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Understanding the effects of land‐use change in drylands on nutrient use efficiencies and losses within the food production‐consumption system provides insights on nutrient management strategies and the effectiveness of land‐use policies. Weibei Dryland is considered an example to use to evaluate changes in nitrogen (N) flows through the food chain following conversion of croplands to apple orchards, using the Nutrient flows in Food Chains, Environment and Resources Use (NUFER) model, and we also modelled the effects of 20% reduction in N fertilizers on apple yields to obtain a balanced fertilization strategy. The apple orchards area increased from 9.6 × 104 ha in 1978 to 72 × 104 ha in 2013, while the area under cereals decreased from 186 × 104 ha in 1978 to 152 × 104 ha in 2013. The increased income due to apple orchards led on‐one‐hand to more N fertilization to get a higher apple yield, and on‐the‐other‐hand promoted the consumption of animal foods. Consequently, nitrogen use efficiency of crop production (NUEc) and the combination of crop and animal production (NUEc+a) decreased, but nitrogen use efficiency of animal production (NUEa) and N losses increased between 1978 and 2013. The modelled scenario of 20% reduction in N fertilization amounts will still keep optimum apple yield but will significantly decrease N losses from the apple orchards. We therefore, suggest that the key measures for improving nitrogen use efficiency and decreasing N losses after conversion to cash crops are: (a) reducing N fertilization as well as balancing various inorganic fertilizers; and (b) implying organic fertilizers. [ABSTRACT FROM AUTHOR]

Rhynchosporium commune is the causal pathogen of scald in barley (Hordeum vulgare), a foliar disease that can reduce yield by up to 40% in susceptible cultivars. R. commune is found worldwide in all temperate growing regions and is regarded as one of the most economically important barley pathogens. It is a polycyclic pathogen with the ability to rapidly evolve new virulent strains in response to resistance genes deployed in commercial cultivars. Hence, introgression and pyramiding of different loci for resistance (qualitative or quantitative) through marker‐assisted selection is an effective way to improve scald resistance in barley. This review summarizes all 148 resistance quantitative trait loci reported at the date of submission of this review and projects them onto the barley physical map, where it is clear many loci co‐locate on chromosomes 3H and 7H. We have summarized the major named resistance loci and reiterated the renaming of Rrs15 (CI8288) to Rrs17. This review provides a comprehensive resource for future discovery and breeding efforts of qualitative and quantitative scald resistance loci. [ABSTRACT FROM AUTHOR]

In-situ analytics have been increasingly adopted by leadership scientific applications to gain fast insights into massive output data of simulations. With the current practice, systems buffer the output data in DRAM for analytics processing, constraining it to DRAM capacity un-used by the simulation. The rapid growth of data size requires alternative approaches to accommodating data-rich analytics, such as using solid-state disks to increase effective memory capacity. For this purpose, this paper explores software solutions for exploring the deep memory hierarchies expected on future high-end machines. Leveraging the fact that many analytics are sensitive to data features (regions-of-interest) hidden in the data being processed, the approach incorporates the knowledge of the data features into in-situ data management. It uses adaptive index creation/refinement to reduce the overhead of index management. In addition, it uses data features to predict data skew and improve load balance through controlling data distribution and placement on distributed staging servers. The experimental results show that such feature-guided optimizations achieve substantial improvements over state-of-the-art approaches for managing output data in-situ. [ABSTRACT FROM AUTHOR]

I/O requests from parallel processes to a disk compete with each other for a single disk head to access data. The disk efficiency can be significantly reduced due to frequent disk head seeks. In this paper, we propose a scheme, named IR+, to eliminate I/O interference for a single MPI program on disks by taking advantage of optimized access patterns and heterogeneous storage devices (e.g., solid-state disks). It identifies segments of files that could be involved in the interfering accesses and replicates them to their respectively designated storage servers. Moreover, it can further improve I/O performance by storing replicas on SSDs when accessing the on-disk replicas cannot sufficiently address the issue. IR+ has been implemented in the MPI library for high portability on top of the Lustre parallel file system. Our experiments with representative benchmarks, such as NPB BTIO and GTS , show that it can significantly improve I/O performance on the production HPC systems. [ABSTRACT FROM AUTHOR]

Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi-DBN performs significantly better than the traditional neural network. The out-of-sample cross-validation R2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 μg/m3. On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 μg/m3. This study provides a new perspective for air pollution monitoring in large geographic regions. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a chronic inflammatory disease. It leads to bone erosion in joints and other complications, which severely affect patients' quality of life. To accurately diagnose and monitor the progression of RA, quantitative imaging and analysis tools are desirable. High-resolution peripheral quantitative computed tomography (HR-pQCT) is such a promising tool for monitoring disease progression in RA. However, automatic erosion detection tools using HR-pQCT images are not yet available. Inspired by the consensus among radiologists on the erosions in HR-pQCT images, in this paper we define erosion as the significant concave regions on the cortical layer, and develop a model-based 3D automatic erosion detection method. It mainly consists of two steps: constructing closed cortical surface, and detecting erosion regions on the surface. In the first step, we propose an initialization-robust region competition methods for joint segmentation, and then fill the surface gaps by using joint bone separation and curvature-based surface alignment. In the second step, we analyze the curvature information of each voxel, and then aggregate the candidate voxels into concave surface regions and use the shape information of the regions to detect the erosions. We perform qualitative assessments of the new method using 59 well-annotated joint volumes. Our method has shown satisfactory and consistent performance compared with the annotations provided by medical experts. • We propose a precise and robust algorithm which automatically constructs 3D joint surfaces from raw HRpQCT volumes. • We design a novel computational pipeline using differential geometry-based features to characterize and detect erosions at joint surface. • Our method is the first 3D fully automatic joint erosion detection algorithm, and its performance agrees well with manual annotation results. [ABSTRACT FROM AUTHOR]

The rhizosphere and detritusphere are hotspots of soil enzyme-mediated microbial processes, but little is known about their spatio-temporal distribution and interactions in situ , especially after straw incorporation. To answer this question, we planted maize in rhizoboxes with three wheat straw localizations: a) straw incorporated in the upper 2 cm of soil (straw localized); b) straw mixed with all the soil (straw homogenized); and 3) without straw (control). Zymography was used to investigate the spatio-temporal distribution of soil C- and N- acquiring enzyme activities in the rhizosphere (living roots) and detritusphere (straw induced), which depends on straw localization (localized vs. homogenized) and time (7 vs. 15 days after planting). The hotspot area of enzyme activities was similar in the detritusphere compared to the rhizosphere at the early stage (day 7), suggesting the importance of detritusphere in driving C and N cycling. Compared with homogenized straw, higher C- and N-acquiring enzyme activities in detritusphere were observed in the treatment with localized straw, which indicates that straw localization had a marked impact on the enzyme activity in the detritusphere. Although enzyme activities in rhizosphere and detritusphere decreased with time from straw application, straw incorporation increased the enzyme activities in the rhizosphere (especially with localized straw) compared to control at the late stage (day 15). Furthermore, homogenized straw greatly reduced plant height (−36 %), leaf area (−49 %), SPAD (−49 %), and shoot weight (−53 %) than control at the late stage, but localized straw had little impact on plant performance. In conclusion, straw incorporation forms another soil enzyme activity hotspot (detritusphere) and promotes enzyme activities in the rhizosphere and soil profile. Collectively, homogenized straw incorporation intensified nutrient competition between microorganisms and roots and restricted plant performance. [Display omitted] • The hotspot area of enzyme activity was similar in rhizosphere and detritusphere. • Enzyme activities in rhizosphere and detritusphere decreased over time. • Straw incorporation increased the enzyme activity in rhizosphere and soil surface. • Straw homogenized inhibited plant growth as nutrient competition between roots and microbes. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most common malignant tumors worldwide, occurring in the colon or rectum portion. Quinoa is a dicotyledonous plant, and the quinoa bran is the outer seed coat of quinoa. Functional molecules in plants play an important role in the treatment and alleviation of chronic diseases. In this study, quinoa bran was used as the raw material. The active components with an anti-colorectal cancer effect were extracted by acetone and separated with an AB-8 macroporous resin column, which was further identified as terpenoids by liquid-mass spectrometry (LMS), named Quinoa bran terpenoids (QBT). The yield of quinoa bran terpenoids was 0.77%. Moreover, the results showed that QBT significantly inhibited the proliferation of colorectal cancer cells DLD-1 and HCT-8 with a concentration gradient dependent manner. Respectively, the IC 50 values of DLD-1 and HCT-8 cells were 0.42 ± 0.02 and 0.54 ± 0.05 mg/mL. Further studies showed that QBT upregulated apoptosis-related proteins such as activated-caspase-3, 8, and 9. In addition, QBT decreased mitochondrial membrane potential, promoted the expression of the pro-apoptotic protein Bax, and inhibited the anti-apoptotic protein Bcl-2. The experiments showed that QBT significantly repressed the growth of tumors in nude mice. In conclusion, these results indicated that QBT largely inhibits the development of colorectal cancer to large extent through upregulation of caspase-3, 8, and 9 and reduction of mitochondrial membrane potential, and has great potential for anti-tumor progression. [Display omitted] [ABSTRACT FROM AUTHOR]

