Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between a Polymorphism in CYP17 with Plasma Hormone Levels and Breast Cancer 1

The A2 allele of CYP17 has been associated with polycystic ovarian syndrome, elevated levels of certain steroid hormones in premenopausal women, and increased breast cancer risk. We prospectively assessed the association between the A2 allele of CYP17 and breast cancer risk in a case-control study nested within the Nurses’ Health Study cohort. We also evaluated associations between this CYP17 genotype and plasma steroid hormone levels among postmenopausal controls not using hormone replacement to assess the biological significance of this genetic variant. Women with the A2 allele were not at an increased risk of incident breast cancer [OR (odds ratio), 0.85; 95% CI (confidence interval), 0.65–1.12] or advanced breast cancer (OR, 0.84; 95% CI, 0.54–1.32). We did observe evidence that the inverse association of late age at menarche with breast cancer may be modified by the CYP17 A2 allele. The protective effect of later age at menarche was only observed among women without the A2 allele ( A1/A1 genotype: for age at menarche > 13 versus < 13; OR, 0.57; 95% CI, 0.36–0.90; A1/A2 and A2/A2 genotypes: OR, 1.05; 95% CI, 0.76–1.45; P for interaction 5 0.07). Among controls, we found women with the A2/A2 genotype to have elevated levels of estrone ( 1 14.3%, P 5 0.01), estradiol ( 1 13.8%, P 5 0.08), testosterone ( 1 8.6%, P 5 0.34), androstenedione ( 1 17.1%, P 5 0.06), dehydroepiandrosterone ( 1 14.4%, P 5 0.02), and dehydroepiandrosterone sulfate ( 1 7.2%, P 5 0.26) compared with women with the A1/A1 genotype. These data suggest that the A2 allele of CYP17 modifies endogenous hormone levels, but is not a strong independent risk factor for breast cancer.
Small RNAs Asserting Big Roles in Mycobacteria

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb), with 10.4 million new cases per year reported in the human population. Recent studies on the Mtb transcriptome have revealed the abundance of noncoding RNAs expressed at various phases of mycobacteria growth, in culture, in infected mammalian cells, and in patients. Among these noncoding RNAs are both small RNAs (sRNAs) between 50 and 350 nts in length and smaller RNAs (sncRNA) < 50 nts. In this review, we provide an up-to-date synopsis of the identification, designation, and function of these Mtb-encoded sRNAs and sncRNAs. The methodological advances including RNA sequencing strategies, small RNA antagonists, and locked nucleic acid sequence-specific RNA probes advancing the studies on these small RNA are described. Initial insights into the regulation of the small RNA expression and putative processing enzymes required for their synthesis and function are discussed. There are many open questions remaining about the biological and pathogenic roles of these small non-coding RNAs, and potential research directions needed to define the role of these mycobacterial noncoding RNAs are summarized.
Quality of Location Awareness Recommendation System Based On User Data

: The area approval system continuously includes huge volumes of information, thus causes the measurability difficulties which not exclusively improved the gap however reason compact accuracy yet. Different techniques of advice systems were developed to hit this downside. Supportive filtering could be a method that features high correctness within the place suggestion system, however, it features a weakness in terms of scalability. Additionally, the context-awareness approach is being developed by utilizing user discourse data, to produce more precise suggestions consistent with the user's preferences. Some studies have shown that to realize qualified advice for the user, their systems used a context-awareness approach. This study aims to review the method of place advice system that uses the context-awareness to improve their presentation in terms of correctness and measurability. The outcome of the study shows that the completion of context-awareness on a suggestion system gave the simplest result for recommending the modified place in its place of a suggestion system while no context-awareness. A study that uses context-awareness within the place recommendation system can do up to fifty-eight accuracy and provide higher recommendations for the user. By developing DBSCAN, Singular worth Decomposition or deep learning algorithmic rule will produce lower measurability with high correctness in place suggestion system.
A new D-threonine aldolase as a promising biocatalyst for highly stereoselective preparation of chiral aromatic β-hydroxy-α-amino acids

