Miracle-dz/UniFAR-FTUs huggingface dataset:
Conhecimento e perceção de futuros professores sobre o impacto da produção animal no aquecimento global

portuguesO aquecimento global do planeta acentuou-se nas ultimas decadas, fruto da acao antropogenica, e muito se tem escrito sobre as suas causas e consequencias. Mas so recentemente o impacto da producao animal nas alteracoes climaticas ganhou alguma notoriedade, impulsionada pelo documentario Cowspiracy: The Sustainability Secret, embora seja dificil de avaliar o impacto deste filme em Portugal. De facto, a producao animal, principalmente de animais ruminantes, e uma das principais causas do aquecimento global, se nao a principal, relacionando-se ainda com a degradacao do solo, poluicao atmosferica, escassez de agua e sua poluicao e perda de biodiversidade (Steinfeld et al. 2006). Assim, neste estudo, quisemos verificar o conhecimento de 38 futuros professores de duas instituicoes de ensino superior acerca da relacao entre a producao animal e o aquecimento global, e como a hierarquizam no quadro de outras causas que conduzem igualmente a este fenomeno. Os resultados, muito semelhantes nos estudantes das duas instituicoes, apontam para uma desvalorizacao desta causa no quadro de outras, embora cerca de 2/3 dos estudantes inquiridos consigam explicar, pelo menos parcialmente, a referida relacao. A desvalorizacao referida permite suspeitar que este assunto podera nao vir a ser abordado no contexto profissional futuro destes docente com a relevância que o mesmo merece. EnglishThe global warming of the planet has increased in recent decades as a result of anthropogenic action, and much has been written about its causes and consequences. But only recently the impact of livestock production on climate change has gained some notoriety, especially due to the documentary Cowspiracy: The Sustainability Secret, although it is difcult to assess the impact of this flm in Portugal. In fact, livestock production, especially of ruminant animals, is a major cause of global warming, if not the most important one, and is also related to land degradation, air pollution, water scarcity and its pollution and loss of biodiversity (Steinfeld et al. 2006). Therefore, in this study, we aimed to verify the knowledge of 38 future teachers of two institutions of higher education about the impact of livestock on global warming, and how they rank this cause when compared with others. The results, which were very similar in the students of both institutions, show that livestock production is considered a less important cause in global warming, although near two thirds of the surveyed students were able to explain it scientifcally. The results also allow us to suspect that this matter will not be addressed in their future professional context with the relevance it deserves.
Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model

The increasing wealth of truck global positioning system (GPS) data has broadened the opportunities for understanding freight logistics activities and enhancing research capabilities to real-world case studies. A very important piece of information from the planning and regulatory perspective concerns the occurrence and location of rest stops. In this study, we propose a data-driven unsupervised machine learning method to impute truck stop events by using a Continuous Hidden Markov Model (CHMM). Specifically, we estimate the joint probability distribution of a mixture of multivariate Gaussian densities, whose parameters depend on the latent states of a Markov chain. Each density represents a cluster of stops that are identified not only from their spatial proximity but also from their temporal proximity as the clustering of the rest stops depends on latent states that are conditional on expected times retrieved from the observed data. In this study, we applied the proposed method to a database containing more than 71 million GPS records of Australian trucks, and we particularly aimed to identify rest stops based on a list of features related to the locations and the load of the trucks. The results showed that the CHMM could account for the location proximity for different activities of truck drivers, and they were validated against complementary data on truck loads and land use by using a stratified sampling technique. Validation results indicated that 94.1% of the rest stops were correctly identified, and highlighted the advantage of the proposed approach without any requirement of labelled data, driver logbook or complimentary survey.
In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host’s Heat Shock Proteins That May Contribute to Viral Replications and Development

The non-structural protein 2 (NSP2) is an RNA-binding protein involved in coronavirus genome replication, and it often decreases human immune response to promote viral invasion and development. It is believed that the NSP2 associates itself with polyamines and heat shock proteins inside the host cell to proceed with viral development. This study aimed to investigate how the SARS-CoV-2 virus’ key non-structural proteins (NSP2) utilize polyamines and heat shock proteins using a molecular docking approach and molecular dynamics (MD). ClusPro and HADDOCK servers were used for the docking and Discovery Studio, chimera, and PyMOL were used for analysis. Docking of the heat shock proteins 40 (HSP40), 70 (HSP70), and 90 (HSP90) with SARS-CoV-2 NSP2 resulted in 32, 28, and 19 interactions, respectively. Molecular dynamics revealed Arg458, Asn508, Met297, Arg301, and Trp417 as active residues, and pharmacophore modeling indicated ZINC395648, ZINC01150525, and ZINC85324008 from the zinc database as possible inhibitors for this NSP2.
The Risk of Drug Interactions in Older Primary Care Patients after Hospital Discharge: The Role of Drug Reconciliation

