ubidots dapat menjadi salah satu sarana positif untuk mengontrol jumlah lembar kertas atau mengamati pergerakan kertas. Alat ini mampu secara otomatis menampilkan jumlah lembar kertas secara realtime. Hasil dari rancangan sistem ini telah mampu memberikan kontribusi kepada perusahaan. Kata kunci : Penghitungan jumlah kertas, Sensor infra red, internet of Things, Microcontroler Wemos
Fruiting and Productivity of the Argan (Argania spinosa) Tree and Evaluation of the Nutritional Value of Its Early and Late Fruits from Western Algeria

ABSTRACT The Argan tree (Argania spinosa (L.) (Skeels)) is a woody species endemic to Algeria (Tindouf region mostly) and Morocco. It is a plant species of oleo-agro-sylvo-pastoral and ecological importance as it contributes to the fight against desertification and climate change. Moreover, the seed is used for extracting oil, which has high dietic and cosmetic qualities. In this work, the fruit harvest period and Argania spinosa productivity are related to the phenological process of flowering and fruiting. Our results show that the experimental argan trees of Mostaganem bloom late to be pollinated in the spring and remain dormant throughout the summer, in contrast to the endemic Tindouf trees that bloom early in September, bear fruit and ripen in the same season. This explains why that the fruits of the Tindouf region are the richest compared to the fruits of the other two regions. Our study shows that the pulp always contains more than almonds, as the oil was extracted and analyzed, the pulp is the fraction richest in phenols, while almond is the poorest fraction (3.43 ± 0.72 mg/g, 1.34 ± 0.03 mg/g), the same thing goes for the total flavonoid content (9 ± 0.5 mg/g, 2.83 ± 0.14 mg/g). The greatest activity was recorded in the fruits of Tindouf. As for the components of the fruit, the highest activity was recorded in the pulps then the almonds and the lowest in the oil (502 ± 11.42 g, 216 ± 8.43 g, 174 ± 3.61 g), respectively. Concerning primary metabolites, the fruit is rich (the almond is rich in proteins, the pulp is rich in sugar and the oil is rich in lipids). GRAPHICAL ABSTRACT
Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications

The human brain’s role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals’ cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200–300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90–140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones.
Antimicrobial Susceptibility of E. coli Isolates from Intra-Abdominal Infections in the Asia-Pacific Region: Trends in Ciprofloxacin, Ceftriaxone, Cefepime, and Piperacillin/Tazobactam Susceptibility

Purpose To investigate the antibiotic susceptibility of Escherichia coli isolates in patients diagnosed with intra-abdominal infections (IAIs) in the Asia-Pacific region. Patients and Methods This study was conducted at 50 medical hospitals across 9 countries/regions as part of the Study for Monitoring Antimicrobial Resistance Trends (SMART) surveillance program from 2014 to 2018. Nonduplicate isolates of aerobic and facultative gram-negative bacilli were collected and processed for further antimicrobial susceptibility testing. Results A total of 10,709 isolates were collected, with E. coli (n=4737, 44.2%) being the leading pathogen causing IAIs, followed by Klebsiella pneumoniae (n=2429, 22.7%) and Pseudomonas aeruginosa (n=931, 8.7%). Community-associated (CA) E. coli isolates generally exhibited higher susceptibility rates for most antibiotics than hospital-associated (HA) isolates. In countries/regions other than Hong Kong, South Korea, and Singapore, HA isolates displayed lower susceptibility rates for multiple classes (≥4) of antibiotics. Among the commonly used antibiotics in IAIs, the overall susceptibility rate for ciprofloxacin was low, with an average of 41.3%. Ceftriaxone susceptibility rates in all selected countries were below 80% starting in 2018, ranging from 23.3% to 75.8%. The cefepime susceptibility rates varied across regions, with consistently reduced susceptibility ranging from 45.5% to 57.8% in India, Thailand, and Vietnam. Piperacillin/tazobactam demonstrated effectiveness against E. coli isolates in almost all countries except India, with a downward trend observed in the Philippines and Taiwan. Carbapenems remained effective against more than 90% of E. coli isolates, except in India. Conclusion Prudent use of fluoroquinolones and ceftriaxone is advised when treating both CA and HA IAIs in the Asia-Pacific region. The low susceptibility rate of cefepime in India, Thailand, and Vietnam needs careful consideration in its administration. Moreover, the increase in nonsusceptibility to piperacillin/tazobactam in the Philippines and Taiwan poses a potential risk that should be closely monitored.
Mask-Reconstruction-Based Decoupled Convolution Network for Hyperspectral Imagery Classification

