Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic characteristics influence the phenotype of marine macroalga Fucus vesiculosus (Phaeophyceae)

Abstract Intraspecific variation is an important component of heterogeneity in biological systems that can manifest at the genotypic and phenotypic level. This study investigates the influence of genetic characteristics on the phenotype of free‐living Fucus vesiculosus using traditional morphological measures and microsatellite genotyping. Two sympatric morphotypes were observed to be significantly genetically and morphologically differentiated despite experiencing analogous local environmental conditions; indicating a genetic element to F. vesiculosus morphology. Additionally, the observed intraclonal variation established divergent morphology within some genets. This demonstrated that clonal lineages have the ability to alter morphological traits by either a plastic response or somatic mutations. We provide support for the potential occurrence of the Gigas effect (cellular/organ enlargement through genome duplication) in the Fucus genus, with polyploidization appearing to correlate with a general increase in the size of morphological features. Phenotypic traits, as designated by morphology within the study, of F. vesiculosus are partially controlled by the genetic characteristics of the thalli. This study suggests that largely asexually reproducing algal populations may have the potential to adapt to changing environmental conditions through genome changes or phenotypic plasticity.
Acquisition during normative code-mixing: Trinidadian children’s varilingual pronoun usage

While monolingual English speakers acquire most pronouns by age 5, acquisition amid prevalent, normative code-mixing, such as in Trinidad, is underexplored. This study examines how Trinidadian 3- to 5-year-olds express third-person subject, object, reflexive and possessive pronouns and factors influencing pronoun choices. Seventy-five preschoolers produced pronouns via a word elicitation task conducted in Trinidadian English Creole and Trinidad and Tobago English. Responses were coded for children’s age, gender, district and socioeconomic status; task language; grammatical gender/number; and response form. Conditional inference trees facilitated statistical analysis. When choices were available, children exhibited variable production, with Creole forms often dominant. Grammatical gender influenced whether English or Creole pronouns were selected. Task language influenced possessive pronoun choices, indicating developing sociolinguistic competence. The non-significance of other variables suggests widespread mixing of English and Creole pronouns. Findings underscore the importance of describing understudied populations, especially where variation is inherent, to ensure accurate language assessment.
Infrared Images of the Young Cluster NGC 2264

Initial results of an extensive IR imaging survey of the young cluster NGC 2264 and a nearby galactic control field are presented. A large portion of the cluster was imaged in each of the three standard near-IR colors (J, H, and K) with an IR array camera. On the basis of a comparison of these observations, the size of the cluster population is estimated, and the nature of its members is investigated. The cluster is found to contain 360 +/- 130 members. The slope of the K luminosity function of the cluster is significantly steeper than that expected for a cluster of ZAMS stars, and appears to flatten out or turn over at an apparent K magnitude of roughly 13.0-14.0. An analysis of the JHK color-color diagrams of the cluster and control fields shows that approximately 170 sources observed toward the cluster have colors indicative of intrinsic excess IR emission.
Inter-Rater Reliability between Structured and Non-Structured Interviews Is Fair in Schizophrenia and Bipolar Disorders—A Systematic Review and Meta-Analysis

We aimed to find agreement between diagnoses obtained through standardized (SDI) and non-standardized diagnostic interviews (NSDI) for schizophrenia and Bipolar Affective Disorder (BD). Methods: A systematic review with meta-analysis was conducted. Publications from 2007 to 2020 comparing SDI and NSDI diagnoses in adults without neurological disorders were screened in MEDLINE, ISI Web of Science, and SCOPUS, following PROSPERO registration CRD42020187157, PRISMA guidelines, and quality assessment using QUADAS–2. Results: From 54231 entries, 22 studies were analyzed, and 13 were included in the final meta-analysis of kappa agreement using a mixed-effects meta-regression model. A mean kappa of 0.41 (Fair agreement, 95% CI: 0.34 to 0.47) but high heterogeneity (Î2 = 92%) were calculated. Gender, mean age, NSDI setting (Inpatient vs. Outpatient; University vs. Non-university), and SDI informant (Self vs. Professional) were tested as predictors in meta-regression. Only SDI informant was relevant for the explanatory model, leaving 79% unexplained heterogeneity. Egger’s test did not indicate significant bias, and QUADAS–2 resulted in “average” data quality. Conclusions: Most studies using SDIs do not report the original sample size, only the SDI-diagnosed patients. Kappa comparison resulted in high heterogeneity, which may reflect the influence of non-systematic bias in diagnostic processes. Although results were highly heterogeneous, we measured a fair agreement kappa between SDI and NSDI, implying clinicians might operate in scenarios not equivalent to psychiatry trials, where samples are filtered, and there may be more emphasis on maintaining reliability. The present study received no funding.
A General Theory of Completeness Proofs

