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Abstract Fetal macrosomia is associated with maternal and newborn complications due to incorrect fetal weight estimation or inappropriate choice of delivery models. The early screening and evaluation of macrosomia in the third trimester can improve delivery outcomes and reduce complications. However, traditional clinical and ultrasound examinations face difficulties in obtaining accurate fetal measurements during the third trimester of pregnancy. This study aims to develop a comprehensive predictive model for detecting macrosomia using machine learning (ML) algorithms. The accuracy of macrosomia prediction using logistic regression, k-nearest neighbors, support vector machine, random forest (RF), XGBoost, and LightGBM algorithms was explored. Each approach was trained and validated using data from 3244 pregnant women at a hospital in southern China. The information gain method was employed to identify deterministic features associated with the occurrence of macrosomia. The performance of six ML algorithms based on the recall and area under the curve evaluation metrics were compared. To develop an efficient prediction model, two sets of experiments based on ultrasound examination records within 1-7 days and 8-14 days prior to delivery were conducted. The ensemble model, comprising the RF, XGBoost, and LightGBM algorithms, showed encouraging results. For each experimental group, the proposed ensemble model outperformed other ML approaches and the traditional Hadlock formula. The experimental results indicate that, with the most risk-relevant features, the ML algorithms presented in this study can predict macrosomia and assist obstetricians in selecting more appropriate delivery models.

Abstract This study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training.

Reducing greenhouse gas (GHG) emissions and quantifying the carbon footprint (CF) of rice-cropping systems in the context of food security is an important step toward the sustainability of rice production. Exploring the key factors affecting emission reduction in rice production is important to properly evaluate the impact of China’s rice-cropping systems on global climate change. This review provides an overview of the direct and indirect CF in rice-cropping systems; analyzes the influencing factors in terms of rice-based cropping systems, varieties and agronomic practices; and proposes mitigation strategies. Different studies have shown that direct and indirect GHG emissions in rice-based cropping systems accounted for 38.3 to 95.5% and 4.5 to 61.7% of total emissions, respectively. And the CFs of ratoon rice, rice–wheat, rice–maize, rice–rapeseed, and rice–fish systems ranged from 316,9 kg CO2-eq kg−1 to 258,47 kg CO2-eq kg−1, which are lower than that in a double-rice planting system. High-yielding rice, drought-resistant rice, and other hybrids can mitigate GHG emissions from paddy fields by 3.7 ~ 21.5%. Furthermore, organic matter, water, tillage, straw incorporation, conservation tillage, reduced nitrogen fertilizer use, and added biochar and methane inhibitors could reduce emissions. Therefore, through reasonable agronomic measures, variety selection and optimal layout of rice-based rotation systems, the carbon neutral rate of rice production can be improved to help the national carbon sequestration and emission reduction target.

Abstract Background Myopia control interventions, such as defocus incorporated multiple segments (DIMS) spectacle lenses, have been adopted in school-aged children to reduce the prevalence of myopia and its complications. This study aimed to investigate the effect of DIMS spectacle lenses on subfoveal choroidal thickness (SfChT) over a period of two years, as the choroidal response to myopic control is a crucial factor in exploring its potential effect on predicting myopia progression. Methods This study involved a secondary analysis of our previous randomized clinical trial. Myopic school-aged children aged 8–13 years were recruited in a two-year study investigating the effect of DIMS spectacle lenses on myopia progression. The treated group received DIMS spectacle lenses (n = 78), while the control group was treated with a pair of single vision (SV) spectacle lenses (n = 80). SfChT was monitored at 1 week, 1, 3, 6, 12, 18 and 24 months post lens wear using spectral-domain optical coherence tomography and a custom made auto-segmentation algorithm utilizing convolutional neural networks. Results SfChT increased significantly after one week of DIMS spectacle lens wear compared to those wearing SV spectacle lenses (adjusted mean change relative to baseline ± SEM at one week; DIMS vs. SV, 6.75 ± 1.52 µm vs. − 3.17 ± 1.48 µm; P