Key message: We report the first study on the unique allele from wild barley that can improve waterlogging tolerance in cultivated barley with a substantially higher contribution to aerenchyma formation. Abstract: Waterlogging is one of the major abiotic stresses that dramatically reduce barley crop yield. Direct selection on waterlogging tolerance in the field is less effective due to its viability to environment. The most effective way of selection is to choose traits that make significant contributions to the overall tolerance and are easy to score. Aerenchyma formation under waterlogging stress is one of the most effective mechanisms to provide adequate oxygen supply and overcome stress-induced hypoxia imposed on plants. In this study, a new allele for aerenchyma formation was identified from a wild barley accession TAM407227 on chromosome 4H. Compared to that identified in cultivated barley, this allele not only produced a greater proportion of aerenchyma but made a greater contribution to the overall waterlogging tolerance. The QTL explained 76.8% of phenotypic variance in aerenchyma formation with a LOD value of 51.4. Markers co-segregating with the trait were identified and can be effectively used in marker assisted selection. [ABSTRACT FROM AUTHOR]

Main conclusion : We projected meta-QTL (MQTL) for drought, salinity, and waterlogging tolerance to the physical map of barley through meta-analysis. The positions of these MQTL were refined and candidate genes were identified. Drought, salinity and waterlogging are three major abiotic stresses limiting barley yield worldwide. Breeding for abiotic stress-tolerant crops has drawn increased attention, and a large number of quantitative trait loci (QTL) for drought, salinity, and waterlogging tolerance in barley have been detected. However, very few QTL have been successfully used in marker-assisted selection (MAS) in breeding. In this study, we summarized 632 QTL for drought, salinity and waterlogging tolerance in barley. Among all these QTL, only 195 major QTL were used to conduct meta-analysis to refine QTL positions for MAS. Meta-analysis was used to map the summarized major QTL for drought, salinity, and waterlogging tolerance from different mapping populations on the barley physical map. The positions of identified meta-QTL (MQTL) were used to search for candidate genes for drought, salinity, and waterlogging tolerance in barley. Both MQTL3H.4 and MQTL6H.2 control drought tolerance in barley. Fine-mapped QTL for salinity tolerance, HvNax4 and HvNax3, were validated on MQTL1H.4 and MQTL7H.2, respectively. MQTL2H.1 and MQTL5H.3 were also the target regions for improving salinity tolerance in barley. MQTL4H.4 is the main region controlling waterlogging tolerance in barley with fine-mapped QTL for aerenchyma formation under waterlogging conditions. Detected and refined MQTL and candidate genes are crucial for future successful MAS in barley breeding. [ABSTRACT FROM AUTHOR]

