Carbon budget of diversified cropping systems in southwestern China: Revealing key crop categories and influencing factors under different classifications.

Cropping systems are considered the largest source of agricultural GHG emissions. Identifying key categories and factors affecting cropping systems is essential for reducing these emissions. Most studies have focused on the carbon budget of cropping systems from the perspective of a single crop or crop category. Comprehensive studies quantifying the carbon budget of diversified cropping systems, including farmland and garden crops, are still limited. This study aims to fill this gap by quantifying the carbon budget of diversified cropping systems, clarifying their carbon attributes, and identifying key crop categories and influencing factors within different classifications of the system. This study analyzed the carbon budget of a diversified cropping system consisting of 19 crops in Yunnan Province, southwestern China, using a crop-based net greenhouse gas balance methodology based on the "cradle-to-farm" life cycle idea. Crops were categorized into three levels of categories to assess the potential impact of categorization within the cropping system on its carbon balance. Results showed that Yunnan's diversified cropping system is a significant carbon sink, with net sequestration of 33.1 Mt CO2 eq, total emissions of 37.4 Mt CO2 eq, and total sequestration of 70.5 Mt CO2 eq. Cereals, vegetables, and hobby crops were the main contributors to carbon emissions, accounting for 41.61%, 21.87%, and 15.37%, respectively. Cereal crops also made the largest contribution to carbon sequestration at 53.18%. Bananas had the highest emissions per unit area (11.45 t CO2 eq ha-1), while walnuts had the highest sequestration (20.64 t CO2 eq ha-1). In addition, this study highlights effective strategies to reduce greenhouse gas emissions, such as reducing nitrogen fertilizer use, minimizing reactive nitrogen losses, and controlling methane emissions from rice fields. By elucidating the impact of carbon dynamics and crop categories, this study provides insights for sustainable agricultural practices and policies.

Long-term residue returning increased subsoil carbon quality in a rice-wheat cropping system.

Residue returning (RR) was widely implemented to increase soil organic carbon (SOC) in farmland. Extensive studies concentrated on the effects of RR on SOC quantity instead of SOC fractions at aggregate scales. This study investigated the effects of 20-year RR on the distribution of labile (e.g., dissolved, microbial biomass, and permanganate oxidizable organic) and stable (e.g., microbial necromass) carbon fractions at aggregate scales, as well as their contribution to SOC accumulation and mineralization. The findings indicated a synchronized variation in the carbon content of bacterial and fungal necromass. Residue retention (RR) notably elevated the concentration of bacterial and fungal necromass carbon, while it did not amplify the microbial necromass carbon (MNC) contribution to SOC when compared to residue removal (R0) in the topsoil (0-5 cm). In the subsoil (5-15 cm), RR increased the MNC contribution to SOC concentration by 21.2%-33.4% and mitigated SOC mineralization by 12.6% in micro-aggregates (P < 0.05). Besides, RR increased soil ß-glucosidase and peroxidase activities but decreased soil phenol oxidase activity in micro-aggregates (P < 0.05). These indicated that RR might accelerate cellulose degradation and conversion to stable microbial necromass C, and thus RR improved SOC stability because SOC occluded in micro-aggregates were more stable. Interestingly, SOC concentration was mainly regulated by MNC, while SOC mineralization was by dissolved organic carbon under RR, both of which were affected by soil carbon, nitrogen, and phosphorus associated nutrients and enzyme activities. The findings of this study emphasize that the paths of RR-induced SOC accumulation and mineralization were different, and depended on stable and labile C, respectively. Overall, long-term RR increased topsoil carbon quantity and subsoil carbon quality.

Ultra-stable perovskite quantum dot composites encapsulated with mesoporous SiO2 and PbBr(OH) for white light-emitting diodes.

Lead halide perovskite quantum dots (QDs) with high fluorescence efficiency and high color purity have a broad application prospect in the field of backlight display, but poor stability has been a key factor limiting their commercialization. Herein, we successfully synthesized CsPbBr3 QDs-KIT-6 (CsPbBr3 -K6) composite by using KIT-6 molecular sieve as the limited template with a simple high temperature solid-phase method. Further, the semi-protected CsPbBr3 QDs in KIT-6 frame will spontaneously hydrolyze when encountering water, and finally the double-encapsulated CsPbBr3 QDs-KIT-6@PbBr(OH) (CsPbBr3 -K6@PbBr(OH)) composite are obtained. CsPbBr3 -K6@PbBr(OH) composite shows excellent green emission properties, including a photoluminescence quantum yield (PLQY) (~73%) and a narrow emission linewidth of 25 nm. It is interesting that, the composite has excellent stability, including water stability without attenuation of fluorescence intensity after soaking in water for 60 days, thermal stability of 120°C heating-cooling cycle, and excellent optical stability without attenuation under continuous ultraviolet irradiation.

