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Underwater Acoustic Sensor Networks (UASNs) have been widely applied in marine monitoring, military reconnaissance, hydrology surveys, etc. Their location information is an important apriori knowledge when they are carried out underwater. However, the complex underwater environments impose great challenges on location acquisition, especially for Autonomous Underwater Vehicles (AUVs), because of their mobility and finite power. In UASNs, existing location and navigation methods can offer AUVs position information, but they may either need a Doppler Velocity Log (DVL), which is inefficient due to the complex underwater environments, or they may require additional localization infrastructure to deploy underwater, which suffers from large communication latency among AUVs, and costs enormous power. In this paper, an efficient Velocity Estimation and Location Prediction method (VELP) in underwater acoustic sensor networks is proposed to avoid the above restrictions. It only utilizes collaborations based on communication among AUVs to achieve higher-precision location with lower-cost. Specifically, we apply an AUV-assisted velocity estimation algorithm with Doppler Shift estimation in physical layer of UASNs, to improve the velocity estimation accuracy instead of the DVL. Meanwhile, we build a belief propagation-neural network-based location prediction model, which decreases the communication requirements and obviates introducing modeling errors. Extensive experimental results show VELP achieves superior performance on both accuracy and efficiency, demonstrating its great advantage in offering AUVs’ location information.

Background Some significant differences exist between the outcomes of left‐ and right‐sided colon cancer patients. The presence of nodal metastases is a critical prognostic factor, especially in the absence of distant metastasis. Our research studied the lymph nodes status of left‐ and right‐sided colon cancer patients to determine the influence of this factor on prognosis. Methods Our data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. We used the chi‐square test to analyze the clinicopathological characteristics. The X‐tile program was adopted to acquire optimal cutoff points of lymph node index. Kaplan–Meier curves were used to analyze prognosis and multivariate Cox regression models were performed to identify the independent factors associated with survival. Nomograms were built to predict the overall survival of patients, Harrell's C‐index and calibration plots were used to validate the nomograms. Results The study included 189,941 patients with colon cancer without metastasis (left 69,885, right 120,056) between 2004 and 2015. There are more patients with adequate examined lymph nodes in right‐sided. Lymph node status in patients with right colon cancer has a more significant impact on the risk of death. LODDS (C‐index: 0.583; AIC: 6875.4) was used to assess lymph node status. The nomograms showed that lymph node status was the main factor to predict the outcome in right‐sided colon patients. Conclusions The influence of lymph node status on predicting prognosis is significantly different between patients with left and right colon cancer without metastasis. The tumor site needs to be considered when lymph node status is used to assess the outcome of patients., The effect of lymph node status on predicting prognosis differed significantly between left‐sided and right‐sided colon cancer patients. The tumor site needs to be considered when lymph node status is used to assess the outcome of patients.

Background: Septic lung injury is associated with excessive neutrophil activation, while neu-trophil extracellular traps formation contributes to inflammatory lung injury in sepsis. C1q/ tumor necrosis factor–related protein-6 (CTRP6) is a paralog of adiponectin and exerts anti-inflammatory and antioxidant properties. The role of CTRP6 in sepsis-associated inflammatory lung injury was investigated in this study. Methods: Mice were injected with lipopolysaccharides (LPS) intraperitoneally to establish the mouse sepsis model. They were first tail-vein injected with adenovirus-mediated overexpression CTRP6 (Ad-CTRP6) and then subjected to the LPS injection. Pathological changes in lungs were detected by hematoxylin and eosin staining. Inflammation cytokine levels in bronchoalveolar lavage fluid were assessed by qRT-PCR and ELISA. Flow cytometry was used to detect the number of neutrophils in bronchoalveolar lavage fluid, and immunofluorescence was performed to assess neutrophil extracellular traps. Results: Lipopolysaccharides induced pulmonary congestion, interstitial edema, and alveolar wall thickening in the lungs, as well as upregulated lung histology score and wet/dry weight ratio. CTRP6 was reduced in lung tissues of septic mice. Injection with Ad-CTRP6 ameliorated extensive histopathological changes in LPS-induced mice and decreased lung histology score and wet/dry weight ratio. Overexpression of CTRP6 reduced the levels of TNF-α, IL-6, and IL-1β in septic mice. Injection with Ad-CTRP6 also decreased the number of neutrophils and down-regulated Cit-H3 and myeloperoxidase polymers in septic mice. Protein expression of p-ERK in septic mice was reduced by overexpression of CTRP6. Conclusion: CTRP6 attenuated septic lung injury, exerted anti-inflammatory effect, and sup-pressed neutrophil extracellular traps formation against sepsis through inactivation of extra-cellular signal-regulated kinase signaling.

