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Drought has become one of the major constraints to agricultural development, particularly in areas that lack water. Studying the effects of different water stresses on the photosynthesis, growth, yield, water use efficiency (WUE) and irrigation water productivity (IWP) of winter wheat will provide data for the development of scientific irrigation strategies for water-saving agricultural methods. According to the size of the field water capacity, four different water stress levels were set, i.e., 30–40% (severe stress), 40–50% (moderate stress), 50–60% (mild stress) and 60–80% (well-watered) of field water capacity, controlling the amount of irrigation through an automatic irrigation system. The results showed that the seasonal changes in photosynthetic parameters, such as net photosynthetic rate (Pn), intercellular carbon concentration (Ci), stomatal conductance (Gs) and transpiration (E), significantly decreased under moderate and severe stress. As a result, the height, biomass and grain size of winter wheat decreased significantly, which led to low WUE and IWP. The Pn of the mild stress group only slightly decreased compared to that of the well-watered group, and was actually higher during the flowering and grain-filling stages, resulting in increases in dry biomass and 1000 grain weight of 2.07% and 1.95%, respectively. Higher WUE and IWP were attributed to higher yields and less water use. Thus, mild stress (60–80% field water capacity) resulted in the optimal use of water resources without a significant reduction in yield in the North China Plain (NCP). Therefore, mild stress can be considered a suitable environment for winter wheat growth in arid areas.

RNA interference (RNAi) is a valuable and revolutionary technology that has been widely applied in medicine and agriculture. The application of RNAi in various industries requires large amounts of low-cost double-stranded RNA (dsRNA). Chemical synthesis can only produce short dsRNAs; long dsRNAs need to be synthesized biologically. Several microbial chassis cells, such as Escherichia coli , Saccharomyces cerevisiae , and Bacillus species, have been used for dsRNA synthesis. However, the titer, rate of production, and yield of dsRNA obtained by these microorganism-based strategies is still low. In this review, we summarize advances in microbial dsRNA production, and analyze the merits and faults of different microbial dsRNA production systems. This review provides a guide for dsRNA production system selection. Future development of efficient microbial dsRNA production systems is also discussed.

The dimensional limit of ferroelectricity has been long explored. The critical contravention is that the downscaling of ferroelectricity leads to a loss of polarization. This work demonstrates a zero-dimensional ferroelectricity by the atomic sliding at the restrained van der Waals interface of crossed tungsten disufilde nanotubes. The developed zero-dimensional ferroelectric diode in this work presents not only non-volatile resistive memory, but also the programmable photovoltaic effect at the visible band. Benefiting from the intrinsic dimensional limitation, the zero-dimensional ferroelectric diode allows electrical operation at an ultra-low current. By breaking through the critical size of depolarization, this work demonstrates the ultimately downscaled interfacial ferroelectricity of zero-dimensional, and contributes to a branch of devices that integrates zero-dimensional ferroelectric memory, nano electro-mechanical system, and programmable photovoltaics in one. Down-scaled ferroelectricity normally diminishes due to the arising depolarization field. Here, the authors realize a 0D ferroelectric diode device taking advantage of the sliding at the van der Waals interface by the two crossed tungsten disulfide nanotubes.

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median–Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management.

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring.

Background To investigate the clinical and genetic characteristics of classic congenital adrenal hyperplasia (CAH) patients. To determine whether gonadotropin-releasing hormone analogs (GnRHa) + recombinant human growth hormone (rhGH) + Anastrozole combined therapy improves the final adult height of CAH patients with central precocious puberty (CPP). Methods We described the clinical and genetic characteristics of 16 classic CAH patients, and performed pathogenic analysis and structural modeling of the newly discovered mutation. By using the method of self-before and after control, we statistically analyzed bone age advancement, predicted adult height (PAH) and other indicators of 7 CAH children with CPP before and after combined treatment to observe its effect on adult height. Results All patients showed high levels of 17-hydroxyprogesterone, testosterone and adrenocorticotropic hormone. All patients had CYP21A2 gene mutations, and the newly discovered mutation c.79 A > G (p.Ser27Gly) may change the hydrophilicity of the protein and affect its function. Seven CAH patients with CPP were diagnosed at 5.6 (3.5 to 7.3) years. Their target height was 0.18 (-1.2 to 0.78) SD, and the PAH at the start of treatment was − 3.01 (-3.75 to -2.89) SD. The ages at which CAH patients with CPP started to be treated with GnRHa, rhGH and Anastrozole were 5.8 (5.5 to 8.7), 7.1 (5.5 to 9.8), 8.7 (7.6 to 10.7) years old, and discontinued them at 8.8 (7.5 to 10.2), 10.4 (9.0 to 12.7), 11.0 (9.7 to 12.7) years old, respectively. The PAH at treatment end was − 0.28 (-1.2 to 0.4) SD. The final height was − 0.28 (-1 to 1.04) SD, significantly higher than the initial PAH ( P  < 0.001) and similar to the target height ( P  = 0.478). Conclusion GnRHa + rhGH + Anastrozole therapy can improve the final adult height of CAH patients with CPP. In addition, this study also discovered a new CYP21A2 gene mutation c.79 A > G.