Abstract Background The neurophysiological mechanisms underlying cognitive deficits in non-alcoholic fatty liver disease (NAFLD) remain unknown. Cognitive changes may be caused by brain alterations in neural activity and functional connectivity (FC). Aim This study aims to investigate the alterations between spontaneous brain neural activity and FC in male NAFLD patients and the relationship of neural activity with cognitive performance. Methods In this prospective study, 33 male pre-cirrhosis NAFLD subjects and 20 male controls matched for age, education level, and body mass index. All participants underwent resting-state functional magnetic resonance imaging scans and neuropsychological examinations. Regional homogeneity (ReHo) analysis was used to investigate the brain function in NAFLD, and regions with significantly altered ReHo were selected as seeds for subsequent FC analysis. Partial correlation analysis was used to assess the relationships between altered ReHo measures and cognitive performance indicators. Results Compared with the controls, the NAFLD patients showed increased ReHo in the opercular part of the right inferior frontal gyrus (IFGoperc) and decreased ReHo in the right middle frontal gyrus (MFG) and left superior parietal gyrus (SPG). The subsequent FC analysis showed increased FC between these regions (right IFGoperc, right MFG, and left SPG) and nodes of the default mode network (DMN) (such as left supraMarginal, left median cingulate and paracingulate gyri, left precuneus, orbital part of left medial frontal gyrus, and bilateral posterior cingulate gyrus). In addition, significant positive correlations were observed between NAFLD patients’ clock drawing test scores and altered ReHo in prefrontal cortices (right IFGoperc and right MFG). Conclusion Before developing cirrhosis, NAFLD patients showed altered neural activity in several brain regions and altered FC between the salience network and DMN. These alterations could potentially be a compensatory mechanism to maintain cognitive function in pre-cirrhosis NAFLD patients.

Ratoon rice cultivation has expanded to improve resource use efficiency, increase economic profits, and ensure food security in China in recent years. This article reviewed the development history and research progress of ratoon rice in Anhui Province, located in the lower reaches of the Yangtze River. Key cultivation technologies related to rice variety selection, planting patterns, fertilizer and water management strategies, as well as harvesting methods and stubble heights of the main crop for high-yielding ratoon rice were summarized. Particularly, we described three types of rice ratooning technologies in Anhui, including the technologies for medium-duration indica hybrid variety with the main crop harvested mechanically at high stubble height, for indica-japonica hybrid variety with the main crop harvested mechanically at medium stubble height, and for short-duration indica variety with the main crop harvested mechanically at low stubble height. Additionally, this review analyzed several challenges for the sustainable development of ratoon rice in Anhui, such as poor understanding of mechanisms underlying simultaneous improvement of high yield and high quality, the lack of rice varieties with strong ratooning ability, the yield loss from mechanical harvesting of the main crop, the unstable grain yield due to adverse weather, and the insufficient financial support for R&D. Finally, the prospects for further development of ratoon rice were proposed, including technology development and dissemination, farmers' training, and profit-boosting from premium products.

Transient elastography (TE), recommended by the WHO, is an established method for characterizing liver fibrosis via liver stiffness measurement (LSM). However, technical barriers remain towards point-of-care application, as conventional TE requires wired connections, possesses a bulky size, and lacks adequate imaging guidance for precise liver localization. In this work, we report the design, phantom validation, and clinical evaluation of a palm-sized TE system that enables simultaneous B-mode imaging and LSM. The performance of this system was validated experimentally using tissue-equivalent reference phantoms (1.45–75 kPa). Comparative studies against other liver elastography techniques, including conventional TE and two-dimensional shear wave elastography (2D-SWE), were performed to evaluate its reliability and validity in adults with various chronic liver diseases. Intra- and inter-operator reliability of LSM were established by an elastography expert and a novice. A good agreement was observed between the Young’s modulus reported by the phantom manufacturer and this system (bias: 1.1–8.6%). Among 121 patients, liver stiffness measured by this system and conventional TE were highly correlated (r = 0.975) and strongly agreed with each other (mean difference: −0.77 kPa). Inter-correlation of this system with conventional TE and 2D-SWE was observed. Excellent-to-good operator reliability was demonstrated in 60 patients (ICCs: 0.824–0.913). We demonstrated the feasibility of employing a fully integrated phased array probe for reliable and valid LSM, guided by real-time B-mode imaging of liver anatomy. This system represents the first technical advancement toward point-of-care liver fibrosis assessment. Its small footprint, along with B-mode guidance capability, improves examination efficiency and scales up screening for liver fibrosis.

