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Appearance-based gaze estimation works well in inferring human gaze under real-world condition. But one of the significant limitations in appearance-based methods is the need for huge amounts of training data. Eye image synthesis addresses this problem by generating huge amounts of synthetic eye images with computer graphics. To fully use the large-scale synthetic eye images, a simple-but-effective appearance-based gaze estimation framework with neighbor selection is proposed in this paper. The proposed framework hierarchically fuses multiple k -NN queries (in head pose, pupil center and eye appearance spaces) to choose closest samples with more relevant features. Considering the structure characters of the closet samples, neighbor regression methods then can be applied to predict the gaze directions. Experimental results demonstrate that the representative neighbor regression methods under the proposed framework achieve better performance for within-subject and cross-subject gaze estimation. [ABSTRACT FROM AUTHOR]

We propose an As 2 Se 3 -based photonic quasi-crystal fiber (PQF) with high nonlinearity and birefringence. By optimizing the structure parameters, a nonlinear coefficient up to 2079 W −1 km −1 can be achieved at the wavelength of 2 μ m; the birefringence reaches up to the order of 10 −2 due to the introduction of large circular air holes in the cladding. Using an optical pulse with a peak power of 6 kW, a pulse width of 150 f s, and a central wavelength of 2.94 μ m as the pump pulse, a mid-infrared polarized supercontinuum is obtained by using a 15 mm long PQF. The spectral width for x - and y -polarizations covers 1 μ m–10.2 μ m and 1 μ m–12.5 μ m, respectively. The polarization state can be well maintained when the incident angle of the input pulse changes within ±2°. The proposed PQF, with high nonlinear coefficient and birefringence, has potential applications in mid-infrared polarization-maintaining supercontinuum generation. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) modules installed on flat roof need to be cooled to improve the power conversion efficiency (PCE). The efficient use of natural wind for cooling is a low-cost alternative compared with the use of artificial cooling devices. In this study, a passive cooling scheme is proposed including components: curved eave and vortex generators (VGs) which is arranged on the roof to enhance the forced convective heat exchange and reduce temperature of rooftop PV module under prevailing wind conditions. The optimum arrangement of VGs was studied, temperature reduction and PCE of rooftop PV with the scheme were evaluated. The results show that the curved eaves installed on the flat-roof leading edge improved the convective heat exchange only in the airflow separation area. And the defect that surface convective heat exchange decreased along with the flow direction was improved when multi-row array VGs were arranged. The overall convective heat exchange on rooftop PV can be improved at a wide range of wind speed by scheme using curved eaves in combination with the symmetrically arranged VGs. After using the passive cooling scheme, the ratio of forced convective heat exchange on the rooftop PV upper surface to 60%, and the maximum temperature reduction is about 5.89 ℃, and PCE of PV is improved in summer. The low-cost passive cooling scheme proposed in this paper is expected to be applied to the new-built and reconstruction of flat-roof mounted PV system. [ABSTRACT FROM AUTHOR]

Rural teachers remain disadvantaged due to the less favorable working conditions and multiple challenges they might encounter, and their career development trajectories vary. Based on interviews with fifteen rural experienced teachers from Gansu Province, China, this study reveals an ABC model of teachers with various valued functionings. Teachers' valued functionings were shaped by resources, conversion factors and capabilities, and all further exerted influence over functionings through the mediation of agency. It is suggested that more attention should be given to rural experienced teachers to understand their various circumstances, and implement corresponding impulses to boost their agency and facilitate their functioning achievement. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Fe3+/sodium percarbonate Fenton-like process using WS 2 as cocatalyst was proposed. • The WS 2 could improve the Fe3+/Fe2+ cycle and H 2 O 2 activation. • The co-catalytic system could efficiently remove metronidazole over a wide pH range. • The metronidazole removal achieved 100 % under the optimized conditions after 10 min. To overcome the drawbacks of traditional Fenton method, this work proposed the application of tungsten disulfide (WS 2) as the cocatalyst to enhance Fe3+ and sodium percarbonate (SPC) Fenton-like system for metronidazole (MTZ) degradation. MTZ was completely removed after 10 min under the optimum conditions with 1.5 g/L WS 2 , 2 mmol/L SPC, 1.5 mmol/L Fe3+, and pH 7. And the Fe3+/SPC/WS 2 could achieve well performance over a wide pH range of 3–11. WS 2 could significantly accelerate the Fe3+/Fe2+ circulation and H 2 O 2 disintegration. The electron paramagnetic resonance and quenching tests discovered that the hydroxyl radicals (HO·) and carbonate radicals (CO 3 ·−) were the crucial active species for MTZ decomposition. The surface valence state of WS 2 before and after the reaction was analyzed by X-ray photoelectron spectroscopy, and the Fe3+/Fe2+ cycle was proved to be triggered by the conversion of W4+ to W6+. After fifth cycle, the Fe3+/SPC/WS 2 still achieved the MTZ degradation efficiency of 75 %. The decomposition byproducts of MTZ and their biotoxicities were determined by mass spectrometry and toxicity estimation software tool, respectively. This investigation provided an effectively idea for antibiotic removal with the WS 2 co-catalytic system. [ABSTRACT FROM AUTHOR]