D-Threonine aldolase is an enzyme belonging to the glycine-dependent aldolases, and it catalyzes the reversible aldol reaction of glycine and acetaldehyde to give D-threonine and/or D-allo-threonine. In this study, a putative D-threonine aldolase gene from Delftia sp. RIT313 was cloned and expressed in Escherichia coli BL21 (DE3). The purified enzyme (DrDTA, 47 KDa) exhibited 21.3 U mg−1 activity for the aldol addition of glycine and acetaldehyde in MES-NaOH buffer (pH 6.0) at 50 °C. Both pyridoxal 5′-phosphate and metal ions were needed for the reaction, and the existence of the metal ions enhanced the stability of the enzyme. It was found that the conversion and Cβ-stereoselectivity were dramatically influenced by the reaction temperature, co-solvent, amount of enzyme and reaction time, and it is possible to enable the reaction under kinetic control to retain suitable conversion and high stereoselectivity at the β-carbon, thus tackling the “Cβ-stereoselectivity problem”. DrDTA showed high activity toward aromatic aldehydes with electron-withdrawing substituents. Under the optimized reaction conditions, phenylserines with a 2′-fluoro- or 3′-nitro-substituent were obtained with >90% conversion and >90% de. In addition, DL-threo-phenylserine and DL-threo-4-(methylsulfonyl)phenylserine were efficiently resolved with an excellent enantiomeric excess value (ee, >99%) using a whole cell biocatalyst in a two-phase system at 1.0 M and 0.3 M, respectively, the highest substrate concentration reported so far. These results suggested that DrDTA might be a promising biocatalyst for producing chiral aromatic β-hydroxy-α-amino acids.
CS-433 Machine Learning - Resource-Efﬁcient Machine Learning Algorithm Design for On-Implant Neurological Symptom Detection

—Recurrent neural networks, such as long short-term memory (LSTM) and gated recurrent units, have revolutionized the analysis of time-series medical data. However, although modern Machine Learning tools generally obtain a high accuracy in neural signal classiﬁcation, their deployment on neural interface implants is severely limited due to rigid power, area
Predicting gene-gene relationship with CNNC model

—In a complex cell system, it is now clear that biological function can not be attributed to an individual molecule in most of the cases. Network analysis of biological molecules has been proved informative. In gene network analysis, research shows that genes with related functions often have similar expression patterns. Pearson correlation coefﬁcient is the most used representation in measuring similarity for gene-expression data. After getting the quantitative representation of the gene-gene relationship, further analysis such as clustering can be performed to further investigate the gene co-expression network. Due to the unsupervised property of the clustering method and the limitation of the Pearson correlation coefﬁcient, some important gene-phenotype relationships may be dismissed while some false positive relationships may arise. Traditional gene sequencing experiments detect gene expression levels in a large number of cells thus only provide an average expression level which may not reﬂect the real interaction between genes which leads to false-negative results. Yuan and Bar-Joseph developed a convolutional neural networks-based method called CNNC analysing single-cell gene expression data and inferring gene-gene relationships. Under the hypothesis that genes with related function often have similar expression pattern, the CNNC model learns the expression pattern between two genes and use the learned parameter to predict whether an unknown gene has similar expression pattern with a known one. CNNC is claimed to be able to overcome the shortcomings of traditional gene relationship analysis method. In our report, 4-Hydroxynonenal(HNE) related genes from former experiment and literature are collected as gene labels and were supposed to have a stronger interaction with each other. CNNC model was applied to see if HNE related gene-gene expression pattern could be separated from HNE non-related gene-gene expression pattern. Some adjustments were done to the CNNC model. A possible explanation for the result and a discussion on CNNC model are given in the report.
Interatomic interaction in fcc metals