Introduction: Drug–drug interactions (DDIs) represent an important clinical problem, particularly in older patients, due to polytherapy, comorbidity, and physiological changes in pharmacodynamic and pharmacokinetic pathways. In this study, we investigated the association between drugs prescribed after discharge from the hospital or clinic and the risk of DDIs with drugs used daily by each patient. Methods: We performed an observational, retrospective, multicenter study on the medical records of outpatients referred to general practitioners. DDIs were measured using the drug interaction probability scale. Potential drug interactions were evaluated by clinical pharmacologists (physicians) and neurologists. Collected data were analyzed using the Statistical Package for the Social Sciences. Results: During the study, we evaluated 1772 medical records. We recorded the development of DDIs in 10.3% of patients; 11.6% of these patients required hospitalization. Logistic regression showed an association among DDIs, sex, and the number of drugs used (p = 0.023). Conclusions: This observational real-life study shows that the risk of DDIs is common in older patients. Physicians must pay more attention after hospital discharge, evaluating the treatment to reduce the risk of DDIs.
Research on accurate recognition of arbitrarily selected mark patterns in alignment of lithography

The alignment of mask and wafer is a very important step in the process of lithography. When a specific pattern in the exposure image is selected as the alignment mark, the traditional automatic alignment methods which are based pre-set markers and image processing are not suitable. To address this issue, we propose a novel accurate image recognition method for arbitrarily selected mark patterns, which combines Support Vector Machine (SVM) with feature extraction to achieve adaptive switching of alignment templates. Firstly, based on the distinct linear contour features of silicon wafer exposure patterns, which lack color and texture characteristics, we extract Histogram of Oriented Gradients (HOG) features from the images to construct feature vectors ; then, we select the optimal SVM kernel function through experimental comparisons, and select regions of interest on silicon wafer exposure images for testing ; finally, we utilize HU-based shape features for secondary matching and recognition decisions. The experimental results demonstrate that the proposed method achieves a recognition accuracy of 100%, enabling the implementation of adaptive alignment template selection and switching.
The small protein MntS evolved from a signal peptide and acquired a novel function regulating manganese homeostasis in Escherichia coli

Small proteins (< 50 amino acids) are emerging as ubiquitous and important regulators in organisms ranging from bacteria to humans, where they commonly bind to and regulate larger proteins during stress responses. However, fundamental aspects of small proteins, such as their molecular mechanism of action, downregulation after they are no longer needed, and their evolutionary provenance are poorly understood. Here we show that the MntS small protein involved in manganese (Mn) homeostasis binds and inhibits the MntP Mn transporter. Mn is crucial for bacterial survival in stressful environments, but is toxic in excess. Thus, Mn transport is tightly controlled at multiple levels to maintain optimal Mn levels. The small protein MntS adds a new level of regulation for Mn transporters, beyond the known transcriptional and post-transcriptional control. We also found that MntS binds to itself in the presence of Mn, providing a possible mechanism of downregulating MntS activity to terminate its inhibition of MntP Mn export. MntS is homologous to the signal peptide of SitA, the periplasmic metal-binding subunit of a Mn importer. Remarkably, the homologous signal peptide regions can substitute for MntS, demonstrating a functional relationship between MntS and these signal peptides. Conserved gene-neighborhoods support that MntS evolved from an ancestral SitA, acquiring a life of its own with a distinct function in Mn homeostasis. Significance This study demonstrates that the MntS small protein binds and inhibits the MntP Mn exporter, adding another layer to the complex regulation of Mn homeostasis. MntS also interacts with itself in cells with Mn, which could prevent it from regulating MntP. We propose that MntS and other small proteins might sense environmental signals and shut off their own regulation via binding to ligands (e.g., metals) or other proteins. We also provide evidence that MntS evolved from the signal peptide region of the Mn importer, SitA. Homologous SitA signal peptides can recapitulate MntS activities, showing that they have a second function beyond protein secretion. Overall, we establish that small proteins can emerge and develop novel functionalities from gene remnants.
Underused Marine Resources: Sudden Properties of Cod Skin Gelatin Gel