• Repetitive wear and multiple collection will cause non-identical distribution. • The traditional model has poor recognition effect on non-identical signals. • Incremental learning has a good recognition effect on non-identical signals. • Deep learning can construct sample incremental learning well. • Incremental learning can be used to build real-time evaluation training systems. Aiming at the problem that the traditional mechanomyography (MMG) pattern recognition model for upper limb rehabilitation action has poor recognition effect on non-identical distribution test data, this research proposes an incremental learning method for deep learning based on MMG. By collecting 8-channels of MMG of 4 types of hand movements (wrist flexion, wrist extension, wrist ulnar flexion, and wrist radial flexion), after signal preprocessing, feature extraction and dimensionality reduction, 12 groups of non-identical distributed data were obtained. The model was trained by using deep neural network (DNN), and five commonly used machine learning algorithms were used as comparison for incremental training. Finally, the recognition rate of DNN was 88.25%, and the final recognition rates of Passive Aggressive (PA), Incremental Support Vector Machine (ISVM), Perceptron, Bernoulli Naive Bayes (BNB) and Multinomial Naive Bayes (MNB) were 85.94%, 84.82%, 81.01%, 73.07% and 61.20% respectively. Among them, both DNN and PA had better upward trends, and DNN had the highest final recognition rate. By comparing the confusion matrices before and after DNN incremental learning, it could be seen that DNN incremental training can significantly improve the accuracy and precision in confusion matrix. The experimental results demonstrate that the method of incremental learning can not only achieve the recognition of non-identically distributed test data, but also improve the recognition rates and the generalization performance of the model. [ABSTRACT FROM AUTHOR]

We propose an As2Se3-based highly nonlinear photonic quasi-crystal fiber with dual zero-dispersion wavelengths (ZDWs). Using a full-vector finite element method, the proposed fiber is optimized to obtain high nonlinear coefficient, low confinement loss and two zero-dispersion points by optimizing the structure parameters. Numerical results demonstrate that the proposed photonic quasi-crystal fiber (PQF) has dual ZDWs and the nonlinear coefficient up to 2600 W−1 km−1within the wavelength range from 2 to 5.5 μm. Due to the introduction of the large air holes in the third ring of the proposed fiber, the ability of confining the fundamental mode field can be improved effectively and thus the low confinement loss can be obtained. The proposed PQF with high nonlinearity and dual ZDWs will have a number of potential applications in four-wave mixing, super-continuum generation, and higher-order dispersion effects. [ABSTRACT FROM AUTHOR]

The exogenous fragment sequence and flanking sequence between the exogenous fragment and recombinant chromosome of transgenic wheat B102-1-2 were successfully acquired using genome walking technology. The newly acquired exogenous fragment encoded the full-length sequence of transformed genes with transformed plasmid and corresponding functional genes including ubi, vector pBANF-bar, vector pUbiGUSPlus, vector HSP, reporter vector pUbiGUSPlus, promoter ubiquitin, and coli DH1. A specific polymerase chain reaction (PCR) identification method for transgenic wheat B102-1-2 was established on the basis of designed primers according to flanking sequence. This established specific PCR strategy was validated by using transgenic wheat, transgenic corn, transgenic soybean, transgenic rice, and non-transgenic wheat. A specifically amplified target band was observed only in transgenic wheat B102-1-2. Therefore, this method is characterized by high specificity, high reproducibility, rapid identification, and excellent accuracy for the identification of transgenic wheat B102-1-2. [ABSTRACT FROM AUTHOR]

Background: Intermediate phenotypes, such as characteristic neuroimaging patterns, offer unique insights into the genetic and stress-related underpinnings of neuropsychiatric disorders like depression. This study aimed to identify neuroimaging intermediate phenotypes associated with depression, bridging etiological factors to behavioral manifestations and connecting insights from animal models to diverse clinical populations. Methods: We analyzed datasets from both rodents and humans. The rodent studies included a genetic model (P11 knockout) and an environmental stress model (chronic unpredictable mild stress), while the human data comprised 748 participants from three cohorts. Using the amplitude of low-frequency fluctuations, we identified neuroimaging patterns in rodent models. We then applied a machine-learning approach to cluster neuroimaging subtypes of depression. To assess the genetic predispositions and stress-related changes associated with these subtypes, we analyzed genotype and metabolite data. Linear regression was employed to determine which neuroimaging features predicted core depression symptoms across species. Results: The genetic and environmental stress models exhibited distinct neuroimaging patterns in subcortical and sensorimotor regions. Consistent patterns emerged in two neuroimaging subtypes identified across three independent depressed cohorts. The subtype resembling P11 knockout demonstrated higher genetic susceptibility, with enriched expression of risk genes in brain tissues and abnormal metabolites linked to tryptophan metabolism. In contrast, the stress animal-like subtype did not show changes in genetic risk scores but exhibited enriched risk gene expression in somatic and endocrine tissues, along with mitochondrial dysfunction in the antioxidant stress system. Notably, these distinct subcortical-sensorimotor neuroimaging patterns predicted anhedonia, a core symptom of depression, in both rodent models and depressed subtypes. Conclusions: This cross-species validation suggests that these neuroimaging patterns may serve as robust intermediate phenotypes, linking etiology to anhedonia and facilitating the translation of findings from animal models to humans with depression and other psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia patients with a metabolically abnormal obese (MAO) phenotype have been shown poor cardiovascular outcomes, but the characteristics of their current psychiatric symptoms have not been characterized. This study mainly explored the psychiatric symptoms of schizophrenia patients with the MAO phenotype. Methods: A total of 329 patients with schizophrenia and 175 sex- and age-matched people without schizophrenia from Anhui Province in China were enrolled. The Positive and Negative Syndrome Scale (PANSS) was used to evaluate the mental symptoms of the schizophrenia patients. The MAO phenotype was defined as meeting 1–4 metabolic syndrome criteria (excluding waist circumference) and having a body mass index (BMI) ≥ 28 kg/m2. And, metabolically healthy normal-weight (MHNW) phenotype was defined as meeting 0 criteria for metabolic syndrome and 18.5 ≤ BMI < 24 kg/m2. Results: Overall, 15.8% of the schizophrenia patients and 9.1% of the control group were consistent with the MAO phenotype, and the prevalence of MAO in the schizophrenia group was higher than that in the control group. Among the patients with schizophrenia, the MAO group had lower negative factor, cognitive factor and total PANSS scores than the MHNW group. However, when confounding factors were controlled, only the negative factor remained lower significantly. Conclusion: We found that schizophrenia patients with the MAO phenotype had reduced negative symptoms, which may indicate an internal mechanism linking metabolic disorders and negative symptoms. Trial registration: This study was registered in the China Clinical Trial Registration Center (No. chiCTR 1,800,017,044). [ABSTRACT FROM AUTHOR]

• A new air distribution system by using multi-vortex flow was proposed for high emission industrial building with large aspect ratio. • The airflow fields and pollutant distributions of the multi-vortex ventilation system were studied. • The influence factors of the subsystem distance and work conditions were evaluated. • An assessment indices for pollutant removal were used to evaluate the multi-vortex ventilation, and compared with commonly used mixing ventilation. Reducing emission and energy consumption are of great concern to sustainable development of industry in modern society. Ventilation system is the key factor to maintain industrial production, its efficiency is related to the pollutant emissions and energy consumption. In this study, we propose a novel ventilation system denoted multi-vortex ventilation (MVV), which is composed of several subsystems for industrial plant with large aspect ratio. This work aims to deal with the flow characteristics and ventilation effectiveness of MVV. The results show that MVV works when the distance between adjacent subsystems is long enough or the ventilation flow rate of adjacent subsystem is similar. When exhaust velocity reaches 5 m/s, there will be low pressure of – 30 Pa and vertical velocity of 2 m/s at vortex core, so as to transport the pollutants to the exhaust outlet rapidly. Moreover, the ventilation effectiveness is used to evaluate a room with MVV and to compare it with that of mixing ventilation (MV). Compared with MV, MVV can achieve 91 % ∼ 351 % higher ventilation effectiveness in the same ventilation flow rate. The results will provide a valuable method in the design of ventilation system in the industrial plants with large aspect ratio. [ABSTRACT FROM AUTHOR]

This paper presents Diff-ReColor, a novel diffusion-based image colorization framework that addresses the challenges of colorizing grayscale images with high semantic fidelity and diversity. Recognizing the limitations of previous approaches, including CNN-based methods and GANs, which often resulted in ambiguous colorization and artifacts, we leverage the recent advancements in diffusion models known for their stable training and diverse output generation. Diff-ReColor integrates an edge-conditional Denoising Diffusion Probabilistic Model (DDPM) with Dual-Tier Attention Color Reference Encoder, and a segmentation-informed sampling technique to produce colorized images that are both semantically consistent and rich in detail. Our edge-conditional DDPM is specifically designed to handle intricate image details, while the segmentation-guided sampling technique ensures the retention of semantic nuances by utilizing edge and segmentation cues during the colorization process. Inputs are also channeled into a dual attention encoder, where spectral-attention filters semantic information enhanced by spatial-attention, acquiring coarse coloring images to assist the denoising process. Empirical validation on the ImageNet benchmark dataset demonstrates the superior performance of Diff-ReColor in terms of color richness, colorization accuracy, and semantic fidelity. The model's generalization capabilities are further highlighted through its performance on COCO-Stuff and CelebA-HQ datasets, where it surpasses the baseline models without the need for fine-tuning. [ABSTRACT FROM AUTHOR]

Subjective well-being (SWB) varies within a person. However, even though previous studies have paid attention to why people with a more proactive personality have higher SWB, they have ignored how proactive personality influences an individual's state SWB using a within-person approach. According to the time perspective, we propose that proactive personality positively influences an individual's weekly SWB. Moreover, we propose that weekly rumination, weekly mindfulness, and weekly future optimism—which represent the past, present, and future time perspective, respectively—mediate the relationship between proactive personality and weekly SWB. Using a multilevel model, including 97 people and 388 within-person data points, we found that proactive personality positively influences an individual's weekly SWB. Only the mediation of weekly future optimism underlying this relationship was supported. We discuss the theoretical and practical implications of our findings. [ABSTRACT FROM AUTHOR]

• A dual hollow-core anti-resonant fiber for ultra-broadband polarization splitter is proposed. • The proposed polarization splitter has a 6.75 cm long splitter length. • The splitter has an ultra-broadband bandwidth of 310 nm from 1.41 μm to 1.72 μm. • The splitter has good single mode performance with high order mode extinction ratio great than 100. A dual hollow-core anti-resonant fiber is proposed and numerically investigated. Two elliptical tubes are introduced to separate the core into two symmetrical cores; further nested tubes are adopted to achieve good single mode performance. Mode coupling takes place in the proposed fiber through an air gap between the two cores; the coupling strength can be controlled by adjusting fiber parameters. A broadband polarization splitter can be constructed by selecting a suitable fiber length. Numerical results show that a polarization splitter based on the proposed fiber has a length of 6.75 cm and a bandwidth of 310 nm, covering the wavelength range from 1.41 μm to 1.72 μm with an extinction ratio as low as −20 dB. The ratio of higher order mode to fundamental mode loss is great than 100, implying that the proposed polarization splitter also possesses effective single mode performance. [ABSTRACT FROM AUTHOR]

Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM. [ABSTRACT FROM AUTHOR]

Aims: Neurodevelopmental impairments are closely linked to the basis of adolescent major psychiatric disorders (MPDs). The visual cortex can regulate neuroplasticity throughout the brain during critical periods of neurodevelopment, which may provide a promising target for neuromodulation therapy. This cross‐species translational study examined the effects of visual cortex repetitive transcranial magnetic stimulation (rTMS) on neurodevelopmental impairments in MPDs. Methods: Visual cortex rTMS was performed in both adolescent methylazoxymethanol acetate (MAM) rats and patients with MPDs. Functional magnetic resonance imaging (fMRI) and brain tissue proteomic data in rats and fMRI and clinical symptom data in patients were analyzed. Results: The regional homogeneity (ReHo) analysis of fMRI data revealed an increase in the frontal cortex and a decrease in the posterior cortex in the MAM rats, representing the abnormal neurodevelopmental pattern in MPDs. In regard to the effects of rTMS, similar neuroimaging changes, particularly reduced frontal ReHo, were found both in MAM rats and adolescent patients, suggesting that rTMS may reverse the abnormal neurodevelopmental pattern. Proteomic analysis revealed that rTMS modulated frontal synapse‐associated proteins, which may be the underpinnings of rTMS efficacy. Furthermore, a positive relationship was observed between frontal ReHo and clinical symptoms after rTMS in patients. Conclusion: Visual cortex rTMS was proven to be an effective treatment for adolescent MPDs, and the underlying neural and molecular mechanisms were uncovered. Our study provides translational evidence for therapeutics targeting the neurodevelopmental factor in MPDs. [ABSTRACT FROM AUTHOR]

Vehicle re-identification is to identify a target vehicle in different cameras with non-overlapping views. It is a challenging task due to various viewpoints of vehicles, diversified illuminations and complicated environments. Most existing vehicle re-identification methods focus on learning global features, while neglecting the importance of local features. In this paper, we propose a Multi-Region Model (MRM) to learn powerful features for vehicle re-identification. In addition to extracting global region features, MRM also extracts features from a series of local regions. For each local region, instead of utilizing the rigid part to extract features directly, a Spatial Transformer Network (STN) based localization model is introduced to localize local regions which contain more distinctive visual cues. In order to further improve the performance of re-identification, we design a context-based ranking method which generates the ranking list by taking context and content into consideration to measure the similarity between neighbors. Experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset. [ABSTRACT FROM AUTHOR]

Featured by multi-charge, multi-process integration, multi-constraint, steelmaking and continuous casting (SCC) scheduling is a complex and industrial synthesis process. Generally, it is solved by the two-stage or multistage approach. To reduce time consumption, we propose a "one-stage" optimization method that integrates the constraints into the cuckoo search algorithm (CICSA). To obtain the minimum total waiting time (TWT), we built an SCC scheduling optimization model. Firstly, we integrate machine uniqueness constraints and the process sequence into the coding of the nests. Then, non-conflict constraints and casting on time constraints are converted into the fitness values of the cuckoo search algorithm (CSA). Thus, the solutions obtained in the population after iteration meet the process constraints. The non-conflict optimal nest is taken as the optimal solution. Simulations are conducted using the actual industrial data. Comparisons among the proposed algorithm, the two-stage algorithm, and the original CSA are presented. The result shows the proposed approach achieves better performance. [ABSTRACT FROM AUTHOR]

• DC-c-AG retains rapid alginate gelation for wet-spinning and improves biostability. • Wet-spun DC-c-AG fibers with hierarchical orientations mimic spinal cord structure. • DC-c-AG fibers align neural cells and promote axon outgrowth along the fiber axis. • DC-c-AG fiber implantation effectively promotes spinal cord injury repair in rats. Spinal cord injury (SCI) repair remains a major challenge in regenerative medicine. While biomaterials have shown promise in tissue engineering, they often lack the directional cues necessary to recapitulate the parallel alignment of ascending and descending nerve tracts in the spinal cord at the mesoscale, and to guide axon elongation at the nanoscale, thus limiting their effectiveness in SCI interventions. Within this context, we developed a hydrogel that retains the rapid ionic crosslinking properties of alginate while enhancing its biostability and mechanical strength to support the prolonged process of tissue engineering, achieved by introducing gelatin for covalent crosslinking and incorporating calcium crystallite deposition. The resulting hydrogel can gelatinize within a few seconds and maintain over 60% for 8 weeks in a complete cell medium. Leveraging this gelation strategy, we enabled to further fabricate the hydrogel into a continuous fibrous scaffold by a facile wet-spinning technique. Intriguingly, the obtained micron-sized fibers feature parallel nanoscale grooves on their surface, providing hierarchical guidance cues to mimic spinal cord architecture. Thanks to this multiscale oriented structure, the wet-spun fibers can not only align neural cell distribution but also promote axonal outgrowth in the direction of the fiber axis. When implanted into rats with complete SCI for long-term treatment, the fibrous scaffolds significantly facilitated motor functional recovery, enhanced tissue integration, and markedly improved neural regeneration and neurite extension at the injured sites. Therefore, our study presents a promising strategy for fabricating hierarchically oriented fibers to achieve efficient SCI treatment. [ABSTRACT FROM AUTHOR]

There is an urgent need to address the contradiction between maize production and soil nutrient shortages to achieve efficient maize production with minimum fertilizer, labor and environmental costs. Determination of rational Nitrogen (N) application patterns is the key to solving this problem. N application is an effective strategy to improve maize N uptake (NU), yield and water use efficiency (WUE). However, the effects of different N application patterns on maize NU, yield and WUE vary greatly, and it is difficult to determine the great-yield and high-efficiency N application pattern for maize in the Loess Plateau region according to a single experimental study. We synthesized 102 studies (102 sites) in the Loess Plateau region of China to evaluate the effects of different N application patterns (BU: basal urea; TU: basal and topdressing urea; S/C: slow/controlled release urea; S/CU: slow/controlled release urea mixed with normal urea) on maize NU, yield, WUE, and N use efficiency (NUE), and explored their responses to different climates, soil physicochemical properties, and field management practices. N application significantly increased the maize NU, yield and WUE. S/CU pattern significantly improved maize NU, yield and WUE the most with 110.74 %, 83.13 % and 86.21 %, respectively, compared to non-N application. S/C pattern showed the greatest increase in NUE of maize (3.47 %). Random forest analysis showed that growing season precipitation (GSP) was the most important determinant of the impact of N fertilizer application on maize NU, yield and WUE, while soil total nitrogen (TN) content was the most important determinant of maize NUE. The greatest increase in S/CU pattern yield and WUE enhanced when GSP and MAT were 200–400 mm and ≤ 10 °C, respectively. N application was more effective in increasing maize yield and WUE when the soil texture was clay loam and SOM < 10 g kg−1. Film mulching also further increased maize NU, yield, and WUE. In addition, variety of "Xianyu 335" had higher effect sizes for NU, yield and WUE than "Zhengdan 958". S/CU pattern obtained greater maize yield and WUE with lower fertilizer and labor costs, the suitable rate of nitrogen application was determined to be 165.20 kg ha−1 and the urea mix ratio was 65 %. The results would provide theoretical support and technical guidance for great-yield and high-efficiency green production of maize in the Loess Plateau of China. ● Slow/controlled release urea mixed with normal urea (S/CU) improved maize yield. ● S/CU was the most effective in improving maize water use efficiency (WUE). ● S/CU was more effective in fields with poor soil and medium and low fertility. ● S/CU reduced fertilizer and labor costs while increasing maize yield and WUE. ● The optimum N application pattern was S/CU with 165.2 kg N ha−1 at 65 % urea. [ABSTRACT FROM AUTHOR]

Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGB Leaf), stem (AGB Stem), and reproductive organs (AGB R) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGB Leaf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGB Leaf inversion hybrid model was coupled with the allometric model to estimate the AGB Stem and AGB R in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn't improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGB Leaf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R2) of wheat and maize AGB Stem were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R2 of AGB R was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model's transferability is necessary to increase the potential for generalization in production practice. [Display omitted] • The hybrid model of PROSAIL, Boruta, and LCE achieved the optimal AGB Leaf estimation. • Wheat and maize organ biomass were estimated by the 6hybrid model and allometric relationships. • LCE combined the advantages of both Bagging and Boosting to obtain higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia.Methods: A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed.Results: In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775-0.918] and the test set (AUC, 0.867; 95% CI, 0.732-949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts.Conclusions: The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]

Previous studies have shown that high homocysteine worsens the occurrence, symptoms, and prognosis of patients with schizophrenia. The purpose of this study was to evaluate the prevalence, clinical correlation, and demographic characteristics of hyperhomocysteinemia in Han Chinese schizophrenia patients. In this study, we enrolled 330 schizophrenia patients and 190 healthy controls. Positive and Negative Syndrome Scale (PANSS) was used to evaluate the psychiatric symptoms of patients with schizophrenia. The plasma homocysteine level was measured by the enzyme cycle method and the concentration of homocysteine > 15 μmol/L was defined as hyperhomocysteinemia. The prevalence of hyperhomocysteinemia in Han Chinese schizophrenia patients and healthy controls was 55.05% and 26.98%, respectively. Schizophrenia patients with hyperhomocysteinemia had more male proportion, older age, higher smoking rate, lower HDL level, higher PANSS total score, and higher negative factor than those patients without hyperhomocysteinemia. Binary logical regression result showed that gender and age were the independent risk factors of hyperhomocysteinemia. Han Chinese patients with schizophrenia had high prevalence hyperhomocysteinemia than healthy controls, and elderly male patients have a higher risk of hyperhomocysteinemia. This study was registered in the China Clinical Trial Registration Center (chiCTR 1800017044). [ABSTRACT FROM AUTHOR]

Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution, and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo. Combining droplet-based ATAC-seq and mitochondrial DNA sequencing reveals clonal variation in human cells and tissues. [ABSTRACT FROM AUTHOR]

Background: Chest CT is used for the assessment of the severity of patients infected with novel coronavirus 2019 (COVID-19). We collected chest CT scans of 202 patients diagnosed with the COVID-19, and try to develop a rapid, accurate and automatic tool for severity screening follow-up therapeutic treatment.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. By taking the advantages of the pre-trained deep neural network, four pre-trained off-the-shelf deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) were exploited to extract the features from these CT scans. These features are then fed to multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, tenfold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines.Results and Conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models shows the promising application in COVID-19 severity screening, whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for tenfold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions. [ABSTRACT FROM AUTHOR]

Here we describe TRACE (T7 polymerase-driven continuous editing), a method that enables continuous, targeted mutagenesis in human cells using a cytidine deaminase fused to T7 RNA polymerase. TRACE induces high rates of mutagenesis over multiple cell generations in genes under the control of a T7 promoter integrated in the genome. We used TRACE in a MEK1 inhibitor-resistance screen, and identified functionally correlated mutations. Efficient diversification of DNA sequences is enabled by a T7 RNA polymerase fused to a cytidine deaminase. [ABSTRACT FROM AUTHOR]

Highlights • Tunable and switchable C- and L-band single-, dual- and triple-wavelength EDFL. • Tuning range for triple-wavelength regimes covers from 1558.71 to 1580.07 nm. • Tuning ranges for single- and dual-wavelength regimes are 11 nm and 6.23 nm. • The SMSR and tuning step for all laser regimes are up to 55 dB and 0.02 nm. Abstract In this paper, we propose and experimentally demonstrate a C- and L-band erbium-doped fiber laser (EDFL) with tunable single-, dual- and triple-wavelength lasing emissions. A tapered structure large-core fiber filter (LCFF) is inserted to serve as tuning component and wavelength selector for tunable and stable multi-wavelength oscillations. Based on the axial strain response of LCFF, two tunable triple-wavelength regimes within the range from 1558.71 to 1579.81 nm are obtained with side mode suppression ratio (SMSR) as high as 55 dB. Moreover, tunable single- and dual-wavelength regimes are also achieved with total tuning range of 11 nm and 6.23 nm, respectively. The tuning step for overall laser regimes is 0.02 nm, and the maximum wavelength sensitivity reaches to 1.87 pm/με. In addition, this proposed versatile EDFL is proved to be very stable since the lasing wavelength shifts and power fluctuations are less than 0.02 nm and 0.78 dB during an hour. [ABSTRACT FROM AUTHOR]

Nitrogen (N) nutrition is essential for crop production and a critical N dilution curve (CNDC) could provide a novel strategy for diagnosing plant N status. CNDCs for several crops under different scenarios have been established to optimize N management. However, few studies have been conducted to establish a CNDC for maize (Zea mays L.) under ridge-furrow planting with film mulching over ridges, a planting pattern that has been widely adopted in semi-arid regions due to its improvement in soil water status. In addition, the effects of varied planting patterns on CNDC remain unknown. Therefore, a four-year (2018–2021) field experiment was conducted to establish CNDCs and provide a precise diagnosis of maize N status under different mulching planting patterns. Four N levels (0, 90, 180, and 270 kg N ha 1 for the 2018–2020 period; and 100, 150, 200, and 300 kg N ha 1 for 2021) were applied under three mulching planting patterns: flat planting without film mulching, ridge-furrow planting with transparent plastic film mulching over ridges, and ridge-furrow planting with biodegradable film mulching over ridges. Taking into consideration of the 95 % credibility intervals, no significant differences in CNDCs and its estimated parameters were observed across years and mulching planting patterns. Therefore, we derived a unified CNDC equation defined as N C = 3.10 W 0.40 based on data obtained from the three mulching planting patterns during the four years. Furthermore, no significant differences were observed in critical N nutrition index thresholds. Consequently, the results supported establishing a universal CNDC model to diagnose maize N status in northwest China. The optimal N application rate for maize to obtain great yield and high N use efficiency in the region was 180–200 kg ha 1. Further studies are required to evaluate CNDC for maize grown under various N fertilizer types, planting patterns, rainfall years, and regions, which would lead to a more precise diagnosis of maize N status and a more accurate N management regime. • A unified critical nitrogen dilution curve (CNDC) was established for maize. • The CNDC (N C = 3.10 W 0.40) was obtained using a Bayesian analysis. • Mulching planting pattern did not obviously change the variability of CNDC. • The optimum N application rate for maize was 180–200 kg ha 1 in northwest China. [ABSTRACT FROM AUTHOR]

Abstract We have numerically investigated the influences of asymmetrical geometric structures on the birefringence of the index-guiding photonic crystal fiber (IG-PCF), by the finite element method. The structure of a reported highly birefringent (HB) PCF with artificial defect in the core is modified to destruct the symmetry. Our proposed structural modifications include elliptical air hole cladding, squeezing lattice of air holes, rotational elliptical air holes, and horizontally shifted air holes, in the cladding. It is found that the birefringence is sensitive to the modifications in different cases due to the different mechanisms. For 1550 nm wavelength, the optimized birefringence can be obtained to be 0.0328 by the elliptical air holes with the major semi-axis in the direction of the long side of the rectangular core region. Furthermore, for the HB-PCF by the asymmetric core shape, the structural factor restricting the further increase of birefringence is that continuous increase of asymmetry leading to the multi-core transmission. It seems to be that This work is not only a proposing of the optimal design of HB-PCF, but also a reference of founding the essence of geometric birefringence. [ABSTRACT FROM AUTHOR]

Highlights • A broad low loss single mode band in the wavelength region from 2.5 to 3.3 μm. • The ultra large mode area of 2314 μm2 is obtained. • Simple structure with high fabrication feasibility. • Bending resistance. Abstract A novel hollow-core anti-resonant fiber with a large mode area and good single mode performance is proposed for high power delivery in mid-infrared region. The structure consists of two-layer circular tubes as the cladding. By optimizing structure parameters, a broad low-loss single-mode band, in which the fundamental mode loss is lower than 4 × 10−3 dB/m and the high order mode extinction ratio (HOMER) is higher than 1000, is achieved in the wavelength region from 2.5 to 3.3 μm in which the lowest loss of 2.87 × 10−4 dB/m occurs at 3.1 μm. The effective mode area is up to 2314 μm2 with the highest HOMER of 16,600 at the wavelength of 2.8 μm. In addition, this fiber also has good bend resistance characteristics. When the fiber is bent at the bending radius of 18 cm, the mode area reaches to 2268 μm2 with the HOMER above 100 at the wavelength of 2.8 μm. Furthermore, the effective mode area can be further increased to 4714 µm2 by increasing core radius at the cost of sacrificing single mode performance and bending resistance. [ABSTRACT FROM AUTHOR]

