Miracle-dz/UniFAR-FTUs huggingface dataset:
A narrative review of current and emerging MRI safety issues: What every MRI technologist (radiographer) needs to know

Magnetic resonance imaging (MRI) has been traditionally regarded as a safe imaging modality due to the absence of ionising radiation. However, MRI is a source of potential hazards with a variety of risks including, but not limited to, those associated with the various electromagnetic fields used for imaging. All MRI technologists (radiographers) require sound knowledge of the physical principles of the MRI scanner and must understand the associated safety risks and how to avoid adverse events from occurring. MRI technologists now assume more responsibility in clinical decision‐making, and their knowledge base has consequently had to expand significantly. In addition, rapid advancements in MRI technology and other correlated areas such as medical implant technology, and the associated increase in MRI safety issues, place increasing demands on the MRI technologist to constantly keep abreast of current and future developments. This article reviews current and emerging MRI safety issues relevant to the three MRI electromagnetic fields and highlights the key information that all MRI technologists should be fully cognisant of to ensure competent and safe practice within the MRI environment.
Emerging and advanced membrane technology for wastewater treatment: A review

Over the years, conventional wastewater treatment processes have achieved to some extent in treating effluents for discharge pints. Development in wastewater treatment processes is essential to make treated wastewater reusable for industrial, agricultural, and domestic purposes. Membrane technology has emerged as an ideal technology for treating wastewater from different wastewater streams. Membrane technology is one of the most up‐to‐date advancements discovered to be successful in fundamentally lessening impurities to desired levels. In spite of having certain impediments, membrane bioreactors (MBRs) for biological wastewater treatment provide many advantages over conventional treatment. This review article covers all the aspects of membrane technology that are widely used in wastewater treatment process such as the principle of membrane technology, the classification of membrane technology processes in accordance to pressure, concentration, electrical and thermal‐driven processes, its application in different industries, advantages, disadvantages and the future prospective.
Environmental factors shaping bacterial, archaeal and fungal community structure in hydrothermal sediments of Guaymas Basin, Gulf of California

The flanking regions of Guaymas Basin, a young marginal rift basin located in the Gulf of California, are covered with thick sediment layers that are hydrothermally altered due to magmatic intrusions. To explore environmental controls on microbial community structure in this complex environment, we analyzed site- and depth-related patterns of microbial community composition (bacteria, archaea, and fungi) in hydrothermally influenced sediments with different thermal conditions, geochemical regimes, and extent of microbial mats. We compared communities in hot hydrothermal sediments (75-100°C at ~40 cm depth) covered by orange-pigmented Beggiatoaceae mats in the Cathedral Hill area, temperate sediments (25-30°C at ~40 cm depth) covered by yellow sulfur precipitates and filamentous sulfur oxidizers at the Aceto Balsamico location, hot sediments (>115°C at ~40 cm depth) with orange-pigmented mats surrounded by yellow and white mats at the Marker 14 location, and background, non-hydrothermal sediments (3.8°C at ~45 cm depth) overlain with ambient seawater. Whereas bacterial and archaeal communities are clearly structured by site-specific in-situ thermal gradients and geochemical conditions, fungal communities are generally structured by sediment depth. Unexpectedly, chytrid sequence biosignatures are ubiquitous in surficial sediments whereas deeper sediments contain diverse yeasts and filamentous fungi. In correlation analyses across different sites and sediment depths, fungal phylotypes correlate to each other to a much greater degree than Bacteria and Archaea do to each other or to fungi, further substantiating that site-specific in-situ thermal gradients and geochemical conditions that control bacteria and archaea do not extend to fungi.
Retinal OCT Image Registration: Methods and Applications

Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed.
Exploring the feasibility, acceptability and value of volunteer peer mentors in supporting self-management of osteoarthritis: a qualitative evaluation