Introduction: Low back pain (LBP) is a prevalent and complex condition that poses significant medical, social, and economic burdens worldwide. The accurate and timely assessment and diagnosis of LBP, particularly non-specific LBP (NSLBP), are crucial to developing effective interventions and treatments for LBP patients. In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of NSLBP patients.Methods: We recruited 52 subjects with NSLBP from the University of Hong Kong-Shenzhen Hospital and collected B-mode ultrasound images and SWE data from multiple sites. The Visual Analogue Scale (VAS) was used as the ground truth to classify NSLBP patients. We extracted and selected features from the data and employed a support vector machine (SVM) model to classify NSLBP patients. The performance of the SVM model was evaluated using five-fold cross-validation and the accuracy, precision, and sensitivity were calculated.Results: We obtained an optimal feature set of 48 features, among which the SWE elasticity feature had the most significant contribution to the classification task. The SVM model achieved an accuracy, precision, and sensitivity of 0.85, 0.89, and 0.86, respectively, which were higher than the previously reported values of MRI.Discussion: In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of non-specific low back pain (NSLBP) patients. Our results showed that combining B-mode ultrasound image features with SWE features and employing an SVM model can improve the automatic classification of NSLBP patients. Our findings also suggest that the SWE elasticity feature is a crucial factor in classifying NSLBP patients, and the proposed method can identify the important site and position of the muscle in the NSLBP classification task.

Introduction: Objective, accurate, and efficient measurement of exophthalmos is imperative for diagnosing orbital diseases that cause abnormal degrees of exophthalmos (such as thyroid-related eye diseases) and for quantifying treatment effects.Methods: To address the limitations of existing clinical methods for measuring exophthalmos, such as poor reproducibility, low reliability, and subjectivity, we propose a method that uses deep learning and image processing techniques to measure the exophthalmos. The proposed method calculates two vertical distances; the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the outer edge of the orbit in axial CT images and the distance from the apex of the anterior surface of the cornea to the highest protrusion point of the upper and lower outer edges of the orbit in sagittal CT images.Results: Based on the dataset used, the results of the present method are in good agreement with those measured manually by clinicians, achieving a concordance correlation coefficient (CCC) of 0.9895 and an intraclass correlation coefficient (ICC) of 0.9698 on axial CT images while achieving a CCC of 0.9902 and an ICC of 0.9773 on sagittal CT images.Discussion: In summary, our method can provide a fully automated measurement of the exophthalmos based on orbital CT images. The proposed method is reproducible, shows high accuracy and objectivity, aids in the diagnosis of relevant orbital diseases, and can quantify treatment effects.

High temperatures caused by climate warming severely affect the grain yield and quality of rice. In this study, the rice cultivars Longliangyou Huazhan (LLYHZ) and Quanliangyou 2118 (QLY2118) were selected as the experimental materials for investigation of an optimal cultivation system under high-temperature treatment. In addition, the heat-resistant cultivar Huanghuazhan (HHZ) and heat-sensitive cultivar Huiliangyou 858 (HLY858) were chosen as the experimental materials to study the effects of exogenous plant growth regulators on heat stress responses under high-temperature treatment. The results showed that mechanical transplanting of carpet seedlings and delayed sowing effectively increased the leaf area index and reduced the canopy temperature of LLYHZ and QLY2118. Furthermore, carpet seedling mechanical transplantation and delayed sowing improved grain yield and quality. Spray application of five plant growth regulators revealed that brassinolide and salicylic acid had the strongest effects on significantly improving antioxidant enzyme activities in the panicle, which would reduce the damage caused by the accumulation of reactive oxygen species and enhance plant tolerance of high-temperature stress. In addition, brassinolide and salicylic acid enhanced the percentage of anther dehiscence and percentage seed set. In this study, a set of simplified eco-friendly cultivation techniques for single-season indica rice adaptation to high-temperature stress was established. These results will be of great importance in alleviating the effects of high-temperature stress on rice production.

