Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological and lipidomic response of exogenous choline chloride alleviating salt stress injury in Kentucky bluegrass (Poa pratensis)

Introduction Choline participates in plant stress tolerance through glycine betaine (GB) and phospholipid metabolism. As a salt-sensitive turfgrass species, Kentucky bluegrass (Poa pratensis) is the main turfgrass species in cool-season areas. Methods To improve salinity tolerance and investigate the effects of choline on the physiological and lipidomic responses of turfgrass plants under salinity stress conditions, exogenous choline chloride was applied to Kentucky bluegrass exposed to salt stress. Results From physiological indicators, exogenous choline chloride could alleviate salt stress injury in Kentucky bluegrass. Lipid analysis showed that exogenous choline chloride under salt-stress conditions remodeled the content of phospholipids, glycolipids, and lysophospholipids. Monogalactosyl diacylglycerol, digalactosyl diacylglycerol, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, and lysophosphatidylcholine content were increased and phosphatidic acid content were decreased in plants after exogenous choline chloride under salt treatment. Plant leaf choline content increased, but GB was not detected in exogenous choline chloride treatment plants under nonstress or salt-stress conditions. Discussion GB synthesis pathway related genes showed no clear change to choline chloride treatment, whereas cytidyldiphosphate‐choline (CDP‐choline) pathway genes were upregulated by choline chloride treatment. These results reveal that lipid remodeling through choline metabolism plays an important role in the salt tolerance mechanism of Kentucky bluegrass. Furthermore, the lipids selected in this study could serve as biomarkers for further improvement of salt-sensitive grass species.
A Comprehensive Review of Battery and Supercapacitor Cells Voltage-Equalizer Circuits

Voltage unbalances of the series-connected battery and supercapacitor (SC) cells are mainly due to their differences in materials, manufacturing technology, internal specifications, temperature, initial charge, etc. This voltage difference can reduce the battery or SC pack capacity, stored energy, efficiency, and lifespan. Various methods have been proposed to overcome this issue to avoid its disadvantages. Generally, voltage-equalizer circuits are divided into active and passive categories. The passive methods consume high-voltage cells energy as heat, and they are widely used in nonsensitive applications, due to their low cost and volume, high reliability, and expandability. However, these equalizer circuits are not used in sensitive applications, due to their power losses, heat generation, and low efficiency. To overcome these problems, the active balancing methods have been proposed, which transfer energy from high-voltage cells to low-voltage cells. However, these methods have complex structures and control circuits. In this article, different types of batteries and SCs and their optimum combinations are investigated, first. Then, the active and passive equalizer circuits are examined and compared, in detail. In addition, their different control methods have been studied to choose the most suitable circuit and its control method for a desired application.
Effect of Different Vitrification Techniques on Viability and Apoptotic Index of Domestic Cat Testicular Tissue Cells

Simple Summary The vitrification process stands out among cryopreservation methods because it is a practical and fast technique that reduces the risk of intracellular ice formation and cell damage. The present study aimed to compare three devices used for the vitrification of adult cat testicular biopsies for use in preservation programs. The efficiency of the methods was evaluated by histological and viability analysis of the testicular cells after the vitrification process. It is shown that these methods are efficient and can be considered an important tool for the genetic conservation of endangered wild cats, with the domestic cat providing an experimental model for the development of reproductive biotechnology in wild cat species. Abstract Vitrification is essential for successful tissue cryopreservation and biobanking in wild cats. This study aimed to compare different methods of vitrification (Ovarian Tissue Cryosystem—OTC, Straws—STW, and Solid Surface vitrification—SSV) for testicular fragment vitrification in tom cats. Testicular fragments were recovered from five adult tom cats and subjected to equilibrium vitrification using different cryovials and methods under the same conditions of vitrification solutions and cryoprotectants. The efficiencies of the methods were evaluated using histological analysis of spermatogonia and Sertoli cell nuclei, seminiferous tubular basement membrane detachment, and the gonadal epithelium shrinkage score scale. Cell viability was assessed using Hoechst PI and Terminal deoxynucleotidyl transferase nick end labeling (TUNEL) assay. The results showed that OTC is an effective vitrification method for maintaining the distinction between spermatogonia and Sertoli cells. OTC was similar to the control for basal membrane detachment parameters (p = 0.05). Epithelial shrinkage was low in the SSV group, which showed the highest percentage of viable cells among the vitrified groups (p = 0.0023). The OTC and SSV vitrification methods were statistically similar in terms of the percentage of TUNEL-positive cells (p = 0.05). Therefore, OTC and SSV provide favorable conditions for maintaining viable cat testicular tissue cells after vitrification.
Nonlinear Gulf Stream Interaction with the Deep Western Boundary Current System: Observations and a Numerical Simulation

