Miracle-dz/UniFAR-FTUs huggingface dataset:
Convective Impact on the Global Lower Stratospheric Water Vapor Budget

Water vapor in the stratosphere is primarily controlled by temperatures in the tropical upper troposphere and lower stratosphere. However, the direct impact of deep convection on the global lower stratospheric water vapor budget is still an actively debated issue. Two complementary modeling approaches are used to investigate the convective impact in boreal winter and summer. Convective influence is diagnosed by tracing trajectories through convective cloud top altitude fields derived from global rainfall and brightness temperature data. Backward trajectory model simulations coupled with a detailed treatment of cloud microphysical processes indicate that convection moistens the global lower stratosphere by approximately 0.3 ppmv in boreal winter and summer 2010. The diurnal peak in convection is responsible for about half of the total convective moistening during winter and nearly all of the convective moistening during summer. Deep convective clouds overshooting the tropopause have relatively minor effect on global lower stratospheric water vapor. A forward trajectory model coupled with a simplified cloud module is used to estimate the relative magnitude of the interannual variability of the convective impact. Combining the results from the two models, we find that the convective impact on the global lower stratospheric water vapor during 2006–2016 is approximately 0.3 ppmv with year‐to‐year variations of up to 0.1 ppmv. An important mechanism of convective hydration of the lower stratosphere is via the detrainment of saturated air and ice into the tropical uppermost troposphere and the subsequent upward transport of some of these moist air parcels across relatively warm and subsaturated tropopause.
Four economic principles of just sustainability transition

Equitable income distribution is desirable for moral, economic, and social reasons. Recent studies, however, indicate that improved income allocation will result in increased environmental impacts due to our socio-economic system’s current settings. Therefore, we explored the key aspects of a system that can more evenly reallocate natural and economic resources while reducing negative environmental impacts. We found that the capital is extremely important as a means of material flows and stocks. Thus, effective policy interventions should target mechanisms at this very market. Based on a comprehensive literature review and statistical analyses at various levels, we proposed a four-step policy framework that includes 1) reducing and targeted savings, 2) reshaping governments’ spatial decisions and 3) role in the housing market, and 4) changing the rates of depreciation in income tax legislation used globally.
Modulating the distribution of thalamocortical synapses on the dendritic tree to facilitate contrast-invariant tuning

A network model with thalamocortical neurons responding to visual stimuli and projecting to a spiny stellate cell (SSC) was simulated using the computer program NEURON. The model is based on the one used by Bannit et al. (NEURON accession no.39948) but with the effects of synaptic depression and intracortical inhibition ignored. A linear section of the SCC was selected as the experimental branch (EB). Thalamocortical neurons were numbered based on their positioning on a 9x10 receptive field. The field was stimulated using a sinusoidal grating at varying orientations and levels of contrasts, and SSC response was measured as the number of spikes per second (Hz). Figure 2. Overview of the experimental methods. A, Modelled receptive field of the dLGN composed of 90 thalamocortical neurons. Black and red circles represent onand off-center activity respectively. Sinusoidal grating is shown at the 0° orientation. B, Morphology of the modeled cell with emphasis on the EB (blue) and soma (red). C, Distribution of thalamocortical inputs on the EB. Left to right: soma, normal, inverse. With all inputs on the soma, the background firing rate of the SSC was set to be approximately 2 Hz, consistent with previous experiments [1]. By changing the synaptic distribution from the control layout to the vertically oriented layouts, the strength of the excitatory postsynaptic potential (ESPS) of synapses at the distal dendrites diminished considerably before arriving to the soma. The background firing rate of the SSC fell to 0 Hz. To be able to effectively compare our results to the control layout, a fixed conductance offset was applied so that the background firing rate returned to 2 Hz.
Influence of pulse repetition rate on the average size of silicon nanoparticles deposited by laser ablation

To investigate the influence of pulse repetition rate on the average size of the nanoparticles, nanocrystalline Si films were prepared by pulsed laser ablation in high-purity Ar gas with a pressure of 10 Pa at room temperature, under the pulse repetition rates between 1 and 40 Hz, using a nanosecond laser. Raman, X-ray diffraction spectra, and scanning electron microscopy images show that with increasing pulse repetition rate, the average size of the nanoparticles in the film first decreases and reach its minimum at 20 Hz, and then increases, which may be attributed to the nonlinear dynamics of the laser-ablative deposition. In our experiment conditions, the duration of the ambient restoration, a characteristic parameter being used to distinguish nonlinear or linear region, is about a few seconds from the order of magnitude, which is consistent with the previous experimental observation. More detailed model to explain quantitively the observed effect is under investigation.
Changes in Bone Metabolism and Antioxidant Defense Systems in Menopause-Induced Rats Fed Bran Extract from Dark Purple Rice (Oryza sativa L. Cv. Superjami)