The parameters of interatomic potential for 10 fcc metals are presented in this paper. The potential is based on the embedded atom method [6]. Parameters are determined empirically by fitting to the equilibrium lattice constant, cohesion energy, vacancy formation energy, bulk modulus and three elastic constants. The proposed potentials are suitable for atomistic computer simulations of practical applications in areas of material science and engineering.
Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview

The global demand for safe and healthy food with minimal synthetic preservatives is continuously increasing. Natural food antimicrobials and especially essential oils (EOs) possess strong antimicrobial activities that could play a remarkable role as a novel source of food preservatives. Despite the excellent efficacy of EOs, they have not been widely used in the food industry due to some major intrinsic barriers, such as low water solubility, bioavailability, volatility, and stability in food systems. Recent advances in nanotechnology have the potential to address these existing barriers in order to use EOs as preservatives in food systems at low doses. Thus, in this review, we explored the latest advances of using natural actives as antimicrobial agents and the different strategies for nanoencapsulation used for this purpose. The state of the art concerning the antibacterial properties of EOs will be summarized, and the main latest applications of nanoencapsulated antimicrobial agents in food systems will be presented. This review should help researchers to better choose the most suitable encapsulation techniques and materials.
Modeling spatiotemporal Aedes aegypti risk in French Guiana using meteorological and remote sensing data

Although the development of vaccines for the prevention of arboviral diseases has been a priority in recent years, prevention strategies continue to depend on vector control. Risk maps at scales appropriate for these strategies can provide valuable information to assess entomological risk levels and guide actions. We used a spatio-temporal modeling approach to predict, at the local scale, the risk of homes potentially harboring Aedes aegypti larvae. The model used integrated larvae risk data collected in the field from September 2011 to February 2013, environmental data obtained from very high spatial resolution Pleiades imagery, and daily meteorological data, collected in the city of Matoury in French Guiana. Various environmental and meteorological conditions were identified as risk or protective factors for the presence of immature stages of Aedes aegypti in homes on a given date and used to produce dynamic maps with high spatial and temporal resolution. Aedes vector risk was modeled between 50 and 200 m, around houses, on a time scale of 3 to 5 days. The resulting model was extrapolated to other municipalities with the same characteristics of urbanization during the 2019-2020 dengue epidemic in French Guiana. This work represents a major opportunity to monitor the evolution of vector risk and constitutes information that could be particularly useful for public health authorities in charge of vector control.
International prevalence of consultation with a naturopathic practitioner: a systematic review and meta-analysis

Objectives Naturopathy is a traditional medicine system informed by codified philosophies and principles, and an emphasis on non-pharmacologic therapeutic interventions. While naturopathy is practised by approximately 75 000–100 000 000 naturopathic practitioners in at least 98 countries, little is known about the international prevalence of history of consultation with a naturopathic practitioner. This study reports a systematic review and meta-analysis of studies describing the global prevalence of history of consultation with a naturopathic practitioner by the general population. Setting The included literature was identified through a systematic search of eight databases between September and October 2019, as well as the grey literature. Participants Studies were included if they reported the prevalence rate of consultations with a naturopathic practitioner by the general population. Interventions Survey items needed to report consultations with a naturopathic practitioner as defined in the country where data was collected, and not combine naturopathic consultations with other health services or only report consulations for illness populations. Primary and secondary outcome measures Primary measures used for the analysis was consultations in the previous 12 months. Other prevalence timeframes were reported as secondary measures. Methods Meta-analysis of prevalence data was conducted using random effects models based on individual countries and WHO world regions. Results The literature search identified eight manuscripts summarising 14 studies reporting prevalence for inclusion in the review. All included studies had a low risk of bias. Meta-analysis of the included studies by world region found the 12-month prevalence of history of naturopathy consultations ranged from 1% in the Region of the Americas to 6% in the European and Western Pacific Regions. Conclusions There are up to sixfold differences in the prevalence of naturopathy consults over 12 months between and within world regions, which may be driven by a range of policy, legislative and social factors. PROSPERO registration number CRD42020145529.
Analysis of Whole Genome Sequencing in a Cohort of Individuals with PHACE Syndrome Suggests Dysregulation of RAS/PI3K Signaling

