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Root hairs and soil water content are crucial in controlling the release and diffusion of root exudates and shaping profiles of biochemical properties in the rhizosphere. But whether root hairs can offset the negative impacts of drought on microbial activity remains unknown. Soil zymography

Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

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By integrating a target-responsive MSN-based controlled release system with a sensitization-SPR co-enhanced thionine/MoS2 QDs/Cu NWs photocathode, a highly sensitive split-type PEC aptasensing platform for AβO detection in blood is constructed.

Although considerable research has shown that attachment is a protective factor for cyberbullying, little research has investigated the mechanisms underlying this relationship among college students. This study examined whether loneliness mediates the association between parental attachment and cyberbullying and whether this process is moderated by interdependent self. A sample of 1125 college students (M

Reactive oxygen species (ROS) play key roles in soil biogeochemical processes, yet the occurrence and accumulation of ROS in the rhizosphere are poorly documented. Herein, we first developed a ROS-trapping membrane to

In this paper, we investigate the existence and multiplicity of nontrivial solutions for poly-Laplacian system on a finite graph $G=(V, E)$ G = ( V , E ) , which is a generalization of the Yamabe equation on a finite graph. When the nonlinear term F satisfies the super-$(p, q)$ ( p , q ) -linear growth condition, by using the mountain pass theorem we obtain that the system has at least one nontrivial solution, and by using the symmetric mountain pass theorem, we obtain that the system has at least dim W nontrivial solutions, where W is the working space of the poly-Laplacian system. We also obtain the corresponding result for the poly-Laplacian equation. In some sense, our results improve some results in (Grigor’yan et al. in J. Differ. Equ. 261(9):4924–4943, 2016).

Photic biofilms widely exist on the surface of paddy soil and play a key role in nutrient transformation and accumulation in the soil–water interface. However, whether and how photic biofilms affec...

The sorting of machine-picked fresh tea leaves after mechanized harvesting remains a challenge because of the complex morphological characteristics and physicochemical properties of fresh tea leaves. First, the recognition results of four types of models, namely, YOLOv5, YOLOv3, Fast RCNN, and SSD, were compared. It was found that YOLOv5, with guaranteed recognition accuracy, had a recognition speed of 4.7 ms/frame (about four times that of the second ranked YOLOv3). Therefore, this study presents a novel fresh tea leaf sorting system that provides rapid and high-precision multi-channel sorting for four grades of tea leaves using a tea leaf recognition model based on the You Only Look Once (YOLOv5) deep learning model. Subsequently, a raw dataset, consisting of 6400 target images of different grades and different moisture contents, was used to evaluate three different optimization methods. Among these, the Stochastic Gradient Descent (SGD) optimization method was found to provide the best model training results with an average recognition accuracy of 98.2%. In addition, the recognition efficacy of the recognition model was found to be positively correlated with the gradient coverage of tea’s moisture content in the training set. Theoretical analysis was then conducted, along with the experimental investigation of the air-blowing force on the fresh tea leaves in the sorting process, with 30° determined to be the optimal air-blowing angle. Finally, the overall results showed that the construction of the full moisture content training set enabled a model recognition accuracy of up to 88.8%, a recall of 88.4%, a recognition speed of 4.7 ms/frame, and an overall sorting accuracy of 85.4%. This result is promising for multi-channel sorting of fresh tea leaf grades in complex situations, and as such provides a strong basis for the application of tea leaf sorting equipment.

In accurately diagnosing Alzheimer's disease (AD) and distinguishing AD from other dementia, the concentration ratio of amyloid-beta 42 (Aβ

Root morphology and the composition of root exudates shape the spatial organization and various processes in the rhizosphere. For instance, root hairs are essential for plant nutrition, while secondary plant metabolites (i.e. benzoxazinoids) ensure plant defence from herbivore and fungal infection. Nevertheless, it is still unknown to which extent root hairs and benzoxazinoids may change the microbiome and enzymatic activities, as well as formation of rhizosphere hot- and coldspots.To study the effect of root hairs and benzoxasinoids on the rhizosphere microbiome structure and its enzymatic activities we compared mutants with defective root hairs rth3 or with reduced benzoxazinoids bx1 with the corresponding wild-type (WT) maize.Root hairs increased acid phosphatase activity by 80 % promoting mineralization of organic phosphorus sources to available forms in the hotspots. In the coldspots, broken root hairs in WT facilitated the intensive microbial hotspots with up to two times higher β-glucosidase and chitinase activities, compared to rth3.The presence of benzoxazinoids in root exudates strongly supported plant defence against pathogenic fungi (i.e., genus Fusarium and Gibberella) while the total microbial biomass remained unaffected. In response to the presence of pathogenic fungi, bx1 exuded 70 % more chitinase for defence purpose to partly compensate for benzoxazinoids deficiency, which was however, less efficient against pathogens than the presence of benzoxazinoids.Overall, we conclude that: i) root hairs facilitate better plant nutrition at the shortage of available nutrients (i.e., coldspots), while; ii) the presence of benzoxazinoids in exudates protect plant from pathogenic microorganisms. This two root traits are promising for plant breeding of genotypes suitable for sustainable agriculture and organic farming.

