Miracle-dz/UniFAR-FTUs huggingface dataset:
The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.
Dictionary learning methods for single-channel source separation

In this thesis we provide three main contributions to blind source separation methods based on NMF. Our first contribution is a group-sparsity inducing penalty specifically tailored for Itakura-Saito NMF. In many music tracks, there are whole intervals where only one source is active at the same time. The group-sparsity penalty we propose allows to blindly indentify these intervals and learn source specific dictionaries. As a consequence, those learned dictionaries can be used to do source separation in other parts of the track were several sources are active. These two tasks of identification and separation are performed simultaneously in one run of group-sparsity Itakura-Saito NMF. Our second contribution is an online algorithm for Itakura-Saito NMF that allows to learn dictionaries on very large audio tracks. Indeed, the memory complexity of a batch implementation NMF grows linearly with the length of the recordings and becomes prohibitive for signals longer than an hour. In contrast, our online algorithm is able to learn NMF on arbitrarily long signals with limited memory usage. Our third contribution deals user informed NMF. In short mixed signals, blind learning becomes very hard and sparsity do not retrieve interpretable dictionaries. Our contribution is very similar in spirit to inpainting. It relies on the empirical fact that, when observing the spectrogram of a mixture signal, an overwhelming proportion of it consists in regions where only one source is active. We describe an extension of NMF to take into account time-frequency localized information on the absence/presence of each source. We also investigate inferring this information with tools from machine learning.
Gut Microbiome Changes in Gestational Diabetes

Gestational diabetes mellitus (GDM), one of the most common endocrine pathologies during pregnancy, is defined as any degree of glucose intolerance with onset or first discovery in the perinatal period. Physiological changes that occur in pregnant women can lead to inflammation, which promotes insulin resistance. In the general context of worldwide increasing obesity in young females of reproductive age, GDM follows the same ascending trend. Changes in the intestinal microbiome play a decisive role in obesity and the development of insulin resistance and chronic inflammation, especially in patients with type 2 diabetes mellitus (T2D). To date, various studies have also associated intestinal dysbiosis with metabolic changes in women with GDM. Although host metabolism in women with GDM has not been fully elucidated, it is of particular importance to analyze the available data and to discuss the actual knowledge regarding microbiome changes with potential impact on the health of pregnant women and newborns. We analyzed peer-reviewed journal articles available in online databases in order to summarize the most recent findings regarding how variations in diet and metabolic status of GDM patients can contribute to alteration of the gut microbiome, in the same way that changes of the gut microbiota can lead to GDM. The most frequently observed alteration in the microbiome of patients with GDM was either an increase of the Firmicutes phylum, respectively, or a decrease of the Bacteroidetes and Actinobacteria phyla. Gut dysbiosis was still present postpartum and can impact the development of the newborn, as shown in several studies. In the evolution of GDM, probiotic supplementation and regular physical activity have the strongest evidence of proper blood glucose control, favoring fetal development and a healthy outcome for the postpartum period. The current review aims to summarize and discuss the most recent findings regarding the correlation between GDM and dysbiosis, and current and future methods for prevention and treatment (lifestyle changes, pre- and probiotics administration). To conclude, by highlighting the role of the gut microbiota, one can change perspectives about the development and progression of GDM and open up new avenues for the development of innovative therapeutic targets in this disease.
The m7G Modification Level and Immune Infiltration Characteristics in Patients with COVID-19

Purpose The 7-methylguanosine (m7G)-related genes were used to identify the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19) and to identify possible therapeutic targets. Patients and Methods The GSE157103 dataset provides the transcriptional spectrum and clinical information required to analyze the expression of m7G-related genes and the disease subtypes. R language was applied for immune infiltration analysis, functional enrichment analysis, and nomogram model construction. Results Most m7G-related genes were up-regulated in COVID-19 and were closely related to immune cell infiltration. Disease subtypes were grouped using a clustering algorithm. It was found that the m7G-cluster B was associated with higher immune infiltration, lower mechanical ventilation, lower intensive care unit (ICU) status, higher ventilator-free days, and lower m7G scores. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between m7G-cluster A and B were enriched in viral infection and immune-related aspects, including COVID-19 infection; Th17, Th1, and Th2 cell differentiation, and human T-cell leukemia virus 1 infection. Finally, through machine learning, six disease characteristic genes, NUDT4B, IFIT5, LARP1, EIF4E, LSM1, and NUDT4, were screened and used to develop a nomogram model to estimate disease risk. Conclusion The expression of most m7G genes was higher in COVID-19 patients compared with that in non-COVID-19 patients. The m7G-cluster B showed higher immune infiltration and milder symptoms. The predictive nomogram based on the six m7G genes can be used to accurately assess risk.
Quantized gravitomagnetic charges from WIMT: cosmological consequences