The purpose of the following treatise is to remark the fact that the method developed in Schiitte [6] is able to give a unification to completeness proofs for several formal systems of logic. Especially in treatments on the predicate logic, the second E-Theorem in the sense of Hilbert-Bernays [2] is used effectively. In this treatise, we comment on the classical and the intuitionistic logic only. Because, application to other logics, in author's opinion, is easy now and peculiarly needed ideas concerning some modal logics are already shown in Schiitte's book [7]. I would like to express my thanks to Prof. G. Takeuti who gave me a remark concerning the fact that the E-Theorem is useful to the completeness proof of the classical predicate calculus and to Dr. D. P. McCullough who got me interested in those problems by sending me his unpublished papers.
Methods for monitoring and imaging nanoparticles in cells

Successful targeting nanoparticles (NP) to specific cells requires reliable feedback about NP accumulation in cells. This task is a challenge for all optical methods due the size of NP and diffraction limit of optical devices. We modified several microscopy-based techniques for imaging and measuring NP in individual cells: photothermal, fluorescent, electron and atomic-force microscopies and flow cytometry. All those techniques were applied for quantitative analysis and imaging of interaction of gold NP (10 and 30 nm) with living tumor cells. Based on experimental results we performed comparison of all methods in terms of sensitivity, speed, sample requirements etc. We have found that standard microscopes may detect NP and their clusters in individual living cells through imaging NP-related thermal, fluorescent and other phenomena.
A New Method on Matching Correspondence Features in Image Warping

This paper introduces a robust method for automatically matching features in images corresponding to the same physical point on an object captured from two arbitrary viewpoints. Starting from detected line segments in two or more images using phase congruency, the pairs of line segments are characterized by indices that encode the relative positions and orientations of those segments. The encoding indices are invariant with respect to viewpoint changes and the corresponding line segments between the images can be found by them. The feature matching is optimized for image warping where we wish to ignore unreliable matches at the expense of reducing the number of feature matches. This approach can be applied to the image warping to realize its automatism. Results are presented on real images.
Applications of stereoscopic particle image velocimetry: Dust acoustic waves and velocity space distribution functionsa)

Two-dimensional particle image velocimetry (2D-PIV) techniques have been applied to dusty plasmas for the past 5 years. During that time, 2D-PIV has been used to provide detailed measurements of microparticle transport in dusty plasmas. However, a measurement of the third velocity vector direction is necessary to fully understand the microparticle transport. In this paper, stereoscopic particle image velocimetry (stereo-PIV) is used as a technique for obtaining all three-velocity vector components. This paper discusses the application of stereo-PIV techniques to measurements of dust acoustic waves and velocity space distribution functions in dusty plasmas.
Effects of Selecting Channel Direction in Improving Performance of Sub-100 nm MOSFETs Fabricated on (110) Surface Si Substrate

We investigated the low field mobility and short channel characteristics of metal-oxide-semiconductor field-effect transistors (MOSFETs) on (110) surface Si substrates with various channel directions from the viewpoints of experiment and numerical simulation. We found that the mobility (µ) ratio of (110) substrates to (001) substrates (µ(110)/µ(001)) does not depend on the vertical electric field due to the identical surface roughness for (110) and (001) substrates. We verified mobility enhancement and its channel direction dependence by conducting a detailed carrier transport simulation using a full band model and relaxation time approximation. We obtained good threshold voltage (Vth) lowering characteristics due to the suppression of channeling at the source and drain (SD) extension by implant sequence control. Our results showed that the improvement in propagation delay time (CV/I) and on-current ratio of nMOS to pMOS (Ionn/Ionp) obtained by using an optimized combination of channel directions and a (110) surface Si substrate is attractive for future LSIs down to the sub-100 nm region.
Association between the Oxidative Balance Score and Incident Chronic Kidney Disease in Adults

Oxidative stress is a novel risk factor for chronic kidney disease (CKD). The oxidative balance score (OBS) was developed to represent the overall oxidative balance based on dietary and lifestyle pro-oxidant and antioxidant components. The aim of this study is to verify the relationship between the OBS and the incidence of CKD. Data from 5795 participants without CKD at the baseline survey of the Korean Genome and Epidemiology Study were analyzed. Participants were classified into sex-specific OBS tertiles. During the mean follow-up period of 13.6 years, 286 men and 382 women newly developed CKD. The Cox proportional hazard spline curve revealed an inverse dose–response association between the OBS and incident CKD in both men and women. Multiple Cox proportional hazard regression analysis revealed that the adjusted hazard ratios (95% confidence intervals) for sex-specific highest (T3) and middle (T2) OBS tertile groups were 0.80 (0.59–1.08) and 0.70 (0.51–0.95), respectively, in men and 0.76 (0.59–0.98) and 0.73 (0.55–0.96), respectively, in women, with the sex-specific lowest OBS tertile group (T1) as the reference. These results suggest that a healthy diet and lifestyle that increases the OBS may help prevent CKD in both men and women.
Multi-material decomposition of spectral CT images

Spectral Computed Tomography (Spectral CT), and in particular fast kVp switching dual-energy computed tomography, is an imaging modality that extends the capabilities of conventional computed tomography (CT). Spectral CT enables the estimation of the full linear attenuation curve of the imaged subject at each voxel in the CT volume, instead of a scalar image in Hounsfield units. Because the space of linear attenuation curves in the energy ranges of medical applications can be accurately described through a two-dimensional manifold, this decomposition procedure would be, in principle, limited to two materials. This paper describes an algorithm that overcomes this limitation, allowing for the estimation of N-tuples of material-decomposed images. The algorithm works by assuming that the mixing of substances and tissue types in the human body has the physicochemical properties of an ideal solution, which yields a model for the density of the imaged material mix. Under this model the mass attenuation curve of each voxel in the image can be estimated, immediately resulting in a material-decomposed image triplet. Decomposition into an arbitrary number of pre-selected materials can be achieved by automatically selecting adequate triplets from an application-specific material library. The decomposition is expressed in terms of the volume fractions of each constituent material in the mix; this provides for a straightforward, physically meaningful interpretation of the data. One important application of this technique is in the digital removal of contrast agent from a dual-energy exam, producing a virtual nonenhanced image, as well as in the quantification of the concentration of contrast observed in a targeted region, thus providing an accurate measure of tissue perfusion.
Racial Disparities and Common Respiratory Infectious Diseases in Children of the United States: A Systematic Review and Meta-Analysis

Due to the lack of sufficient data on the relationship between racial disparities and the occurrence of infectious respiratory diseases in children, the aim of this systematic review and meta-analysis is to evaluate the presence of racial gaps in the occurrence of respiratory infectious diseases in children. This study follows the PRISMA flow guidelines for systematic reviews and the standards of meta-analysis for 20 quantitative studies conducted from 2016 to 2022 including 2,184,407 participants. As evidenced from the review, in the U.S., racial disparities are present among children, with Hispanic and Black children carrying the burden of infectious respiratory disease occurrence. Several factors are contributory to these outcomes among Hispanic and Black children, including higher rates of poverty; higher rates of chronic conditions, such as asthma and obesity; and seeking care outside of the home. However, vaccinations can be used to reduce the risk of infection among Black and Hispanic children. Whether a child is very young or a teen, racial disparities are present in occurrence rates of infectious respiratory diseases, with the burden resting among minorities. Therefore, it is important for parents to be aware of the risk of infectious diseases and to be aware of resources, such as vaccines.
CIFNet: context information fusion network for cloud and cloud shadow detection in optical remote sensing imagery

Abstract. Accurately and automatically detecting cloud and cloud shadow is one of the key steps in the analysis of optical remote sensing imagery. Currently, most cloud and cloud shadow detection methods are prone to false detection, and some clouds and cloud shadows may be missing from the detection results. Due to the lack of contextual information extraction and fusion capabilities, the accuracy of these cloud detection algorithms cannot be guaranteed. This article proposes a deep learning-based convolutional neural network context information fusion network (named CIFNet) to solve the problem of cloud and cloud shadow detection on Gaofen-1 wide field of view optical remote sensing imageries. First, we preprocess the dataset and make the corresponding labels. Next, we design a Resblock-cloud to capture global and local features and prevent the network from degrading. Then, we utilize a global context fusion module to fuse different levels of global context information by dense skip-connections. Finally, a multiscale context fusion module is designed to extract multiscale contextual relations between cloud and cloud shadow. Experimental results show that the proposed CIFNet method can obtain better cloud and cloud shadow boundaries in complex situations and therefore outperforms the competing methods.
The global evolution of the primordial solar nebula

Complete radial, time-dependent calculations of the structure and evolution of the primordial solar nebula during the viscous diffusion stage are presented. The viscous stress is derived from analytic one-zone models of the vertical nebular structure based on detailed grain opacities. Comparisons with full numerical integrations indicate that the effective viscous alpha parameter is about 0.01. The evolution time of a minimum mass nebula is one-million yr or less. The flow pattern of fluid elements in the disk is examined and the implications the results have on the theory of the formation of the solar system are discussed.
Optimization of Ultrasonic-Assisted Extraction Conditions for Bioactive Components and Antioxidant Activity of Poria cocos (Schw.) Wolf by an RSM-ANN-GA Hybrid Approach

