Miracle-dz/UniFAR-FTUs huggingface dataset:
Group-velocity-matched noncollinear second-harmonic generation in quasi-phase matching

We demonstrate group-velocity-matched second-harmonic generation of 80-fs pulses under noncollinear quasi-phase-matching (QPM) geometry. Second-harmonic pulses of 100-fs duration, 11-nm bandwidth, and0.78-µm center wavelength were generated with 50% efficiency with 3-mm-long periodically poled lithium niobate. We have also numerically studied the dependence of conversion efficiency and pulse durations on pump intensity, propagation length, and noncollinear angle. It is confirmed that our group-velocity-matching scheme requires a much lower pump intensity than the conventional collinear QPM scheme.
Bayesian Inference for the Spatial Random Effects Model

Spatial statistical analysis of massive amounts of spatial data can be challenging because computation of optimal procedures can break down. The Spatial Random Effects (SRE) model uses a fixed number of known but not necessarily orthogonal (multiresolutional) spatial basis functions, which gives a flexible family of nonstationary covariance functions, results in dimension reduction, and yields optimal spatial predictors whose computations are scalable. By modeling spatial data in a hierarchical manner with a process model that includes the SRE model, the choice is whether to estimate the SRE model’s parameters or to take a Bayesian approach and put a prior distribution on them. In this article, we develop Bayesian inference for the SRE model when the spatial basis functions are multiresolutional. Then the covariance matrix of the random effects decomposes naturally in terms of Givens angles and eigenvalues, for which a new class of prior distributions is developed. This approach to prior specification of a spatial covariance matrix offers remarkable improvement over other types of priors used in the random-effects literature (e.g., Wishart priors), as demonstrated in a simulation experiment. Further, a large remote-sensing dataset of aerosol optical depth (AOD), from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on the Terra satellite, is analyzed in a fully Bayesian framework, using the new prior, and compared to an empirical-Bayesian analysis.
Biophysics in reverse: Using blood cells to accurately calibrate force-microscopy cantilevers

We report on the refinement and validation of one of the earliest ideas of “reverse” biophysics: the use of individual red blood cells as reliable, ultrasensitive mechanotransducers. Our analysis is based on the numerical prediction of the force exerted by a micropipette-held red cell as it is pushed against a test object. Examining this red-cell transducer, in conjunction with a custom-built “horizontal” force microscope, we were able to soundly corroborate its utility, while at the same time, accurately calibrating the spring constants of atomic-force microscope cantilevers.
Measuring Progress towards a Circular Economy: A Monitoring Framework for Economy‐wide Material Loop Closing in the EU28

The concept of a circular economy (CE) is gaining increasing attention from policy makers, industry, and academia. There is a rapidly evolving debate on definitions, limitations, the contribution to a wider sustainability agenda, and a need for indicators to assess the effectiveness of circular economy measures at larger scales. Herein, we present a framework for a comprehensive and economy‐wide biophysical assessment of a CE, utilizing and systematically linking official statistics on resource extraction and use and waste flows in a mass‐balanced approach. This framework builds on the widely applied framework of economy‐wide material flow accounting and expands it by integrating waste flows, recycling, and downcycled materials. We propose a comprehensive set of indicators that measure the scale and circularity of total material and waste flows and their socioeconomic and ecological loop closing. We applied this framework in the context of monitoring efforts for a CE in the European Union (EU28) for the year 2014. We found that 7.4 gigatons (Gt) of materials were processed in the EU and only 0.71 Gt of them were secondary materials. The derived input socioeconomic cycling rate of materials was therefore 9.6%. Further, of the 4.8 Gt of interim output flows, 14.8% were recycled or downcycled. Based on these findings and our first efforts in assessing sensitivity of the framework, a number of improvements are deemed necessary: improved reporting of wastes, explicit modeling of societal in‐use stocks, introduction of criteria for ecological cycling, and disaggregated mass‐based indicators to evaluate environmental impacts of different materials and circularity initiatives. This article met the requirements for a gold – gold JIE data openness badge described at http://jie.click/badges.
Diet can alter the cost of resistance to a natural parasite in Caenorhabditis elegans

