Miracle-dz/UniFAR-FTUs huggingface dataset:
Unified Interpretations of Two Kinds of Needle-Shaped Precipitates Using Transmission Electron Microscopy and Small-Angle Neutron Scattering in Aged Al–Mg2Si(-Cu) Alloys

This study investigates the nanostructural properties of pseudo-binary Al–1.0Mg2Si (mass%) alloys with and without 0.5Cu using transmission electron microscopy (TEM) and small-angle neutron scattering (SANS). The TEM results show that both alloys exhibit extra electron diffraction spots related to MgSiMg second clusters at peak-aged conditions. High-resolution TEM images have revealed that the second cluster exists as a needle-shaped precipitate that is shorter and thicker than the β″ phase. We found that the second cluster, which we referred to as the R phase in this paper, is more likely to form partially along the longitudinal axis of a random-type precipitate. Thus, the atomic arrangement in the random-type precipitate is not completely random. SANS is used to quantify the size and volume fraction of the observed needle-shaped precipitates since the R phase is difficult to observe with TEM. The R phase forms even in the Cu-free alloy, but the volume fraction is low, and the growth and formation are retarded near the peak-aged conditions. Undoubtedly, the Cu addition has the effect of stabilizing the growth of the R phase and also promoting its formation. Therefore, the R phase also contributes to the increase in hardness at both under- and peak-aged conditions in the Cu-containing alloy in addition to the strengthening β″ phases.
Constraints on Solar Wind Density and Velocity Based on Coronal Tomography and Parker Solar Probe Measurements

Previous work has established an empirical relationship between densities gained from coronal rotational tomography near the ecliptic plane with solar wind outflow speeds at heliocentric distance r 0 = 8R ⊙. This work aims to include solar wind acceleration, and thus velocity profiles out to 1 au. Inner boundary velocities are given as a function of normalized tomographic densities, ρ N , as V0=75∗e−5.2*ρN+108 , and typically range from 100 to 180 km s−1. The subsequent acceleration is defined as V(r)=V01+αIP1−e−r−r0/rH , with α IP ranging between 1.75 and 2.7, and r H between 50 and 35 R ⊙ dependent on V 0. These acceleration profiles approximate the distribution of in situ measurements by Parker Solar Probe (PSP) and other measurements at 1 au. Between 2018 November and 2021 September these constraints are applied using the HUXt model and give good agreement with in situ observations at PSP, with a ∼6% improvement compared with using a simpler constant acceleration model previously considered. Given the known tomographical densities at 8 R ⊙, we extrapolate density to 1 au using the model velocities and assuming mass flux conservation. Extrapolated densities agree well with OMNI measurements. Thus coronagraph-based estimates of densities define the ambient solar wind outflow speed, acceleration, and density from 8 R ⊙ to at least 1 au. This sets a constraint on more advanced models, and a framework for forecasting that provides a valid alternative to the use of velocities derived from magnetic field extrapolations.
MAMMOTH-Subaru. III. Lyα Halo Identified by Stacking ∼3300 Lyα Emitters at z = 2.2–2.3

In this paper, we present a Lyα halo (LAH) identified by stacking ∼3300 Lyα emitters (LAEs) at z = 2.2–2.3. We carry out imaging observations and data reduction with Subaru/Hyper Suprime-Cam. Our total survey area is ∼12 deg2 and the imaging depths are 25.5–27.0 mag. Using the imaging data, we select 1240 and 2101 LAE candidates at z = 2.2 and 2.3, respectively. We carry out spectroscopic observations of our LAE candidates and data reduction with Magellan/IMACS to estimate the contamination rate of our LAE candidates. We find that the contamination rate of our sample is low (8%). We stack our LAE candidates with a median stacking method to identify the LAH at z = 2. We show that our LAH is detected until ∼100 kpc at the 2σ significance level and likely extended to ∼200 kpc at a surface brightness level of ∼10−20 erg s−1 cm−2 arcsec−2. Compared to those of previous studies, our LAH is brighter at radii of ∼25–100 kpc, which is not likely caused by the contamination in our sample but by the different redshifts, fields, and selection methods instead. To investigate how central galaxies affect surrounding LAHs, we divide our LAEs into subsamples based on the Lyα luminosity (L Lyα ), rest-frame Lyα equivalent width (EW0), and UV magnitude (M uv). We stack the subsamples and find that higher L Lyα , smaller EW0, and brighter M uv cause more extended halos. Our results suggest that more massive LAEs generally have more extended LAHs.
A rare haplotype of the GJD3 gene segregating in familial Meniere Disease interferes with connexin assembly