In this study, a response surface methodology and an artificial neural network coupled with a genetic algorithm (RSM-ANN-GA) was used to predict and estimate the optimized ultrasonic-assisted extraction conditions of Poria cocos. The ingredient yield and antioxidant potential were determined with different independent variables of ethanol concentration (X1; 25–75%), extraction time (X2; 30–50 min), and extraction solution volume (mL) (X3; 20–60 mL). The optimal conditions were predicted by the RSM-ANN-GA model to be 55.53% ethanol concentration for 48.64 min in 60.00 mL solvent for four triterpenoid acids, and 40.49% ethanol concentration for 30.25 min in 20.00 mL solvent for antioxidant activity and total polysaccharide and phenolic contents. The evaluation of the two modeling strategies showed that RSM-ANN-GA provided better predictability and greater accuracy than the response surface methodology for ultrasonic-assisted extraction of P. cocos. These findings provided guidance on efficient extraction of P. cocos and a feasible analysis/modeling optimization process for the extraction of natural products.
Multi-Objective, Multi-Armed Bandits: Algorithms for Repeated Games and Application to Route Choice

Multi-objective decision-making in multi-agent scenarios poses multiple challenges. Dealing with multiple objectives and non-stationarity caused by simultaneous learning are only two of them, which have been addressed separately. In this work, reinforcement learning algorithms that tackle both issues together are proposed and applied to a route choice problem, where drivers must select an action in a single-state formulation, while aiming to minimize both their travel time and toll. Hence, we deal with repeated games, now with a multi-objective approach. Advantages, limitations and differences of these algorithms are discussed. Our results show that the proposed algorithms for action selection using reinforcement learning deal with non-stationarity and multiple objectives, while providing alternative solutions to those of centralized methods.
A fast matching algorithm based on local gradient histograms

Image matching is an important task in image processing. Basically two different problems are distinguished: detection of a reference image in a scene and estimation of its exact position. Recently many matching algorithms have been proposed. In this work, we propose a hybrid matching algorithm based on recursive calculation of local gradient histograms and pyramidal representation of matched images. The proposed algorithm is fast and invariant to affine transformations such as rotation, translation, and scaling. Computer simulation results obtained with the suggested algorithm are presented and compared with those of common matching techniques.
A split‐and‐merge Bayesian variable selection approach for ultrahigh dimensional regression

We propose a Bayesian variable selection approach for ultrahigh dimensional linear regression based on the strategy of split and merge. The approach proposed consists of two stages: split the ultrahigh dimensional data set into a number of lower dimensional subsets and select relevant variables from each of the subsets, and aggregate the variables selected from each subset and then select relevant variables from the aggregated data set. Since the approach proposed has an embarrassingly parallel structure, it can be easily implemented in a parallel architecture and applied to big data problems with millions or more of explanatory variables. Under mild conditions, we show that the approach proposed is consistent, i.e. the true explanatory variables can be correctly identified by the approach as the sample size becomes large. Extensive comparisons of the approach proposed have been made with penalized likelihood approaches, such as the lasso, elastic net, sure independence screening and iterative sure independence screening. The numerical results show that the approach proposed generally outperforms penalized likelihood approaches: the models selected by the approach tend to be more sparse and closer to the true model.
CoFe2O4@SiO2@TiO2 core-shell nanoparticles for photocatalytic water decontamination

In the present work is reported, following a facile process, the synthesis of cobalt ferrite nanoparticles covered with a shell of SiO2, and then, covered with a shell of TiO2. In the first step, cobalt ferrite nanoparticles were prepared by coprecipitation of Co+2 and Fe +2 ions in basic medium, followed by a simple controlled oxidation process carried out by nitrate ions in basic medium with inert atmosphere at 95°C for 24h. In the second step, SiO2 particles were deposited by heterogeneous nucleation onto the surface of the ferrite in alcohol medium by alkalinization of tetraethylortosilane solutions, finally, in the third step, the TiO2 shell film is deposited by using sol-gel technique. Characterization techniques were performed to determine the particle morphology and size distribution (Scanning electron microscopy), crystalline structure (X-ray diffraction). Results showed that cobalt ferrite nanoparticles can be obtained following this synthesis route without using surfactants as size drivers, which is a common reagent in nanoparticle preparation, giving a size distribution of 162 ± 30 nm and a polyhedral geometry. Also, it was observed that SiO2 is homogeneously distributed onto the surface of the cobalt ferrite, and that TiO2 shell films covered well, creating a catalyst that also presents magnetic response. This kind of catalyst nanomaterial, presents a magnetic response, and is a stable and environmentally safe, then could be separated easily from the aqueous medium at the end of the purification process by applying an external magnetic field.