Many complex networks can be modeled as evolving graphs. Existing in-memory graph data structures are designed for DRAM. Thus, they cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent for NVMM. (2) NVMM writes and reads and may incur higher latency than DRAM. In this article, we propose a novel persistent evolving graph data structure, named NVG RAPH , for both computing and in-memory storage of evolving graphs in NVMM. We devise NVG RAPH as a multi-version data structure, wherein a minimum of one version of its data is stored in NVMM to provide the desired durability at runtime for failure recovery, and another version is stored in both DRAM and NVMM to reduce the NVMM-induced memory latency. NVG RAPH is also a partitioned data structure. We dynamically transform the layout of NVG RAPH (e.g., changing the size of its partition in DRAM) exploiting network properties and data access patterns of workloads. For the evaluation of NVG RAPH , we implement four representative real-world graph applications: pagerank, BFS, influence maximization, and rumor source detection. The experimental results show that the performance of NVG RAPH is comparable to other in-memory data structure (e.g., CSR and LLAMA) while using 70 percent less DRAM. It scales well up to 10 billion edges and 201 snapshots and supports crash consistency. It offers up to the 21X speedup of execution time compared to the scale-up graph computation approaches (e.g., GraphChi and X-stream).

As the performance gap between processors and storage devices keeps increasing, I/O performance becomes a critical bottleneck of modern high-performance computing systems. In this paper, we propose a pattern-directed and layout-aware data replication design, named PDLA, to improve the performance of parallel I/O systems. PDLA includes an HDD-based scheme H-PDLA and an SSD-based scheme S-PDLA . For applications with relatively low I/O concurrency, H-PDLA identifies access patterns of applications and makes a reorganized data replica for each access pattern on HDD-based servers with an optimized data layout. Moreover, to accommodate applications with high I/O concurrency, S-PDLA replicates critical access patterns that can bring performance benefits on SSD-based servers or on HDD-based and SSD-based servers. We have implemented the proposed replication scheme under MPICH2 library on top of OrangeFS file system. Experimental results show that H-PDLA can significantly improve the original parallel I/O system performance and demonstrate the advantages of S-PDLA over H-PDLA.

The emerging distribution-level phasor measurement unit (D-PMU) is expected to play an important role in enhancing distribution system observability and situational-awareness. It is a demanding yet challenging task to develop advanced D-PMU data management and analytics tools to improve D-PMU data usability and further promote D-PMU projects deployment. This paper focuses on D-PMU data processing and storage. It presents a brief review of existing D-PMU data storage systems and points out their limitations on high-performance, flexibility, and scalability. To overcome the limitations, a unified PMU-data storage system (UPS) is proposed. Specifically, a unified I/O interface between storage servers and computing jobs is developed to effectively reduce the overhead of managing various computing jobs and data analytics over multiple storage infrastructures; and PMUCache with PMUCache partitioning and PMUCache replacement algorithms are designed to support in-situ data processing and shared distributed data storage and further serve the computing jobs/queries with different quality of service (QoS) requirements. Through a series of experiments, it is demonstrated that UPS achieves high performance on fast and big data processing and storage, and efficiently increases the flexibility and scalability of PMU data management systems.

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms., Accepted by IEEE Transactions on Multimedia (TMM)

The teeth and their supporting tissues provide an easily accessible source of oral stem cells. These different stem cell populations have been extensively studied for their properties, such as high plasticity and clonogenicity, expressing stem cell markers and potency for multilineage differentiation in vitro. Such cells with stem cell properties have been derived and characterised from the dental pulp tissue, the apical papilla region of roots in development, as well as the supporting tissue of periodontal ligament that anchors the tooth within the alveolar socket and the soft gingival tissue. Studying the dental pulp stem cell populations in a continuously growing mouse incisor model, as a traceable in vivo model, enables the researchers to study the properties, origin and behaviour of mesenchymal stem cells. On the other side, the oral mucosa with its remarkable scarless wound healing phenotype, offers a model to study a well-coordinated system of healing because of coordinated actions between epithelial, mesenchymal and immune cells populations. Although described as homogeneous cell populations following their in vitro expansion, the increasing application of approaches that allow tracing of individual cells over time, along with single-cell RNA-sequencing, reveal that different oral stem cells are indeed diverse populations and there is a highly organised map of cell populations according to their location in resident tissues, elucidating diverse stem cell niches within the oral tissues. This review covers the current knowledge of diverse oral stem cells, focusing on the new approaches in studying these cells. These approaches "decode" and "map" the resident cells populations of diverse oral tissues and contribute to a better understanding of the "stem cells niche architecture and interactions. Considering the high accessibility and simplicity in obtaining these diverse stem cells, the new findings offer potential in development of translational tissue engineering approaches and innovative therapeutic solutions.

Paddy soils regularly experience redox oscillations during the wetting and draining stages, yet the effects of short-term presence of oxygen (O

Graphic Processing Units (GPUs) have limited memory capacity. Training popular deep neural networks (DNNs) often requires a larger amount of memory than that a GPU may have. Consequently, training data needs to be swapped between CPUs and GPUs. Data swapping may become a bottleneck when its latency is longer than the latency of DNN computations. Tensor compression in GPUs can reduce the data swapping time. However, existing works on compressing tensors in the virtual memory of GPUs have two major issues: sub-optimal compression performance for varying tensor sparsity and sizes and lack of portability because its implementation requires additional (de)compression units in memory controllers. We propose a self-tuning tensor compression framework, named CSWAP, for improving the virtual memory management of GPUs. It has high portability and is minimally dependent on GPU architecture features. Furthermore, its runtime only applies compression on tensors that are deemed to be cost-effective considering their sparsity and size and the characteristics of compression algorithms. Finally, our framework is fully automated and can customize the compression policy for different neural network architectures and GPU architectures. Our experimental results using six representative memory-intensive DNN models show that CSWAP reduces tensor swapping latency by up to 50.9% and reduces the DNN training time by 20.7% on average with NVIDIA V100 GPUs compared to vDNN.