Familial Meniere Disease (FMD) is a rare polygenic disorder of the inner ear. Mutations in the connexin gene family, which encodes gap junction proteins, can also cause hearing loss, but their role in FMD is largely unknown. Here, we found an enrichment of rare missense variants in the GJD3 gene when comparing allelic frequencies in FMD (N=94) with the Spanish reference population (OR=3.9[1.92-7.91], FDR=2.36E-03). In the GJD3 sequence, we identified a rare haplotype (TGAGT) composed of two missense, two synonymous, and one downstream variants. This haplotype was found in five individuals with FMD, segregating in three unrelated families with a total of ten individuals; and in another eight Meniere Disease individuals. GJD3 encodes the gap junction protein delta 3, also known as human connexin 31.9 (CX31.9). The protein model predicted that the NP_689343. 3:p.(His175Tyr) missense variant could modify the interaction between connexins and the connexon assembly, affecting the homotypic GJD3 gap junction between cells. Our studies in mice revealed that the mouse ortholog Gjd3 - encoding Gjd3 or mouse connexin 30.2 (Cx30.2) - was expressed in the organ of Corti and vestibular organs, particularly in the tectorial membrane, the base of inner and outer hair cells and the nerve fibers. The present results describe a novel association between GJD3 and familial FMD, providing evidence that FMD is related to changes in the inner ear channels; in addition, it supports a new role of tectorial membrane proteins in FMD.
The DESI One-Percent Survey: A Concise Model for the Galactic Conformity of Emission-line Galaxies

Galactic conformity is the phenomenon whereby a galaxy of a certain physical property is correlated with its neighbors of the same property, implying a possible causal relationship. The observed auto correlations of emission-line galaxies (ELGs) from the highly complete DESI One-Percent Survey exhibit a strong clustering signal on small scales, providing clear evidence for the conformity effect of ELGs. Building upon the original subhalo abundance-matching (SHAM) method developed by Gao et al., we propose a concise conformity model to improve the ELG–halo connection. In this model, the number of satellite ELGs is boosted by a factor of ∼5 in the halos whose central galaxies are ELGs. We show that the mean ELG satellite number in such central halos is still smaller than 1 and that the model does not significantly increase the overall satellite fraction. With this model, we can well recover the ELG auto correlations to the smallest scales explored with the current data (i.e., r p > 0.03 Mpc h −1 in real space and at s > 0.3 Mpc h −1 in redshift space), while the cross correlations between luminous red galaxies and ELGs are nearly unchanged. Although our SHAM model has only eight parameters, we further verify that it can accurately describe the ELG clustering in the entire redshift range from z = 0.8 to 1.6. We therefore expect that this method can be used to generate high-quality ELG lightcone mocks for DESI.
Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach

The upper limb of the body is a vital for various kind of activities for human. The complete or partial loss of the upper limb would lead to a significant impact on daily activities of the amputees. EMG carries important information of human physique which helps to decode the various functionalities of human arm. EMG signal based bionics and prosthesis have gained huge research attention over the past decade. Conventional EMG-PR based prosthesis struggles to give accurate performance due to off-line training used and incapability to compensate for electrode position shift and change in arm position. This work proposes online training and incremental learning based system for upper limb prosthetic application. This system consists of ADS1298 as AFE (analog front end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing). The system has been tested for both intact and amputated subjects. Time derivative moment based features have been implemented and utilized for effective pattern classification. Initially, system have been trained for four classes using the on-line training process later on the number of classes have been incremented on user demand till eleven, and system performance has been evaluated. The system yielded a completion rate of 100% for healthy and amputated subjects when four motions have been considered. Further 94.33% and 92% completion rate have been showcased by the system when the number of classes increased to eleven for healthy and amputees respectively. The motion efficacy test is also evaluated for all the subjects. The highest efficacy rate of 91.23% and 88.64% are observed for intact and amputated subjects respectively.
Diagnostic Utility of Integration of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MR Perfusion Employing Split Bolus Technique in Differentiating High-Grade Glioma

Abstract Background : Despite documented correlation between glioma grades and dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion-derived parameters, and its inherent advantages over dynamic susceptibility contrast (DSC) perfusion, the former remains underutilized in clinical practice. Given the inherent spatial heterogeneity in high-grade diffuse glioma (HGG) and assessment of different perfusion parameters by DCE (extravascular extracellular space volume [Ve] and volume transfer constant in unit time [k-trans]) and DSC (rCBV), integration of the two into a protocol could provide a holistic assessment. Considering therapeutic and prognostic implications of differentiating WHO grade 3 from 4, we analyzed the two grades based on a combined DCE and DSC perfusion. Methods : Perfusion sequences were performed on 3-T MR. Cumulative dose of 0.1 mmol/kg of gadodiamide, split into two equal boluses, was administered with an interval of 6 minutes between the DCE and DSC sequences. DCE data were analyzed utilizing commercially available GenIQ software. Results : Of the 41 cases of diffuse gliomas analyzed, 24 were WHO grade III and 17 grade IV gliomas (2016 WHO classification). To differentiate grade III and IV gliomas, Ve cutoff value of 0.178 provided the best combination of sensitivity (88.24%) and specificity (87.50%; AUC: 0.920; p < 0.001). A relative cerebral blood volume (rCBV) of value 3.64 yielded a sensitivity of 70.59% and specificity of 62.50% ( p = 0.018). The k-trans value, although higher in grade III than in grade IV gliomas, did not reach statistical significance ( p = 0.108). Conclusion : Uniqueness of employed combined perfusion technique, treatment naïve patients at imaging, user-friendly postprocessing software utilization, and ability of Ve and rCBV to differentiate between grade III and IV gliomas ( p < 0.05) are the strengths of the present study, contributing to the existing literature and moving a step closer to achieving accurate MR perfusion-based glioma grading.
NorA efflux pump mediates Staphylococcus aureus response to Pseudomonas aeruginosa pyocyanin toxicity

ABSTRACT Endogenous transporters protect Staphylococcus aureus against antibiotics and also contribute to bacterial defense from environmental toxins. We evaluated the effect of overexpression of four efflux pumps, NorA, NorB, NorC, and Tet38, on S. aureus survival following exposure to pyocyanin (PYO) of Pseudomonas aeruginosa, using a well diffusion assay. We measured the PYO-created inhibition zone and found that only an overexpression of NorA reduced S. aureus susceptibility to pyocyanin killing. The MICPYO of the NorA overexpressor increased threefold compared to that of wild-type RN6390 and was reduced 2.5-fold with reserpine, suggesting that increased NorA efflux caused PYO resistance. The PYO-created inhibition zone of a ΔnorA mutant was consistently larger than that of a plasmid-borne NorA overexpressor. PYO also produced a modest increase in norA expression (1.8-fold at 0.25 µg/mL PYO) that gradually decreased with increasing PYO concentrations. Well diffusion assays carried out using P. aeruginosa showed that ΔnorA mutant was less susceptible to killing by PYO-deficient mutants PA14phzM and PA14phzS than to killing by PA14. NorA overexpression led to reduced killing by all tested P. aeruginosa. We evaluated the NorA–PYO interaction using a collection of 22 clinical isolates from adult and pediatric cystic fibrosis (CF) patients, which included both S. aureus (CF-SA) and P. aeruginosa (CF-PA). We found that when isolated alone, CF-PA and CF-SA expressed varying levels of PYO and norA transcripts, but all four CF-PA/CF-SA pairs isolated concurrently from CF patients produced a low level of PYO and low norA transcript levels, respectively, suggesting a partial adaptation of the two bacteria in circumstances of persistent co-colonization.
Sub-minimum Inhibitory Concentrations of Zinc Oxide Nanoparticles Reduce the Expression of the Staphylococcus aureus α-Haemolysin