Abstract : Gulf Stream (GS) separation near its observed Cape Hatteras (CH) separation location, and its ensuing path and dynamics, is a challenging ocean modeling problem. If a model GS separates much farther north than CH, then northward GS meanders, which pinch off warm core eddies (rings), are not possible or are strongly constrained by the Grand Banks shelfbreak. Cold core rings pinch off the southward GS meanders. The rings are often re-absorbed by the GS. The important warm core rings enhance heat exchange and, especially, affect the northern GS branch after GS bifurcation near the New England Seamount Chain. This northern branch gains heat by contact with the southern branch water upstream of bifurcation, and warms the Arctic Ocean and northern seas, thus playing a major role in ice dynamics, thermohaline circulation and possible global climate warming. These rings transport heat northward between the separated GS and shelf slope/Deep Western Boundary Current system (DWBC). This region has nearly level time mean isopycnals. The eddy heat transport convergence/divergence enhances the shelfbreak and GS front intensities and thus also increases watermass transformation. The fronts are maintained by warm advection by the Florida Current and cool advection by the DWBC. Thus, the GS interaction with the DWBC through the intermediate eddy field is climatologically important.
Inertial Fusion Energy reactor design studies: Prometheus-L, Prometheus-H. Volume 1, Final report

This report contains a review of design studies for inertial confinement reactors. The first of three volumes briefly discusses the following: Introduction; Key objectives, requirements, and assumptions; Systems modeling and trade studies; Prometheus-L reactor plant design overview; Prometheus-H reactor plant design overview; Key technical issues and RD Comparison of IFE designs; and study conclusions.
Estimation of antigenotoxic properties of Ginger (Zingibe officinale) for DNA Damage after Exposure to Patulin in rats

Patulin, a known fungal poison, is an important pollutant in apples and products derived from apples and fodder. Ginger improved the liver function and prevents hepatotoxicity against many toxins and used in treatment of various diseases. The genetic toxicity of patulin in the liver of rats using the comet assay in vivo was investigated and effect of ginger (Zingibe officinale) on DNA damage induced by patulin. Five groups of adult male Sprague Dawley rats were used. The rats were given 3.75 mg kg-1 intraperitoneal (i.p) patulin. The rats were treated with ginger at a dose (100 mg kg-1) for four weeks and eight weeks. DNA damage was measured in the liver of rats using a standard genetic toxicity tests comet assay. Tail length and percentage of tail DNA within the comet increased significantly (p <0.05) in patulin group (p =0.01), (p =0.000) respectively, compared with control group a time-dependent manner, indicating an increase in DNA damage. The liver tissue showed signs of improvement in DNA damage after treating rats with ginger. In conclusion, these results indicate that patulin is genotoxic at the injected dose and that liver is an important target organ. Instead, ginger therapy can reduce DNA damage in liver cells from the toxicity caused by patulin.
Advances in CRISPR/Cas9-based research related to soybean [Glycine max (Linn.) Merr] molecular breeding

Soybean [Glycine max (Linn.) Merr] is a source of plant-based proteins and an essential oilseed crop and industrial raw material. The increase in the demand for soybeans due to societal changes has coincided with the increase in the breeding of soybean varieties with enhanced traits. Earlier gene editing technologies involved zinc finger nucleases and transcription activator-like effector nucleases, but the third-generation gene editing technology uses clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9). The rapid development of CRISPR/Cas9 technology has made it one of the most effective, straightforward, affordable, and user-friendly technologies for targeted gene editing. This review summarizes the application of CRISPR/Cas9 technology in soybean molecular breeding. More specifically, it provides an overview of the genes that have been targeted, the type of editing that occurs, the mechanism of action, and the efficiency of gene editing. Furthermore, suggestions for enhancing and accelerating the molecular breeding of novel soybean varieties with ideal traits (e.g., high yield, high quality, and durable disease resistance) are included.
Analysis of Sensitivity, Specificity, and Predictive Values of High-Sensitivity Troponin T in a Secondary Care Setting: A Retrospective Cohort Study