Prognostic value of dynamic cardiac biomarkers in patients with acquired refractory thrombocytopenic purpura: A retrospective study in Chinese population.

INTRODUCTION: Thrombotic thrombocytopenic purpura (TTP) is becoming a curable disease with the introduction of therapeutic plasma exchange (TPE). However, cardiovascular complications remain essential causes of mortality in patients with refractory TTP, while the association of cardiac biomarkers with the prognosis of TTP warrants further investigation. METHODS: Patients admitted to the First Affiliated Hospital of Soochow University for refractory TTP from 2013 through 2020 were included in this retrospective study. Clinical characteristics were collected from electronic health records. Biomarker levels on admission and post TPE were recorded. Logistic regression was adopted to identify risk factors for mortality. RESULTS: A total of 78 patients with refractory TTP were included in this study. Twenty-one patients died during hospitalization, with a mortality rate of 26.9%. High-sensitivity cardiac troponin T (hs-cTnT), N-terminal probrain natriuretic peptide (NT-proBNP), and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) ratios (AAR) were increased in deceased patients compared with the survival group. Multivariate analysis showed that AAR after TPE was associated with overall mortality (OR: 4.45, 95% CI 1.09-18.19). The areas under the receiver operator characteristic curve (AUC) of AAR, hs-cTnT, and NT-proBNP for the association with mortality were 0.814, 0.840, and 0.829, respectively. CONCLUSION: Higher post-TPE cardiac biomarker levels are associated with increased in-hospital mortality in patients with refractory TTP.

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

Subcortical and cerebellar volume differences in bilingual and monolingual children: An ABCD study.

Research suggests that bilingual children experience an extension or delay in the closing of the sensitive/critical period of language development due to multiple language exposure. Moreover, bilingual experience may impact the development of subcortical regions, although these conclusions are drawn from research with adults, as there is a scarcity of research during late childhood and early adolescence. The current study included 1215 bilingual and 5894 monolingual children from the ABCD Study to examine the relationship between subcortical volume and English vocabulary in heritage Spanish bilingual and English monolingual children, as well as volumetric differences between the language groups. We also examined the unique effects of language usage in bilingual children's subcortical volumes. In general, bilingual children had less cerebellar volume and greater volume in the putamen, thalamus, and globus pallidus than monolingual children. English vocabulary was positively related to volume in the cerebellum, thalamus, caudate, putamen, nucleus accumbens, and right pallidum in all children. Moreover, the positive relationship between vocabulary and volume in the nucleus accumbens was stronger for monolingual adolescents than bilingual adolescents. The results are somewhat in line with existing literature on the dynamic volume adaptation of subcortical brain regions due to bilingual development and experience. Future research is needed to further explore these regions longitudinally across development to examine structural changes in bilingual brains.

Organic fertilizer substitution benefits microbial richness and wheat yield under warming.

Climate warming poses a serious threat to soil biodiversity and crop yield. Application of organic fertilizer has been extensively practiced to improve soil health and crop productivity. However, information is limited about the effects of organic fertilizer on microbial communities and diversity (richness) under warming. Thus, to investigate the interactive effects of temperature (ambient temperature and warming) and fertilizer (chemical fertilizer and partial substitution of chemical fertilizer with organic fertilizer) on microbial properties and wheat yield, a two-factorial pot experiment was conducted using soils with high and low fertility The results showed that warming and organic fertilizer had minor effects on bacterial Shannon and Simpson indexes. Due to concomitant reductions in soil moisture, warming decreased the average Chao index by 5.4 % and Ace index by 3.8 % for soils with high and low fertility (P < 0.05). High-throughput sequence presented that dominated genus was Bacillus with spore-forming ability. Under warming and drying conditions, microbes with adaptive traits (spore-forming ability) would outcompete the other microbes, and decrease microbial Chao and Ace index (richness). However, organic fertilizer counteracted the adverse effects of warming on microbial richness attributed to positive interaction between temperature and fertilizer on soil nutrients and organic carbon. The strong relationships between bacterial richness and wheat yield, as well as soil nutrients, highlighted the importance of soil biodiversity in improving soil nutrients and crop productivity. Partial substitution of chemical fertilizer with organic fertilizer significantly increased wheat yield by 27.1 % and 14.9 % under ambient temperature and by 28.0 % and 19.6 % under warming for soils with high and low fertility, respectively. Overall, this study provided the possibility to increase bacterial richness related to nutrient turnover and crop production by organic fertilizer application with reduced chemical fertilizer, especially under climate warming.