Using the formalism of Weitzenb\"ock induced matter theory (WIMT) we calculate the gravito-magnetic charge on a topological string which is induced through a foliation on a five-dimensional (5D) gravito-electromagnetic vacuum defined on a 5D Ricci-flat metric, which produces a symmetry breaking on an axis. We obtain the resonant result that the quantized charges are induced on the effective four-dimensional hypersurface. This quantization describes the behavior of a test gravito-electric charge in the vicinity of a point gravito-magnetic monopole, both geometrically induced from a 5D vacuum. We demonstrate how gravito-magnetic monopoles would decrease exponentially during the inflationary expansion of the universe.
Longitudinal Follow-Up Using the Heel Enthesitis Magnetic Resonance Imaging Scoring System (HEMRIS) Shows Minimal Changes in Heel Enthesitis Assessed in Spondyloarthritis and Psoriasis Patients

Enthesitis is a common clinical feature of spondyloarthritis (SpA). For reliable assessment of enthesitis the Heel Enthesitis Magnetic Resonance Imaging Scoring System (HEMRIS) was developed. The aims of this study were to evaluate changes in HEMRIS over time and to evaluate whether these changes correlated with changes in clinical parameters. This single-center observational study followed patients with SpA and psoriasis, regardless of presence of clinical heel enthesitis, for two years. Clinical evaluation and ankle MRIs were performed annually. Changes in HEMRIS were compared at one-year intervals using the Wilcoxon signed-rank test. The association between changes in the HEMRIS with changes in clinical parameters was evaluated using Spearman’s correlation coefficient. In total, 38 patients were included. An increase in the inflammatory and structural HEMRIS was identified in, respectively, 12 (17.9%) and 4 (6.0%) patients in one-year intervals. We found non-significant changes in the HEMRIS during longitudinal follow-up. Changes in the HEMRIS did not correlate with changes in local or general disease activity. Our results show that MRI-findings of enthesitis assessed with HEMRIS changed in a small number of patients in a one-year interval in an observational setting. Changes in HEMRIS were not associated with changes in clinical disease activity.
Evaluating Virtual and Inpatient Pulmonary Rehabilitation Programs for Patients with COPD

Chronic obstructive pulmonary disease (COPD) is an increasingly frequent disorder that is likely to become the third leading cause of morbidity worldwide. It significantly degrades the quality of life of patients affected and poses a significant financial burden to the healthcare systems providing treatment and rehabilitation. Consequently, our study’s purpose was to compare conventional inpatient pulmonary rehabilitation (PR) with virtual (online) PR using a mobile phone application. During a three-month period, two groups of patients followed the research protocol by participating in a pulmonary rehabilitation program administered and supervised by a physical therapist five times per week. A number of respiratory variables were examined before and after the test. At the end of the study period, a total of 72 patients completed the rehabilitation in the inpatient group, respectively 58 in the online group. It was observed that post-test comparison between patients undergoing the traditional and online rehabilitation methods did not show any significant differences. However, the calculated mean differences between pre-test and post-test results were significantly higher in favor of the virtual method. The most significant variations were encountered in maximal inspiratory pressure (MIP) (6.6% vs. 8.5%, p-value < 0.001), 6-min walking test (6MWT) (6.7% vs. 9.4%, p-value < 0.001), and COPD assessment test (CAT) values (4.8 vs. 6.2, p-value < 0.001), respectively. However, the maximal expiratory pressure (MEP) variation was significantly higher in patients undergoing the traditional rehabilitation method, from an average of 4.1% to 3.2% (p-value < 0.001). In this preliminary study, the online pulmonary rehabilitation program proved non-inferiority to the traditional method, with significantly better results in several measurements. Additional studies using larger cohorts of patients and longer exposure to the online rehabilitation program are required to validate these findings.
Identification of the Angle Errors of the LED Parallel-Light Module in PCB Exposure Device by Using Neural Network Learning Algorithms

