Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-inclusive inelastic electron scattering from nuclei

A survey is presented of the physics of the electroproduction of hadrons from nuclear targets, eA . -->. e'hX. Variables and structure functions are specified. The parton model description of electroproduction is summarized; fragmentation functions are defined and their properties are listed. Specific measurements are suggested. Predictions of the pion exchange model are presented for the nuclear dependence of eA . -->. e'hX, including a discussion of the special subprocess e.pi. -->. e'. pi. in which scattering occurs from the pion constituents of nuclei.
Analysis of Discrete Wavelet Spectra of Broand Signals

The article is devoted to the development of a mathematical model for autoregressive and autocoherent analysis of discrete wavelet spectra with approbation on known experimental data. The model is used to detect anomalies or emergency states of the object after wavelet filtering of noise and zero crossing. The proposed method for processing noisy signals, combining noise filtering and Mahalanobis proximity analysis, shows better machine learning results than neural networks and other methods for detecting classification and recognition anomalies. The wavelet spectra were processed using autoregressive and autocoherent analyses. The possibility of using these functions as classification features for identifying possible anomalous states of devices is shown. Before forming the wavelet spectra, the procedure of direct with filtering and inverse transformation of the already filtered signal is carried out. As a result, it seems possible to identify the characteristic features of the signal - anomalies or emergency conditions. The main factor in the classification of device status signals is the moment when changes in the signal spectrum begin to develop, therefore, all analysis, both in the time and frequency domain, is tied either to the number of samples by the time the signal was received, and in some cases even to the extended date format - month number.
Case Driven TLC Model Checker Analysis in Energy Scenario

Today, model checking techniques and corresponding tools are widely applied in diverse case driven scenarios, the safety critical ones in particular. Addressing current situation in Ukraine, an energy domain is among the topical spheres, where safety critical business processes take place. To foster the functional safety of corresponding program-algorithmic solutions, the model checking techniques and related tools are applied to the formal specifications of named solutions. Doing so is not a trivial task: it depends on a particular use case scenario determining the architecture (structure and couplings) of the resulting design artifact. Moreover, the outcomes of formal techniques and tools application directly depend on specification atomicity level chosen – as a tradeoff between the complexity of program-algorithmic constituent addressed to be represented in formal specification and available computational and spatial resources of the computing platform with model checking technique implementation – because of an exponential growth of transition system state space. To this end, to foster the effectiveness of model checking technique application, with respect to a particular case driven scenario, the analysis of broadly applied TLC model checker has been conducted on the basis of a role model from energy domain. Experimentation has been conducted by addressing two alternative implementations of the TLC method. Both – computational and spatial properties – have been covered. To estimate also the domain related spatial expenses on verification, with respect to the number of software threads utilized, the approximation task has been resolved.
Identification of a novel glycolysis-related prognosis risk signature in triple-negative breast cancer

Introduction Triple-negative breast cancer (TNBC) is a particularly aggressive cluster of breast cancer characterized by significant molecular heterogeneity. Glycolysis is a metabolic pathway that is significantly associated with cancer progression, metastasis, recurrence and chemoresistance. However, the potential roles of glycolysis-related genes in TNBC remain unclear. Methods In the present study, we identified 108 glycolysis-related differentially expressed genes (DEGs) between breast cancer (BRCA) tumor tissues and normal tissues, and we divided patients into two different clusters with significantly distinct molecular characteristics, clinicopathological features, prognosis, immune cell infiltration and mutation burden. We then constructed a 10-gene signature that classified all TNBCs into low- and high-risk groups. Results The high-risk group had significantly lower survival than the low-risk group, which implied that the risk score was an independent prognostic indicator for TNBC patients. Consequently, we constructed and validated a prognostic nomogram, which accurately predicted individual overall survival (OS) of TNBC. Moreover, the risk score predicted the drug sensitivity of chemotherapeutic agents and immunotherapy for TNBC patients. Discussion The present comprehensive analysis of glycolysis-related DEGs in TNBC provides new methods for prognosis prediction and more effective treatment strategies.
Butanol as a major product during ethanol and acetate chain elongation