• Deep learning algorithm developed for fast/high-precision fresh tea leaf sorting. • The parallel robotic arm's stepper motor running time model was established. • Results were optimized based on a multi-objective continuous sorting model. • The recognition accuracy of the training/validation sets reached 100%/99.82%. • Results demonstrate that the proposed method achieves excellent sorting effects. Tea is one of the most popular beverages worldwide. It is rich in substances, such as tea polyphenols and polysaccharides, that are closely related to human health. However, fresh tea leaves picked by machines are neither neat nor uniform. In this study, we apply a deep learning algorithm and the DELTA parallel robotic arm for high-precision and fast sorting of machine-picked fresh tea leaves. First, a convolutional neural network algorithm and a regional segmentation method are used to achieve fast identification and localization of machine-picked fresh tea leaves. Second, the actual running time model of the stepper motor at different pulse frequencies was established by the interpolation fitting method. The running times of the parallel mechanical arm associated with S-curve type acceleration and deceleration motions were calculated accurately using this model, and the sorting point of tea fresh leaves was then determined. A multi-objective, continuous sorting model of machine-picked fresh tea leaves is constructed to verify and optimize the model effects. The results of the robot's arm running-time test show that the running time from the end of the robotic arm's application to the fresh tea leaf sorting plane is in the range 500–1800 ms. The experimental results of fresh tea leaf recognition and classification show that after 150 iterations, the recognition accuracy of the validation set can reach 99.82%. Finally, the average sorting accuracy of the four experiments reach 89%, with the highest sorting accuracy reaching up to 92%. These results demonstrate that the proposed method leads to an excellent sorting effect pertaining to machine-picked fresh tea leaves. This approach could be easily applied to any agricultural product sorting application. [ABSTRACT FROM AUTHOR]

• Efficient photoelectric conversion at 2D TAPP-DHTA porphyrin COF photocathode. • Immuno-HCR-CRISPR/Cas12a-based multiple signal amplification for IL-4 immunoassay. • MQD@CNW nanozyme-catalyzed in situ generation of PANI/Porphyrin COF heterojunctions. • An ultralow detection limit of 0.84 fg/mL and a wide linear range of 2 fg/mL to 0.2 ng/mL. The level of Interleukin-4 (IL-4, a classical anti-inflammatory cytokine) in cerebrospinal fluid is in response to brain injury, infection and neurodegenerative diseases. For early prevention and treatment of related diseases, sensitive and reliable monitoring of IL-4 is urgently needed. Herein, by employing 5,10,15,20-tetrakis(4-aminophenyl)porphyrin-2,5-dihydroxyterephthaldeyde COF (TAPP-DHTA COF) as an efficient photocathode and in situ generated-polyaniline (PANI) as the signal label, we establish an immuno-HCR assisted CRISPR/Cas12a-based bioassay platform for IL-4 determination. The TAPP-DHTA COF provides an outstanding cathodic photoelectrochemical (PEC) response, profiting from the unique structural feature and high light harvesting capability. The immuno-HCR-CRISPR/Cas12a system magnifies the target signal multiply, through initiation of the unique trans-cleavage function of CRISPR/Cas12a. During the trans-cleavage process, the nanozyme that consists of MoS 2 QDs@Cu nanowires (MQD@CNW) is released and a PANI layer is in situ catalytically deposited onto the porphyrin COF, leading to significantly enhanced photocurrent. As a result, an ultralow detection limit of 0.32 fg mL−1 and wide linear range of 2 fg mL−1 to 0.2 μg mL−1 are obtained. This work exemplifies a high performance 2D porphyrin-COF cathodic PEC sensing platform with a promise of biomedical applications. [Display omitted] [ABSTRACT FROM AUTHOR]