Abstract Resistance to parasites confers a fitness advantage, yet hosts show substantial variation in resistance in natural populations. Evolutionary theory indicates that resistant and susceptible genotypes can coexist if resistance is costly, but there is mixed evidence that resistant individuals have lower fitness in the absence of parasites. One explanation for this discrepancy is that the cost of resistance varies with environmental context. We tested this hypothesis using Caenorhabditis elegans and its natural microsporidian parasite, Nematocida ironsii. We used multiple metrics to compare the fitness of two near‐isogenic host genotypes differing at regions associated with resistance to N. ironsii. To quantify the effect of the environment on the cost associated with these known resistance regions, we measured fitness on three microbial diets. We found that the cost of resistance varied with both diet and the measure of fitness. We detected no cost to resistance, irrespective of diet, when fitness was measured as fecundity. However, we detected a cost when fitness was measured in terms of population growth, and the magnitude of this cost varied with diet. These results provide a proof of concept that, by mediating the cost of resistance, environmental context may govern the rate and nature of resistance evolution in heterogeneous environments.
Purification and Characterization of Authentic 30S Ribosomal Precursors Induced by Heat Shock

Ribosome biogenesis is a complex and multistep process that depends on various assembly factors. To understand this process and identify the ribosome assembly intermediates, most studies have set out to delete or deplete these assembly factors. Instead, we took advantage of the impact of heat stress (45 °C) on the late stages of the biogenesis of the 30S ribosomal subunit to explore authentic precursors. Under these conditions, reduced levels of the DnaK chaperone proteins devoted to ribosome assembly lead to the transient accumulation of 21S ribosomal particles, which are 30S precursors. We constructed strains with different affinity tags on one early and one late 30S ribosomal protein and purified the 21S particles that form under heat shock. A combination of relative quantification using mass spectrometry-based proteomics and cryo-electron microscopy (cryo-EM) was then used to determine their protein contents and structures.
The effects of larval organic fertiliser exposure on the larval development, adult longevity and insecticide tolerance of zoophilic members of the Anopheles gambiae complex (Diptera: Culicidae)

Zoophilic members of the Anopheles gambiae complex are often associated with cattle. As such, it is likely that the immature aquatic stages will be exposed to cattle faeces as a pollutant. This study aimed to examine the effect of cattle manure on members of the An. gambiae complex found in South Africa. In this study, a commercial organic fertiliser originating from cattle manure was used as a proxy for cattle faeces. Laboratory strains of An. merus, An. quadriannulatus as well as four An. arabiensis strains (SENN and MBN: insecticide susceptible, MBN-DDT: insecticide resistant, unselected, SENN-DDT: insecticide resistant: selected for resistance) were used in this study. The effect of larval fertiliser exposure on larval development rate and adult longevity was assessed in all three species. The effect of larval fertiliser exposure on subsequent adult size, insecticide tolerance and detoxification enzyme activity of the four strains of the malaria vector An. arabiensis was also assessed. Following fertiliser treatment, all strains and species showed a significantly increased rate of larval development, with insecticide susceptible strains gaining the greatest advantage. The adult longevities of An. merus, An. quadriannulatus, insecticide susceptible and resistant An. arabiensis were significantly increased following fertiliser treatment. Insecticide susceptible and resistant An. arabiensis adults were significantly larger after larval organic fertiliser exposure. Larval fertiliser exposure also increased insecticide tolerance in adult An. arabiensis, particularly in the insecticide resistant, selected strain. This 4.7 fold increase in deltamethrin tolerance translated to an increase in pyrethroid resistance intensity, which could exert operational effects. In general, larval exposure to cattle faeces significantly affects the life histories of members of the An. gambiae complex.
Autoregressive Order Identification for VAR Models with Non Constant Variance