Background: Food-borne diseases cause serious harm to public health and food safety. The snail species Galba pervia is an intermediate host for an array of parasitic trematodes. In this study, we performed a prevalence investigation on zoonotic trematode in G. pervia in Guangxi, China, and assessed the zoonotic potential of Trematode in the region.Methods: G. pervia was collected from 61 sites in 9 cities throughout Guangxi Provinces between 2012 to 2014. The larvae species were determined by combing morphological and molecular characteristics. Phylogenetic trees were constructed using neighbor-joining method with ITS2 sequences. The developmental cycles of the isolated trematodes were examined by experimental infection in ducks. The developmental characteristics of Echinostoma revolutum was recorded by dissecting infected ducklings from 1 dpi to 10 dpi. Results: Species identification showed E. revolutum, Australapatemon sp., Hypoderaeum conoideum, Pharyngostomum cordatum and Echinostoma sp. parasitized in G. pervia. However, no Fasciola larvae had been detected. A Neighbor-Joining tree analysis of ITS2 sequences resulted in monophyletic clades comprised of all sequences from isolated larvae with high bootstrap support. The overall prevalence of Trematode larvae in G. pervia was 22.0% (1818/8258), while E. revolutum presented with the highest infection rate of 12.9% from 11 sampling sites. Ducklings exposed to Echinostoma sp., E. revolutum, and H. conoideum larvae were successfully infected. E. revolutum larvae matured at 10 dpi in the intestine of the duck, and the developmental characteristics of E. revolutum were characterized by the maturation of the reproductive and digestive organs around 6~8 dpi.Conclusions: The present investigation revealed the high prevalence of trematodes in the G. pervia in Guangxi, China. With existing trematode infection human cases together with wide geographical distribution of G. pervia, more insight into the risks of human health and its link to human infections are needed.

Chrysosplenium macrophyllum Oliv., belonging to the family Saxifragaceae, is a traditional and unique Chinese herbal medicine. However, the lack of adequate molecular markers has hampered the progress regarding population genetics and evolution within this species. In this research, we used the DNBSEQ-T7 Sequencer (MGI) sequencing assay to analyze the transcriptome profiles of C. macrophyllum. SSR markers were developed on the basis of transcriptomic sequences and further validated on C. macrophyllum and other Chrysosplenium species. The genetic diversity and structure of the 12 populations were analyzed by using polymorphic expressed sequence tag simple sequence repeat (EST-SSR) markers. A potential pool of 3127 non-redundant EST-SSR markers were identified for C. macrophyllum in this study. The developed EST-SSR markers had high amplification rates and cross-species transferability in Chrysosplenium. Our results also showed that the natural populations of C. macrophyllum had a high level of genetic diversity. Genetic distance, principal component analysis, and popular structure analysis revealed that all 60 samples clustered into two major groups that were consistent with their geographical origins. This study provided a batch of highly polymorphic EST-SSR molecular markers that were developed via transcriptome sequencing. These markers will be of great significance for the study of the genetic diversity and evolutionary history of C. macrophyllum and other Chrysosplenium species.

