Miracle-dz/UniFAR-FTUs huggingface dataset:
Geoprivacy in Neighbourhoods and Health Research: A Mini-Review of the Challenges and Best Practices in Epidemiological Studies

Neighbourhood and health research often relies on personal location data (e.g., home address, daily itineraries), despite the risks of geoprivacy breaches. Thus, geoprivacy is an important emerging topic, contemplated in international regulations such as the General Data Protection Regulation. In this mini-review, we briefly assess the potential risks associated with the usage of personal location data and provide geoprivacy-preserving recommendations to be considered in epidemiological research. Risks include inference of personal information that the individual does not wish to disclose, reverse-identification and security breaches. Various measures should be implemented at different stages of a project (pre-data collection, data processing, data analysis/publication and data sharing) such as informed consent, pseudo-anonymization and geographical methods.
The use of habitat suitability modelling for seagrass: A review

Coastal ecosystems, including coral reefs, mangroves, and seagrass, are in global decline. Mitigation approaches include restoration and other managed recovery interventions. To maximise success, these should be guided by an understanding of the environmental niche and geographic limits of foundational species. However, the choices of data, variables, and modelling approaches can be bewildering when embarking on such an exercise, and the biases associated with such choices are often unknown. We reviewed the current available knowledge on methodological approaches and environmental variables used to model and map habitat suitability for coastal ecosystems. While our focus is on seagrass, we draw on information from all marine macrophyte studies for greater coverage of approaches at different scales around the world. We collated 75 publications, of which 35 included seagrasses. Out of all the publications, we found the most commonly used predictor variables were temperature (64%), bathymetry (61%), light availability (49%), and salinity (49%), respectively. The same predictor variables were also commonly used in the 35 seagrass Habitat Suitability Models (HSM) but in the following order: bathymetry (74%), salinity (57%), light availability (51%), and temperature (51%). The most popular method used in marine macrophyte HSMs was an ensemble of models (29%) followed by MaxEnt (17%). Cross-validation was the most commonly used selection procedure (24%), and threshold probability was the favoured model validation (33%). Most studies (87%) did not calculate or report uncertainty measures. The approach used to create an HSM was found to vary by location and scale of the study. Based upon previous studies, it can be suggested that the best approach for seagrass HSM would be to use an ensemble of models, including MaxEnt along with a selection procedure (Cross-validation) and threshold probability to validate the model with the use of uncertainty measures in the model process.
Effects of neutrino mass and asymmetry on cosmological structure formation

Light but massive cosmological neutrinos do not cluster significantly on small scales, due to their high thermal velocities. With finite masses, cosmological neutrinos become part of the total matter field and contribute to its smoothing. Structure formation in the presence of massive neutrinos is therefore impeded compared to that in the standard ΛCDM cosmology with massless neutrinos. Neutrinos' masses also distort the anisotropy power spectrum of cosmic microwave background (CMB). Furthermore, a finite chemical potential μ for cosmological neutrinos, still allowed by current data, would have a non-negligible impact on CMB and structure formation. We consistently evaluate effects of neutrino masses and chemical potentials on the matter power spectrum by use of a neutrino-involved N-body simulation, with cosmological parameters obtained from a Markov-Chain Monte-Carlo (MCMC) refitting of CMB data. Our results show that while a finite averaged neutrino mass mν tends to suppress the matter power spectrum in a range of wave numbers, the neutrino degeneracy parameters ξi ≡ μi /T (i= 1, 2, 3) enhance the latter, leading to a large parameter degeneracy between mν and ξi. We provide an empirical formula for the effects on the matter power spectrum in a selected range of wave numbers induced by mν and η ≡ √∑i ξ2i. Observing a strong correlation between mν and η, we propose a single redshift-independent parameter mν − 4/3 η2 to characterize the neutrino effects on the matter power spectrum.
Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy

Background Growing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN. Methods Gene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein–protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed. Results Four gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases. Conclusions This study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.
FunOrder 2.0 – a method for the fully automated curation of co-evolved genes in fungal biosynthetic gene clusters