Menopause is a matter of concern for women’s health due to a deficiency of female hormones; additionally, reactive oxygen species and aging can cause osteoporosis. Food becomes increasingly interesting as a menopausal woman’s alternative to hormone therapy. The effects of ethanol extracts from dark purple Superjami rice bran on bone metabolism and antioxidant defense systems in menopause-induced animal models were evaluated. Female rats underwent sham surgery or were ovariectomized to induce a menopause-like state. Rats were divided into a sham control group (SHAM), an ovariectomized control group (OVX), and an ovariectomized grou supplemented with Superjami rice bran extract group (OVX-S) and fed for 8 weeks. The OVX groups exhibited significantly more weight gain, amounts of bone turnover biochemical markers (alkaline phosphatase, osteocalcin, and C-terminal telopeptide), bone loss, lipid-peroxidation and oxidative stress than the SHAM group. However, Superjami bran extract added to the diet resulted in a significant reduction in body weight and lipid peroxidation, as well as enhanced bone metabolism and antioxidant enzyme activities, in ovariectomized rats. These results propound that extracts from Superjami rice bran have therapeutic potentiality against bone loss and oxidative stress in menopause-induced states and will be useful in preventing postmenopausal osteoporosis and oxidative damage.
Natural Radiative Lifetimes in Ce II

New radiative lifetimes of 18 levels (energy range between 30 000 cm-1 and 39 000 cm-1) in Ce II have been measured using the time-resolved laser-induced fluorescence method. A tunable frequency-doubled dye laser with 1 ns pulse duration was used to excite selectively singly ionized cerium in a laser-produced plasma. The experimental results are compared with theoretical data and the agreement is found to be very good.
Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module

Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Comparative experiments prove the effectiveness of our approach. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters.
Generalized covariance functions associated with the Laplace Equation and Their use in interpolation and inverse problems

This work examines which generalized covariance function when used in the stochastic approach produces the flattest possible estimate of an unknown function that is consistent with the data. Such an estimate is the plainest possible continuous function, thus in a sense eliminating details that are irrelevant or unsupported by data. The answer is found from the solution of the following variational problem: Determine the function that reproduces the data, has the smallest gradient (in the square norm sense), and has a gradient that vanishes at large distances from the observations. The generalized covariance functions are shown to be the Green's functions for the free‐space Laplace equation: the linear distance, in one dimension; the logarithmic distance in two dimensions; and the inverse distance in three dimensions. It is demonstrated that they are appropriate covariance functions for intrinsic random fields, a modification is proposed to facilitate numerical implementation, and a couple of examples are presented to illustrate the applicability of the methodology.
A Comparison of Certain Bootstrap Confidence Intervals in the Cox Model

Abstract We study bootstrap confidence intervals for three types of parameters in Cox's proportional hazards model: the regression parameter, the survival function at fixed time points, and the median survival time at fixed values of a covariate. Several types of bootstrap confidence intervals are studied, and the type of interval is determined by two factors. One factor is the method of drawing the bootstrap sample. We consider three such methods: (1) ordinary resampling from the empirical cumulative distribution function, (2) resampling conditional on the covariates, and (3) resampling conditional on the covariates and the censoring pattern. Another factor is the method of forming the confidence interval from a given sample; the methods considered are the percentile, hybrid, and bootstrap-t. All the methods of forming confidence intervals are compared to each other and to the standard asymptotic method via a Monte Carlo study. The data sets for this Monte Carlo study are simulated conditionally on the c.
Study on particle swarm optimization inversion of mine transient electromagnetic method in whole-space

In underground mining detecting using the mine transient electromagnetic method(MTEM),electromagnetic field is of whole-space distribution.Mine transient electromagnetic inversion in whole-space is nonlinear and complicated.At present,whole-space response is mainly obtained by the product of half-space response and the whole-space response coefficient in conventional inversion,which makes the anomalies of roof and floor(or front and rear)overlap together and difficult to separate,thus the resolution decreases.In this paper,particle swarm optimization(PSO)was adopted for whole-space inversion of MTEM.Based on theoretical analysis and standard particle swarm optimization algorithm,a new evolution formulation was proposed to improve the optimization ability.Particle swarm optimization inversion program was compiled based on whole-space transient electromagnetic field theory,and then the inversions ofacomplex five-layer model in whole-space were made respectively.Combined with a case of detecting water-bearing zone of mine roadway roof and floor rock mass and fault,the results of inversion calculation and interpretation for the surveyed data were confirmed by drilling.The research showed that the inversion fitted well for theoretical model and the surveyed data respectively by the improved particle swarm algorithm,the separation of the mine roof and floor apparent resistivity anomalies was realized,and the accuracy and resolution of whole-space transient electromagnetic detection were improved.
Transcription factors and potential therapeutic targets for pulmonary hypertension