For the smart manufacturing development of printed-circuit-board (PCB) exposure devices, the LED parallel-light (LPL) module is investigated and the angle errors of those LPL units are identified by neural network learning algorithms. At present, in PCB manufacturing, most circuit boards use photoresist covering etching. After exposure and development, unwanted copper foil is etched and removed to make circuit boards. The exposure process is its key process, and the equipment used in this process is an exposure machine. The LPL unit is designed and the LPL exposure module is searched under the principle of higher irradiance uniformity. The learning data of supervised learning for the convolutional neural network (CNN) include a 2D irradiance distribution image constructed by the ray tracing simulation tool. In these supervised learning data, all units of LPL-EM are randomly added with a self-specific angle error. By using Fast Region-based CNN, the identification of the multi-LPL module with the specific errors of inclination and azimuth angle is verified. Those results preliminarily illustrate that supervised learning techniques should be able to help identify the errors of inclination and azimuth angle for the single LPL unit and multi-light module of PCB exposure devices. In other words, this technology should serve as a reference for the development of the PCB exposure process towards smart manufacturing.
Excitonic resonance effects and Davydov splitting in circularly polarized Raman spectra of few-layer WSe2

Few-layer tungsten diselenide (WSe2) is investigated using circularly polarized Raman spectroscopy with up to eight excitation energies. The main E2g1 and A1g modes near 250 cm−1 appear as a single peak in the Raman spectrum taken without consideration of polarization but are resolved by using circularly polarized Raman scattering. The resonance behaviors of the E2g1 and A1g modes are examined. Firstly, both the E2g1 and A1g modes are enhanced near resonances with the exciton states. The A1g mode exhibits Davydov splitting for trilayers or thicker near some of the exciton resonances. The low-frequency Raman spectra show shear and breathing modes involving rigid vibrations of the layers and also exhibit strong dependence on the excitation energy. An unidentified peak at ~19 cm−1 that does not depend on the number of layers appears near resonance with the B exciton state at 1.96 eV (632.8 nm). The strengths of the intra- and inter-layer interactions are estimated by comparing the mode frequencies and Davydov splitting with the linear chain model, and the contribution of the next-nearest-neighbor interaction to the inter-layer interaction turns out to be about 34% of the nearest-neighbor interaction. Fano resonance is observed for 1.58 eV excitation, and its origin is found to be the interplay between two-phonon scattering and indirect band transition.
The dipole anisotropy of WISE × SuperCOSMOS number counts

We probe the isotropy of the Universe with the largest all-sky photometric redshift dataset currently available, namely WISE~$\times$~SuperCOSMOS. We search for dipole anisotropy of galaxy number counts in multiple redshift shells within the $0.10 < z < 0.35$ range, for two subsamples drawn from the same parent catalogue. Our results show that the dipole directions are in good agreement with most of the previous analyses in the literature, and in most redshift bins the dipole amplitudes are well consistent with $\Lambda$CDM-based mocks in the cleanest sample of this catalogue. In the $z<0.15$ range, however, we obtain a persistently large anisotropy in both subsamples of our dataset. Overall, we report no significant evidence against the isotropy assumption in this catalogue except for the lowest redshift ranges. The origin of the latter discrepancy is unclear, and improved data may be needed to explain it.
Losses in Monolayer Graphene Nanoresonators due to Joule Dissipation Caused by Synthetic Electric Fields and the ways of Joule Losses Minimizations

We consider losses in monolayer graphene nanoresonator connected with Joule dissipation (heating) caused by valley currents stipulated by synthetic electric fields . These synthetic electric fields arise in graphene membrane due to periodic in time gauge fields generated there by external periodic electromotive force. This mechanism accounts for essential part (about 40 percents) of losses in graphene nanoresonator and is specific just for graphene. The ways of the minimization of these Joule dissipation (increase of the quality-factor of the electromechanical system) are discussed. It is explained why one can increase quality-factor by correctly chosen combination of strains (by strain engineering). Besides it is shown that quality-factor can be increased by switching on a magnetic field perpendicular to graphene membrane.
Full-data Results of Hubble Frontier Fields: UV Luminosity Functions at z ∼ 6–10 and a Consistent Picture of Cosmic Reionization

We present UV luminosity functions of dropout galaxies at with the complete Hubble Frontier Fields data. We obtain a catalog of ∼450 dropout-galaxy candidates (350, 66, and 40 at , 8, and 9, respectively), with UV absolute magnitudes that reach mag, ∼2 mag deeper than the Hubble Ultra Deep Field detection limits. We carefully evaluate number densities of the dropout galaxies by Monte Carlo simulations, including all lensing effects such as magnification, distortion, and multiplication of images as well as detection completeness and contamination effects in a self-consistent manner. We find that UV luminosity functions at have steep faint-end slopes, , and likely steeper slopes, at . We also find that the evolution of UV luminosity densities shows a non-accelerated decline beyond in the case of , but an accelerated one in the case of . We examine whether our results are consistent with the Thomson scattering optical depth from the Planck satellite and the ionized hydrogen fraction QH ii at based on the standard analytic reionization model. We find that reionization scenarios exist that consistently explain all of the observational measurements with the allowed parameters of and for , where is the escape fraction, Mtrunc is the faint limit of the UV luminosity function, and is the conversion factor of the UV luminosity to the ionizing photon emission rate. The length of the reionization period is estimated to be (for ), consistent with the recent estimate from Planck.
The Association between Iron and Folic Acid Supplementation and Malaria Prophylaxis and Linear Growth among Children and Neonatal Mortality in Sub-Saharan Africa—A Pooled Analysis

