The development and nutritional quality of Lyophyllum decastes affected by monochromatic or mixed light provided by light-emitting diode.

Edible fungi has certain photo-sensitivity during the mushroom emergence stage, but there has been few relevant studies on the responses of Lyophyllum decastes to different light quality. L. decastes were planted in growth chambers with different light qualities that were, respectively, white light (CK), monochromatic red light (R), monochromatic blue light (B), mixed red and blue light (RB), and the mixture of far-red and blue light (FrB). The photo-sensitivity of L. decastes was investigated by analyzing the growth characteristics, nutritional quality, extracellular enzymes as well as the light photoreceptor genes in mushroom exposed to different light treatments. The results showed that R led to mycelium degeneration, fungal skin inactivation and failure of primordial formation in L. decastes. The stipe length, stipe diameter, pileus diameter and the weight of fruiting bodies exposed to RB significantly increased by 8.0, 28.7, 18.3, and 58.2% respectively, compared to the control (p < 0.05). B significantly decreased the stipe length and the weight of fruiting body, with a decrease of 8.5 and 20.2% respectively, compared to the control (p < 0.05). Increased color indicators and deepened simulated color were detected in L. decastes pileus treated with B and FrB in relative to the control. Meanwhile, the expression levels of blue photoreceptor genes such as WC-1, WC-2 and Cry-DASH were significantly up-regulated in mushroom exposed to B and FrB (p < 0.05). Additionally, the contents of crude protein and crude polysaccharide in pileus treated with RB were, respectively, increased by 26.5 and 9.4% compared to the control, while those in stipes increased by 5.3 and 58.8%, respectively. Meanwhile, the activities of extracellular enzyme such as cellulase, hemicellulase, laccase, manganese peroxidase, lignin peroxidase and amylase were significant up-regulated in mushroom subjected to RB (p < 0.05), which may promote the degradation of the culture materials. On the whole, the largest volume and weight as well as the highest contents of nutrients were all detected in L. decastes treated with RB. The study provided a theoretical basis for the regulation of light environment in the industrial production of high quality L. decastes.

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.

Effects of irrigation scheduling on the yield and irrigation water productivity of cucumber in coconut coir culture.

Optimum irrigation scheduling is important for ensuring high yield and water productivity in substrate-cultivated vegetables and is determined based on information such as substrate water content, meteorological parameters, and crop growth. The aim of this study was to determine a precise irrigation schedule for coconut coir culture in a solar greenhouse by comparing the irrigation, evapotranspiration (ET), substrate water content (VWC), as well as the crop growth indices and yield of cucumber, and irrigation water productivity (IWP) under three irrigation schedules: the soil moisture sensor-based method (T-VWC), the accumulated radiation combined with soil moisture sensor-based method (Rn-VWC), and the crop evapotranspiration estimated method using the hourly PM-ETo equation with an improved calculation of Kc (T-ETc). The results showed that the daily irrigation and evapotranspiration amount were the highest under T-VWC treatment, while the lowest under T-ETc treatment. In different meteorological environments, the change in irrigation amount was more consistent with the ET,and the VWC was relatively stable in T-ETc treatment compared with that under T-VWC or Rn-VWC treatments. The plant height, leaves number, leaf area, and stem diameter of T-VWC and Rn-VWC treatments were higher than those of the T-ETc treatments, but there was no significant difference in cucumber yield. Compared with the T-VWC treatment, total irrigation amount under Rn-VWC and T-ETc treatments significantly decreased by 25.75% and 34.04%, respectively ([Formula: see text]). The highest IWP values of 25.07 kg m[Formula: see text] was achieved from T-ETc treatment with significantly increasing by 44.33% compared to the T-VWC treatment (17.37 kg m[Formula: see text]). In summary, the T-ETc treatment allowed more reasonable irrigation management and was appropriate for growing cucumber in coconut coir culture.

High-Similarity-Pass Attention for Single Image Super-Resolution.

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation.

Antibody-based binding domain fused to TCRγ chain facilitates T cell cytotoxicity for potent anti-tumor response.