The scaling law of deep learning, which governs the relationship between model size and performance, has led to critical concerns regarding efficiency and sustainability. To address these challenges, this study presents a computational approach using self-organized submillimeter-long tungsten disulfide nanotube cluster as a 3D very-large-scale physical reservoir. The reservoir, with its 0D van der Waals interfaces on the order of 10 8 , or 1.0×10 10 mm -3 , matches the synaptic quantity and density of the fruit fly’s brain. The reservoir demonstrates the capability to perform a wide range of tasks from monomodal challenges to multimodal endeavors such as speech-to-image and medical image generation. The photosensitive mimetic synaptic connections in the very large scale reservoir emulate the optogenetic modulation of neuron circuits in in-vivo biological systems. By integrating the principles of the scaling law, multimodal task capabilities, and mimetic optogenetic mechanisms, this research paves a path toward advanced computing architectures tailored for next-generation energy-efficient artificial intelligence. Han et al. present a very-large-scale 3D computing reservoir self-assembled from 1D WS 2 nanotubes, with dense mimetic optogenetic synapses matching a fruit fly’s brain. It efficiently handles monomodal and multimodal tasks, featuring optogenetics-inspired, light-tunable computing dynamics.

To assess risk factors of reintubation in intensive care unit patients on mechanical ventilation. We conducted a systematic review of literature (inception to May 2022) and a meta-analysis. Data are reported as pooled odds ratios for categorical variables and mean differences for continuous variables. A total of 2459 studies were retrieved of which 38 studies were included in a meta-analysis involving 22,304 patients. Risk factors identified were: older age, higher APACHE II scores, COPD, pneumonia, shock, low SaO2, low PaO2, low PaO2/FiO2, low hemoglobin, low albumin, high brain natriuretic peptide, low pH, high respiratory rate, low tidal volume, a higher rapid shallow breathing index, a lower vital capacity, a higher number of spontaneous breathing trials, prolonged length of mechanical ventilation, weak cough, a reduced patient's cough peak flow and positive cuff leak test. Subgroup analysis showed that risk factors substantially overlap when reintubation was considered within 48 hours or within 72 hours after extubation. We identified 21 factors associated with increased risk for reintubation. These allow to recognize the patient at high risk for reintubation at an early stage. Future studies may combine these factors to develop comprehensive predictive algorithms allowing appropriate vigilance.

Anticipated changes in the diurnal cloud cycle (DCC) under global warming carry significant implications for future climate predictions. However, there is a notable dearth of studies specifically focusing on DCC shifts. Here, we establish a connection between DCC and its radiative effects (DCCRE), revealing that the DCC has tended to warm the climate over the past 13 years. We found that the centroid position determines the warming or cooling effect of DCCRE, while the magnitude of the amplitude influences its strength. As global mean temperature (GMT) rises, low cloud centroid tends to shift from daytime to nighttime, enhancing nighttime warming effects and diminishing solar radiation reflection. Conversely, high clouds exhibit the opposite, reducing nighttime warming effects and displaying stronger DCCRE compared to low clouds. For each 1 °C increase in GMT, the total cloud DCCRE is expected to rise by 2.90 W m−2, mainly driven by low clouds contributing to an increase of 1.85 W m−2. This suggests that DCCRE may continue to warm the climate as GMT rises. These results indicate that the changes in DCC are significant for understanding climate variability. However, it should be noted that these findings are based on short-term regression analysis and further research is needed to determine whether they are related to long-term responses.

Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis, and is considered as a direct and non-invasive indicator of the functional status of photosynthetic machinery; the normalized difference vegetation index (NDVI) can also reflect photosynthetic characteristics. Both are related to surface soil moisture (SSM), which is susceptible to drought, and phenology plays an important role in these vegetation-moisture relationships. Based on a variety of gridded SIF, NDVI, and SSM datasets obtained from satellite sensors, we presented the detailed relationships of SSM with SIF and NDVI in different phenological stages. Results showed that SIF and NDVI were significantly correlated with SSM in different phenological stages, especially during the maturity and senescence stages. For different vegetation types, SIF was more sensitive than NDVI to SSM anomalies in grasslands and forests during the maturity stage and rainfed croplands during the senescence stage. These relationships were regulated by precipitation and mean temperature, and decreased precipitation had the most significant impact when accompanied by increased temperature during the maturity stage or decreased temperature during the senescence stage. Our findings shed light on the role that phenology has in the relationships of SSM with SIF and NDVI on a large scale, which can further improve the understanding of vegetation-moisture relationships.