The main object of this work was to characterize the structure and properties of laboratory-made fish gelatin from cod skin in comparison with known commercial gelatins of fish and mammalian origin. This is one way we can contribute to the World Food Program and characterize foodstuff resources from alternative natural sources. Our research was based on the combination of an expanded set of complementary physical–chemical methods to study the similarities and distinctions of hydrogels from traditional and novel gelatin sources from underused marine resources. In this work, we have compared the morphology, supramolecular structure and colloid properties of two commercial (mammalian and fish) gelatins with gelatin we extracted from cold-water cod skin in laboratory conditions. The obtained results are novel, showing that our laboratory-produced fish gelatin is much closer to the mammalian one in terms of such parameters as thermal stability and strength of structural network under temperature alterations. Especially interesting are our experimental observations comparing both fish gelatins: it was shown that the laboratory-extracted cod gelatin is essentially more thermally stable compared to its commercial analogue, being even closer in its rheological properties to the mammalian one.
Successful treatment with trastuzumab plus chemotherapy as the first‑line regimen in advanced small bowel adenocarcinoma harboring HER2 amplification: A report of two cases

Therapeutic options are limited for individuals with unresectable or metastatic small bowel adenocarcinoma (SBA), necessitating palliative chemotherapy. Human epidermal growth factor receptor 2 (HER2) gene amplification or protein overexpression in SBA is exceedingly rare. HER2 amplification mutations/overexpression serves as a potential target for treatment in various malignancies. However, research on targeted therapies for SBA with HER2 mutation is lacking. In this context, the present study reports two cases of advanced SBA with a HER2 amplification mutation. Both patients received the anti-HER2 agent trastuzumab in combination with an oxaliplatin-based chemotherapy regimen as a first-line treatment. Following disease progression, trastuzumab was used in conjunction with other palliative chemotherapy regimens. Notably, anti-HER2 treatment resulted in significantly extended overall survival times without the occurrence of serious treatment-related adverse events. The overall survival times of the two patients were 31 and 15 months. Additionally, a review of the existing literature was conducted with regard to the effectiveness of anti-HER2 agents in the treatment of advanced SBA. It can be concluded that it is imperative to ascertain the HER2 status prior to the initiation of palliative treatment.
Role of Glycolytic and Glutamine Metabolism Reprogramming on the Proliferation, Invasion, and Apoptosis Resistance through Modulation of Signaling Pathways in Glioblastoma

Simple Summary Metabolic reprogramming in glioblastoma is a hallmark of malignancy. Metabolic enzymes and metabolites participate in energetic metabolism and function as signaling molecules associated with genomic instability, mutations, epigenetic changes, and the activation of oncogenic signaling pathways. In addition, metabolites obtained from the metabolic network regulate several metabolic enzymes facilitating malignancy, therapeutic resistance, and tumor recurrence at the transcriptional and post-translational levels. In this review, we described the role of the glycolytic and glutamine metabolic network as an inductor of the cellular pathophysiology of glioblastoma as well as a therapeutic target in glioma cells. Abstract Glioma cells exhibit genetic and metabolic alterations that affect the deregulation of several cellular signal transduction pathways, including those related to glucose metabolism. Moreover, oncogenic signaling pathways induce the expression of metabolic genes, increasing the metabolic enzyme activities and thus the critical biosynthetic pathways to generate nucleotides, amino acids, and fatty acids, which provide energy and metabolic intermediates that are essential to accomplish the biosynthetic needs of glioma cells. In this review, we aim to explore how dysregulated metabolic enzymes and their metabolites from primary metabolism pathways in glioblastoma (GBM) such as glycolysis and glutaminolysis modulate anabolic and catabolic metabolic pathways as well as pro-oncogenic signaling and contribute to the formation, survival, growth, and malignancy of glioma cells. Also, we discuss promising therapeutic strategies by targeting the key players in metabolic regulation. Therefore, the knowledge of metabolic reprogramming is necessary to fully understand the biology of malignant gliomas to improve patient survival significantly.
Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications.
Network-level Attacks Against Ethereum PoS

This study delves into the feasibility of network-level attacks against Ethereum’s Proof-of-Stake (PoS) consensus mechanism, specifically focusing on the Proposer-Builder-Separation (PBS) scheme. We simulate this environment on a custom development network and run two attack models. We find that in the new network landscape that PBS introduces, if a network attacker can successfully execute an attack that includes dropping or delaying packets sent between the relays and a validator, they are able to force the validator to build its own block, censor transactions, influence the profitability of certain blocks, and thus influence the outcome of the blockchain. We find that the attacker succeeds at a higher frequency with increasing drop rates and that adding a 500ms delay of outgoing packets from the relay suffices to get a 100% success rate.
BRAFV600E, BANCR, miR-203a-3p and miR-204-3p in Risk Stratification of PTC Patients