Chimeric antigen receptor T-cell (CAR-T) therapy has demonstrated potent clinical efficacy in the treatment of hematopoietic malignancies. However, the application of CAR-T in solid tumors has been limited due in part to the expression of inhibitory molecules in the tumor microenvironment, leading to T-cell exhaustion. To overcome this limitation, we have developed a synthetic T-cell receptor (TCR) that targets programmed death-ligand 1 (PD-L1), a molecule that is widely expressed in various solid tumors and plays a pivotal role in T-cell exhaustion. Our novel TCR platform is based on antibody-based binding domain, which is typically a single-chain variable fragment (scFv), fused to the γδ TCRs (TCRγδ). We have utilized the T-cell receptor alpha constant (TRAC) locus editing approach to express cell surface scFv of anti-PD-L1, which is fused to the constant region of the TCRγ or TCRδ chain in activated T cells derived from peripheral blood mononuclear cells (PBMCs). Our results indicate that these reconfigured receptors, both γ-TCRγδ and δ-TCRγδ, have the capability to transduce signals, produce inflammatory cytokines, degranulate and exert tumor killing activity upon engagement with PD-L1 antigen in vitro. Additionally, we have also shown that γ-TCRγδ exerted superior efficacy than δ-TCRγδ in in vivo xenograft model.

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing.

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multilayer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an alternating graph-regularized neural network (AGNN) composed of graph convolutional layer (GCL) and graph embedding layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multilayer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with the state-of-the-art models.

Challenges in αCD38-chimeric antigen receptor (CAR)-expressing natural killer (NK) cell-based immunotherapy in multiple myeloma: Harnessing the CD38dim phenotype of cytokine-stimulated NK cells as a strategy to prevent fratricide.

BACKGROUND AIMS: Adoptive cell therapy with chimeric antigen receptor (CAR)-expressing natural killer (NK) cells is an emerging approach that holds promise in multiple myeloma (MM). However, the generation of CAR-NK cells targeting CD38 is met with obstacles due to the expression of CD38 on NK cells. Knock-out of CD38 is currently explored as a strategy, although the consequences of the lack of CD38 expression with regards to engraftment and activity in the bone marrow microenvironment are not fully elucidated. Here, we present an alternative approach by harnessing the CD38dim phenotype occurring during long-term cytokine stimulation of primary NK cells. METHODS: Primary NK cells were expanded from peripheral blood mononuclear cells by long-term IL-2 stimulation. During expansion, the CD38 expression was monitored in order to identify a time point when introduction of a novel affinity-optimized αCD38-CAR confered optimal viability, i.e. prevented fratricide. CD38dim NK cells were trasduced with retroviral vectors encoding for the CAR trasngene and their functionality was assessed in in vitro activation and cytotoxicity assays. RESULTS: We verified the functionality of the αCD38-CAR-NK cells against CD38+ cell lines and primary MM cells. Importantly, we demonstrated that αCD38-CAR-NK cells derived from patients with MM have increased activity against autologous MM samples ex vivo. CONCLUSIONS: Overall, our results highlight that incorporation of a functional αCD38-CAR construct into a suitable NK-cell expansion and activation protocol results in a potent and feasible immunotherapeutic strategy for the treatment of patients with MM.

Responses of butter leaf lettuce to mixed red and blue light with extended light/dark cycle period.

To investigate the effects of extended light/dark (L/D) cycle period (relative to the diurnal L/D cycle) on lettuce and explore potential advantages of abnormal L/D cycles, butter leaf lettuce were grown in a plant factory with artificial light (PFAL) and exposed to mixed red (R) and blue (B) LED light with different L/D cycles that were respectively 16 h light/8 h dark (L16/D8, as control), L24/D12, L48/D24, L96/D48 and L120/D60. The results showed that, all the abnormal L/D cycles increased shoot dry weight (DW) of lettuce (by 34-83%) compared with the control, and lettuce DW increased with the L/D cycle period prolonged. The contents of soluble sugar and crude fiber in lettuce showed an overall upward trend with the length of L/D cycle extended, and the highest vitamin C content as well as low nitrate content were both detected in lettuce treated with L120/D60. The light use efficiency (LUE) and electric use efficiency (EUE) of lettuce reached the maximum (respectively 5.37% and 1.76%) under L120/D60 treatment and so were DW, Assimilation rate (A), RC/CS, ABS/CS, TRo/CS and DIo/CS, indicating that longer L/D cycle period was beneficial for the assimilation efficiency and dry matter accumulation in lettuce leaves. The highest shoot fresh weight (FW) and nitrate content detected in lettuce subjected to L24/D12 may be related to the vigorous growth of root, specific L/D cycle seemed to strengthen root growth and water absorption of lettuce. The openness level of RC in PSII (Ψo), ETo/CS, and PIabs were all the highest in lettuce treated with L24/D12, implying that slightly extending the L/D cycle period might promote the energy flowing to the final electron transfer chain. In general, irradiation modes with extended L/D cycle period had the potential to improve energy use efficiency and biomass of lettuce in PFAL. No obvious stress or injury was detected in lettuce subjected to prolonged L/D cycles in terms of plant growth and production. From the perspective of shoot FW, the optimal treatment in this study was L24/D12, while L120/D60 was the recommended treatment as regards of the energy use efficiency and nutritional quality.