In this study, the effect of sub-minimum inhibitory concentrations of the zinc oxide nanoparticle on the expression of the α-haemolysin gene as a significant virulence factor of Staphylococcus aureus is assessed. The effect of sub-minimum inhibitory concentration (312.5 μg/ml) of the zinc oxide nanoparticle on the haemolysis phenomenon was studied phenotypically on blood agar media with and without zinc oxide nanoparticles and genetically by real time polymerase chain reaction method in media containing and without zinc oxide. The haemolysis phenomena of all S. aureus isolates in a blood agar with a sub-minimum inhibitory concentration and 1/2, 1/4 and 1/8 minimum inhibitory concentration of zinc oxide nanoparticles was completely restrained. After performing real time polymerase chain reaction, the amount of hla gene expression in 12 h culture in media with 1/2 and 1/4 minimum inhibitory concentration of zinc oxide nanoparticles was reduced, but gene expression in the 1/8 minimum inhibitory concentration was did not decrease. Haemolysis by S. aureus was decreased in the presence of sub-minimum inhibitory concentration of zinc oxide nanoparticles. The use of zinc oxide nanoparticle in sub-minimum inhibitory concentration as cover of artificial instruments such as catheter, intravascular catheters or shunts to control bacterial infection is suggested for further study.
Force measurement using a multi-section mechatronic module for localization of the patient’s support areas during the formation of his spatial position

In the modern world, there is a rapid deployment of mechatronic modules and devices into various spheres of human activity. One of the directions that has been developed in recent years is the use of mechatronic devices to create surfaces of complex shape by using solutions with a large number of linear mobility. Of particular interest is the use of these solutions in the field of medicine. The implementation of such systems for the tasks of forming the spatial position of the patient will significantly improve the quality of surgical operations. However, the task of forming the spatial position of the patient is inextricably linked with the task of determining its support areas. The purpose of this article is to verify the possibility of implementing the method of localization of the patient’s support areas using the functionality of a complex of a multi-section mechatronic module and a mechatronic clamping module capable of forming complex surfaces by changing the position of 98 composite plates by controlled movement of rods with integrated force sensors. The article describes the principle of operation of this mechatronic module, synthesizes the communication protocols of the sections and, based on the resulting protocol, develops algorithms and control programs. Experiments with a sensor system measuring the applied forces are also described in order to verify the possibility of localization of the patient’s support area during the formation of his spatial position.
Research on entity relations extraction based on deep learning

The Internet has brought strong scientific and technological support to social development. With the hardware development rapidly in information technology field, such as digital devices, the application demand of Internet knowledge services by network users has been highly valued by scholars. Entity relation extraction technology is an important technical support for various search engines and machine response artificial intelligence applications, which can help users extract knowledge from massive text data on the Internet. This article use deep learning to study how to enhance the effectiveness and accuracy of entity relationship extraction. Firstly, the basic structure, algorithm principle and process of the most important basic algorithm in neural network are described. The main ways and types of building in-depth model in entity relationship extraction are discussed. After that, the concept of attention-based entity relationship extraction model is put forward, and the structure and implementation method of the model are analyzed. The scheme of improving the model to promote the accuracy and the learning rate. The experimental results indicate that optimization scheme improves the model learning rate and improves the effectiveness of entity phrase pre-training. Compared with other in-depth learning methods, the optimized in-depth learning model proposed can identify the weight of local features more accurately, thus further improving the judgment of the relationship between entity semantics. The model research results text entity relationship extraction have excellent prospect in the field of Web Text Knowledge Service application.
Research on data asset pricing optimization model considering risk factors