Accelerated fractional ventilation imaging with hyperpolarized Gas MRI.

PURPOSE: To investigate the utility of accelerated imaging to enhance multibreath fractional ventilation (r) measurement accuracy using hyperpolarized gas MRI. Undersampling shortens the breath-hold time, thereby reducing the O2 -induced signal decay and allows subjects to maintain a more physiologically relevant breathing pattern. Additionally, it may improve r estimation accuracy by reducing radiofrequency destruction of hyperpolarized gas. METHODS: Image acceleration was achieved using an eight-channel phased array coil. Undersampled image acquisition was simulated in a series of ventilation images and data was reconstructed for various matrix sizes (48-128) using generalized auto-calibrating partially parallel acquisition. Parallel accelerated r imaging was also performed on five mechanically ventilated pigs. RESULTS: Optimal acceleration factor was fairly invariable (2.0-2.2×) over the range of simulated resolutions. Estimation accuracy progressively improved with higher resolutions (39-51% error reduction). In vivo r values were not significantly different between the two methods: 0.27 ± 0.09, 0.35 ± 0.06, 0.40 ± 0.04 (standard) versus 0.23 ± 0.05, 0.34 ± 0.03, 0.37 ± 0.02 (accelerated); for anterior, medial, and posterior slices, respectively, whereas the corresponding vertical r gradients were significant (P < 0.001): 0.021 ± 0.007 (standard) versus 0.019 ± 0.005 (accelerated) (cm(-1) ). CONCLUSION: Quadruple phased array coil simulations resulted in an optimal acceleration factor of ∼2× independent of imaging resolution. Results advocate undersampled image acceleration to improve accuracy of fractional ventilation measurement with hyperpolarized gas MRI.

Impact of livelihood capital and rural site conditions on livelihood resilience of farm households: evidence from contiguous poverty-stricken areas in China.

Farm households around the world are increasingly exposed to both external and internal shocks and stressors. Enhancing the resilience of farm households to frequent disturbances holds paramount importance in fostering the sustainability of their livelihoods and the revitalization of rural areas. Based on 1500 household samples from 14 contiguous poverty-stricken areas (CPSA) in China, this study explores the causal pathways between livelihood capitals of farm households and rural site conditions of rural communities, as well as quantifying their impacts on farm households' livelihood resilience using structural equation models. In particular, the livelihood resilience of farm households is measured based on the "Exposure-Sensitivity-Adaptability" framework. The results show that livelihood resilience is positively represented by exposure and adaptability, but is negatively correlated with sensitivity. Specifically, households with lower mean health and higher dependency ratio are more sensitive to risks, while exposure to agroforestry pests and diseases will lead farm households to diversify their livelihood activities and increase crop and livestock variety to enhance their adaptability. The livelihood capital of farm households has a significant positive effect on livelihood resilience (ß = 0.874, p < 0.001). Rural site conditions have both significant direct and indirect impacts on livelihood resilience, with the direct impact (ß = - 0.207, p < 0.05) being negative and a bit larger than the positive indirect impact (ß = 0.163, p < 0.05), as mediated by livelihood capital. The government should, therefore, invest more in health insurance, education and training, financial support, and infrastructure, and implement village planning to enhance both the quality of household livelihood capitals and rural site conditions in CPSA.

Robust and efficient abdominal CT segmentation using shape constrained multi-scale attention network.

