Long-term survivals of immune checkpoint inhibitors as neoadjuvant and adjuvant therapy in dMMR/MSI-H colorectal and gastric cancers.

BACKGROUND: The long-term survival benefit of immune checkpoint inhibitors (ICIs) in neoadjuvant and adjuvant settings is unclear for colorectal cancers (CRC) and gastric cancers (GC) with deficiency of mismatch repair (dMMR) or microsatellite instability-high (MSI-H). METHODS: This retrospective study enrolled patients with dMMR/MSI-H CRC and GC who received at least one dose of neoadjuvant ICIs (neoadjuvant cohort, NAC) or adjuvant ICIs (adjuvant cohort, AC) at 17 centers in China. Patients with stage IV disease were also eligible if all tumor lesions were radically resectable. RESULTS: In NAC (n = 124), objective response rates were 75.7% and 55.4%, respectively, in CRC and GC, and pathological complete response rates were 73.4% and 47.7%, respectively. The 3-year disease-free survival (DFS) and overall survival (OS) rates were 96% (95%CI 90-100%) and 100% for CRC (median follow-up [mFU] 29.4 months), respectively, and were 84% (72-96%) and 93% (85-100%) for GC (mFU 33.0 months), respectively. In AC (n = 48), the 3-year DFS and OS rates were 94% (84-100%) and 100% for CRC (mFU 35.5 months), respectively, and were 92% (82-100%) and 96% (88-100%) for GC (mFU 40.4 months), respectively. Among the seven patients with distant relapse, four received dual blockade of PD1 and CTLA4 combined with or without chemo- and targeted drugs, with three partial response and one progressive disease. CONCLUSION: With a relatively long follow-up, this study demonstrated that neoadjuvant and adjuvant ICIs might be both associated with promising DFS and OS in dMMR/MSI-H CRC and GC, which should be confirmed in further randomized clinical trials.

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion.

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

AUV-Aided Optical-Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning.

Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

Research on Error Correction Technology in Underwater SINS/DVL Integrated Positioning and Navigation.

Underwater vehicles are key carriers for underwater inspection and operation tasks, and the successful implementation of these tasks depends on the positioning and navigation equipment with corresponding accuracy. In practice, multiple positioning and navigation devices are often combined to integrate the advantages of each equipment. Currently, the most common method for integrated navigation is combination of the Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL). Various errors will occur when SINS and DVL are combined together, such as installation declination. In addition, DVL itself also has errors in the measurement of speed. These errors will affect the final accuracy of the combined positioning and navigation system. Therefore, error correction technology has great significance for underwater inspection and operation tasks. This paper takes the SINS/DVL integrated positioning and navigation system as the research object and deeply studies the DVL error correction technology in the integrated system.

Prognosis of hepatocellular carcinoma and its association with immune cells using systemic inflammatory response index.

Aim: To explore the prognostic value of the systemic inflammatory response index (SIRI) and peripheral blood T-cell subsets in patients with hepatocellular carcinoma (HCC) and the relationship between them. Materials & methods: We treated 352 patients with HCC with sorafenib and/or immune checkpoint inhibitors (ICIs) and analyzed SIRI and peripheral blood T cells. Results: SIRI was an independent prognostic factor for patients with HCC receiving systemic therapy. Patients with high SIRI and low baseline peripheral blood T-cell counts showed a poor response to ICIs. SIRI was significantly and negatively correlated with CD3+, CD4+ and CD8+ T-cell counts. Conclusion: SIRI markers can be employed to noninvasively assess the presence of cancer-promoting inflammation in the tumor microenvironment and predict the efficacy of targeted therapy and immunotherapy.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. The change of immune microenvironment plays an important role in the occurrence and development of HCC. Recently, targeted therapy and immunotherapy have brought new hope to patients with advanced HCC. However, owing to the complexity of the immune microenvironment, not all patients can benefit from it. This study explores a simple, non-invasive method based on blood cell count to assess the immune microenvironment of HCC and predict the efficacy of treatment.

Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network.

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things.

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion-malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing.

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

Diversity of short interspersed nuclear elements (SINEs) in lepidopteran insects and evidence of horizontal SINE transfer between baculovirus and lepidopteran hosts.

BACKGROUND: Short interspersed nuclear elements (SINEs) belong to non-long terminal repeat (non-LTR) retrotransposons, which can mobilize dependent on the help of counterpart long interspersed nuclear elements (LINEs). Although 234 SINEs have been identified so far, only 23 are from insect species (SINEbase: http://sines.eimb.ru/ ). RESULTS: Here, five SINEs were identified from the genome of Plutella xylostella, among which PxSE1, PxSE2 and PxSE3 were tRNA-derived SINEs, PxSE4 and PxSE5 were 5S RNA-derived SINEs. A total of 18 related SINEs were further identified in 13 lepidopteran insects and a baculovirus. The 3'-tail of PxSE5 shares highly identity with that of LINE retrotransposon, PxLINE1. The analysis of relative age distribution profiles revealed that PxSE1 is a relatively young retrotransposon in the genome of P. xylostella and was generated by recent explosive amplification. Integration pattern analysis showed that SINEs in P. xylostella prefer to insert into or accumulate in introns and regions 5 kb downstream of genes. In particular, the PxSE1-like element, SlNPVSE1, in Spodoptera litura nucleopolyhedrovirus II genome is highly identical to SfSE1 in Spodoptera frugiperda, SlittSE1 in Spodoptera littoralis, and SlituSE1 in Spodoptera litura, suggesting the occurrence of horizontal transfer. CONCLUSIONS: Lepidopteran insect genomes harbor a diversity of SINEs. The retrotransposition activity and copy number of these SINEs varies considerably between host lineages and SINE lineages. Host-parasite interactions facilitate the horizontal transfer of SINE between baculovirus and its lepidopteran hosts.

Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network.

Non-orthogonal multiple access (NOMA) has been extensively studied to improve the performance of the Terrestrial-Satellite Integrated Network (TSIN) on account of the shortage of frequency band resources. In this paper, the terrestrial network and satellite network synergistically provide complete coverage for ground users, and based on the architecture, we first formulate a constrained optimization problem to maximize the sum rate of the TSIN under the limited spectrum resources. As the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, in which the optimal power control factor expression is derived. Then, by constructing the relationship model between user elevation angle, beam angle and distance, we develop a dynamic group pairing schemes to ensure the effective pairing of NOMA users. Based on the user pairing, to obtain the optimal resource allocation, a joint optimization algorithm of power allocation, beam channel and base station channel resource is proposed. Finally, simulation results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with the existing works.

Effects of tumor distance from anal verge on survival outcomes for rectal NENs and lymphatic metastasis risk score for colorectal NENs.

OBJECTIVE: To explore whether the distance of rectal neuroendocrine neoplasms from the anal margin has an impact on the prognosis of patients and evaluate lymphatic metastases risk score for colorectal neuroendocrine neoplasms (NENs). METHODS: Clinical pathological and follow-up data of 71 patients identified as colorectal neuroendocrine neoplasms by pathology from July 2011 to July 2019 were carefully collected. RESULTS: Among 71 patients with colorectal NENs, most of the tumors were rectal NENs (62 cases). A total of 26 patients were in the presence of lymph node metastasis, and 44 patients had negative lymph nodes. Patients with lesions from the anal margin > 5 cm in rectum have a better prognosis (P = 0.022). Tumor stage (P = 0.034) and grade (P = 0.001) were independent risk predictors of lymphatic metastases. We developed a lymphatic metastasis risk score for rectal NENs, and patients with the score ≥ 7.5 were more likely to develop lymph node metastases (area 0.958, 95% CI 0.903-1.000, P = 0.000) with a sensitivity of 72.2% and a specificity of 97.3%. CONCLUSION: Patients with lesions from the anal margin > 5 cm and lymphatic metastasis risk score ≥ 7.5 should be treated actively.