Coevolution is an important biological process that shapes interacting proteins – may it be physically interacting proteins or consecutive enzymes in a metabolic pathway, such as the biosynthetic pathways for secondary metabolites. Previously, we developed FunOrder, a semi-automated method for the detection of co-evolved genes, and demonstrated that FunOrder can be used to identify essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. In the improved version, we use several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output to automatically detect co-evolved genes. Further, we replaced the BLAST tool with the DIAMOND tool as a prerequisite for using larger proteome databases. Potentially, FunOrder 2.0 may be used for the assessment of complete genomes, which has not been attempted yet. However, the introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity.
Applications of DFT + DMFT in Materials Science

First-principles methods can provide insight into materials that is otherwise impossible to acquire. Density functional theory (DFT) has been the first-principles method of choice for numerous applications, but it falls short of predicting the properties of correlated materials. First-principles DFT + dynamical mean field theory (DMFT) is a powerful tool that can address these shortcomings of DFT when applied to correlated metals. In this brief review, which is aimed at nonexperts, we review the basics and some applications of DFT + DMFT.
Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers

We determine the mass-ratio, eccentricity and binary inclination angles that maximize the contribution of the higher-order waveform multipoles $(\ell, \, |m|)= \{(2,\,2),\, (2,\,1),\, (3,\,3),\, (3,\,2), \, (3,\,1),\, (4,\,4),\, (4,\,3),\, (4,\,2),\,(4,\,1)\}$ for the gravitational wave detection of eccentric binary black hole mergers. We carry out this study using numerical relativity waveforms that describe non-spinning black hole binaries with mass-ratios $1\leq q \leq 10$, and orbital eccentricities as high as $e_0=0.18$ fifteen cycles before merger. For stellar-mass, asymmetric mass-ratio, binary black hole mergers, and assuming LIGO's Zero Detuned High Power configuration, we find that in regions of parameter space where black hole mergers modeled with $\ell=|m|=2$ waveforms have vanishing signal-to-noise ratios, the inclusion of $(\ell, \, |m|)$ modes enables the observation of these sources with signal-to-noise ratios that range between 30\% to 45\% the signal-to-noise ratio of optimally oriented binary black hole mergers modeled with $\ell=|m|=2$ numerical relativity waveforms. Having determined the parameter space where $(\ell, \, |m|)$ modes are important for gravitational wave detection, we construct waveform signals that describe these astrophysically motivate scenarios, and demonstrate that these topologically complex signals can be detected and characterized in real LIGO noise with deep learning algorithms.
The nature of strong H i absorbers probed by cosmological simulations: satellite accretion and outflows

We use cosmological hydrodynamic zoom simulations to study the neutral gas distribution in and around galaxies that gives rise to high column density H i Ly α absorption (damped Ly α systems (DLAs) and sub-DLAs) in background quasar spectra. Such simulations often sacrifice numerical resolution for volume that affects the lower density galaxy halo gas, and simulations have difficulties reproducing the span of projected separations (b) between absorbing clouds and their hosts. Our simulations produce (sub-)DLAs over the entire probed parameter space ($b\lesssim 50\, \mathrm{kpc}$ and metallicity −4 ≲[M/H]≲ 0.5) at all redshifts (z ∼ 0.4 − 3.0), enclosing spectroscopically confirmed absorber-galaxy pairs. Recovering (sub-)DLAs at b ≳ 20 − 30 kpc from a massive host galaxy requires high numerical resolution and efficient feedback, and we show that these lines-of-sight are associated with dwarf satellites in the main halo, stripped metal-rich gas, and outflows. H i disc- and halo gas significantly contributes to (sub-)DLAs around galaxies. At large redshifts the halo plays an increasingly important role, while at 0.4 < z < 1 the disc and halo contribute with ∼60(80) and ∼40(20) per cent to column densities above the sub-DLA(DLA) lower limits. The distribution of b for sub-DLAs and DLAs overlap at z ∼ 2 − 3, but evolves so that sub-DLAs on average are located at twice larger b by z ∼ 0.5. A weak correlation suggests that sub-DLA covering fractions increase with stellar mass more rapidly than those of DLA. This can explain why sub-DLAs are preferentially selected in more massive galaxies in the low-z Universe.
Detection of asymptomatic recurrence following curative surgery improves survival in patients with gastric cancer: A systematic review and meta-analysis