The acronym PHACE stands for the co-occurrence of posterior brain fossa malformations, hemangiomas, arterial anomalies, cardiac defects, and eye abnormalities. The majority of patients have a segmental hemangioma and at least one developmental structural anomaly. The etiology and pathogenesis are unknown. Here we discuss the candidate causative genes identified in a de novo analysis of whole genome sequencing of germline samples from 98 unrelated trios in which the probands had PHACE, all sequenced as part of the Gabriella Miller Kids First Pediatric Research Program. A g:Profiler pathway analysis of the genes with rare, de novo variants suggested dysregulation of the RAS/MAPK and PI3K/AKT pathways that regulate cell growth, migration, and angiogenesis. These findings, along with the developmental anomalies and the vascular birthmark, support including PHACE within the RASopathy family of syndromes.
COVID-19 Outcomes Among Users of CD20 Inhibitors for Immune-Mediated Diseases: A Comparative Cohort Study

Objective: Patients with immune-mediated diseases treated with CD20 inhibitors may have worse COVID-19 outcomes due to impaired humoral immunity, but differences versus the general population are unknown. Methods: We identified patients with immune-mediated diseases who received CD20 inhibitors within one year prior to the index date of PCR-confirmed COVID-19 between January 31, 2020, and January 31, 2021. Comparators with COVID-19 were matched up to 5:1 by age, sex, and PCR date. Hazard ratios (HRs) and 95% confidence intervals (CIs) for hospitalization, mechanical ventilation, and death in CD20 inhibitor users versus comparators were estimated using Cox regression. Results: We identified 114 cases with COVID-19 who had received CD20 inhibitors for immune-mediated diseases (mean age 55 years, 70% female) and 559 matched comparators with COVID-19 (mean age 54 years, 70% female). CD20 inhibitor-treated cases had higher mortality (aHR 2.16; 95% CI: 1.03 to 4.54) than matched comparators. Risks of hospitalization (aHR 0.88; 95% CI: 0.62 to 1.26) and mechanical ventilation (aHR 0.82; 95% CI: 0.36 to 1.87) were similar. Similar trends were seen in analyses according to type of indication (e.g., rheumatic or neurologic disease) and duration of CD20 inhibitor use (<1 or [≥]1 year), and after excluding patients with interstitial lung disease, cancer, and those on glucocorticoids prior to COVID-19 diagnosis. Conclusions: Patients who received CD20 inhibitors for immune-mediated diseases prior to COVID-19 had higher mortality following COVID-19 than matched comparators, highlighting the urgent need to mitigate excess risks in CD20 inhibitor users during the ongoing pandemic.
Quantum Holography from Fermion Fields

We demonstrate, in the context of Loop Quantum Gravity, the Quantum Holographic Principle, according to which the area of the boundary surface enclosing a region of space encodes a qubit per Planck unit. To this aim, we introduce fermion fields in the bulk, whose boundary surface is the two-dimensional sphere. The doubling of the fermionic degrees of freedom and the use of the Bogoliubov transformations lead to pairs of spin network’s edges piercing the boundary surface with double punctures, giving rise to pixels of area encoding a qubit. The proof is also valid in the case of a fuzzy sphere.
Effectiveness of smearing and tetrahedron methods: best practices in DFT codes

Density functional theory (DFT) codes are commonly treated as a ‘black box’ in high-throughput screening of materials, with users opting for the default values of the input parameters. Often, non-experts may not sufficiently consider the effect of these parameters on prediction quality. In this work, we attempt to identify a robust set of parameters related to smearing and tetrahedron methods that return numerically accurate and efficient results for a wide variety of metallic systems. The effects of smearing and tetrahedron methods on the total energy, number of self-consistent field cycles, and forces on atoms are studied in two popular DFT codes: the Vienna ab initio Simulation Package and Quantum Espresso. From nearly 40 000 computations, it is apparent that the optimal smearing depends on the system, smearing method, smearing parameter, and k-point density. The benefit of smearing is a minor reduction in the number of self-consistent field cycles, which is independent of the smearing method or parameter. A large smearing parameter—what is considered large is system dependent—leads to inaccurate total energies and forces. Blöchl’s tetrahedron method leads to small improvements in total energies. When treating diverse systems with the same input parameters, we suggest using as little smearing as possible due to the system dependence of smearing and the risk of selecting a parameter that gives inaccurate energies and forces.
Data mining on ‘UIP’ and immunization coverage in India