The identification of the lag length for vector autoregressive (VAR) models by mean of Akaike information criterion (), partial autoregressive, and correlation matrices (PAM and PCM hereafter) is studied in the framework of processes with time-varying variance. It is highlighted that the use of the standard tools is not justified in such a case. As a consequence considering the adequate expression of the likelihood of the model and the adaptive estimator of the autoregressive parameters, we propose new identifying tools. More precisely we approximate the correct for VAR models with time-varying variance using an adaptive . It is found that the adaptive is robust to the presence of unconditional heteroscedasticity. Corrected confidence bounds are proposed for the usual PAM and PCM obtained from the ordinary least squares (OLS) estimation. We also use the unconditional variance structure of the innovations to develop adaptive PAM and PCM. Noting that the adaptive estimator is more accurate than the usual OLS estimator, it is underlined that the adaptive PAM and PCM have a greater ability than the OLS PAM and PCM for detecting unnecessary large lag length for VAR models. Monte Carlo experiments show that the adaptive selects satisfactorily the correct autoregressive order of VAR processes. An illustrative application using US international finance data is presented.
Possibility of stereoscopic displays by using a viewing angle dependence of twisted nematic liquid crystal cells

Stereoscopic displays that use the viewing angle dependence of twisted nematic (TN) liquid crystal cells to direct light to either the left or right eye have been investigated. From a calculation that combined Frank's elastic theory and Berreman's 4/spl times/4 matrix method, it is observed that a luminance ratio defined as the luminance of viewing direction divided by that from the opposite direction largely depends on a pretilt angle of liquid crystal material and the cell thickness. The maximum luminance ratio is obtained at the cell thickness of the second-minimum. From experimental results, clear transmittance inversion between left and right directions observed. The experimental results of the viewing angle dependence are in good agreement with calculation results. The luminance ratios were 4.7 in the cell with polyimide alignment layer (the pretilt angle 4.5/spl deg/) and 17 in the cell with alignment layer of SiO oblique evaporation (the pretilt angle=40/spl deg/).
The Role of Artificial Intelligence in Surveillance in Barrett’s Esophagus and Gastric Intestinal Metaplasia

Artificial Intelligence (AI) has been increasingly applied in medical imaging for improved accuracy and efficiency. One area that has seen significant advances is the use of AI in the detection of Barrett’s esophagus (BE) and gastric intestinal metaplasia (GIM). BE is a pre-cancerous condition that occurs in the esophagus and can lead to esophageal adenocarcinoma, while GIM is a precancerous lesion that occurs in the stomach. In this review article, we will provide an overview of current AI techniques used in the detection and diagnosis of BE and GIM, including computer-aided diagnosis (CAD) systems, deep learning algorithms, and image analysis techniques. The performance of AI in detecting these conditions will be evaluated and compared to traditional methods. The challenges and limitations of using AI in the diagnosis of BE and GIM will also be discussed. The article aims to provide a comprehensive understanding of the use of AI in BE and GIM detection and to guide future research in this field.
Novel optical cryptography using PANDA ring resonator for highly secured communication

We propose a novel method of an optical cryptography using the dark-bright soliton conversion control in a modified add/drop optical filter. By using the control arrangement, the obtained outputs of the dynamic states of dark-bright soliton can be used to form the key suppression for communication security application, in which the key recovery can be obtained by controlling the add/drop filter outputs. The optical cryptography consists of an add/drop filter which is used to generate the encryption and decryption keys from the optical keys (LIP signals). A Mach-Zehnder interferometer is used for multiplexing and de-multiplexing operations. Simulation results obtained shows, that the proposed system can be used to form a high security communication system.
Classification and Prediction of Skyrmion Material Based on Machine Learning