Abstract Background Hip and knee osteoarthritis (OA) affect a large and growing proportion of the population. Treatment options are typically conservative making self-management a priority. Using trained peers to support individuals with OA has potential to improve self-management. Purpose To explore the process of engaging and training volunteers to become peer mentors; and to qualitatively evaluate the feasibility, acceptability and value of being a peer mentor to support others’ self-management of OA. Materials and methods A qualitative evaluation of a peer mentorship support intervention reporting the processes of recruitment and training; and semi-structured interviews conducted with nine active peer mentors. Transcribed interviews were coded and analysed using framework analysis. Results It was possible to recruit, train and retain volunteers with OA to become peer mentors. The peer mentors benefitted from their training and felt equipped to deliver the intervention. They enjoyed social elements of the mentorship intervention and gained satisfaction through delivering valued support to mentees. Peer mentors perceived the mentorship intervention to have a positive impact on self-management of OA for mentees. Conclusion Training volunteers with OA to become peer mentors was feasible and acceptable. Peer mentors perceived their support benefitted others with OA. They positively rated their experience of providing mentorship support. IMPLICATIONS FOR REHABILITATION This study demonstrates that it is possible to recruit, train and engage older volunteers to become peer mentors for people with osteoarthritis. Training should highlight the significance of employing key self-management techniques such as goal-setting. Peer mentors acknowledged that they benefitted from training and delivering the mentorship intervention, and this impacted positively on their own osteoarthritis self-management. Careful consideration of matching mentors and mentees appears to enhance the success of mentorship support. Recognising the impact of mentorship support on mentees’ self-management is central to peer mentors’ sustained engagement with the intervention.
Synthesis, anticancer activity and molecular docking of new triazolo[4,5-d]pyrimidines based thienopyrimidine system and their derived N-glycosides and thioglycosides

Abstract A series of new substituted triazolo[4,5-d]pyrimidine derivatives linked to thienopyrimidine ring system were prepared as a hybrid heterocyclic systems, as possible nucleobases analogs, starting from the key carboxamide derivative 2 and its azide precursor via heterocyclization reactions and their structures were characterized. Glycosylation of the prepared triazolopyrimidine derivatives was performed and afforded, regioselctively, the corresponding thienopyrimidine-triazolopyrimidine hybrid N 1-glycosides and their thioglycoside analogues in good yields. The synthesized glycosyl heterocycles were studied for their cytotoxic activity against HepG-2 and MCF-7 human cancer cells and significant results were obtained. Compounds 7a, 8 b, 9 b, 9a and 7 b demonstrated promising activities comparable to the activity of the doxorubicin for (HepG-2) cell line. Furthermore, a number of the afforded triazolopyrimidine glycosides were found potent against cancer cells (MCF-7). Furthermore, docking simulation the promising thienopyrimidine analogues 7-13 was done against EGFR kinase to provide a binding model that could serve in discovery of further anticancer agents.
Dysregulated Circulating Apoptosis- and Autophagy-Related lncRNAs as Diagnostic Markers in Coronary Artery Disease