Chain elongation is a relevant bioprocess in support of a circular economy as it can use a variety of organic feedstocks for production of valuable short and medium chain carboxylates, such as butyrate (C4), caproate (C6), and caprylate (C8). Alcohols, including the biofuel, butanol (C4), can also be generated in chain elongation but the bioreactor conditions that favor butanol production are mainly unknown. In this study we investigated production of butanol (and its precursor butyrate) during ethanol and acetate chain elongation. We used semi-batch bioreactors (0.16 L serum bottles) fed with a range of ethanol concentrations (100–800 mM C), a constant concentration of acetate (50 mM C), and an initial total gas pressure of ∼112 kPa. We showed that the butanol concentration was positively correlated with the ethanol concentration provided (up to 400 mM C ethanol) and to chain elongation activity, which produced H2 and further increased the total gas pressure. In bioreactors fed with 400 mM C ethanol and 50 mM C acetate, a concentration of 114.96 ± 9.26 mM C butanol (∼2.13 g L−1) was achieved after five semi-batch cycles at a total pressure of ∼170 kPa and H2 partial pressure of ∼67 kPa. Bioreactors with 400 mM C ethanol and 50 mM C acetate also yielded a butanol to butyrate molar ratio of 1:1. At the beginning of cycle 8, the total gas pressure was intentionally decreased to ∼112 kPa to test the dependency of butanol production on total pressure and H2 partial pressure. The reduction in total pressure decreased the molar ratio of butanol to butyrate to 1:2 and jolted H2 production out of an apparent stall. Clostridium kluyveri (previously shown to produce butyrate and butanol) and Alistipes (previously linked with butyrate production) were abundant amplicon sequence variants in the bioreactors during the experimental phases, suggesting the microbiome was resilient against changes in bioreactor conditions. The results from this study clearly demonstrate the potential of ethanol and acetate-based chain elongation to yield butanol as a major product. This study also supports the dependency of butanol production on limiting acetate and on high total gas and H2 partial pressures.
Digital ethnography: ethics through the case of QAnon

Introduction Digital ethnography is a relatively new practice with unclear standards and guidelines. As a result, the ethics of the practice remain unclear. Scholarly debates have emerged surrounding the decision of many researchers and institutional review boards to treat social media data as public. Concerns have also been raised about how informed consent can be adapted to online fieldwork. How does a researcher make their presence known when they are not visible in the traditional sense? Which online interactions should be considered public, and which are private? How can we protect the anonymity of social media users? Methods This article leverages original digital ethnographic research on QAnon social media spaces to suggest ethical guidelines for digital ethnographic practices. Discussion It begins with a description of the research, followed by discussions of the public-private binary, lurking, data reconstruction, and institutional review boards. Results This article advocates for rethinking the public-private binary as it applies to the digital world, ameliorating the “lurker” concern by making the presence of the researcher known in appropriate spaces, and maintaining the integrity of the data by avoiding reconstruction. Although many digital ethnographers have chosen to reconstruct or paraphrase online data to protect privacy, this practice comes with its own ethical dilemmas. The ethical dilemmas and guidance discussed in this article are critical lessons for digital and in-person ethnographers alike.
From lab to mass production: a policy for enabling the licensing of mRNA vaccines

Using the South African vaccine technology transfer hub supported by the WHO as an example, we show that the know-how needed to move mRNA vaccines from prototype to mass-production acts as an invisible barrier to market entry of mRNA vaccines. Overcoming this barrier relies on scarce human capital. In view of this scarcity and in preparation for the next pandemic, we propose broadening the scope of an existing WHO program, the WHO Academy, so that it coordinates knowledge diffusion initiatives by forming a systematized repository of know-how and a register of experts. As we explain, this proposal has an advantage in overcoming barriers to entry over current approaches of know-how acquisition.
Nitric oxide-mediated the therapeutic properties of induced pluripotent stem cell for paraquat-induced acute lung injury

The mortality rate associated with acute lung injury (ALI) and its severe form, acute respiratory distress syndrome, is high. Induced pluripotent stem cell (iPSC) therapy is a potential treatment method for ALI, but its therapeutic efficacy is limited in injured lungs. Nitric oxide (NO) has various physiological actions. The current study investigated the effect of iPSCs pretreated with NO donors in paraquat (PQ)-induced ALI mouse model. Male C57BL/6 mice were intraperitoneally injected with PQ, followed by infusion of phosphate-buffered saline, iPSCs, L-arginine pretreated iPSCs, or Nitro-L-arginine methylester (L-NAME) pretreated iPSCs through the tail veins. Histopathological changes, pulmonary microvascular permeability, and inflammatory cytokine levels were analyzed after 3 or 28 d. The effects on iPSC proliferation, migration, and adhesion were evaluated in vitro. More L-arginine-pretreated iPSCs were selectively trafficked into the injured pulmonary tissue of mice with LPS-induced ALI, drastically diminishing the histopathologic changes and inflammatory cytokine levels (IL-1β and IL-6). There was also markedly improved pulmonary microvascular permeability and pulmonary function. The NO inhibitor abolished the protective effects of iPSCs. In addition, the ability of L-arginine to promote the proliferation and migration of iPSCs was decreased by L-NAME pretreatment, suggesting that NO might mediate the therapeutic benefits of iPSC. The improvement of the iPSC physiological changes by the endogenous gaseous molecule NO reduces lung injury severity. L-Arginine represents a pharmacologically important strategy for enhancing the therapeutic potential of iPSCs.
Fully automating LI-RADS on MRI with deep learning-guided lesion segmentation, feature characterization, and score inference

Introduction Leveraging deep learning in the radiology community has great potential and practical significance. To explore the potential of fitting deep learning methods into the current Liver Imaging Reporting and Data System (LI-RADS) system, this paper provides a complete and fully automatic deep learning solution for the LI-RADS system and investigates its model performance in liver lesion segmentation and classification. Methods To achieve this, a deep learning study design process is formulated, including clinical problem formulation, corresponding deep learning task identification, data acquisition, data preprocessing, and algorithm validation. On top of segmentation, a UNet++-based segmentation approach with supervised learning was performed by using 33,078 raw images obtained from 111 patients, which are collected from 2010 to 2017. The key innovation is that the proposed framework introduces one more step called feature characterization before LI-RADS score classification in comparison to prior work. In this step, a feature characterization network with multi-task learning and joint training strategy was proposed, followed by an inference module to generate the final LI-RADS score. Results Both liver segmentation and feature characterization models were evaluated, and comprehensive statistical analysis was conducted with detailed discussions. Median DICE of liver lesion segmentation was able to achieve 0.879. Based on different thresholds, recall changes within a range of 0.7 to 0.9, and precision always stays high greater than 0.9. Segmentation model performance was also evaluated on the patient level and lesion level, and the evaluation results of (precision, recall) on the patient level were much better at approximately (1, 0.9). Lesion classification was evaluated to have an overall accuracy of 76%, and most mis-classification cases happen in the neighboring categories, which is reasonable since it is naturally difficult to distinguish LI-RADS 4 from LI-RADS 5. Discussion In addition to investigating the performance of the proposed model itself, extensive comparison experiment was also conducted. This study shows that our proposed framework with feature characterization greatly improves the diagnostic performance which also validates the effectiveness of the added feature characterization step. Since this step could output the feature characterization results instead of simply generating a final score, it is able to unbox the black-box for the proposed algorithm thus improves the explainability.
Screening of immunotherapy-related genes in bladder cancer based on GEO datasets

Background As one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy. Methods Two GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment. Result We screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages. Conclusion These four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.
Oral administration of Pinus koraiensis cone essential oil reduces rumen methane emission by altering the rumen microbial composition and functions in Korean native goat (Capra hircus coreanae)

This study aimed to investigate Pinus koraiensis cone essential oil (PEO) as a methane (CH4) inhibitor and determine its impact on the taxonomic and functional characteristics of the rumen microbiota in goats. A total of 10 growing Korean native goats (Capra hircus coreanae, 29.9 ± 1.58 kg, male) were assigned to different dietary treatments: control (CON; basal diet without additive) and PEO (basal diet +1 g/d of PEO) by a 2 × 2 crossover design. Methane measurements were conducted every 4 consecutive days for 17–20 days using a laser CH4 detector. Samples of rumen fluid and feces were collected during each experimental period to evaluate the biological effects and dry matter (DM) digestibility after PEO oral administration. The rumen microbiota was analyzed via 16S rRNA gene amplicon sequencing. The PEO oral administration resulted in reduced CH4 emission (eructation CH4/body weight0.75, p = 0.079) without affecting DM intake; however, it lowered the total volatile fatty acids (p = 0.041), molar proportion of propionate (p = 0.075), and ammonia nitrogen (p = 0.087) in the rumen. Blood metabolites (i.e., albumin, alanine transaminase/serum glutamic pyruvate transaminase, creatinine, and triglyceride) were significantly affected (p < 0.05) by PEO oral administration. The absolute fungal abundance (p = 0.009) was reduced by PEO oral administration, whereas ciliate protozoa, total bacteria, and methanogen abundance were not affected. The composition of rumen prokaryotic microbiota was altered by PEO oral administration with lower evenness (p = 0.054) observed for the PEO group than the CON group. Moreover, PICRUSt2 analysis revealed that the metabolic pathways of prokaryotic bacteria, such as pyruvate metabolism, were enriched in the PEO group. We also identified the Rikenellaceae RC9 gut group as the taxa potentially contributing to the enriched KEGG modules for histidine biosynthesis and pyruvate oxidation in the rumen of the PEO group using the FishTaco analysis. The entire co-occurrence networks showed that more nodes and edges were detected in the PEO group. Overall, these findings provide an understanding of how PEO oral administration affects CH4 emission and rumen prokaryotic microbiota composition and function. This study may help develop potential manipulation strategies to find new essential oils to mitigate enteric CH4 emissions from ruminants.
Comprehensive pan-cancer analysis of PTGES3 and its prognostic role in hepatocellular carcinoma

Introduction PTGES3, also known as p23, is a molecule chaperone of Hsp90 that is involved in the pathogenesis of malignant tumors. Increasing studies have shown that PTGES3 plays a nonnegligible role in tumor development. However, analysis of PTGES3 in pan-cancer has not been performed yet. Methods We explored the role of PTGES3 in 33 types of tumors and depicted the potentialimmune-related pathways among them. Using multiple databases includingTCGA, LinkedOmics, GDSC, and TIMER, we made a comprehensive analysis to explore whether there was an interaction between PTGES3 and prognosis, DNA methylation, copy number variation (CNV), tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TME). Results Our study revealed that PTGES3 expression level was upregulated in most cancers. PTGES3 was also associated with a positive or negative prognosis in a variety of cancers, which was mainly associated with DNA methylation, CNV, MSI, TMB, andmismatch repair-related genes. High PTGES3 expression was related to the infiltration of Th2 subsets of CD4+ T cells and immune checkpoint-related genes in most cancers, especially in hepatocellular carcinoma (HCC). Enrichment analysis demonstrated that PTGES3 was involved in cellular processes including DNA replication and spliceosome. The relationship between PTGES3 expression and HCC progression was verified at the protein level through immune histochemical analysis. Conclusions Our research demonstrated theprognostic predictive value of PTGES3 in a wide range of cancers, which was alsoassociated with the process of tumor immune infiltration. As a result, it suggestedthat PTGES3 was a valuable prognostic biomarker in HCC treatment.
Effects of early introduction of solid foods on nutrient intake in preterm infants during their 1st year of life: a secondary outcome analysis of a prospective, randomized intervention study

Very low birth weight (VLBW) infants have higher nutritional needs even after hospital discharge. However, data concerning current nutrient intakes at different time points after the introduction of solid foods and whether dietary reference values are being met are scarce. To adress this issue, this secondary analysis of a prospective, two-arm interventional study in 177 VLBW infants 21 investigates dietary intake comparing early and late (early: 10–12 weeks corrected for gestational age, late: 16–18 weeks corrected for gestational age) introduction of standardized complementary food during the first year of life. Nutritional intake was assessed using self-reported monthly 3-day dietary records from 3 until 12 months, corrected for gestational age. The time point of the introduction of solid foods did not influence nutrient intake, but the early introduction of solids tended toward a higher proportional intake of protein and carbohydrates and a lower intake of fat as a percentage of total energy) during the 1st year of life, corrected for gestational age. The results of this study indicate that this standardized feeding concept was sufficient for zinc, calcium, and phosphorus intake. However, dietary iron and vitamin D intakes did not meet the recommendations. Thus, prolonged iron supplementation should be considered beyond the introduction of meat and vitamin D supplementation at least until 12 months, corrected for gestational age. Trial registration number ClinicalTrials.gov: NCT01809548.
Assessment of validity and comparison of two Spanish versions of the Geriatric Depression Scale

Introduction The Geriatric Depression Scale is an instrument used to identify depression in people of an older age. The original English version of this scale has been translated into Spanish (GDS-VE); two shorter versions of 5- (GDS-5) and 15-items (GDS-15) have been developed. Aim of the study To assess the validity and compare the 5- and 15-item Spanish versions of the GDS among the Spanish population. Materials and methods 573 Galicia residents aged >50 years participated in this study. The following instruments were applied: the 19-item Control, Autonomy, Self-Realization and Pleasure scale, the Subjective Memory Complaints Questionnaire, the Mini-Mental State Examination test, the GDS-5, and the GDS-15. Results We found differences in total score between GDS-5 and GDS-15 regarding the variable sex. Internal reliability for GDS-5 and GDS-15 was 0.495 and 0.715, respectively. Sensitivity and specificity for GDS-5 – with a cut-off value of 1 – was 0.517 and 0.650, respectively; for GDS-15 – with a cut-off value of 3 points – sensitivity was 0.755 and specificity 0.668. GDS-5 has a ROC curve of 0.617 and GDS-15 of 0.764. Conclusion GDS-15, and to a greater extent GDS-5, should be revised or even reformulated to improve their diagnostic usefulness by choosing higher discriminative ability items or even include new items with greater sensitivity that consider currently prevailing psychosocial factors.