The majority of research on linear growth among children is confined to South Asia and focuses on iron and folic acid (IFA) supplementation during pregnancy, without considering malaria prophylaxis. Similarly, there is limited evidence on the association of antenatal IFA supplementation and malaria prophylaxis with neonatal mortality in sub-Saharan Africa (SSA). This study aims to address these gaps. A pooled analysis of demographic and health survey (DHS) data from 19 countries in SSA was conducted to study the association between IFA supplementation and malaria prophylaxis and linear growth and neonatal mortality. Multivariate logistic and linear regression models were used. Malaria prophylaxis was significantly associated with stunting, height-for-age Z scores (HAZ scores), and neonatal mortality, but IFA supplementation was not associated with these outcomes. When women’s height and body mass index (BMI) were introduced in the model, a significant association between combined malaria prophylaxis and IFA supplementation was found with HAZ scores only. For severe stunting, no significant association was found with either in the two models. In conclusion, this study underscores the importance of antenatal malaria prophylaxis as a potential intervention for nutrition outcomes (linear growth) and neonatal mortality, as well as the importance of coordinating efforts between malaria and the health and nutrition sectors to improve these outcomes in the countries of SSA.
NFE2L3 as a Potential Functional Gene Regulating Immune Microenvironment in Human Kidney Cancer

With the increasing incidence and mortality of renal cancer, it is pressing to find new biomarkers and drug targets for diagnosis and treatment. However, as one negative upstream regulator of p53, the prognostic and immunological role of NFE2L3 in renal cancer is still barely known. We investigated the expression, prognostic value, and relevant pathways of NFE2L3 using the datasets from public databases, including The Cancer Genome Atlas Program (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and UALCAN. Furthermore, we analyzed the relationship between NFE2L3 expression and the immune microenvironment using distinct methods. We found that NFE2L3 was higher expressed in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) tissues than adjacent normal tissues. Additionally, we identified NFE2L3 as one survival-related factor for KIRC and KIRP. The enrichment analyses revealed that NFE2L3 was associated with a variety of immune-relevant pathways in KIRC and related to the infiltration ratios of 17 types of immune cells in KIRC patients. Ultimately, we demonstrated nine significantly enriched mutations, such as TP53 and MET, in NFE2L3-expression-changing groups. The elevated expression of NFE2L3 in renal cancerous tissues versus normal tissues is associated with poor outcomes in patients. Besides, NFE2L3 has a role in the regulation of the immune microenvironment in renal cancer patients. The findings of our study provide a potential prognostic biomarker and a new drug target for renal cancer.
Optimizing the Extraction of Keratin from Cattle Hoof Using Central Composite Design

Cattle hoofs are abundantly available by-product sources of organic material from the slaughterhouse. It can be successfully converted into keratin protein. Keratin extracted using alkali hydrolysis has a better conservancy of keratin structure. The purpose of this study is optimizing the extraction of keratin from cuttle hoof. Designing of the experiment, analysis of the results, and optimization of the process parameters have been conducted by central composite design (CCD). Three factors (temperature (A), time (B), and concentration of NaOH (C)) each at five levels have been used to extract the keratin protein from cattle hoof and two response variables (dissolution % and purity %) have been considered in the study. The results obtained demonstrate that extraction temperature, alkali concentration, and time showed a significant effect on the purity and dissolution of keratin. The regression model shows that all factors have positive and significant relation with dissolution percentage whereas only temperature and concentration of NaOH have significant and negative relation with purity percentage. It is observed that the Biuret and Fourier-transform infrared spectroscopy (FTIR) tests showed better preservation of protein structure in extractive keratin. The FTIR spectra, indicates that amide I and amide II occur at a wave length of 1633 and 1542 cm−1 for raw cattle hoof and at wave length of 1650 and 1542 cm−1 for keratin protein, respectively. Thus, it can be concluded that the cattle hoof by-product could offer an alternative keratin source. Finally, the extraction process had an optimum value of 0.5 M NaOH, 60 minutes reaction time, and 55°C temperature with 85% dissolution and 89.6% purity.