Objective Increasing evidence emphasizes the implications of dysregulated apoptosis and autophagy cellular processes in coronary artery disease (CAD). Herein, we aimed to explore apoptosis- and autophagy-related long noncoding RNAs (lncRNAs) in peripheral blood of CAD patients. Methods The mRNA and lncRNA expression profiles were retrieved from the Gene Expression Omnibus (GEO) database. With ∣fold change | >1.5 and adjusted p value < 0.05, differentially expressed apoptosis- and autophagy-related mRNAs were screened between CAD and healthy blood samples. Also, differentially expressed lncRNAs were identified for CAD. Using the psych package, apoptosis- and autophagy-related lncRNAs were defined with Spearson's correlation analysis. Receiver operating characteristic (ROC) curves were conducted for the assessment of the diagnosed efficacy of these apoptosis- and autophagy-related lncRNAs. Results Our results showed that 24 apoptosis- and autophagy-related mRNAs were abnormally expressed in CAD than normal controls. 12 circulating upregulated and 1 downregulated apoptosis- and autophagy-related lncRNAs were identified for CAD. The ROCs confirmed that AC004485.3 (AUC = 0.899), AC004920.3 (AUC = 0.93), AJ006998.2 (AUC = 0.776), H19 (AUC = 0.943), RP5-902P8.10 (AUC = 0.956), RP5-1114G22.2 (AUC = 0.883), RP11-247A12.1 (AUC = 0.885), RP11-288L9.4 (AUC = 0.928), RP11-344B5.2 (AUC = 0.858), RP11-452C8.1 (AUC = 0.929), RP11-565A3.1 (AUC = 0.893), and XXbac-B33L19.4 (AUC = 0.932) exhibited good performance in differentiating CAD from healthy controls. Conclusion Collectively, our findings proposed that circulating apoptosis- and autophagy-related lncRNAs could become underlying diagnostic markers for CAD in clinical practice.
On the Robustness of Forward and Inverse Artificial Neural Networks for the Simulation of Ethylene/1‐Butene Copolymerization

Forward and inverse artificial neural network (ANN) models are used to describe ethylene/1-butene copolymerization with a model catalyst having two site types. The forward ANN predicts number and weight average molecular weights, average comonomer content, and polymer yield as a function of a set of polymerization conditions, while the inverse model estimates polymerization conditions needed to produce copolymers with desired microstructures. The forward model is found to be robust and resilient to random noise introduced into the datasets. The inverse model, however, leads to multiple solutions (several polymerization conditions can produce polymers with similar microstructures) and is sensitive to random noise in the data. Although the polymerization conditions estimated from inverse ANN are different from the model data, the estimated polymerization conditions are found to provide similar microstructures even with the random noise.
Tumor Biomarker In-Solution Quantification, Standard Production, and Multiplex Detection

The diagnosis and monitoring of cancer have been facilitated by discovering tumor “biomarkers” and methods to detect their presence. Yet, for certain cancers, we still lack sensitive and specific biomarkers or the means to quantify subtle concentration changes successfully. The identification of new biomarkers of disease and improving the sensitivity of detection will remain key to changing clinical outcomes. Patient liquid biopsies (serum and plasma) are the most easily obtained sources for noninvasive analysis of proteins that tumor cells release directly and via extracellular microvesicles and tumor shedding. Therefore, an emphasis on creating reliable assays using serum/plasma and “direct, in-solution” ELISA approaches has built an industry centered on patient protein biomarker analysis. A need for improved dynamic range and automation has resulted in the application of ELISA principles to paramagnetic beads with chemiluminescent or fluorescent detection. In the clinical testing lab, chemiluminescent paramagnetic assays are run on automated machines that test a single analyte, minimize technical variation, and are not limited by serum sample volumes. This differs slightly from the R&D setting, where serum samples are often limiting; therefore, multiplexing antibodies to test multiple biomarkers in low serum volumes may be preferred. This review summarizes the development of historical biomarker “standards”, paramagnetic particle assay principles, chemiluminescent or fluorescent biomarker detection advancements, and multiplexing for sensitive detection of novel serum biomarkers.
Protein Folding Neural Networks Are Not Robust

Deep neural networks such as AlphaFold and RoseTTAFold predict remarkably accurate structures of proteins compared to other algorithmic approaches. It is known that biologically small perturbations in the protein sequence do not lead to drastic changes in the protein structure. In this paper, we demonstrate that RoseTTAFold does not exhibit such a robustness despite its high accuracy, and biologically small perturbations for some input sequences result in radically different predicted protein structures. This raises the challenge of detecting when these predicted protein structures cannot be trusted. We define the robustness measure for the predicted structure of a protein sequence to be the inverse of the root-mean-square distance (RMSD) in the predicted structure and the structure of its adversarially perturbed sequence. We use adversarial attack methods to create adversarial protein sequences, and show that the RMSD in the predicted protein structure ranges from 0.119\r{A} to 34.162\r{A} when the adversarial perturbations are bounded by 20 units in the BLOSUM62 distance. This demonstrates very high variance in the robustness measure of the predicted structures. We show that the magnitude of the correlation (0.917) between our robustness measure and the RMSD between the predicted structure and the ground truth is high, that is, the predictions with low robustness measure cannot be trusted. This is the first paper demonstrating the susceptibility of RoseTTAFold to adversarial attacks.
MutualGraphNet: A novel model for motor imagery classification