In this review, the significance of ratoon rice was introduced, and the research status and development trends of ratoon rice were also summarized. It is pointed out that mechanically harvested ratoon rice is the developing direction of future ratoon rice. On this basis, we analyzed the relationship between the yield of ratoon rice and many factors, such as variety characteristics, sowing date, water control, fertilizer, and many others. It is important to construct a comprehensive and practical evaluation system for rice regeneration that can provide a basis for high-yield cultivation of machine-harvested ratoon rice. At the same time, it is suggested that combining high-yield cultivation with the green ecological efficiency of rice can achieve better production and improve the quality of rice. Finally, some problems with ratoon rice development were put forward. An in-depth study on the rhizosphere biology and regulation techniques of ratoon rice and the effective ecological compensation mechanism increased the capacity and quality of ratoon rice. Further, the functioning of such research can enhance the planting area for ratoon rice and improve food security.

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69–0.71 for MG region, 0.89–0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment.

IntroductionUsing wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics.Methods31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method.ResultsThe low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 (P < 0.05), the daily rhythm trend of the high physical activity state was generally lower in the MCI group than in the cognitively normal group (P < 0.05). The peak rhythms in the sedentary state appeared at 12:00–15:00 and 20:00. The peak rhythms of rMSSD, HRV high frequency output power, and HRV low frequency output power in the 6h HRV parameters at night in the MCI group disappeared at 3:00 a.m., and the amplitude of fluctuations decreased; the amplitude of fluctuations of LHratio nocturnal rhythm increased and the phase was disturbed; the oxygen saturation was between 90 and 95% and less than 90% were increased in all time periods (P < 0.05). The F1 value of the two machine learning algorithms for MCI classification of multi-feature data combined with subjective scales were XGBoost (78.02) and GBDT (84.04).ConclusionBy collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.

A fixed-frequency beam-scanning leaky-wave antenna (LWA) array with three switchable dual-polarized beams is proposed and experimentally demonstrated. The proposed LWA array consists of three groups of spoof surface plasmon polaritons (SPPs) LWAs with different modulation period lengths and a control circuit. Each group of SPPs LWAs can independently control the beam steering at a fixed frequency by loading varactor diodes. The proposed antenna can be configured in both multi-beam mode and single-beam mode, where the multi-beam mode with optional two or three dual-polarized beams. The beam width can be flexibly adjusted from narrow to wide by switching between multi-beam and single-beam states. The prototype of the proposed LWA array is fabricated and measured, and both simulation and experimental results show that the antenna can accomplish a fixed frequency beam scanning at an operating frequency of 3.3 to 3.8 GHz with a maximum scanning range of about 35° in multi-beam mode and about 55° in single-beam mode. It could be a promising candidate for application in the space–air–ground integrated network scenario in satellite communication and future 6G communication systems.

Late-stage nitrogen (N) fertilization is an important crop management for improving rice production and also affects the quality of rice, whereas information on the influence of late N on structural and functional properties of indica rice starch is lacking. In this study, effects of various N rates (0, 60, and 120 kg ha−1 referred to as N0, N60 and N120, respectively) on structural and functional properties of grain starches from two rice cultivars (OM052 and Chaoyou1000) were investigated. Comparison of different N treatments showed that late N application exhibited strong influence on starch properties, and the sensitivity to N application varied with rice genotypes. Compared to non N rate, higher input of N (N60 for OM052, and N120 for Chaoyou1000) significantly reduced apparent amylose content (AAC), relative crystallinity (RC), gelatinization enthalpy, swelling power, solubility and degree of hydrolysis, but significantly increased the proportion of short chains with degree of polymerization 6–12 in amylopectin and gelatinization onset temperature. Further comparison and principal component analyses revealed that the variations in the starch functional properties were due mainly to the changes in RC. The solubility was positively associated with AAC. The present results can provide important information for understanding the environmental regulation of starch biosynthesis in rice grain.