Big data has become the core strategy of China's national development. However, most of the current research on data asset pricing do not consider risk factors. First, this paper combs the aspects involved in the data asset pricing currently, and makes a literature review on the risk of data value realization; Secondly, it constructs a data asset pricing optimization model, and incorporates two risk factors, data value realization risk and market supply and demand, into the optimization model; Finally, the validity of the data asset pricing optimization model is verified by taking the enterprise energy efficiency monitoring product as an example.
Multicenter benchmarking of short and long read wet lab protocols for clinical viral metagenomics

Metagenomics is gradually being implemented for diagnosing infectious diseases. However, in-depth protocol comparisons for viral detection have been limited to individual sets of experimental workflows and laboratories. In this study, we present a benchmark of metagenomics protocols used in clinical diagnostic laboratories initiated by the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS). A mock viral reference panel was designed to mimic low biomass clinical specimens. The panel was used to assess the performance of twelve metagenomic wet-lab protocols in use in the diagnostic laboratories of participating ENNGS member institutions. Both Illumina and Nanopore, shotgun and targeted capture probe protocols were included. Performance metrics sensitivity, specificity, and quantitative potential were assessed using a central bioinformatics pipeline. Overall, viral pathogens with loads down to 10^4 copies/ml (corresponding to CT values of 31 in our assays) were detected by all the evaluated metagenomic wet lab protocols. In contrast, lower abundant mixed viruses of CT values of 35 and higher were detected only by a minority of the protocols. Considering the reference panel as the gold standard, optimal thresholds to define a positive result were determined per protocol, based on the horizontal genome coverage. Implementing these thresholds, sensitivity and specificity of the protocols ranged from 67 to 100% and 87 to 100%, respectively. A variety of metagenomic protocols are currently in use in clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implying the need for standardization of metagenomic analysis for use in clinical settings.
Creating a computer assisted ICD coding system: performance metric choice and use of the ICD hierarchy

Objective: Machine learning methods hold the promise of leveraging available data and generating higher quality data while alleviating the data collection burden on healthcare professionals. International Classification of Diseases (ICD) diagnoses data, collected globally for billing and epidemiological purposes, represents a valuable source of structured information. However, ICD coding is a challenging task. While numerous previous studies reported promising results in automatic ICD classification, they often describe input data specific model architectures, that are heterogeneously evaluated with different performance metrics and ICD code subsets. This study aims to explore the evaluation and construction of more effective Computer Assisted Coding (CAC) systems using generic approaches, focusing on the use of ICD hierarchy, medication data and a feed forward neural network architecture. Methods: We conduct comprehensive experiments using the MIMIC-III clinical database, mapped to the OMOP data model. Our evaluations encompass various performance metrics, alongside investigations into multitask, hierarchical, and imbalanced learning for neural networks. Results: We introduce a novel metric, RE@R, tailored to the ICD coding task, which offers interpretable insights for healthcare informatics practitioners, aiding them in assessing the quality of assisted coding systems. Our findings highlight that selectively cherry-picking ICD codes diminish retrieval performance without performance improvement over the selected subset. We show that optimizing for metrics such as NDCG and AUPRC outperforms traditional F1-based metrics in ranking performance. We observe that Neural Network training on different ICD levels simultaneously offers minor benefits for ranking and significant runtime gains. However, our models do not derive benefits from hierarchical or class imbalance correction techniques for ICD code retrieval. Conclusion: This study offers valuable insights for researchers and healthcare practitioners interested in developing and evaluating CAC systems. Using a straightforward sequential neural network model, we confirm that medical prescriptions are a rich data source for CAC systems, providing competitive retrieval capabilities for a fraction of the computational load compared to text-based models. Our study underscores the importance of metric selection and challenges existing practices related to ICD code sub-setting for model training and evaluation.