Deep learning has attracted much attention in hyperspectral imagery (HSI) classification. However, most deep learning methods ignore the information loss during spatial–spectral feature extraction, which potentially affects the classification performance. In this article, mask-reconstruction-based decoupled convolution network (MrDCN) is proposed, which includes the decoupled feature extraction module (DFEM) to extract spectral information and spatial information of the target HSI patch, respectively. The reconstruction modules are designed to maintain the feature extraction ability of DFEM and ensure that discriminative information in high-dimensional and low-dimensional features is preserved. MrDCN outperforms the state-of-the-art methods in classification on three datasets of various scenarios, which indicates its effectiveness, and experiments on embedded devices are executed to affirm the efficiency of MrDCN.
Identification of an endoplasmic reticulum stress-related prognostic risk model with excellent prognostic and clinical value in oral squamous cell carcinoma

Background: Recently, endoplasmic reticulum stress related gene (ERS) markers have performed very well in predicting the prognosis of tumor patients. Methods: The differentially expressed genes in Oral squamous cell carcinoma (OSCC) were obtained from TCGA and GTEx database. Three prognosis-related and differentially expressed ERSs were screened out by Least Absolute Selection and Shrinkage Operator (Lasso) regression to construct a prognostic risk model. Receiver Operating Characteristic Curve (ROC), riskplots and survival curves were used to verify the model’s accuracy in predicting prognosis. Multi-omics analysis of immune infiltration, gene mutation, and stem cell characteristics were performed to explore the possible mechanism of OSCC. Finally, we discussed the model’s clinical application value from the perspective of drug sensitivity. Results: Three genes used in the model (IBSP, RDM1, RBP4) were identified as prognostic risk factors. Bioinformatics analysis, tissue and cell experiments have fully verified the abnormal expression of these three genes in OSCC. Multiple validation methods and internal and external datasets confirmed the model’s excellent performance in predicting and discriminating prognosis. Cox regression analysis identified risk score as an independent predictor of prognosis. Multi-omics analysis found strong correlations between risk scores and immune cells, cell stemness index, and tumor mutational burden (TMB). It was also observed that the risk score was closely related to the half maximal inhibitory concentration of docetaxel, gefitinib and erlotinib. The excellent performance of the nomogram has been verified by various means. Conclusion: A prognostic model with high clinical application value was constructed. Immune cells, cellular stemness, and TMB may be involved in the progression of OSCC.
Full Coverage of Confined Irregular Polygon Area for Marine Survey

This paper considers the coverage path planning (CPP) problem for marine surveys, where the survey vehicles’ movements are confined in the irregular polygon search area. So this is a sort of milling problem with the minimum number of turns in order for improvement of the acquired sonar image quality. For this purpose, this paper proposes a novel method called CbSPSA (calculation based shortest path search algorithm). Especially in the irregular corner area, this algorithm can easily calculate a kind of shortest path for its full coverage of this irregular area. For any given polygon, it can always be partitioned into convex polygon(s). In this paper, we only consider the case where these divided convex polygons are all connected one by one. In this case, the shortest path with minimum turns in each convex polygon can be easily searched by CbSPSA. And further by simply linking all of these pieces, the total coverage path for any given polygon area can be constructed. On the other hand, for a given polygon, usually there are several different cases of partition. Among the searched coverage paths for each of these different partitions, the final optimal coverage path is determined through a predefined criteria. Numerical simulation and analyses are carried out to demonstrate the effectiveness of the proposed method.