Motor imagery classification is of great significance to humans with mobility impairments, and how to extract and utilize the effective features from motor imagery electroencephalogram(EEG) channels has always been the focus of attention. There are many different methods for the motor imagery classification, but the limited understanding on human brain requires more effective methods for extracting the features of EEG data. Graph neural networks(GNNs) have demonstrated its effectiveness in classifying graph structures; and the use of GNN provides new possibilities for brain structure connection feature extraction. In this paper we propose a novel graph neural network based on the mutual information of the raw EEG channels called MutualGraphNet. We use the mutual information as the adjacency matrix combined with the spatial temporal graph convolution network(ST-GCN) could extract the transition rules of the motor imagery electroencephalogram(EEG) channels data more effectively. Experiments are conducted on motor imagery EEG data set and we compare our model with the current state-of-the-art approaches and the results suggest that MutualGraphNet is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.
Matrix Completion of World Trade

This work applies Matrix Completion (MC) – a class of machine-learning methods commonly used in the context of recommendation systems – to analyze economic complexity. MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced by yearly trade flows. A high-accuracy binary classifier is derived from the MC application, with the aim of discriminating between elements of the RCA matrix that are, respectively, higher/lower than one. We introduce a novel Matrix cOmpletion iNdex of Economic complexitY (MONEY) based on MC, and related to the degree of predictability of the RCA entries of different countries (the lower the predictability, the higher the complexity). Differently from previously-developed economic complexity indices, MONEY takes into account several singular vectors of the matrix reconstructed by MC, whereas other indices are based only on one/two eigenvectors of a suitable symmetric matrix, derived from the RCA matrix. Finally, MC is compared with a state-of-the-art economic complexity index (GENEPY), showing that the false positive rate per country of a binary classifier constructed starting from the average entry-wise output of MC is a proxy of GENEPY.
Post percutaneous coronary intervention hemoglobin levels predict in-hospital mortality in patients with STEMI treated with primary percutaneous coronary intervention.

OBJECTIVE In this study, we aimed to determine whether admission hemoglobin versus post-percutaneous coronary intervention (PCI) hemoglobin level at 24 hours is a predictor of in-hospital mortality for patients with ST elevation myocardial infarction (STEMI) without evidence of clinical hemorrhage who underwent primary PCI. METHODS In this study, we included 1,444 consecutive patients with STEMI who underwent primary PCI at a tertiary heart hospital. The primary outcome of the study was the in-hospital all-cause mortality. We used the penalized maximum likelihood estimation (PMLE) logistic regression method to examine the relationship between primary outcome and candidate predictors. RESULTS In total, 172 (11.9%) patients died during the in-hospital course. According to a PMLE logistic regression analysis, age, KILLIP class ≥2, pre-PCI thrombolysis in myocardial infarction (TIMI) flow <3, systolic blood pressure, creatinine, glycoprotein IIb/IIIa inhibitor use, and post-PCI hemoglobin levels at 24 hours were predictors of in-hospital mortality. The relative importance of post-PCI hemoglobin at 24 hours (contributing 6% of the explainable outcome in the model) was significantly higher than admission hemoglobin (contributing only 0.1% of the explainable outcome in the model). CONCLUSION This study demonstrated that post-PCI hemoglobin levels were independently associated with in-hospital survival in patients with STEMI without evidence of bleeding following primary PCI. In addition, post-PCI hemoglobin was a better predictor of in-hospital mortality than admission hemoglobin for patients with STEMI who underwent primary PCI.
Spectral density of individual trajectories of an active Brownian particle