PURPOSE: Although many deep learning-based abdominal multi-organ segmentation networks have been proposed, the various intensity distributions and organ shapes of the CT images from multi-center, multi-phase with various diseases introduce new challenges for robust abdominal CT segmentation. To achieve robust and efficient abdominal multi-organ segmentation, a new two-stage method is presented in this study. METHODS: A binary segmentation network is used for coarse localization, followed by a multi-scale attention network for the fine segmentation of liver, kidney, spleen, and pancreas. To constrain the organ shapes produced by the fine segmentation network, an additional network is pre-trained to learn the shape features of the organs with serious diseases and then employed to constrain the training of the fine segmentation network. RESULTS: The performance of the presented segmentation method was extensively evaluated on the multi-center data set from the Fast and Low GPU Memory Abdominal oRgan sEgmentation (FLARE) challenge, which was held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) were calculated to quantitatively evaluate the segmentation accuracy and efficiency. An average DSC and NSD of 83.7% and 64.4% were achieved, and our method finally won the second place among more than 90 participating teams. CONCLUSIONS: The evaluation results on the public challenge demonstrate that our method shows promising performance in robustness and efficiency, which may promote the clinical application of the automatic abdominal multi-organ segmentation.

The optimal spatial delineation method for the service level of urban park green space from the perspective of opportunity equity.

Urban park green spaces (UPGS) constitute a vital component of urban ecosystems, and the unequal distribution of UPGS can significantly impact the well-being of residents. Therefore, investigating the spatial delineation methods of UPGS service levels from the perspective of opportunity equity contributes to enhancing people's quality of life and promoting social harmony. Taking the Yingze District of Taiyuan City as an example, this study uses a modified accessibility measurement method of UPGS with the smallest clustered unit (building) as the service demand point and the UPGS entrance/exit as the service provision point, to establish a micro-scale evaluation framework for spatial equity considering the service radius and service quality of UPGS. The findings are as follows: after setting different service radius for UPGS at different levels, additional areas not covered by UPGS service were identified compared to setting the same service radius uniformly, which could prevent these areas from being overlooked in urban plans. After considering the quality of UPGS services, additional areas with low and high UPGS service levels were identified. Accurate spatial delineation of UPGS service level can avoid wasting public resources by including areas with high service levels in the scope of new UPGS requirements, while areas with low service levels lose opportunities for consideration in future urban infrastructure planning. This study emphasizes the residents' demand for both the quantity and quality of UPGS, facilitating an accurate assessment of whether urban residents can enjoy UPGS, the number of UPGS options available to them, and evaluate the quality of UPGS services experienced. Overall, this research provides new insights for evaluating the spatial equity of urban public facilities.

Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics.

BACKGROUND: This study aimed to develop a model that automatically predicts the neoadjuvant chemoradiotherapy (nCRT) response for patients with locally advanced cervical cancer (LACC) based on T2-weighted MR images and clinical parameters. METHODS: A total of 138 patients were enrolled, and T2-weighted MR images and clinical information of the patients before treatment were collected. Clinical information included age, stage, pathological type, squamous cell carcinoma (SCC) level, and lymph node status. A hybrid model extracted the domain-specific features from the computational radiomics system, the abstract features from the deep learning network, and the clinical parameters. Then, it employed an ensemble learning classifier weighted by logistic regression (LR) classifier, support vector machine (SVM) classifier, K-Nearest Neighbor (KNN) classifier, and Bayesian classifier to predict the pathologic complete response (pCR). The area under the receiver operating characteristics curve (AUC), accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and precision were used as evaluation metrics. RESULTS: Among the 138 LACC patients, 74 were in the pCR group, and 64 were in the non-pCR group. There was no significant difference between the two cohorts in terms of tumor diameter (p = 0.787), lymph node (p = 0.068), and stage before radiotherapy (p = 0.846), respectively. The 109-dimension domain features and 1472-dimension abstract features from MRI images were used to form a hybrid model. The average AUC, ACC, TPR, TNR, and precision of the proposed hybrid model were about 0.80, 0.71, 0.75, 0.66, and 0.71, while the AUC values of using clinical parameters, domain-specific features, and abstract features alone were 0.61, 0.67 and 0.76, respectively. The AUC value of the model without an ensemble learning classifier was 0.76. CONCLUSIONS: The proposed hybrid model can predict the radiotherapy response of patients with LACC, which might help radiation oncologists create personalized treatment plans for patients.

A multislice single breath-hold scheme for imaging alveolar oxygen tension in humans.