100+ FPS detector of personal protective equipment for worker safety: A deep learning approach for green edge computing.

In industrial production, personal protective equipment (PPE) protects workers from accidental injuries. However, wearing PPE is not strictly enforced among workers due to all kinds of reasons. To enhance the monitoring of workers and thus avoid safety accidents, it is essential to design an automatic detection method for PPE. In this paper, we constructed a dataset called FZU-PPE for our study, which contains four types of PPE (helmet, safety vest, mask, and gloves). To reduce the model size and resource consumption, we propose a lightweight object detection method based on deep learning for superfast detection of whether workers are wearing PPE or not. We use two lightweight methods to optimize the network structure of the object detection algorithm to reduce the computational effort and parameters of the detection model by 32% and 25%, respectively, with minimal accuracy loss. We propose a channel pruning algorithm based on the BN layer scaling factor γ to further reduce the size of the detection model. Experiments show that the automatic detection of PPE using our lightweight object detection method takes only 9.5 ms to detect a single video frame and achieves a detection speed of 105 FPS. Our detection model has a minimum size of 1.82 MB and a model size compression rate of 86.7%, which can meet the strict requirements of memory occupation and computational resources for embedded and mobile devices. Our approach is a superfast detection method for green edge computing.

Red and blue wavelengths affect the morphology, energy use efficiency and nutritional content of lettuce (Lactuca sativa L.).

Since red (R) and blue (B) LED light has different quantum efficiency and photoelectric conversion efficiency, mixed RB with different proportions of R and B results in varied energy consumption. In order to improve the energy use efficiency of the closed-type plant production systems, the effects of R and B proportions on the electric use efficiency (EUE), light use efficiency (LUE) as well as the quality of butter leaf lettuce were evaluated in this study. Lettuce seedlings were cultivated in a plant factory with artificial lighting (PFAL) and subjected to eleven combinations of R and B (100%R, 90%R, 80%R, 70%R, 60%R, 50%R, 40%R, 30%R, 20%R, 10%R, 0%R; the rest of the photons in each treatment were B) with the same total photosynthetic photon flux density (PPFD) and photoperiod (200 ± 3 µmol·m-2·s-1, 16 h) for 35 days. The results showed that palpable petiole distortion appeared when R proportion was more than 70% and the distortion was aggravated with the increase of R proportion. The highest EUE and LUE were both detected in lettuce under 90%R treatment, which were respectively 3.64% and 1.20%. The least number of photons and the least electricity amount required to produce 1 g dry weight of lettuce was respectively 2.92 mol and 1.67 MJ, which were both detected in lettuce treated with 90%R. The sucrose content in lettuce treated with more than 50%R was significantly higher than those treated with less than 50%R (50%R included). Lettuce treated with 80%R possessed the highest soluble sugar content as well as the lowest crude fiber and nitrate content (not significantly different with the minimum values). R proportion exceeding 50% in mixed RB light was beneficial to the accumulation of hexose and sucrose, as well as the decomposition of nitrate in lettuce. The vitamin C content in lettuce treated with 100%R was significantly higher than that in lettuce under other treatments in the study. On the whole, the study indicated that the proportions of R and B affected the energy use efficiency and quality of lettuce in closed plant factory, however the responses of plants to the proportions of R and B varied according to different indexes. Thus, some indexes of top priority should be determined before choosing the optimal proportions of R and B.

HDR-GAN: HDR Image Reconstruction From Multi-Exposed LDR Images With Large Motions.

Synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) exposures in dynamic scenes is challenging. There are two major problems caused by the large motions of foreground objects. One is the severe misalignment among the LDR images. The other is the missing content due to the over-/under-saturated regions caused by the moving objects, which may not be easily compensated for by the multiple LDR exposures. Thus, it requires the HDR generation model to be able to properly fuse the LDR images and restore the missing details without introducing artifacts. To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images. To our best knowledge, this work is the first GAN-based approach for fusing multi-exposed LDR images for HDR reconstruction. By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content. In addition, we also propose a novel generator network, with a reference-based residual merging block for aligning large object motions in the feature domain, and a deep HDR supervision scheme for eliminating artifacts of the reconstructed HDR images. Experimental results demonstrate that our model achieves state-of-the-art reconstruction performance over the prior HDR methods on diverse scenes.

Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings.

An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, several knowledge graph (KG) embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new KG embedding framework by introducing adversarial autoencoders (AAEs) based on Wasserstein distances and Gumbel-Softmax relaxation for DDI tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation-an inherent flaw in traditional generative models-we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks: link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines. Supplementary information: Supplementary data and code are available at https://github.com/dyf0631/AAE_FOR_KG.

Boundary-aware RGBD Salient Object Detection with Cross-modal Feature Sampling.

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection. In particular, to enhance the fusion of color and depth features, we present a cross-modal feature sampling module to balance the contribution of the RGB and depth features based on the statistics of their channel values. In addition, in our multi-scale dense fusion network architecture, we not only incorporate edge-sensitive losses to preserve the boundary of the detected salient region, but also refine its structure by merging the estimated saliency maps of different scales. We accomplish the multi-scale saliency map merging using two alternative methods which produce refined saliency maps via per-pixel weighted combination and an encoder-decoder network. Extensive experimental evaluations demonstrate that our proposed framework can achieve the state-of-the-art performance on several public RGBD-based datasets.

Novel decoy cellular vaccine strategy utilizing transgenic antigen-expressing cells as immune presenter and adjuvant in vaccine prototype against SARS-CoV-2 virus.

A novel approach modifying cells to express viral markers to elicit protective immunity responses (decoy cellular vaccination) in the prevention of COVID-19 disease is currently being explored. Our approach entails utilizing SARS-CoV-2 Spike antigen-expressing, non-replicating cells as carriers and presenters of immunogenic antigens, so called "I-cells". By using irradiated cells as presenting vehicles of SARS-CoV-2 viral antigens(s) in a cellular context, these presented viral proteins can be recognized by the host immune system, thus, an efficient protective immune response might be elicited. Another advantage of this strategy is that the manufacturing process is scalable and yields uniform cell products allowing for "off-the-shelf" frozen supply availability. To prevent engraftment and proliferation of the cells after administration, the cells will be irradiated post-harvesting abolishing in vivo replication potential. Specifically, immunoreactive Spike-1 proteins from SARS-CoV-2 are expressed on the surface of irradiated target I-cells. Utilizing this innovative strategy, these viral antigen-displaying decoy cells will be developed as a vaccine to protect against COVID-19 disease.

Two Projection Neural Networks With Reduced Model Complexity for Nonlinear Programming.

Recent reports show that projection neural networks with a low-dimensional state space can enhance computation speed obviously. This paper proposes two projection neural networks with reduced model dimension and complexity (RDPNNs) for solving nonlinear programming (NP) problems. Compared with existing projection neural networks for solving NP, the proposed two RDPNNs have a low-dimensional state space and low model complexity. Under the condition that the Hessian matrix of the associated Lagrangian function is positive semi-definite and positive definite at each Karush-Kuhn-Tucker point, the proposed two RDPNNs are proven to be globally stable in the sense of Lyapunov and converge globally to a point satisfying the reduced optimality condition of NP. Therefore, the proposed two RDPNNs are theoretically guaranteed to solve convex NP problems and a class of nonconvex NP problems. Computed results show that the proposed two RDPNNs have a faster computation speed than the existing projection neural networks for solving NP problems.

Sugar accumulation and growth of lettuce exposed to different lighting modes of red and blue LED light.