Background. Triple-negative breast cancer (TNBC) remains the most incurable subtype of breast cancer owing to high heterogeneity, aggressive nature, and lack of treatment options. It is generally acknowledged that epithelial-mesenchymal transition (EMT) is the key step in tumor metastasis. Methods. With the application of TCGA and GEO databases, we identified EMT-related lncRNAs by the Cox univariate regression analysis. Optimum risk scores were calculated and used to divide TNBC patients into high-/low-risk subgroups by the median value using the Lasso regression analysis. The Kaplan–Meier and ROC curve analyses were applied for model validation. Then, we assessed the risk model from multi-omic aspects including immune infiltration, drug sensitivity, mutability spectrum, signaling pathways, and clinical indicators. We also analyzed the expression pattern of lncRNAs involved in the model using qRT-PCR in TNBC cell lines and constructed the ceRNA network. Results. The risk model was composed of EMT-related long noncoding RNAs (lncRNAs), which seemed to be valuable in the prognostic prediction of TNBC patients. The model could act as an independent prognostic factor of TNBC and showed a robust prognostic ability in the stratification analysis. Further investigation demonstrated that the expression of lncRNAs was different between high aggressive and low aggressive TNBC cell lines, as well as TNBC patients. Conclusions. Together, our study successfully established a risk model with great accuracy and efficacy in the prognostic prediction of TNBC patients.

A variety of mechanisms are involved in sex determination in vertebrates. The orange-spotted grouper (Epinephelus coioides), a teleost fish, functions first as females and later as a male and is an ideal model to investigate the regulation of sexual fate. Here, we report female-to-male sex reversal in juvenile orange-spotted groupers caused by overexpressing anti-Müllerian hormone (Amh). Tissue distribution analyses showed that amh and amhrII primarily expressed in the gonad, and expression level in the testis was much higher than that in the ovary. In gonads, the expression of amh was located in the Sertoli cells around spermatogonia of the testis and in the zona pellucida of the mature ovary, and the expression of amhrII was located in the Sertoli cells of the testis and in the oocytes of the ovary. Decrease in female-related genes and serum 17β-estradiol level, increase in male-related genes and serum 11-ketotestosterone, ovarian regression, and spermatogonia proliferation were observed during plasmid feeding experiment. These results illustrate that amh overexpression plasmid feeding can induce a female-to-male transition in grouper.

Shared micromobility programs, including dockless electric scooter-share (E-scooter), are popular in many U.S. cities, and with their adoption brings the hope that they may uphold better car-free accessibility. However, few studies provide clear answers to what activities drive its travel demand or whether it could actually generate more visiting activities. To fill this gap, we conducted a spatiotemporal similarity analysis between E-scooter use and visit patterns to leisure facilities. We find that E-scooter use is significantly correlated with daily dining and drinking, shopping, and recreational activities, in that order. Moreover, we find higher scooter-visit correlation clusters in downtown and university campus areas. We then used the Difference-in-Differences approach to examine if E-scooter use can generate more visiting activities. Surprisingly, the results show that E-scooter use is insignificant to the overall visit increase.

Hyperspectral imaging technology has great potential in the identification of crop varieties because it contains both image information and spectral information for the object. But so far most studies only used the spectral information, the image information has not been effectively utilized. In this study, hyperspectral images of single seed of three types including strong gluten wheat, medium gluten wheat, and weak gluten wheat were collected by near infrared hyperspectra imager, 12 morphological characteristics such as length, width, rectangularity, circularity and eccentricity were extracted, the average spectra of endosperm and embryo were acquired by the mask which was created by image segmentation. Partial least squares discriminant analysis (PLADA) and least squares support vector machine (LSSVM) were used to construct the classification model with image information, results showed that the binary classification accuracy between strong gluten wheat and weak gluten wheat could achieve 98%, for strong gluten wheat and medium gluten wheat, it was only 74.22%, which indicated that hyperspectral images could reflect the differences of varieties, but the accuracy might be poor when recognizing the varieties just by image information. Soft independent modeling of class analogy (SIMCA), PLSDA and LSSVM were used to established the classification model with spectral information, the classification effect of endosperm is slightly better than the embryo, it demonstrated that the grain shape could influence the classification accuracy. Then, we fused the spectral and image information, SIMCA, PLSDA and LSSVM were used to established the identification model, the fusion model showed better performance than the individual image model and spectral model, the classification accuracy which used the PLSDA raise from 96.67% to 98.89%, it showed that digging the morphological and spectral characteristics of the hyperspectral image could effectively improve the classification effect.

