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.

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.

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 .

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.

Predominant role for activation-induced cytidine deaminase in generating IgG anti-nucleosomal antibodies of murine SLE.

Serum IgG anti-nuclear antibodies (ANA) directed to complexes of DNA and histones are a hallmark of systemic lupus erythematosus (SLE) and reflect a failure in lymphocyte self-tolerance. A prior study utilizing spontaneously autoimmune B6.Nba2 mice deficient in terminal deoxynucleotidyl transferase (TdT) and with heterozygous deficiencies in Jh and Igk loci underscored the importance of somatic hypermutation (SHM) as a major generator of SLE-associated ANA. This interpretation had to be qualified because of severely limited opportunities for receptor editing and restricted VHCDR3 diversity. Therefore, we performed the converse study using mice that carried functional Tdt genes and wild type Jh and Igk loci but that could not undergo SHM. Analyses of ANA and ANA-producing hybridomas from B6.Nba2 Aicda(-/-) mice revealed that few animals produced high titers of the prototypical ANA directed to complexes of histones and DNA, that this response was delayed and that those cells that did produce such antibody exhibited limited clonal expansion, unusual Jk use and only infrequent dual receptor expression. This, together with the additional finding of an intrinsic propensity for SHM to generate Arg codons selectively in CDRs, reinforce the view that most IgG autoimmune clones producing prototypical anti-nucleosome antibodies in wild type mice are created by SHM.

[Analysis of inorganic elements in hydroponic Taraxacum mongolicum grown under different spectrum combinations by ICP-AES].

Dandelion (Taraxacum mongolicum) was hydroponically cultured in a completely enclosed plant factory, in which fluorescence and LED emitting spectra of different bands were used as the sole light source for plant growth. Effects of spectral component on the growth of dandelion were studied and the contents of ten inorganic elements such as K, P, Ca, Mg, Na, Fe, Mn, Zn, Cu and B in dandelion were analyzed by ICP-AES technology. The results showed that: (1) Under the condition of similar photosynthetic active radiation (PAR), single R or combined spectrums of FLRB were beneficial for biomass accumulation, while single B was the contrary; (2) Macroelements content ratio in Taraxacum mongolicum grown under FLwas K:Ca:P:Mg : Na=79.74:32.39:24.32:10.55:1.00, microelements content ratio was Fe:Mn:B:Zn:Cu = 9.28:9.71:3.82:2.08:1.00; (3) Red light (peak at 660 nm) could promote the absorptions of Ca, Fe, Mn, Zn, while absorption of Cu was not closely related to spectral conditions; (4) Thehighest accumulation of Ca, Na, Mn and Zn were obtained in aerial parts of Taraxacum mongolicum plants grown under pure red spectrum R, while the accumulation of the rest six elements reached the highest level under the mixed spectrum FLRB.

Antigen-specific suppression of humoral immunity by anergic Ars/A1 B cells.

Autoreactive anergic B lymphocytes are considered to be dangerous because of their potential for activation and recruitment into autoimmune responses. However, they persist for days and constitute ∼5% of the B cell pool. We assessed their functional potential in the Ars/A1 transgene model, where anergic B cells express a dual-reactive Ag receptor that binds, in addition to a self-Ag, the hapten p-azophenylarsonate (Ars). When Ars/A1 B cells were transferred into adoptive recipients that were immunized with foreign proteins covalently conjugated with Ars, endogenous IgG immune responses to both were selectively and severely diminished, and the development of T helper cells was impaired. Approximately 95% inhibition of the anti-Ars response was attained with ∼4000 transferred Ars/A1 B cells through redundant mechanisms, one of which depended on their expression of MHC class II but not upon secretion of IL-10 or IgM. This Ag-specific suppressive activity implicates the autoreactive anergic B cell as an enforcer of immunological tolerance to self-Ags.

Reliable adaptive data aggregation route strategy for a trade-off between energy and lifetime in WSNs.

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.

[Effects of LED spectrum combinations on the absorption of mineral elements of hydroponic lettuce].