The present study evaluated the growth response and sugar accumulation of lettuce exposed to different lighting modes of red and blue LED light based on the same daily light integral (7.49 µmol·m-2). Six lighting treatments were performed, that were monochromatic red light (R), monochromatic blue light (B), simultaneous red and blue light as the control (RB, R:B = 1:1), mixed modes of R, B and RB (R/RB/B, 4 h R to 4 h RB and then 4 h B), and alternating red and blue light with alternating intervals of 4 h and 1 h respectively recorded as R/B(4 h) and R/B(1 h). The Results showed that different irradiation modes led to obvious morphological changes in lettuce. Among all the treatments, the highest fresh and dry weight of lettuce shoot were both detected with R/B(1 h), significantly higher than the other treatments. Compared with plants treated with RB, the contents of fructose, glucose, crude fiber as well as the total sweetness index (TSI) of lettuce were significantly enhanced by R treatment; meanwhile, monochromatic R significantly promoted the activities of sucrose degrading enzymes such as acid invertase (AI) and neutral invertase (NI), while obviously reduced the activity of sucrose synthesizing enzyme (SPS). Additionally. The highest contents of sucrose and starch accompanied with the strongest activity of SPS were detected in plants treated with R/B(1 h). The alternating treatments R/B(4 h) and R/B(1 h) inhibited the activity of SS, while enhanced that of SPS compared with the other treatments, indicating that different light environment might influence sugar compositions via regulating the activities of sucrose metabolism enzymes. On the whole, R/B(1 h) was the optimal lighting strategy in terms of lettuce yield, taste and energy use efficiency in the present study.

Soil moisture and nitrate-nitrogen dynamics and economic yield in the greenhouse cultivation of tomato and cucumber under negative pressure irrigation in the North China Plain.

A field experiment was carried out for two years to investigate the benefits of negative pressure water supply on surface soil water content, nitrate-nitrogen (NO3--N) distribution in the soil profile, economic yield and water and fertilizer use efficiency of tomato and cucumber under greenhouse cultivation in the North China Plain. The experiment included two irrigation treatments: drip irrigation with nutrient solution (DIN) and negative pressure irrigation with nutrient solution (NIN). The results showed that the NIN treatment had a relatively stable soil moisture (about 87% of field capacity), and the fluctuation of soil water content in the 0-20 cm soil layer was 20.6%-25.0% during the experiment period in 2014-2015, which was less than the range of 19.2%-28.1% in the DIN treatment. In both the DIN and NIN treatments, the NO3--N at the end of the four growing seasons was mainly distributed in the 0-40 cm soil layer and showed a gradually increasing trend as the number of cultivation years increased. Compared with the DIN treatment, the NO3--N content in the 0-60 cm layer of the NIN treatment was significantly decreased by 19.7%-28.0% after the fourth growing season. The NIN treatment produced the highest economic yield with lower water and nutrient input than the DIN treatment, however, no significant difference was observed in tomato and cucumber yield in the two years. Average irrigation water use efficiency (WUEi) and partial factor productivity of fertilizer (PFPf) over the study period were all significantly improved under the NIN treatment relative to the DIN treatment, with increases of 26.2% and 25.7% (P < 0.05), respectively. Negative pressure water supply not only maintained a high fruit yield, but significantly increased WUEi and PFPf, indicating a great advantage in water and fertilizer saving compared with drip irrigation.

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks.

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .

Comparative Transcriptomic Analysis of Vernalization- and Cytokinin-Induced Floral Transition in Dendrobium nobile.

Vernalization is required for floral initiation in Dendrobium. Interestingly, those beneficial effects can also be achieved by exogenous cytokinin application in greenhouses. Thus, an as yet unknown crosstalk/interaction may exist between vernalization and cytokinin signaling pathways. In this study, we showed, by de novo transcriptome assembly using RNA-seq data from both vegetative and reproductive tissue samples, that some floral transition-related genes-DnVRN1, FT, SOC1, LFY and AP1-were differentially expressed in low-temperature-challenged (LT) or thidiazuron (TDZ)-treated plants, compared to those mock-treated (CK). Both LT and TDZ upregulated SOC1, LFY and AP1, while the upregulation of DnVRN1 and FT was only LT-induced. We further found that LT promoted the upregulation of some key cytokinin signaling regulators, including several cytokinin biosynthesis-related genes and type-B response regulator (RR)-encoding genes, and that both LT and TDZ triggered the significant upregulation of some marker genes in the gibberellin (GA) signaling pathway, indicating an important low temperature-cytokinin-GA axis in flowering. Our data thus have revealed a cytokinin-GA signal network underlying vernalization, providing a novel insight into further investigation of the molecular mechanism of floral initiation in Dendrobium.

A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.

Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.