The fugitive emissions from industrial buildings in urban areas have significant impacts on the surrounding environment. Therefore, it is necessary to investigate the key factors affecting the dispersion of these fugitive emissions and to find methods to mitigate their impact on the surrounding environment. This study investigates the dispersion characteristics of fugitive emissions from industrial buildings in relation to atmospheric stability and emission location. Additionally, this study explores the efficacy of wind catchers in mitigating the impact of fugitive emissions on the surrounding environment. The findings reveal that as atmospheric stability increases, the area range of building wake effect expands, and the turbulence intensity of the air flow diminishes, leading to more fugitive emissions entering the building wake area and diffusing near the ground. The wind catchers heighten the flow height of the polluted airflow and amplifies the turbulence intensity, effectively preventing fugitive emissions from entering the building wake area and accelerating the dilution of pollutants dispersed into the atmosphere. Consequently, the installation of wind catchers is highly effective in reducing the downstream impact of fugitive emissions, thereby improving the air quality of the surrounding environment. • The fugitive emissions emitted from industrial buildings mainly flow out from the middle of the street canyon. • Wind catchers can enhance the mixing and dilution of fugitive emissions into the atmosphere. • The pollutant concentration in the downstream residential area decreased by 31–65% after installation the wind catcher. [ABSTRACT FROM AUTHOR]

An evaluation method based on the total variation model (TV) in wavelet domain is proposed for modeling optical properties of real photonic crystal fibers (PCFs). The TV model in wavelet domain is set up to suppress the noise of the original image effectively and rebuild the cross section images of real PCFs with high accuracy. The optical properties of three PCFs are evaluated, including two kinds of PCFs that supplied from the Crystal Fiber A/S and a homemade side-leakage PCF, by using the combination of the proposed model and finite element method. Numerical results demonstrate that the proposed method can obtain high noise suppression ratio and effectively reduce the noise of cross section images of PCFs, which leads to an accurate evaluation of optical properties of real PCFs. To the best of our knowledge, it is the first time to denoise the cross section images of PCFs with the TV model in the wavelet domain. [ABSTRACT FROM AUTHOR]

[Display omitted] • Natural CuFeS 2 could effectively activate PAA for metronidazole degradation. • The surface-bonded Cu(I) and Fe(II) on CuFeS 2 were responsible for PAA activation. • The reductive sulfur species could regenerate Cu(I) and Fe(II) active sites on CuFeS 2. In this work, we reported the effective activation of PAA by natural chalcopyrite (CuFeS 2) to eliminate metronidazole (MTZ) for the first time. The influence of PAA concentration, CuFeS 2 dosage, initial pH, initial MTZ concentration, and water matrix on MTZ decontamination was examined. The effective MTZ degradation (83.92%) was achieved after 30 min under conditions of 460 μM PAA, 4 g L−1 CuFeS 2 , and initial pH 3. CuFeS 2 exhibited satisfactory recyclability after five cycles. Hydroxyl radical (OH), organic radicals, and ferryl ion species were proved to exist in the synergistic system based on the scavenging and probe tests, and electron paramagnetic resonance spectrograph, and OH contributed mainly to the MTZ degradation. As expected, surface-bonded Cu(I) and Fe(II) were verified as the main active sites, and surface-bonded Cu(I) had a more significant effect than that of surface-bonded Fe(II). The sulfur atoms of CuFeS 2 could capture protons to form sulfur vacancies, facilitating the exposure of active sites. More importantly, the rich reductive sulfur species in CuFeS 2 could enhance the regeneration of active sites. In addition, major degradation products of MTZ were identified by mass spectrometry, and their toxicity was assessed. The study proposed a new method for combining natural chalcopyrite and peracetic acid for water treatment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Protocatechuic acid (PCA) remarkably enhanced peroxydisulfate (PDS)/Fe2+ performance. • PCA improved Fe3+/Fe2+ cycle and active species generation in PDS/Fe2+/PCA. • Sulfate radical was the dominant radical in PDS/Fe2+/PCA for MO removal. In this study, a boosted peroxydisulfate (PDS)/Fe2+ Fenton-like system that employing protocatechuic acid (PCA) as complexing reagent to mediate Fe3+/ Fe2+ circulation was established. The outcomes presented typical dyes and antibiotics, such as methyl orange (MO), rhodamine B tetracycline, and metronidazole all can be efficiently eliminated in this synergistic process. Under the optimal conditions (1 mmol L-1 PDS, 30 µmol L-1 Fe2+ and 30 µmol L-1 PCA), the MO removal reached 100% within 80 min at pH 6.7. Furthermore, this system could perform well under the broad pH span (3–7) and realize the effective Fe3+/Fe2+ cycle. The masking test and electron paramagnetic resonance demonstrated sulfate radical (SO 4 ·-), hydroxyl radical, and singlet oxygen were responsible for MO elimination in the cooperative process, and SO 4 ·- was the dominant active substance. The MO decomposition byproducts and their evolutions were researched based on the mass spectrum analysis. The effect of inorganic anions in natural water on the PDS/Fe2+/PCA system was conducted, and the consequences indicated that Cl-, HCO 3 – and NO 3 – all negatively influenced the MO elimination. The proposed organic co-catalytic Fenton-like method could promote the development of advanced oxidation processes in water treatment. [ABSTRACT FROM AUTHOR]