This paper examines the effect of Universal Immunization Program (UIP) on the coverage of essential vaccines in various Indian states. Our empirical findings confirm that UIP by itself does not always significantly affect the coverage, however, the effect of UIP on coverage becomes significantly positive in the presence of good health infrastructure. Health infrastructure not only helps the coverage by itself, but it also raises the effectiveness of vaccination funds on the coverage. Health infrastructure combined with UIP funds also serve as good predictors of immunization coverage based on application of LASSO and LARS variable selection techniques. We also compare the nonparametric kernel densities of response variable to that of its in-sample prediction and find that our model fits the data almost perfectly for each vaccine measures considered. The policy prescription that follows from our study is that the immunization program should focus on promoting the required infrastructure in addition to providing funds in order to facilitate effective usage of funds. Some states that are consistently underperforming require more targeted policies.
Authors’ reply to referee comments

The authors would like to thank the reviewers for their comments. These have helped us improve the manuscript through improving clarity, correcting errors, and expanding discussion on some aspects. In particular, some new model runs undertaken in response to referee comments have uncovered an interesting new result regarding radical emissions. We believe we have addressed all of the issues raised by both referees. We discuss each comment in turn in the text below. Most comments have resulted in a change to the manuscript, which we outline in each case. As well as the changes discussed herein, we also made some minor corrections to spelling, grammar, and phrasing in the revised manuscript. In response to these comments we have removed one figure and added another, which means that the numbering of some Figures have changed in the new manuscript. We have not changed such references where we quote the referees’ comments, however in our responses we use the new figure numbers.
Deep Residual Networks for Single Image De-snowing

Atmospheric particle removal is a challenging task and attacks wide interests in computer vision filed. In this paper, we proposed a single image snow removal framework based on deep residual networks. According to the fact that there are various snow sizes in a snow image, the inception module which consists of different filter kernels was adopted to extract multiple resolution features of the input snow image. Except the traditional mean square error loss, the perceptual loss and total variation loss were employed to generate more clean images. Experimental results on synthetic and realistic snow images indicated that the proposed method achieves superior performance in respect of visual perception and objective evaluation
Influence of Phytohormones on Callus Indication and Micrpropagation on Rose (Rosa indica L.)

Roses ( Rosa indica L.) is one of the greatest significant flower collection in the biosphere and have commercial value in decorative, therapeutic and cosmetic trade. Speedy development and construction of disease free plants through in vitro technique have played a dynamic part in broadcast of salable rose cultivars. The five diverse meditations of growth controller such as (MS + 2.00 mg L -1 ), (IBA + 2.00 mg L -1 ), (AA + 30 gL -1 sucrose), (MS + 2.00 mg L -1 ), (BAP + 3.00 mg L -1 ), (NAA +30 mg L -1 sucrose), (MS + 2.00 mg L -1 ), (BAP + 3.00 mg L -1 ), ( IAA + 30 mg L -1 sucrose), (MS + 4 mg L -1 ), (BAP + 2.00 mg L -1 ), (( IAA + 30 mg L -1 sucrose) and (MS + 3.00 mg L -1 ), (BAP + 3.00 mg L -1 ), (IAA + 30 mg L -1 sucrose for shoot induction. After establishment of plantlets, stem was cast off as explants foundation for the initiation of callus. It is concluded that the two different concentrations 2.00 and 4.00 mg L -1 of 2 to 4 D were better worked out for callus induction.