Reliable, noninvasive, and high-resolution imaging of alveolar partial pressure of oxygen (p(A)O(2)) is a potentially valuable tool in the early diagnosis of pulmonary diseases. Several techniques have been proposed for regional measurement of p(A)O(2) based on the increased depolarization rate of hyperpolarized (3) He. In this study, we explore one such technique by applying a multislice p(A)O(2) -imaging scheme that uses interleaved-slice ordering to utilize interslice time-delays more efficiently. This approach addresses the low spatial resolution and long breath-hold requirements of earlier techniques, allowing p(A)O(2) measurements to be made over the entire human lung in 10-15 s with a typical resolution of 8.3 × 8.3 × 15.6 mm(3). PO(2) measurements in a glass syringe phantom were in agreement with independent gas analysis within 4.7 ± 4.1% (R = 0.9993). The technique is demonstrated in four human subjects (healthy nonsmoker, healthy former smoker, healthy smoker, and patient with COPD), each imaged six times on 3 different days during a 2-week span. Two independent measurements were performed in each session, consisting of 12 coronal slices. The overall p(A)O(2) mean across all subjects was 95.9 ± 12.2 Torr and correlated well with end-tidal O(2) (R = 0.805, P < 0.0001). The alveolar O(2) uptake rate was consistent with the expected range of 1-2 Torr/s. Repeatable visual features were observed in p(A)O(2) maps over different days, as were characteristic differences among the subjects and gravity-dependent effects.

Multislice fractional ventilation imaging in large animals with hyperpolarized gas MRI.

The noninvasive assessment of regional lung ventilation is of critical importance in the quantification of the severity of disease and evaluation of response to therapy in many pulmonary diseases. This work presents, for the first time, the implementation of a hyperpolarized (HP) gas MRI technique to measure whole-lung regional fractional ventilation (r) in Yorkshire pigs (n = 5) through the use of a gas mixing and delivery device in the supine position. The proposed technique utilizes a series of back-to-back HP gas breaths with images acquired during short end-inspiratory breath-holds. In order to decouple the radiofrequency pulse decay effect from the ventilatory signal build-up in the airways, the regional distribution of the flip angle (α) was estimated in the imaged slices by acquiring a series of back-to-back images with no interscan time delay during a breath-hold at the tail end of the ventilation sequence. Analysis was performed to assess the sensitivity of the multislice ventilation model to noise, oxygen and the number of flip angle images. The optimal α value was determined on the basis of the minimization of the error in r estimation: α(opt) = 5-6º for the set of acquisition parameters in pigs. The mean r values for the group of pigs were 0.27 ± 0.09, 0.35 ± 0.06 and 0.40 ± 0.04 for the ventral, middle and dorsal slices, respectively (excluding conductive airways r 0.9). A positive gravitational (ventral-dorsal) ventilation gradient effect was present in all animals. The trachea and major conductive airways showed a uniform near-unity r value, with progressively smaller values corresponding to smaller diameter airways, and ultimately leading to lung parenchyma. The results demonstrate the feasibility of the measurement of the fractional ventilation in large species, and provide a platform to address the technical challenges associated with long breathing time scales through the optimization of acquisition parameters in species with a pulmonary physiology very similar to that of humans.

Deep reaction network exploration at a heterogeneous catalytic interface.

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

Requirements and Metabolism for Calcium, Phosphorus and Vitamin D3 in the Growing-Furring Blue Foxes.

A 3 × 3 factorial experiment was conducted to investigate the influence of dietary calcium, phosphorus, and vitamin D3 (VD3) supplement levels on the growth performance, nutrient digestibility, and serum biochemical indices of growing-furring blue foxes. One hundred and thirty-five 120-day-old male blue foxes were randomly allocated into nine groups. The nine treatment diets were supplemented with 0%, 0.4%, or 0.8% Ca, and 1000, 2000, or 4000 IU·kg−1 VD3. The base diet contained 0.8% Ca and 327 IU·kg−1 VD3. The dietary calcium level had a significant effect on the average daily gain (ADG) of blue foxes at 121 to 135 days of age and 136 to 150 days (p < 0.05). The ADG of blue foxes at 121 to 135 days of age was significantly decreased by VD3 level (p < 0.05). The Ca dosage decreased the nutrient digestibility (p < 0.05). The Ca dosage increased the fecal Ca and P and decreased the P digestibility (p < 0.05). Interactions were found between the Ca and VD3 levels, which affected the digestibility of Ca and P (p < 0.05). In conclusion, this research determined the suitable doses of Ca and VD3 for growing-furring blue foxes.

Effect of Dietary Supplementation with Calcium, Phosphorus and Vitamin D3 on Growth Performance, Nutrient Digestibility, and Serum Biochemical Parameters of Growing Blue Foxes.