With the advent of powerful network processors (NPs) in the market, many computation-intensive tasks such as routing table look-up, classification, IPSec, and multimedia transcoding can now be accomplished more easily in a router. An NP consists of a number of on-chip processors to carry out packet level parallel processing operations. Ensuring good load balancing among the processors increases throughput. However, such multiprocessing also gives rise to increased out-of-order departure of processed packets. In this paper, we first propose an Ordered Round Robin (ORR) scheme to schedule packets in a heterogeneous network processor assuming that the workload is perfectly divisible. The processed loads from the processors are ordered perfectly. We analyze the throughput and derive expressions for the batch size, scheduling time and maximum number of schedulable processors. To effectively schedule variable length packets in an NP, we propose a Packetized Ordered Round Robin (P-ORR) scheme by applying a combination of deficit round robin (DRR) and surplus round robin (SRR) schemes. We extend the algorithm to handle multiple flows based on a fair scheduling of flows depending on their reservations. Extensive sensitivity results are provided through analysis and simulation to show that the proposed algorithms satisfy both the load balancing and in-order requirements for parallel packet processing.

We consider a cluster-based multimedia Web server that dynamically generates video units to satisfy the bit rate and bandwidth requirements of a variety of clients. The media server partitions the job into several tasks and schedules them on the backend computing nodes for processing. For stream-based applications, the main design criteria of the scheduling are to minimize the total processing time and maintain the order of media units for each outgoing stream. In this paper, we first design, implement, and evaluate three scheduling algorithms, first fit (FF), stream-based mapping (SM), and adaptive load sharing (ALS), for multimedia transcoding in a cluster environment. We determined that it is necessary to predict the CPU load for each multimedia task and schedule them accordingly due to the variability of the individual jobs/tasks. We, therefore, propose an online prediction algorithm that can dynamically predict the processing time per individual task (media unit). We then propose two new load scheduling algorithms, namely, prediction-based least load first (P-LLF) and prediction-based adaptive partitioning (P-AP), which can use prediction to improve the performance. The performance of the system is evaluated in terms of system throughput, out-of-order rate of outgoing media streams, and load balancing overhead through real measurements using a cluster of computers. The performance of the new load balancing algorithms is compared with all other load balancing schemes to show that P-AP greatly reduces the delay jitter and achieves high throughput for a variety of workloads in a heterogeneous cluster. It strikes a good balance between the throughput and output order of the processed media units

Several companies have introduced powerful network processors (NP) that can be placed in active routers to execute application level tasks in the network. An NP consists of a number of on-chip processors to carry out packet level parallel processing operations. We propose to employ them for multimedia streaming (transcoding) to convert the incoming video streams to low bit-rate media units as per the requirements of the clients. To effectively schedule the parallel transcoding operations in an active router, we propose a static sequentialized batch-coscheduling (SSBC) scheme to meet both load balancing and real-time requirements for media streaming, based on divisible load theory (DLT). We first analyze the feasibility and optimality of the load distribution schemes from the theoretical perspectives, and then present separate solutions for non-delay-sensitive streams and delay-sensitive streams. Rigorous simulations and experiments have been carried out to evaluate the performance.

Active routers allow computation to be performed within the network by processing packets when they pass through the routers. We design and implement a cluster-based active router system that provides multimedia stream transcoding service. The performance of the system is evaluated with three different load balancing schemes. We evaluate the out-of-order phenomenon and analyze the tradeoff between this phenomenon and the processing speed. We present a stream-based round robin algorithm for the transcoding service offered in the router and demonstrate its superiority over the conventional round-robin scheme. The main design criteria are to minimize the total transcoding time and maintain the order of media units for each outgoing stream.