In order to enhance the risk stratification of papillary thyroid carcinoma (PTC) patients, we assessed the presence of the most common mutation in PTC (BRAFV600E) with the expression profiles of long non-coding RNA activated by BRAFV600E (BANCR) and microRNAs, which share complementarity with BANCR (miR-203a-3p and miR-204-3p), and thereafter correlated it with several clinicopathological features of PTC. BRAFV600E was detected by mutant allele-specific PCR amplification. BANCR and miRs levels were determined by quantitative RT-PCR. Bioinformatic analysis was applied to determine the miRs’ targets. The expression profile of miR-203a-3p/204-3p in PTC was not affected by BRAFV600E. In the BRAFV600E-positive PTC, high expression of miR-203a-3p correlated with extrathyroidal invasion (Ei), but the patients with both high miR-203a-3p and upregulated BANCR were not at risk of Ei. In the BRAFV600E-negative PTC, low expression of miR-204-3p correlated with Ei, intraglandular dissemination and pT status (p < 0.05), and the mutual presence of low miR-204-3p and upregulated BANCR increased the occurrence of Ei. Bioinformatic analysis predicted complementary binding between miR-203a-3p/204-3p and BANCR. The co-occurrence of tested factors might influence the spreading of PTC. These findings partially describe the complicated network of interactions that may occur during the development of PTC aggressiveness, potentially providing a new approach for high-risk PTC patient selection.
Carotid plaque score and ischemic stroke risk stratification through a combination of B-mode and contrast-enhanced ultrasound in patients with low and intermediate carotid stenosis

Objective The occurrence of ischemic stroke (IS) is closely related to the characteristics of carotid plaque (CP). Due to the effect of stroke risk stratification based on B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) that has not been studied in patients with low and intermediate carotid stenosis, we construct and validate a CP score and ischemic stroke risk stratification (ISRS) using a combination of B-mode and CEUS, in order to provide new convenient strategies to stratify these patients to prevent stroke. Materials and methods This retrospective study evaluated 705 patients with low and intermediate carotid stenosis who underwent B-mode and CEUS from November 2021 to April 2023. Qualitative B-mode and CEUS features of carotid plaques were analyzed using a univariable and multivariable logistic regression to construct the CP score. Then, we combined the CP score with Essen stroke risk score (ESRS) to develop ISRS. Results This study included a total of 705 patients with low and intermediate carotid stenosis, of which 394 were symptomatic patients (with a mean age of 71.03 ± 10.48 years) and 311 were asymptomatic patients (with a mean age of 65.13 ± 10.31 years). Plaque echogenicity, plaque morphology, carotid intima-media thickness in B-mode US and intraplaque neovascularization grading and perfusion pattern in CEUS were significantly associated with IS. The ISRS incorporating these five predictors and ESRS showed good discrimination and calibration in both primary cohort [area under the curve (AUC), 0.91; Hosmer–Lemeshow test, p = 0.903] and validation cohort (AUC, 0.84; Hosmer–Lemeshow test, p = 0.886). Conclusion We developed an effective and practical tool to identify and stratify patients with low and intermediate carotid stenosis, based on the CP score and ISRS estimation. Our study may provide new insights into managing patients with no indication of surgery.
Gene signatures associated with prognosis and chemotherapy resistance in glioblastoma treated with temozolomide

Background: Glioblastoma (GBM) prognosis remains extremely poor despite standard treatment that includes temozolomide (TMZ) chemotherapy. To discover new GBM drug targets and biomarkers, genes signatures associated with survival and TMZ resistance in GBM patients treated with TMZ were identified. Methods: GBM cases in The Cancer Genome Atlas who received TMZ (n = 221) were stratified into subgroups that differed by median overall survival (mOS) using network-based stratification to cluster patients whose somatic mutations affected genes in similar modules of a gene interaction network. Gene signatures formed from differentially mutated genes in the subgroup with the longest mOS were used to confirm their association with survival and TMZ resistance in independent datasets. Somatic mutations in these genes also were assessed for an association with OS in an independent group of 37 GBM cases. Results: Among the four subgroups identified, subgroup four (n = 71 subjects) exhibited the longest mOS at 18.3 months (95% confidence interval: 16.2, 34.1; p = 0.0324). Subsets of the 86 genes that were differentially mutated in this subgroup formed 20-gene and 8-gene signatures that predicted OS in two independent datasets (Spearman’s rho of 0.64 and 0.58 between actual and predicted OS; p < 0.001). Patients with mutations in five of the 86 genes had longer OS in a small, independent sample of 37 GBM cases, but this association did not reach statistical significance (p = 0.07). Thirty-one of the 86 genes formed signatures that distinguished TMZ-resistant GBM samples from controls in three independent datasets (area under the curve ≥ 0.75). The prognostic and TMZ-resistance signatures had eight genes in common (ANG, BACH1, CDKN2C, HMGA1, IFI16, PADI4, SDF4, and TP53INP1). The latter three genes have not been associated with GBM previously. Conclusion: PADI4, SDF4, and TP53INP1 are novel therapy and biomarker candidates for GBM. Further investigation of their oncologic functions may provide new insight into GBM treatment resistance mechanisms.