Background High-sensitivity cardiac troponin (hs-cTn) assays have significantly improved the early detection of myocardial injury and the diagnosis of acute coronary syndrome (ACS). Different diagnostic algorithms exist for the interpretation of hs-cTn in the management of patients with suspected ACS. This study analysed the diagnostic efficacy of hs-cTn using serial and single measurements while also shedding light on the challenges associated with the use of this assay. Methods We reviewed 189 results belonging to 120 unique patient episodes and records for troponin tests performed in a two-week period obtained from the West Cumberland Hospital, North Cumbria Integrated Care (NCIC), Whitehaven, England. These troponin tests were carried out based on the NCIC trust guidelines for the use of troponin assays in the management of acute coronary syndrome (ACS). A positive troponin test is defined using the NCIC trust guidelines and the National Academy of Clinical Biochemistry (NACB) guidelines. The case notes of the unique patients were reviewed to determine the outcome, which is defined as the clinical diagnosis on discharge of the patient following a cardiologist review. These outcomes were then used to calculate the sensitivity, specificity, and predictive values. We also determined the alternate diagnosis for false-positive tests. Results Using both guidelines to assess the clinical effectiveness of the troponin assay yields slightly varying results, with the single positive test of NACB demonstrating a higher sensitivity of 92.8% (>71.4%) and a slightly better negative predictive value of 97.8% (>96%). However, using the serial troponin measurements as per the NCIC trust guideline demonstrates a better specificity of 95.2% (>42.4%) and a positive predictive value of 66% (>17.5%). False positive results are identified, which are due to alternate diagnoses such as stable angina, myocarditis, heart failure, sepsis, and malignancy. Conclusion High-sensitivity troponin (hs-cTn) assays play a crucial role in the early detection and management of patients with suspected ACS. This study supports evidence that serial troponin measurements are more diagnostically accurate than single troponin measurements. Although hs-cTn assays offer significant advantages, there remain challenges and limitations that require careful interpretation and clinical correlation.
AN INTEGRATED SOLUTION FOR INCREASED CIRCULARITY IN BUILDINGS: A METHODOLOGICAL FRAMEWORK

The transformation from a linear to a circular economy remains very slow worldwide. The abundance of materials measured in billions of tonnes is transferred annually within the economy, with minimal materials being cycled back. The construction sector uses approximately half of the materials extracted while generating a third or more of all waste. The current circular potential in the construction industry is yet to be exploited. This paper addresses the issue of materials embedded in buildings and the possibility of their reversible capacities. This paper aims to develop a systemic methodological framework on how to create, collect, process and present data that could facilitate the improvement of a more efficient recovery process of embedded building materials, especially their reuse and high-quality recycling during the renovation and demolition processes. The proposed framework defines a methodological approach for developing an interactive digital solution, a digital map, to increase circularity in a built environment by integrating spatial data and building information models (BIMs). To this end, a four-step methodological approach is introduced: (1) development of BIMs for the existing building stock; (2) allocation of material inventory data to BIMs; (3) calculation of circular economy indicators; and (4) digital solution integration into 3D Geographic Information Systems (GIS). This map may facilitate maximising the circular potential of buildings, support data transparency and broaden accessibility to a broad spectrum of market participants. It could be imbued with tools such as a digital building logbook with which it could share data.
Experimental Investigation of Acoustic Features to Optimize Intelligibility in Cochlear Implants