Background To date, there is no evidence that intensive follow-up provides survival benefit in gastric cancer patients undergoing curative gastrectomy. The aim of this study is to investigate the efficacy of detection of asymptomatic recurrence using intensive surveillance strategy in long-term survival after curative gastric cancer surgery. Methods A systematic review of electronic databases including PubMed, Embase, Web of Science, the Cochrane Library and China National Knowledge Infrastructure, Clinical Trials Registry and Google Scholar was performed up to April 2022. The primary outcomes were survival outcomes: overall survival, recurrence-free survival and post-recurrence survival. The secondary endpoints were clinicopathological features, recurrence patterns and treatment after recurrence. The registration number of this protocol is PROSPERO CRD42022327370. Results A total of 11 studies including 1898 participants were included. In the pooled analysis, the detection of asymptomatic recurrence was significantly associated with an improved overall survival compared to patients showing symptoms of recurrence (HR=0.67; 95%CI: 0.57-0.79; P<0.001), which was primarily driven by the prolongation of post-recurrence survival (HR=0.51; 95%CI: 0.42-0.61; P<0.001), since there was no significant difference observed in recurrence-free survival (HR=1.12; 95%CI: 0.81-1.55; P=0.48) between the two groups. Meanwhile, male sex and advanced T stage were more frequently observed in the symptomatic recurrence group. Furthermore, patients in the symptomatic recurrence group had a higher proportion of peritoneal relapse but lower proportion of distant lymph node metastasis. Additionally, patients in the symptomatic recurrence group were less likely to receive surgery treatment and post-recurrence chemotherapy. Conclusion The detection of asymptomatic recurrence using intensive follow-up was associated with an appreciable improvement in overall survival. However, more robust data from high-quality studies are still required to verify this issue. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=327370, identifier CRD42022327370.
A genome-wide cell-free DNA methylation analysis identifies an episignature associated with metastatic luminal B breast cancer

Breast cancers of the luminal B subtype are frequent tumors with high proliferation and poor prognosis. Epigenetic alterations have been found in breast tumors and in biological fluids. We aimed to profile the cell-free DNA (cfDNA) methylome of metastatic luminal B breast cancer (LBBC) patients using an epigenomic approach to discover potential noninvasive biomarkers. Plasma cfDNA was analyzed using the Infinium MethylationEpic array in a cohort of 14 women, including metastatic LBBC patients and nontumor controls. The methylation levels of cfDNA and tissue samples were validated with droplet digital PCR. The methylation and gene expression data of 582 primary luminal breast tumors and 79 nontumor tissues were obtained from The Cancer Genome Atlas (TCGA). We found an episignature of 1,467 differentially methylated CpGs that clearly identified patients with LBBC. Among the genes identified, the promoter hypermethylation of WNT1 was validated in cfDNA, showing an area under the ROC curve (AUC) of 0.86 for the noninvasive detection of metastatic LBBC. Both paired cfDNA and primary/metastatic breast tumor samples showed hypermethylation of WNT1. TCGA analysis revealed significant WNT1 hypermethylation in the primary tumors of luminal breast cancer patients, with a negative association between WNT1 methylation and gene expression. In this proof-of-principle study, we discovered an episignature associated with metastatic LBBC using a genome-wide cfDNA methylation approach. We also identified the promoter hypermethylation of WNT1 in cfDNA as a potential noninvasive biomarker for luminal breast cancer. Our results support the use of EPIC arrays to identify new epigenetic noninvasive biomarkers in breast cancer.
Estimates of global research productivity in primary ovarian insufficiency from 2000 to 2021: Bibliometric analysis

Background Primary ovarian insufficiency (POI) is a heterogeneous disease with diverse clinical phenotypes and etiologies, which is defined as ovarian dysfunction under the age of 40 years. The global prevalence of POI is approximately about 1.1%, and it severely affects female fertility. Nevertheless, bibliometric analysis in this field is extremely limited. We aimed to visualize the research hotspots and trends of POI using bibliometric analysis and tried to predict the future development of this field. Methods The original articles regarding POI were culled from the Web of Science Core Collection. Countries, institutions, journals, authors, and keywords in this field were visually analyzed by employing CiteSpace software and Microsoft Excel 2021 software. Results A total of 2,999 publications were included for further bibliometric analysis after screening the titles and abstracts stringently. The number of literature regarding POI significantly increased yearly. These publications come from 78 countries. The USA was dominant in the field of POI in terms of the number of publications (865), average citations per item (57.36), and h-index (112). The Institut National De La Sante Et De La Recherche Medicale Inserm is the most high-yield institution in this field with 351 publications. Fertility and Sterility ranked first with the highest number of publications (152), followed by Human Reproduction (138). According to the keyword cluster analysis from 2000 to 2021, the eight keyword clusters encountered frequently were apoptosis, osteoporosis, fertility preservation, mutation, fragile x syndrome, adrenal insufficiency, DNA repair, ovarian reserve. Keyword citation burst analysis revealed that whole-exome sequencing, ovarian tissue cryopreservation, and DNA repair had a citation burst until 2021. Conclusions Great progress has been made in POI research over the past 20 years, which is widely researched but unevenly developed in the world. In terms of influence, the United States may be in the lead. The research hotspots in POI are mainly pathogenesis and treatment, including genetic mutation, hormone therapy, fertility preservation, and stem cell transplantation.
Simulation of diffusive solute transport in heterogeneous porous media with dipping anisotropy

Dipping anisotropy is a common feature in heterogeneous porous media that can substantially affect solute transport. For problems with complex geometry the influence of dipping anisotropy must be analyzed using numerical models, since suitable analytical solutions are not available. The most straightforward approach is to use a Cartesian coordinate system aligned with the material coordinate system. However, this approach is usually not practical, especially in 3D simulation domains with dipping layers and heterogeneous material properties. Furthermore, in the case of diffusion-dominated transport, the effect of anisotropy is often neglected. In this research, a general-purpose, fully 3-D unstructured grid code was developed to simulate diffusion-dominated solute transport in systems with dipping anisotropy, while accounting for complex geometry. The code has been verified against both 2-D and 3-D analytical solutions and has then been applied to two anisotropic diffusion problems, including an in-situ diffusion experiment and a hypothetical deep geologic repository, respectively. The simulation results indicate that consideration of anisotropy is required if the solute distribution in the rock matrix is of importance, in particular for assessing long-term evolution in layered systems. The formulation presented provides a versatile method for assessing diffusion-dominated solute transport in systems with dipping anisotropy subject to complex geometry.
Association between level of depression and coronary heart disease, stroke risk and all-cause and cardiovascular mortality: Data from the 2005–2018 National Health and Nutrition Examination Survey

Research on the association between level of depression and coronary heart disease (CHD), stroke risk, and all-cause and cardiovascular mortality is lacking in large-scale or population-based studies incorporating cardiovascular disease (CVD) endpoints. We aim to assess the relationship between the level of a person's depression and their risk of CHD, stroke, and all-cause and cardiovascular mortality. Utilizing data from the United States National Health and Nutrition Examination Survey (NHANES), multicycle cross-sectional design and mortality linkage studies were conducted. The study sample included 30918 participants aged 20–85 years old during the 2005–2018 period. Depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), with scores of 5, 10, 15, and 20 being the cut-off points for mild, moderate, moderately severe, and severe depression, respectively. A series of weighted logistic regression analyses and Cox proportional hazards models were utilized to examine the relationship between the level of depression with the risk of CHD, stroke, all-cause, and cardiovascular mortality. Trend analyses were conducted by entering the level of depression as a continuous variable and rerunning the corresponding regression models. Weighted logistic regression models consistently indicated a statistically significant association between the level of depression and increased risk of CHD and stroke, and those linear trend tests were statistically significant (P for trend < 0.001). Furthermore, weighted Cox regression analyses consistently indicated that participants who had a more severe degree of depression were at a higher risk of all-cause death, and trend analyses suggested similar results (P for trend < 0.001). Another weighted Cox regression analysis also consistently indicated that except for severe depression, the hazard of cardiovascular death was increased with each additional level increase of depression. Our study confirmed that the level of depression was strongly associated with CHD, stroke, and all-cause and cardiovascular mortality, even after accounting for other factors that could impact risk, including variables of age, gender, ethnicity, income, education, body mass index (BMI), marital, and smoking status.