Pulmonary hypertension (PH) is a refractory and fatal disease characterized by excessive pulmonary arterial cell remodeling. Uncontrolled proliferation and hypertrophy of pulmonary arterial smooth muscle cells (PASMCs), dysfunction of pulmonary arterial endothelial cells (PAECs), and abnormal perivascular infiltration of immune cells result in pulmonary arterial remodeling, followed by increased pulmonary vascular resistance and pulmonary pressure. Although various drugs targeting nitric oxide, endothelin-1 and prostacyclin pathways have been used in clinical settings, the mortality of pulmonary hypertension remains high. Multiple molecular abnormalities have been implicated in pulmonary hypertension, changes in numerous transcription factors have been identified as key regulators in pulmonary hypertension, and a role for pulmonary vascular remodeling has been highlighted. This review consolidates evidence linking transcription factors and their molecular mechanisms, from pulmonary vascular intima PAECs, vascular media PASMCs, and pulmonary arterial adventitia fibroblasts to pulmonary inflammatory cells. These findings will improve the understanding of particularly interactions between transcription factor-mediated cellular signaling pathways and identify novel therapies for pulmonary hypertension.
Metaphors We Love By: The Shift from Animal to Fruit Metaphors in Classical Arabic Ghazal

ABSTRACT Classical Arabic poetry is replete with animal and fruit metaphors commonly used for endearment purposes. The comparative analysis of love metaphors in classical ghazal shows, however, a shift in the poetics of love from the use of animal metaphors in Badi poetry to the occurrence of fruit imagery in Bedouin ghazal. Based on a selection of classical Arabic love poetry, the paper traces the journey of love and sexuality to illustrate the conceptual change from the prevalence of the gazelle metaphor in Bedouin ghazal of pre- and early Islam times to the emergence of fruit metaphors in Badi poetry of the Abbasid era. Evidenced in poetry, the metaphorical shit mirrors a change in the portrayal of women, who cease to be conceived as wild preys, fearing and fleeing male hunters to become exotic ripe fruits, waiting for the male to pick. Seemingly fortuitous, the shift in love imagery is reminiscent of sociocultural changes that help redefine the poetics of love in classical Arabic literature and inform gender dynamics in medieval Arabia.
Relevant Day/Night Temperatures Simulating Belgian Summer Conditions Reduce Japanese Encephalitis Virus Dissemination and Transmission in Belgian Field-Collected Culex pipiens Mosquitoes

Japanese encephalitis virus (JEV), a zoonotic mosquito-borne Flavivirus, can be considered an emerging infectious disease. Therefore, vector competence studies with indigenous mosquitoes from regions where JEV is not yet endemic are of great importance. In our study, we compared the vector competence of Culex pipiens mosquitoes emerged from Belgian field-caught larvae under two different temperature conditions: a constant 25 °C and a 25/15 °C day/night temperature gradient representing typical summer temperatures in Belgium. Three- to seven-day-old F0-generation mosquitoes were fed on a JEV genotype 3 Nakayama strain spiked blood-meal and incubated for 14 days at the two aforementioned temperature conditions. Similar infection rates of 36.8% and 35.2% were found in both conditions. The observed dissemination rate in the gradient condition was, however, significantly lower compared to the constant temperature condition (8% versus 53.6%, respectively). JEV was detected by RT-qPCR in the saliva of 13.3% of dissemination positive mosquitoes in the 25 °C condition, and this transmission was confirmed by virus isolation in 1 out of 2 RT-qPCR positive samples. No JEV transmission to saliva was detected in the gradient condition. These results suggest that JEV transmission by Culex pipiens mosquitoes upon an accidental introduction in our region is unlikely under current climatic conditions. This could change in the future when temperatures increase due to climate change.
Complement Binding Anti-HLA Antibodies and the Survival of Kidney Transplantation