Genetic improvement has been devoted to increasing rice yield by increasing the spikelet number per panicle and the spikelet/leaf ratio. As a result, indica-japonica hybrid rice “Yongyou” varieties with large panicles and superhigh yield potential have been developed. These varieties exhibit significantly higher grain yield and nitrogen use efficiency for grain (NUEg) under moderate and high N supply conditions due to their large sink size, but their yield performance remains obscure under low N input and low soil fertility conditions. In the present study, we investigated four varieties including Yongyou2640 (YY2640, large-panicle india-japonica hybrid variety), Yangliangyou6 (YLY6, two-line indica hybrid variety), Quanyou6 (QY6, three-line indica hybrid variety), and Huanghuazhan (HHZ, indica inbred variety) under two low soil fertility treatments [LF (removing half of soil depth) and CK] and two N fertilizer rates (0 and 100 kg N ha−1) in Central China. The results showed that the grain yield of YY2640 was more responsive to fertility than that of other varieties, which was 19.4–42.3% higher than that of the other three varieties under CK N100 treatment, but it was 14.5–19.4% lower than that of YLY6 and QY6 under LF N0 and LF N100. A higher spikelet/leaf ratio resulted in more biomass and N partition to panicles rather than to leaves under LF N0 and N100. Slightly more post-flowering dry matter obtained from higher leaf N content and crop growth rate failed to compensate for the adverse effects of reduced pre-flowering dry matter accumulation and stem-to-grain translocation during grain filling. This led to the lower NUEg of YY2640 than YLY6 and QY6 under low soil fertility conditions. Based on these findings, the present study suggested that the source–sink relationship of the super hybrid varieties should be optimized according to the soil N supply condition.

Abstract An accurate segmentation and quantification of the superficial foveal avascular zone (sFAZ) is important to facilitate the diagnosis and treatment of many retinal diseases, such as diabetic retinopathy and retinal vein occlusion. We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography (OCTA) images with robustness to brightness and contrast (B/C) variations. A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth (GT) was manually segmented subsequently. A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class. Subsequently, we applied largest-connected-region extraction and hole-filling to fine-tune the automatic segmentation results. A maximum mean dice similarity coefficient (DSC) of 0.976 ± 0.011 was obtained when the automatic segmentation results were compared against the GT. The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997. In all nine parameter groups with various brightness/contrast, all the DSCs of the proposed method were higher than 0.96. The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods. In conclusion, we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations. For clinical applications, this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.

Abstract The effects of global warming on crop production have received great attention in recent years. However, inconsistent results have been obtained with regard to major crops such as rice and wheat in rice–wheat rotation systems of China. Therefore, we undertook a comprehensive meta‐analysis of literature data. The yield response to warming differed for rice and wheat in the rice–wheat rotation system. Overall, warming significantly increased wheat yield (by 4.8%) but significantly reduced rice yield (by 14.8%). A number of factors were responsible for these observations. For example, rice yield responses varied with the temperature increase, variety and nitrogen (N) rate. Of these, N was the most important variable influencing yield responses to warming. The yield loss for rice was lower at temperature increases of 0.40–1.40°C and at a high N rate than that at temperature increases of 1.60–2.49°C and at a low N rate, respectively. The yield loss of indica rice was lower than that of japonica rice. For wheat, however, yield responses to warming varied with the warming regime, ambient temperature, temperature increases, and CO2 concentrations. Of these factors, ambient temperature was the most important variable influencing yield in response to warming. Warming increased wheat yield at a mean growth season temperature ≤10°C, temperature increases of 0.80–1.50°C, and at ambient CO2 concentrations. In addition, either daytime warming or nighttime warming significantly increased wheat yield. However, all‐day warming had no effect on wheat yields. We conclude that global warming will benefit wheat production but result in a serious loss of rice yield in rice–wheat rotations in China.

Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.