The discovery and study of skyrmion materials play an important role in basic frontier physics research and future information technology. The database of 196 materials, including 64 skyrmions, was established and predicted based on machine learning. A variety of intrinsic features are classified to optimize the model, and more than a dozen methods had been used to estimate the existence of skyrmion in magnetic materials, such as support vector machines, k-nearest neighbor, and ensembles of trees. It is found that magnetic materials can be more accurately divided into skyrmion and non-skyrmion classes by using the classification of electronic layer. Note that the rare earths are the key elements affecting the production of skyrmion. The accuracy and reliability of random undersampling bagged trees were 87.5% and 0.89, respectively, which have the potential to build a reliable machine learning model from small data. The existence of skyrmions in LaBaMnO is predicted by the trained model and verified by micromagnetic theory and experiments.
Bayesian Comparison of Two Regression Lines

The comparison of two regression lines is often meaningful or of interest over a finite interval I of the independent variable. When the prior distribution of the parameters is a natural conjugate, the posterior distribution of the distances between two regression lines at the end points of I is bivariate t. The posterior probability that one regression line lies above the other uniformly over I is numerically evaluated using this distribution.
Projection Estimators for Autoregressive Panel Data Models

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable. This approach yields estimators which coincide with known generalised method of moments (GMM) estimators for models where stationarity is not imposed on the initial conditions and for models which satisfy mean stationarity. Our approach allows us to obtain a simple linear estimator for models which satisfy covariance stationarity, which although not fully efficient performs very well in simulations.
Social Entrepreneurship and Complex Thinking: Validation of SEL4C Methodology for Scaling the Perception of Achieved Competency

This article aims to show the validated results of implementing a self-created methodology for developing the perceived achievement of social entrepreneurship competency and how this methodology is equally valid in developing the perceived achievement of complex thinking competency. Presenting a multivariate descriptive statistical analysis, this article offers the results of an educational intervention carried out on a sample group of students from a Mexican university before and after implementing the SEL4C (Social Entrepreneurship Learning for Complexity) methodology (developed by the Interdisciplinary Research Group IRG Reasoning for Complexity R4C at the Institute for the Future of Education IFE of the Tecnologico de Monterrey). It corroborates that the proposed methodology impacts the perceived achievement of social entrepreneurship competency and its sub-competencies and also manages to develop the perception of achievement of the complex thinking competency. This article contributes to the studies associated with the formation of competencies and the social entrepreneurship approach as a valid pedagogical tool for developing transversal skills.
A surface-based DNA algorithm for the minimal vertex cover problem

Abstract DNA computing was proposed for solving a class of intractable computational problems, of whichthe computing time will grow exponentially with the problem size. Up to now. many achievements have been made to improve its performance and increase its reliability. It has been shownmany times that the surface-based DNA computing technique has very low error rate, but the technique has not been widely used in the DNA computing algorithms design. In this paper, a surface-based DNA computing algorithm for minimal vertex cover problem, a problem well-known for its exponential difficulty, is introduced. This work provides further evidence for the ability of surface-based DNA computing in solving NP-complete problems.
Hyperglycemia and T Cell infiltration are associated with stromal and epithelial prostatic hyperplasia in the nonobese diabetic mouse

Prostatic inflammation and various proinflammatory systemic comorbidities, such as diabetes and obesity are associated with human benign prostatic hyperplasia (BPH). There is a paucity of in vivo models reflecting specific aspects of BPH pathogenesis. Our aim was to investigate the nonobese diabetic (NOD) mouse as a potential model for subsequent intervention studies.
Stiffness-Modulation of Collagen Gels by Genipin-Crosslinking for Cell Culture