Background: Antibody-mediated rejection (AMR) is one of the most important challenges in the context of renal transplantation, because the binding of de novo donor-specific antibodies (dnDSA) to the kidney graft triggers the activation of the complement, which in turn leads to loss of transplant. In this context, the objective of this study was to evaluate the association between complement-fixing dnDSA antibodies and graft loss as well as the possible association between non-complement-fixing antibodies and transplanted organ survival in kidney transplant recipients. Methods: Our study included a cohort of 245 transplant patients over a 5-year period at Virgen de las Nieves University Hospital (HUVN) in Granada, Spain. Results: dnDSA was observed in 26 patients. Of these patients, 17 had non-complement-fixing dnDSA and 9 had complement-fixing dnDSA. Conclusions: Our study demonstrated a significant association between the frequency of rejection and renal graft loss and the presence of C1q-binding dnDSA. Our results show the importance of the individualization of dnDSA, classifying them according to their ability to activate the complement, and suggest that the detection of complement-binding capacity by dnDSA could be used as a prognostic marker to predict AMR outcome and graft survival in kidney transplant patients who develop dnDSA.
Total bounded variation‐based Poissonian images recovery by split Bregman iteration

This paper presents a new total bounded variation regularization‐based Poissonian images deconvolution scheme. Computationally, an extended split Bregman iteration is described to obtain the optimal solution recursively. Moreover, the rigorous convergence analysis of the proposed algorithm is also expatiated here. Compared with the computational speed and the recovered results of the total variation‐based method, numerical simulations definitely demonstrate the competitive performance of the proposed strategy in Poissonian images restoration. Copyright © 2012 John Wiley & Sons, Ltd.
Network anomal detection wavelet neural network based on QPSO

In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection,a novel method of network anomaly detection based on constructing wavelet neural network(WNN) using quantum-behaved particle swarm optimization(QPSO) algorithm is proposed.The WNN is trained by QPSO.A multidimensional vector composed of WNN parameters is regarded as a particle in learning algorithm.The parameter vector,which has a best adaptation value,is searched globally.The well-known KDD CUP 99 Intrusion Detection Data Set is used as the experimental data.Experimental result on KDD 99 intrusion detection datasets shows that this learning algorithm has more rapid convergence,better global convergence ability compared with the traditional gradient descent(GD) algorithm and particle swarm optimization(PSO),and the accuracy of anomaly detection is enhanced.It also shows the remarkable ability of this novel algorithm to detect new type of attacks.
Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms

Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent analysis. Focusing on binary classification problems, we analyzed how missing value imputation under MCAR as well as MAR missingness with different missing patterns affects the predictive performance of subsequent classification. To this end, we compared imputation methods such as several MICE variants, missForest, Hot Deck as well as mean imputation with regard to the classification performance achieved with commonly used classifiers such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and regularized logistic regression. Our simulation results showed that Random Forest based imputation (i.e., MICE Random Forest and missForest) performed particularly well in most scenarios studied. In addition to these two methods, simple mean imputation also proved to be useful, especially when many features (covariates) contained missing values.
Effect of Urinary Albumin Creatinine Ratio on Cardiovascular Morbidity and Mortality in Diabetes Patients with Atherosclerotic Disease

Background Diabetes mellitus (DM) patients with increased urinary albumin creatinine ratio (uACR) have higher risk of mortality, while it is unclear in DM patients with atherosclerotic cardiovascular disease (ASCVD). Methods We analysed 2832 DM patients with ASCVD in this multi-center registry cohort study Cardiorenal ImprovemeNt II (CIN-II) in 5 Chinese tertiary hospitals from 2007 to 2020. Patients were divided into 3 groups according to their uACR level (normal group: uACR <30mg/g, moderately increased group: 30mg/g≤ uACR <300mg/g, severely increased group: 300mg/g≤ uACR). The main outcome of the study was cardiovascular mortality and all-cause mortality. Results During a median follow-up of 2.1 years, among 2832 patients (mean age: 63.3 ± 9.9 years, 29.1% women), 434 patients (15.3%) had moderately increased uACR, and 203 patients (7.2%) had severely increased uACR. Compared to patients in normal group, patients had higher cardiovascular mortality in moderately increased group and severely increased group (2.5% vs 9.9% vs 16.7%, P < 0.001), as well as all-cause mortality. After adjusting confounders, the risk of cardiovascular mortality remained higher in moderately increased group (adjusted hazard ratio [aHR]: 3.13; 95% confidence interval [CI]: 2.04–4.81) and severely increased group (aHR: 4.54; 95% CI: 2.58–8.01) than in normal group, as well as all-cause mortality. Conclusion In our study, we found nearly a quarter of DM patients with ASCVD had increased uACR, and they have over 2- or 3-fold risk of cardiovascular mortality than those with normal uACR. UACR is a helpful indicator for risk stratification and treatment target for DM patients with ASCVD.