Based on the randomized design, a 3 × 3 factorial experiment was designed to examine the effects of dietary calcium (Ca), phosphorus (P), and vitamin D3 (VD3) supplemental levels with a fixed 1.5/1 ratio of Ca to P on the growth performance, nutrient digestibility, and serum biochemical indices blue foxes' growth. In total, 135 male blue foxes with the age of 60 days were randomly divided into 9 groups each with 15 blue foxes. The blue foxes belonging to the nine treatment groups were fed Ca supplementation (0%, 0.4%, or 0.8%) and VD3 supplementation (1000, 2000, or 4000 IU/kg DM). The base diet contained 0.8% Ca and 327 IU/kg VD3. The dosage of VD3 in blue foxes showed a significant impact on their growth performance (p < 0.05). The Ca dosage had a linear effect on the digestibility of the CP and carbohydrates (CHO) (p < 0.05). In conclusion, the results indicated that the Ca and VD3 doses showed promising effects on growth performance and nutrient digestibility in growing blue foxes and could reduce fecal N and P via improvement in protein and P utilization.

Nitrogen Footprint of a Recycling System Integrated with Cropland and Livestock in the North China Plain.

Nitrogen-based pollution from agriculture has global environmental consequences. Excessive use of chemical nitrogen fertilizer, incorrect manure management and rural waste treatment are key contributors. Circular agriculture combining cropland and livestock is an efficient channel to reduce the use of chemical nitrogen fertilizers, promote the recycling of livestock manure, and reduce the global N surplus. The internal circulation of organic nitrogen resources in the cropland-livestock system can not only reduce the dependence on external synthetic nitrogen, but also reduce the environmental impacts of organic waste disposal. Therefore, this study tried to clarify the reactive nitrogen emissions of the crop-swine integrated system compared to the separated system from a life cycle perspective, and analyze the reasons for the differences in nitrogen footprints of the two systems. The results showed that the integrated crop production and swine production increased the grain yield by 14.38% than that of the separated system. The nitrogen footprints of crop production and swine production from the integrated system were 12.02% (per unit area) and 19.78% lower than that from the separated system, respectively. The total nitrogen footprint of the integrated system showed a reduction of 17.06%. The reduction was from simpler waste manure management and less agricultural inputs for both chemical fertilizer and raw material for forage processing. In conclusion, as a link between crop planting and pig breeding, the integrated system not only reduces the input of chemical fertilizers, but also promotes the utilization of manure, increases crop yield, and decreases environmental pollution. Integrated cropland and livestock is a promising model for agriculture green and sustainable development in China.

Semi-hydrogenation of α,ß-unsaturated aldehydes over sandwich-structured nanocatalysts prepared by phase transformation of thin-film Al2O3 to Al-TCPP.

The semi-hydrogenation of α,ß-unsaturated aldehydes to the desired unsaturated alcohols with both high conversion and high selectivity remains a big challenge. Herein, we designed a sandwich-structured nanocatalyst for the highly selective hydrogenation of various α,ß-unsaturated aldehydes (e.g., cinnamaldehyde, furfural, crotonaldehyde, and 3-methyl-2-butenal) to the targeted unsaturated alcohols. Highly accessible platinum nanoparticles were sandwiched between a metal-organic framework (MOF) core (i.e., MIL-88B(Fe)) and a MOF shell (i.e., Al-TCPP). In particular, the growth of the Al-TCPP shell was achieved by atomic layer deposition (ALD) of thin-film Al2O3 followed by phase transformation with a tetrakis(4-carboxyphenyl)porphyrin (H4TCPP) linker. The thickness of the Al-TCPP shell can be finely controlled by adjusting the cycle number of alumina ALD and the concentration of the H4TCPP linker during the phase transformation of Al2O3 to Al-TCPP. It was proven that the permeable MOF shells could serve as selectivity regulators for the activation of the CO bonds in α,ß-unsaturated aldehydes (in preference to the CC bonds), leading to higher selectivity towards unsaturated alcohols as compared to the conventional surface supported Pt catalysts. Mechanistic insights showed that the enhanced catalytic performance was attributed to (i) the modified electronic state of sandwiched Pt nanoparticles by the two MOF layers and (ii) the steric hindrance effect on substrate diffusion through the sandwich-structured catalysts.

Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge.

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.