Mechanized production plays an important role in fulfilling food security demands during the period of labor shortage. Despite its benefits, the mechanical transplanted method (MET) has not been widely adopted due to a lack of awareness. Thus, this study aimed to evaluate the effect of the MET, the manual transplanted method (MAT), and the directed seeded method (DS) on rice yield and quality in farmers’ fields. A two-years field experiment (2016 and 2017) and a one-year survey (2020) were conducted to compare rice yield and quality among the MAT, MET, and DS methods. MET exhibited a higher-yielding population, increased biomass production, enhanced yield, and improved grain quality, compared with MAT. Moreover, japonica rice in MET (MET-JR) produced the maximum yield, 0.6 t hm−2 to 3.1 t hm−2 higher than in other treatments. However, japonica rice showed a poorer appearance quality than indica rice, as well as large panicle size (grains number per panicle ≥ 190); hybrid indica rice (HIR) also presented a high yield with poor appearance quality. These results confirmed that the application of MET could be useful in attaining high panicles per m2, high biomass production, high rice yield, and considerably improved rice quality in farmers’ fields under labor shortage circumstances. Furthermore, it is also imperative to consider balancing the yield and quality of japonica rice and large panicle HIR and employing MET at a broader scale in China, as well as other developing countries having rice–based cropping systems.

Rice growth is constantly challenged by severe environmental conditions, especially high-temperature stress, brought about by global warming. How to increase rice yields is a current issue in many countries. By analyzing the meteorological data from Anhui Province over the past 60 years, the frequency and level of high-temperature damage were comprehensively analyzed. On this basis, five indica rice varieties with strong high-temperature tolerance, relatively high yield and stable yield were selected by laboratory-simulated high-temperature and field high-temperature experiments: Longliangyou Huazan (LLYHZ), 9you-6 (9Y-6), Longliangyou-1988 (LLY-1988), Fengliangyou-4 (FLY-4) and Huanghuazan (HHZ). These are suitable for planting in the middle and lower reaches of the Yangtze River. This study provides a theoretical reference and scientific basis for the study of rice tolerance to high-temperature stress.

Rice (Oryza sativa L.) is cultivated in a wide range of climatic conditions, thereby inducing great variations in the rice growth, yield and quality. However, the comprehensive effects of temperature and solar radiation under different ecological regions on the rice growth, yield and quality are not well understood, especially in a middle rice cropping system. The rice growth, yield- and quality-related traits were investigated under different ecological regions. Among different areas, the days before the heading stage and after the heading stage of six cultivars ranged from 80 to 120 and from 30 to 50. The gaps of the grain yield, head rice rate, chalky grain rate and chalkiness level were about 1.2–52.4%, 1.0–3.0%, 2.7–12.7% and 0.3–4.5%, respectively. This study demonstrated that in these regions, temperature is a limiting factor compared with radiation. Moreover, the rice growth, yield and quality were closely associated with daily air (DT), maximum (MaT), minimum (MiT) and effective accumulated temperatures (EAT). An excellent rice growth, a high grain yield and an excellent quality could be achieved if the EAT was higher than 1592 °C·d and the MiT was lower than 23.1 °C before the heading stage, and if the DT, MiT and MaT were lower than 25.7 °C, 22.0 °C and 30 °C after the heading stage, respectively. These findings served as an important reference for optimizing cultivar selection for a specific area and determining suitable areas for a certain variety.

Plasmonics can bind light to their surface while increasing its intensity. The confinement and enhancement of light allows high–density, independent, subwavelength sensor elements to be constructed in micrometer–sized arrays. Plasmonic nanostructures have been widely used in the sensing field because of their fast, real–time and label–free characteristics. Numerous plasmonic metasensors have been configured for next–generation technologies since the emergence of metamaterials and metasurfaces. Among these applications, the development of high–sensitivity sensors based on new physical mechanisms has received tremendous interest recently. This review focuses on high–sensitivity plasmonic nanosensors and metasensors based on new physical mechanisms, especially based on Fano resonance and the exceptional point (EP). The asymmetric Fano resonance generated by the interference of different resonance modes has a narrower bandwidth, while an EP occurs whenever two resonant modes coalesce both in their resonant frequency and their rate of decay or growth. Both physical mechanisms could tremendously improve the sensitivity of the plasmonic sensors. We summarize the working principles, the latest development status and the development trends of these plasmonic nanosensors and metasensors. It is believed that these new sensing mechanisms can inspire more fruitful scientific research.

Abstract Non-conventional yeasts are playing important roles as cell factories for bioproduction of biofuels, food additives and proteins with outstanding natural characteristics. However, the precise genome editing is challenging in non-conventional yeasts due to lack of efficient genetic tools. In the past few years, CRISPR-based genome editing worked as a revolutionary tool for genetic engineering and showed great advantages in cellular metabolic engineering. Here, we review the current advances and barriers of CRISPR–Cas9 for genome editing in non-conventional yeasts and propose the possible solutions in enhancing its efficiency for precise genetic engineering.

Yield loss of ratoon crops caused by the rolling damage to the left stubble from mechanically harvesting main crops has been reported in previous studies. However, limited attention has been paid to identify the effect of the mechanical harvesting of the main crop on the yield of a non-rolled zone (NRZ) of a ratoon crop. A two-year field experiment on ratoon rice with two harvest methods (mechanical harvesting: MEH; manual harvesting: MAH) of a main crop was conducted to identify whether there is a border effect on the yield of the NRZ, and to evaluate whether the border effect will compensate for the yield loss of the ratoon crop. On average, MEH reduced the grain yield of the ratoon crop by 17.6% compared with MAH. The yield of the rolled zone (RZ) in MEH was 66.1–70.3% lower than that of MAH, which was attributed to the lower panicle m−2, grain filling percentage, total dry weight, and harvest index. In contrast, the yield of the NRZ in MEH was 2.7–10.8% higher than that of MAH. The yield differences of the non-rolled single row (R1), side row (R2), and middle row (R3) in the NRZ compared with MAH were 22.9%, 8.5%, and −10.1%, respectively, which were mainly explained by the panicle m−2, spikelets m−2, total dry weight, and regeneration rate. These results suggest that rolling during the mechanical harvesting of the main crop induced a border effect on the yield of the NRZ, which reduced the yield loss of the ratoon crop. The yield gain of the NRZ was attributed to the increased yield of R1 and R2 rather than R3.

Object: Diabetes is associated with cerebral vascular dysfunction and increased vascular cognitive impairment. The objective of this study was to use arterial spin labeling (ASL) perfusion-weighted magnetic resonance imaging to investigate whether cerebral perfusion was changed in newly-diagnosed children with type 1 diabetes mellitus (T1DM) and the possible relationship between aberrant cerebral blood flow (CBF) with cognitive as well as clinical variables.Methods: Between January 2017 and February 2018, 34 children with newly-diagnosed T1DM and 34 age, gender, and education-matched healthy controls were included. Three dimensional pseudo-continuous ASL perfusion MRI was used to evaluate CBF. A conventional T2WI sequence was added to exclude intracranial disease. Regions with CBF differences between T1DM children and the controls were detected via voxel-wise comparisons in REST software. Associations among the result of neuropsychological test, clinical variables, and CBF values of different brains were investigated by using partial correlation analysis.Results: Compared with the controls, T1DM children show decreased CBF in the left calcarine and postcentral gyrus, and right precentral gyrus. The perfusion in the postcentral gyrus was positively correlated with IQ performance. No significant correlations were found between CBF and HbA1c, blood glucose level before imaging and IQ in other brain regions in T1DM children.Conclusion: There is an abnormal cerebral perfusion in children with newly diagnosed T1DM. The visual and sensorimotor areas are brain areas where perfusion is prone to change at the beginning of T1DM. Our study provided clues for cerebral pathophysiological changes in the initial stage of T1DM.