Characteristics of the sigK Deletion Mutant from Bacillus thuringiensis var. israelensis Strain Bt-59.

All major insecticidal genes of Bacillus thuringiensis var. israelensis (Bti) are controlled by the sporulation-specific sigma factor Sigma E (sigE), while sigE is negatively regulated by Sigma K (sigK). Therefore, knocking out sigK plays an important role in regulating the expression of insecticidal genes in Bti. A sigK deletion mutant of B. thuringiensis var. israelensis strain Bt-59, Bt59(ΔsigK), was constructed by homologous recombination and characterized. The sigK deletion resulted in no mature spores and delayed mother cell lysis from T25 to T60, while the genetically complemented strain, Bt59(HFsigK), had mother cell lysis at T25. Compared to Bt-59, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis indicated that the expression of Cry4Aa2/4Ba1 and Cyt1Aa1 proteins in Bt59(ΔsigK) increased approximately 1.67 and 1.21 times, respectively. However, there was no significant change in Cry11Aa1 protein expression between the two strains. Bioassay results showed that the sigK deletion mutation slightly reduced the insecticidal activity of Bt-59 against Culex pipiens pallens and did not obviously affect activity against Aedes albopictus.

Clinical efficacy and safety of paclitaxel liposomes as first-line chemotherapy in advanced gastric cancer.

Aim: To compare the performance of first-line paclitaxel liposome + oxaliplatin and SOX (tegafur/gimeracil/oteracil + oxaliplatin) in advanced gastric cancer patients. Materials & methods: Stage IIb-IV gastric cancer patients underwent either first-line paclitaxel liposome + oxaliplatin (n = 52) or SOX (n = 69) between 2010-2013, and followed up until 2015 or death. Results: Both groups had similar objective response rate (p = 0.48) and disease control rate (p = 0.992) after two chemotherapy cycles, median progression-free survival (p = 0.495) and median overall survival (p = 0.208). Liposome group had significantly lower rate of grade I-II platelet decline and liver function damage (p = 0.04 and 0.019). Multivariate COX regression identified pre-treatment neutrophil-to-lymphocyte ratio as an independent prognostic factor. Conclusion: First-line paclitaxel liposome + oxaliplatin has comparable efficacy, but causes reduced adverse reactions in advanced gastric cancer as compared with SOX.

A Cluster Sleep-Wake Scheduling Algorithm Based on 3D Topology Control in Underwater Sensor Networks.

3D topology control in underwater sensor networks is of great significance to ensuring reliable and efficient operation of the network. In this paper, by analyzing the characteristics of an underwater sensor network, we take the cube as the basic unit to perform 3D partition of the monitoring area, define the 3D partition unit and basic cluster structure of the underwater sensor network, and arrange rotating temporary control nodes in the cluster. Then, a cluster sleep-wake scheduling algorithm is proposed that compares the remaining node energy. It selects the node with the largest remaining energy as the working node, and the remaining nodes complete the transition of dormancy and waiting states as long as they reach the preset dormancy time. The node state settings of this phase are completed by the temporary control node. Temporary control nodes selecting and sleep-wake scheduling are used in the algorithm through 3D topology control, which reduces energy consumption and guarantees maximum sensing coverage of the entire network and the connection rate of active nodes. Simulation results further verify the effectiveness of the proposed algorithm.

A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks.

Deployment of surface-level gateways holds potential as an effective method to alleviate high-propagation delays and high-error probability in an underwater wireless sensor network (UWSN). This promise comes from reducing distances to underwater nodes and using radio waves to forward information to a control station. In an UWSN, a dynamic energy efficient surface-level gateway deployment is required to cope with the mobility of underwater nodes while considering the remote and three-dimensional nature of marine space. In general, deployment problems are usually modeled as an optimization problem to satisfy multiple constraints given a set of parameters. One previously published static deployment optimization framework makes assumptions about network workload, routing, medium access control performance, and node mobility. However, in real underwater environments, all these parameters are dynamic. Therefore, the accuracy of performance estimates calculated through static UWSN deployment optimization framework tends to be limited by nature. This paper presents the Prediction-Assisted Dynamic Surface Gateway Placement (PADP) algorithm to maximize the coverage and minimize the average end-to-end delay of a mobile underwater sensor network over a specified period. PADP implements the Interacting Multiple Model (IMM) tracking scheme to predict the positions of sensor nodes. The deployment is determined based on both current and predicted positions of sensor nodes, which enables better coverage and shorter end-to-end delay. PADP uses a branch-and-cut approach to solve the optimization problem efficiently, and employs a disjoint-set data structure to ensure connectivity. Simulation results illustrate that PADP significantly outperforms a static gateway deployment scheme.

TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction.

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions.

Downlink Cooperative Broadcast Transmission Based on Superposition Coding in a Relaying System for Future Wireless Sensor Networks.

This study investigates the superiority of cooperative broadcast transmission over traditional orthogonal schemes when applied in a downlink relaying broadcast channel (RBC). Two proposed cooperative broadcast transmission protocols, one with an amplify-and-forward (AF) relay, and the other with a repetition-based decode-and-forward (DF) relay, are investigated. By utilizing superposition coding (SupC), the source and the relay transmit the private user messages simultaneously instead of sequentially as in traditional orthogonal schemes, which means the channel resources are reused and an increased channel degree of freedom is available to each user, hence the half-duplex penalty of relaying is alleviated. To facilitate a performance evaluation, theoretical outage probability expressions of the two broadcast transmission schemes are developed, based on which, we investigate the minimum total power consumption of each scheme for a given traffic requirement by numerical simulation. The results provide details on the overall system performance and fruitful insights on the essential characteristics of cooperative broadcast transmission in RBCs. It is observed that better overall outage performances and considerable power gains can be obtained by utilizing cooperative broadcast transmissions compared to traditional orthogonal schemes.

Lack of cross-resistance between neonicotinoids and sulfoxaflor in field strains of Q-biotype of whitefly, Bemisia tabaci, from eastern China.

Control of Bemisia tabaci has depended primarily and heavily on insecticides, especially neonicotinoids. The novel sulfoximine insecticide sulfoxaflor exhibits high potency against a broad range of sap-feeding insect species, including those resistant to neonicotinoids. The resistance levels of Q-biotype B. tabaci field strains collected from 8 locations in eastern China to neonicotinoids and sulfoxaflor were investigated, and single nucleotide polymorphisms (SNPs) of nicotinic acetylcholine receptor ß1 subunit gene (Btß1) were detected. Compared with the reference strain, the field strains had developed low to moderate levels of resistance to imidacloprid and nitenpyram with the resistance ratios (RR) ranging between 4.07 and 21.75-fold and 3.37 and 16.14-fold, respectively. While YZ strain exhibited high resistance (RF 40.38) to thiamethoxam, only low levels of resistance to thiamethoxam (RF 3.50-8.58) was observed in other strains. All strains were relatively susceptible to both dinotefuran (RF 0.50-2.55) and sulfoxaflor (RF 0.40-3.07). Sequence analysis of Btß1 cDNA fragments revealed 23 SNPs representing 19 amino acid replacements in these strains. Notably, a 45bp fragment deletion was detected in JY strain, which encodes 15 amino acid residues (positions 66-80) containing arginine at position 79 (R79) corresponding to the R81T mutation in Loop D of nAChR ß1 subunit in Myzus persicae resistant to neonicotinoids. The lack of cross-resistance indicates that both dinotefuran and sulfoxaflor could play an important role in the control of B. tabaci already resistant to the first and second generation neonicotinoids.

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks.

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.