Although cochlear implants work well for people with hearing impairment in quiet conditions, it is well-known that they are not as effective in noisy environments. Noise reduction algorithms based on machine learning allied with appropriate speech features can be used to address this problem. The purpose of this study is to investigate the importance of acoustic features in such algorithms. Acoustic features are extracted from speech and noise mixtures and used in conjunction with the ideal binary mask to train a deep neural network to estimate masks for speech synthesis to produce enhanced speech. The intelligibility of this speech is objectively measured using metrics such as Short-time Objective Intelligibility (STOI), Hit Rate minus False Alarm Rate (HIT-FA) and Normalized Covariance Measure (NCM) for both simulated normal-hearing and hearing-impaired scenarios. A wide range of existing features is experimentally evaluated, including features that have not been traditionally applied in this application. The results demonstrate that frequency domain features perform best. In particular, Gammatone features performed best for normal hearing over a range of signal-to-noise ratios and noise types (STOI = 0.7826). Mel spectrogram features exhibited the best overall performance for hearing impairment (NCM = 0.7314). There is a stronger correlation between STOI and NCM than HIT-FA and NCM, suggesting that the former is a better predictor of intelligibility for hearing-impaired listeners. The results of this study may be useful in the design of adaptive intelligibility enhancement systems for cochlear implants based on both the noise level and the nature of the noise (stationary or non-stationary).
Exploring the potential relationship between frozen shoulder and Dupuytren’s disease through bioinformatics analysis and machine learning

Background Frozen shoulder (FS) and Dupuytren’s disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions. Methods GSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein−protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS. Results We identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration. Conclusion We identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.
The Relationship between Post-COVID Syndrome and the Burden of Comorbidities Assessed Using the Charlson Comorbidity Index

Introduction: The post-COVID-19 syndrome is a clinical entity characterized by the manifestation of signs and symptoms that develop after the acute phase of COVID-19, which persist for a duration of more than 12 weeks and are not explained by any alternative diagnosis. It has been observed that individuals with pre-existing chronic diseases, including cardiovascular and pulmonary diseases, are at a greater risk of developing post-COVID-19 syndrome. The Charlson Comorbidity Index (CCI) is a useful tool employed to evaluate the burden of comorbidities and predict the prognosis of patients with post-COVID-19 syndrome. The present study aims to assess whether the burden of comorbidities, evaluated using the CCI, correlates with post-COVID-19 syndrome. Materials and Methods: Between 21 April 2020 and 15 May 2023, we enrolled all consecutive outpatients with previous COVID-19 admissions to a post-acute day-hospital service three months after a negative SARS-CoV-2 molecular test. We assessed age, sex, BMI, acute COVID-19 and post-COVID-19 signs, and symptoms and calculated CCI according to its current definition. Post-COVID-19 syndrome was defined as the persistence of at least one sign or symptom lasting more than 12 weeks after COVID-19 resolution and not explained by an alternative diagnosis. The relationship between post-COVID-19 and CCI was explored first with the chi-squared test, then with different binary logistic regression models. We considered significant values of p lower than 0.05. Results: We obtained a cohort of 3636 patients and observed a significant association between the number of post-COVID-19 symptoms and CCI. Patients developing post-COVID-19 were more commonly affected by a greater burden of comorbidities. Patients with at least one CCI point had an increased risk of post-COVID-19 syndrome (OR:2.961; 95%CI: 2.269–3.863; p < 0.0001), which increased further for CCI ≥ 4 (OR:6.062; 95%CI: 3.163–11.618; p < 0.0001). Conclusions: Patients affected by post-COVID-19 show a greater clinical complexity and a larger burden of comorbidities, synthesized by a higher CCI; moreover, a higher CCI seems to correlate with an increasing post-COVID-19 risk, being the presence of ≥1 or ≥4 CCI points associated with a 3-fold and 6-fold increased risk of post-COVID-19 syndrome, respectively.
Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models

Abstract Aims Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting. Methods and results Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69–0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk. Conclusion Machine learning–derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted. Registration Clinicaltrial.gov identifier is NCT02188355.
Can Surface Topography Give Us Best Light Positions for Reflectance Transformation Imaging?

Reflectance Transformation Imaging (RTI) is a technique that provides an enhanced visualization experience. The current acquisition methods for Reflectance Transformation Imaging (RTI) are time-consuming and computationally expensive. This work investigates the idea of getting the best light positions for RTI acquisition using surface topography. We propose automating the RTI acquisition by estimating the surface topography using a deep learning method followed by estimating light positions using unsupervised clustering method. This is a one-shot method which only needs one image. We also created RTI Synthetic dataset in order to carry out experiments. We found that surface topography alone is not sufficient to estimate best light positions for RTI without putting constraints.