The stiffness of extracellular matrices (ECMs) is critical for cellular functions. Therefore, modulating the stiffness of ECMs in vitro is necessary to investigate the role of stiffness in cellular phenomena. Collagen gels are widely used for cell culture matrices in vitro. However, modulation of the stiffness in collagen gels for cell culture is challenging owing to the limited knowledge of the method to increase the stiffness while maintaining low cytotoxicity. Here, we established a novel method to modulate collagen gel stiffness from 0.0292 to 12.5 kPa with low cytotoxicity. We prepared collagens with genipin, a low-cytotoxic crosslinker of amines, at different concentrations and successfully modulated the stiffness of the gels. In addition, on 10 mM genipin-mixed collagen gels (approximately 12.5 kPa), H1299 human lung cancer cells showed spreading morphology and nuclear localization of yes-associated protein (YAP), typical phenomena of cells on stiff ECMs. Mouse mesenchymal stromal cells on 10 mM genipin-mixed collagen gels differentiated to vascular smooth muscle cells. On the other hand, the cells on 0 mM genipin-mixed collagen gels (approximately 0.0292 kPa) differentiated to visceral smooth muscle cells. Our new method provides a novel way to prepare stiffness-modulated collagen gels with low cytotoxicity in cell culture.
I Believe I Can Try: Self-Efficacy, Pandemic Behaviors, Coping, and Learning

While colleges and universities grapple with delivering instruction face-to-face during the pandemic, there is still a lot to learn from remote teaching experiences. The present study aimed to predict self-reported learning during the first year of the pandemic. Building on previous scholarship on the topic, we focus on the moderating effects of self-efficacy, and the mediating effects of coping styles on the relationship between stress and self-reported learning experiences. We also included self-perceptions of class effort, the instructor, and changes in class, personal, professor, and health behaviors. Students (N = 272) in Introductory Psychology classes participated in an online survey as part of a class research requirement. Analyses demonstrated that self-efficacy predicted differences in many measures associated with learning and predicted learning over and above all other variables entered in a hierarchical regression. The relationship between stress and learning was mediated by coping, but not moderated by self-efficacy. These results suggest student beliefs about their ability to perform online are important to learning outcomes, but coping mechanisms mediate the relationship of stress and learning. Especially while teaching during pandemic times using different modalities, instructors will do well to directly address students’ perceptions of their own ability and build self-efficacy.
Asymptotic Distribution of Smoothers Based on Local Means and Local Medians under Dependence

The asymptotic distribution of k th nearest neighbor regression and autoregression estimates (local means) is obtained for ? -mixing processes. Local medians, as considered by Truong and Stone (1991, Ann. Statist.20 77-97), are also shown to be asymptotically normally distributed.
Land-use effects on aquatic macroinvertebrate diversity in subtropical highland grasslands streams

Knowledge of the frequency and occurrence of macroinvertebrates throughout landscapes may clarify the effects of anthropic impacts on aquatic systems and help guide conservation actions for watersheds. We evaluated macroinvertebrate α and β diversity in streams in four different subtropical phytophysiognomies: highland grasslands with arboreal riparian vegetation, highland grasslands without arboreal riparian vegetation, mixed ombrophilous forest and silviculture. We also evaluated how environmental factors influence α diversity. We sampled macroinvertebrate communities (by Surber sampler), litter input (by nets) and physicochemical variables of the water (by multiparameter and chemical analysis) at 10 sites in riparian zones of a highland system. A total of 2124 individuals were sampled representing 41 taxa, with high α and β diversity in mixed ombrophilous forest. The results indicate that environmental heterogeneity increases food resource availability, as well as macroinvertebrate diversity. Furthermore, the β diversity was found to increase with distance among the unconnected streams. Areas of silviculture had the highest density values among the sampled areas due high organic material stock in the soil. Therefore, higher leaf litter input may decrease the toxicity effect of secondary compounds released by leaf plant decomposition compared to environments with low systemic leaf litter input, such as highland grasslands. The presence of arboreal riparian vegetation drives canopy openness, water temperature, dissolved oxygen, and orthophosphate in streams, which control the frequency and occurrence of benthic macroinvertebrates. Homogeneous systems, as naturally observed in highland grasslands, plus high canopy openness decrease macroinvertebrate richness and density. The naturally low richness of highland grasslands, although with endemic species, may be an important guideline for legislation on anthropic impacts, management, and conservation of streams in this specific subtropical zone.