Type 1 diabetes mellitus (T1DM) causes cognitive changes in children, which may be due to deficits in brain functions. It is unclear whether T1DM children will have brain functional changes during the initial stage of the disease. We aimed to investigate the changes in the functional brain network topology in children with new-onset T1DM. In this study, 35 new-onset T1DM children and 33 age-, sex-matched healthy controls underwent resting-state fMRI. The whole brain functional connectivity density (FCD) analysis and seed-based functional connectivity (FC) analysis were performed to investigate the changes in functional brain networks in new-onset T1DM children when compared with the controls. Pearson correlational analysis was used to explore the correlation between FCD value of differential brain areas and clinical variables in T1DM children. Compared with the controls, children with new-onset T1DM exhibited significantly decreased FCDs of the right inferior temporal gyrus (ITG) and the right posterior cingulate cortex (PCC). In the subsequent FC analysis, decreased FC was found between right PCC and right cuneus and increased FC was found between right ITG and left orbital part of inferior frontal gyrus in children with new-onset T1DM compared to the controls. The FCD values of right ITG and PCC did not correlate with HbA1c, blood glucose level before imaging, and full-scale intelligence quotient (IQ) in T1DM children. These results revealed that T1DM affect the functional activity of the immature brain at the initial stage. These findings also indicate a decrease in regional brain function and abnormalities in temporal-frontal and limbic-occipital circuitry in children with new-onset T1DM, and highlight the effects of T1DM on children's brain networks involved in visual process and memory, which may contribute to the cognition impairments observed in children with T1DM.

Purpose. Considering that intraocular pressure (IOP) is an important confounding factor in corneal biomechanical evaluation, the notion of matching IOP should be introduced to eliminate any potential bias. This study aimed to assess the capability of a novel parameter set (NPS) derived from IOP-matched scenario to diagnose keratoconus. Methods. Seventy samples (training set; 35 keratoconus and 35 normal corneas; pairwise matching for IOP) were used to determine NPS by forward logistic regression. A large validation dataset comprising 62 matching samples (31 keratoconus and 31 normal corneas) and 203 unmatching samples (112 keratoconus and 91 normal corneas) was used to evaluate its clinical significance. To further assess its diagnosis capability, NPS was compared with the other two prior biomechanical indexes. Results. NPS was comprised of three biomechanical parameters, namely, DA Ratio Max 1 mm (DRM1), the first applanation time (AT1), and an energy loading parameter (Eload). NPS was successfully applied to the validation dataset, with a higher accuracy of 96.8% and 95.6% in the IOP-matched and -unmatched scenarios, respectively. More surprisingly, accuracy of NPS was 95.5% in the combined validation, an improvement compared to the two prior biomechanical indexes. Conclusions. This is the first study taking IOP bias into consideration to determine a biomechanical parameter set. Our study shows that NPS indeed offers comparable performance in keratoconus diagnosis. Translational Relevance. Determining a parameter set after eliminating the influence from IOP is useful in revealing the essential differences between keratoconus and normal corneas and possibly facilitating further progress in keratoconus diagnosis.

As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease (PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (R = 0.705, p < 0.001; R = 0.697, p < 0.001 in Test 1; R = 0.592, p = 0.0018, R = 0.528, p = 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.

Diabetic patients often present with comorbid depression. However, the pathogenetic mechanisms underlying diabetic depression (DD) remain unclear. To explore the mechanisms underpinning the pathogenesis of the disease, we used ex vivo 1H nuclear magnetic resonance spectroscopy and immunohistochemistry to investigate the main metabolic and pathological changes in various rat brain areas in an animal model of DD. Compared with the control group, rats in the DD group showed significant decreases in neurotransmitter concentrations of glutamate (Glu) and glutamine (Gln) in the prefrontal cortex (PFC), hippocampus, and hypothalamus and aspartate and glycine in the PFC and hypothalamus. Gamma-aminobutyric acid (GABA) was decreased only in the hypothalamus. Levels of the energy product, lactate, were higher in the PFC, hippocampus, and hypothalamus of rats with DD than those in control rats, while creatine was lower in the PFC and hippocampus, and alanine was lower in the hypothalamus. The levels of other brain metabolites were altered, including N-acetyl aspartate, taurine, and choline. Immunohistochemistry analysis revealed that expressions of both glutamine synthetase and glutaminase were decreased in the PFC, hippocampus, and hypothalamus of rats with DD. The metabolic changes in levels of Glu, Gln, and GABA indicate an imbalance of the Glu-Gln metabolic cycle between astrocytes and neurons. Our results suggest that the development of DD in rats may be linked to brain metabolic changes, including inhibition of the Glu-Gln cycle, increases in anaerobic glycolysis, and disturbances in the lactate-alanine shuttle, and associated with dysfunction of neurons and astrocytes.

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.