We study analytically the single-trajectory spectral density (STSD) of an active Brownian motion (BM) as exhibited, for example, by the dynamics of a chemically-active Janus colloid. We evaluate the standardly-defined spectral density, i.e. the STSD averaged over a statistical ensemble of trajectories in the limit of an infinitely long observation time T, and also go beyond the standard analysis by considering the coefficient of variation γ of the distribution of the STSD. Moreover, we analyse the finite-T behaviour of the STSD and γ, determine the cross-correlations between spatial components of the STSD, and address the effects of translational diffusion on the functional forms of spectral densities. The exact expressions that we obtain unveil many distinctive features of active BM compared to its passive counterpart, which allow to distinguish between these two classes based solely on the spectral content of individual trajectories.
Genomic Profiling of Premenopausal HR+ and HER2– Metastatic Breast Cancer by Circulating Tumor DNA and Association of Genetic Alterations With Therapeutic Response to Endocrine Therapy and Ribociclib

PURPOSE This analysis evaluated the genomic landscape of premenopausal patients with hormone receptor–positive and human epidermal growth factor receptor 2–negative advanced breast cancer and the association of genetic alterations with response to ribociclib in the phase III MONALEESA-7 trial. METHODS Premenopausal patients were randomly assigned 1:1 to receive endocrine therapy plus ribociclib or placebo. Plasma collected at baseline was sequenced using targeted next-generation sequencing for approximately 600 relevant cancer genes. The association of circulating tumor DNA alterations with progression-free survival (PFS) was evaluated to identify biomarkers of response and resistance to ribociclib. RESULTS Baseline circulating tumor DNA was sequenced in 565 patients; 489 had evidence of ≥ 1 alteration. The most frequent alterations included PIK3CA (28%), TP53 (19%), CCND1 (10%), MYC (8%), GATA3 (8%), receptor tyrosine kinases (17%), and the Chr8p11.23 locus (12%). A treatment benefit of ribociclib was seen with wild-type (hazard ratio [HR] 0.45 [95% CI, 0.33 to 0.62]) and altered (HR 0.57 [95% CI, 0.36 to 0.9]) PIK3CA. Overall, patients with altered CCND1 had shorter PFS regardless of treatment, suggesting CCND1 as a potential prognostic biomarker. Benefit with ribociclib was seen in patients with altered (HR 0.21 [95% CI, 0.08 to 0.54]) or wild-type (HR 0.52 [95% CI, 0.39 to 0.68]) CCND1, but greater benefit was observed with altered, suggesting predictive potential of CCND1. Alterations in TP53, MYC, Chr8p11.23 locus, and receptor tyrosine kinases were associated with worse PFS, but ribociclib benefit was independent of alteration status. CONCLUSION In this study—to our knowledge, the first large study of premenopausal patients with hormone receptor–positive and human epidermal growth factor receptor 2–negative advanced breast cancer—multiple genomic alterations were associated with poor outcome. A PFS benefit of ribociclib was observed regardless of gene alteration status, although in this exploratory analysis, a magnitude of benefits varied by alteration.
LSB: Local Self-Balancing MCMC in Discrete Spaces

We present the Local Self-Balancing sampler (LSB), a local Markov Chain Monte Carlo (MCMC) method for sampling in purely discrete domains, which is able to autonomously adapt to the target distribution and to reduce the number of target evaluations required to converge. LSB is based on (i) a parametrization of locally balanced proposals, (ii) a newly proposed objective function based on mutual information and (iii) a self-balancing learning procedure, which minimises the proposed objective to update the proposal parameters. Experiments on energy-based models and Markov networks show that LSB converges using a smaller number of queries to the oracle distribution compared to recent local MCMC samplers.