Lettuce (Lactuca sativa) was hydroponically cultured in a completely enclosed plant factory, in which spectrum proportion-adjustable LED panels were used as sole light source for plant growth. Absorption and content of eleven mineral elements such as K, P, Ca, Mg, Na, Fe, Mn, Zn, Cu, B and Mo in Lactuca sativa under different spectral component conditions were studied by ICP -AES technology. The results showed that: (1) Single or combined spectrums corresponding to the absorbing peaks of chlorophyll a and b (450, 660 nm) could enhance the absorbing ability of roots especially for mineral elements Na, Fe, Mn, Cu and Mo, the single red spectrum had the most significant promoting effect under which contents of those four elements were respectively 7. 8, 4. 2, 4. 0 and 3. 7 times more than that under FL; (2) Absorption of K and B was the highest under FL which was 10. 309 mg g-1 and 32. 6 microg g-1 while the values decreased significantly under single or combined spectrum of red and blue; (3) Plants grown under single blue spectrum had the lowest absorption of Ca and Mg which respectively decreased by 35% and 33% than FL; (4) Lettuce grown under the spectrum combination of 30% blue and 70% red had the highest accumulations of biomass while those grown under 20% blue and 80% red had the highest accumulations of the following seven elements Ca, Mg, Na, Fe, Mn, Zn and B. The results provided theoretical basis for adjusting nutrient solution formula and selecting light spectrum of hydroponic lettuce.

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.

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.

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.

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.

[Analysis of the mineral elements of Lactuca sativa under the condition of different spectral components].

Mineral elements absorption and content of Lactuca sativa under different spectral component conditions were studied by ICP-AES technology. The results showed that: (1) For Lactuca sativa, the average proportion for Ca : Mg : K : Na : P was 5.5 : 2.5 : 2.3 : 1.5 : 1.0, the average proportion for Fe : Mn : Zn : Cu : B was 25.9 : 5.9 : 2.8 : 1.1 : 1.0; (2) The absorptions for K, P, Ca, Mg and B are the largest under the LED treatment R/B = 1 : 2.75, red light from fluorescent lamps and LED can both promote the absorptions of Fe and Cu; (3)The LED treatments exhibiting relatively higher content of mineral elements are R/B = 1 : 2.75 and R/W = 1 : 1 while higher dry matter accumulations are R/B = 1 : 2.75 and B/W = 1 : 1.

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.

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.

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.

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.

A tritherapy combination of a fusion protein vaccine with immune-modulating doses of sequential chemotherapies in an optimized regimen completely eradicates large tumors in mice.

Tumor-induced immunosuppression plays a critical role in both impeding tumor-specific immune responses and limiting the effects of cancer immunotherapy. Analyses of regulatory cells recruited during the growth of the E7-expressing tumor, TC-1, revealed a high percentage of regulatory T cells (Tregs) as well as myeloid-derived suppressor cells (MDSCs) in spleens and tumors. In this study, we proposed that treatment with immune-modulating doses of cyclophosphamide (CTX) and all-trans retinoic acid (ATRA) would result in a beneficial tumor microenvironment with the suppression of Tregs and MDSCs and, thus, enhance the effect of a human papillomavirus protein vaccine. Our results showed that CTX preconditioning and persistent ATRA treatment along with the vaccine achieved long-term survival and induced long-term memory responses. However, the effect of the antitumor response sharply declined when the tritherapy was initiated after the optimal therapeutic time. The more intensive regimen could rescue the effect of the tritherapy accompanied by the decreased percentage of Tregs and MDSCs in spleens and tumors. Besides, a favorable host environment was created by the reduced secretion of interleukin-10 and 6 and vascular endothelial growth factor (VEGF) in the tumor niche and decreased the expression of phosphorylation-signal transducer and activator of transcription 3 of TC-1 tumors. Our data shed light on the immune-modulating doses of sequential chemoimmunotherapeutic strategy targeting not only the tumor but also its microenvironment, which suggests a potential clinical benefit for the immunotherapy of HPV-associated malignancies.

Design and analysis of self-adapted task scheduling strategies in wireless sensor networks.

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm's ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms.