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The study of the ancient subterranean river system in the Tahe Oilfield in the Tarim Basin is still in its initial stage. Subterranean river caves are mainly classified based on the characteristics of the depth distribution and structural patterns of the subterranean rivers. However, there is a lack of comprehensive analysis regarding the spatial development patterns of complex subterranean river systems from geological perspectives such as structure, fault, paleogeomorphology, and water table. This hinders the understanding of primary and secondary relationships of subterranean rivers, spatial superposition patterns, and original connectivity relationships, thereby impeding comprehensive management research in the later stages of Tahe Oilfield development. To address this gap, an investigation was carried out in the S67 well block to clarify the developmental characteristics of the Ordovician ancient subterranean river system in the thrust anticline area of the main Tahe Oilfield. The study utilized methods like structural fault analysis, paleogeomorphology restoration, seismic attribute characterization, and vertical section interpretation to identify river types, classify systems, and examine geological origins. Noteworthy findings include the identification and analysis of a phreatic loop river for the first time. Results show that the S67 well block is located in a low-lying region at the southern edge of the karst platform within the main Tahe Oilfield area. This region features peak cluster depressions and karst hill depressions with minimal amplitude differences and shallow incision depths of surface water systems. The low-angle thrust structural style of the thrust anticline, combined with a network of faults, provides favorable dissolution channels for the multi-layered subterranean river system. In the study area, the Ordovician period is characterized by the emergence of distinct water-table subterranean river systems and phreatic loop river systems, all of which display a dendritic structure. The water-table subterranean rivers are divided into main, branch, and abandoned types, while the phreatic loop rivers are classified as ascending or symmetrical. The formation of ancient subterranean rivers in the thrust anticline area is primarily influenced by factors such as paleogeomorphology, water table levels, thrust anticline structure, and secondary fault networks.

Wolfberry (Lycium, of the family Solanaceae) has special nutritional benefits due to its valuable metabolites. Here, 16 wolfberry-specific metabolites were identified by comparing the metabolome of wolfberry with those of six species, including maize, rice, wheat, soybean, tomato and grape. The copy numbers of the riboflavin and phenyllactate degradation genes riboflavin kinase (RFK) and phenyllactate UDP-glycosyltransferase (UGT1) were lower in wolfberry than in other species, while the copy number of the phenyllactate synthesis gene hydroxyphenyl-pyruvate reductase (HPPR) was higher in wolfberry, suggesting that the copy number variation of these genes among species may be the main reason for the specific accumulation of riboflavin and phenyllactate in wolfberry. Moreover, the metabolome-based neighbor-joining tree revealed distinct clustering of monocots and dicots, suggesting that metabolites could reflect the evolutionary relationship among those species. Taken together, we identified 16 specific metabolites in wolfberry and provided new insight into the accumulation mechanism of species-specific metabolites at the genomic level.

Identification of accident black spots has gained tremendous popularity among road agencies and safety specialists for evaluating and subsequently enhancing road traffic safety. However, there is still limited understanding of the internal relationship between black spots and microscopic vehicle kinetic parameters. To address this gap, this paper describes a project that was undertaken using the real-time tire force data (kinetic response) obtained from road experiments on Wenli Expressway. First, factor analysis was applied to extracted three independent indicators (power-braking, handling stability, and ride comfort) from seven original kinetic indicators with multiple collinearities. Afterward, the main indicators were given vehicle kinetic meaning by analyzing the characteristics of original indicators associated with them. A compelling correlation was established among kinetic parameters, vehicle running qualities, and accident risk. Additionally, an integrated evaluation framework was established to identify accident black spots based on applying ordered logit models and PLS-entropy-TOPSIS approaches. The recognition results exhibited that the overall recognition accuracy obtained by the latter was found to be comparable to that achieved using the previous one. The compound evaluation model proposed in this paper has been proven to present many advantages for black spot identification. It is evidently clear from the findings that the vehicle kinetic parameters have significant correlations with road accident risk. This paper could provide some insightful knowledge for identifying and preventing the black spots from ameliorating traffic safety.

Abstract tsRNAs (tRNA-derived small RNAs), as products of the stress response, exert considerable influence on stress response and injury regulation. However, it remains largely unclear whether tsRNAs can ameliorate liver injury. Here, we demonstrate the roles of tsRNAs in alleviating liver injury by utilizing the loss of NSun2 (NOP2/Sun domain family, member 2) as a tsRNAs-generating model. Mechanistically, the loss of NSun2 reduces methyluridine-U5 (m5U) and cytosine-C5 (m5C) of tRNAs, followed by the production of various tsRNAs, especially Class I tsRNAs (tRF-1s). Through further screening, we show that tRF-Gln-CTG-026 (tG026), the optimal tRF-1, ameliorates liver injury by repressing global protein synthesis through the weakened association between TSR1 (pre-rRNA-processing protein TSR1 homolog) and pre-40S ribosome. This study indicates the potential of tsRNA-reduced global protein synthesis in liver injury and repair, suggesting a potential therapeutic strategy for liver injury.

Seismic and drilling data were obtained to study the paleo-karst development conditions and cave developmental characteristics of the Middle-Lower Ordovician in the incised meandering canyon area on the western slope of Tahe Oilfield, Tarim Basin. Using layer leveling, tectonic trend surface, amplitude anomaly and other methods, the paleo-hydrogeographic conditions and paleo-karst fluid system were restored, the types of karst caves in the incised meandering canyon area were divided, and the development model of karst caves under the background of incised meandering canyons was re-constructed. Results showed that: (1) Early Hercynian paleo-karst was developed in the study area. The paleo-geomorphology was generally high in the east and low in the west. The karst geomorphic units were mainly valleys, karst hills and depressions. The incised meandering of the main surface water system was the lowest karst water discharge benchmark in the study area, and the underground and surface branch water systems converged from the southern and northern platforms to the central main body. (2) The underground river caves in the incised meandering canyon area were identified as subterranean river type, swallet stream type, perforated type, phreatic type along river valley and vadose type of sink hole, which were mainly distributed 0-90 m below unconformity surface. The perforated type and phreatic type along river valley were easy to be filled by mud. (3) Incised meandering canyon, karst residual hill and fault controlled the development of ancient karst caves, forming a central confluence karst cave model in the Early Hercynian incised meandering canyon area of the Tahe karst basin.

Heavy-duty diesel truck (HDDT) is one of the major sources of air pollution and energy consumption. To reduce the estimation bias and improve the interpretability, the random parameters logit (RPL) model was employed to examine the effects of influencing factors on fuel consumption of HDDTs in the real world. The unobserved heterogeneity effects varying across the samples on fuel efficiency were extracted from the long-term daily trip-based data. In order to further illustrate the advantages of the RPL model in explaining the impacts of factors, a fixed parameters logit model with twenty parameters was constructed and compared. The Akaike information criterion and the Bayesian information criterion were used to select a more reasonable model structure. The findings show that the RPL model performs better and the unobserved heterogeneity would affect the effects of factors of rolling without engine load proportion and temperature and, consequently, map the level of fuel consumption. This reveals the variability of the fuel consumption among the samples. Driving compensation effects were also identified in this study (i.e., the drivers tend to perform the fuel-saving operations in adverse driving circumstances and vice versa). The methodology proposed in this paper can provide a new insight for researchers to identify the instability of energy-related factors under real road conditions. Future research could be implemented to assess the similar effects of alternative fuel vehicles.

Complement receptor 3 (CD11b/CD18) is an important receptor that mediates adhesion, phagocytosis and chemotaxis in various immunocytes. The conidia of the medically-important pathogenic fungus, Aspergillus fumigatus can be internalized into alveolar epithelial cells to disseminate its infection in immunocompromised host; however, the role of CR3 in this process is poorly understood. In the present study, we investigated the potential role of CR3 on A. fumigatus internalization into type II alveolar epithelial cells and its effect on host intracellular PA content induced by A. fumigatus. We found that CR3 is expressed in alveolar epithelial cells and that human serum and bronchoalveolar lavage fluid (BALF) could improve A. fumigatus conidial internalization into A549 type II alveolar epithelial cell line and mouse primary alveolar epithelial cells, which were significantly inhibited by the complement C3 quencher and CD11b-blocking antibody. Serum-opsonization of swollen conidia, but not resting conidia led to the increase of cellular phosphatidic acid (PA) in A549 cells during infection. Moreover, both conidial internalization and induced PA production were interfered by CD11b-blocking antibody and dependent on FAK activity, but not Syk in alveolar epithelial cells. Overall, our results revealed that CR3 is a critical modulator of Aspergillus fumigatus internalization into alveolar epithelial cells.

Wolfberry (Lycium barbarum) is a traditional cash crop in China and is well-known worldwide for its outstanding nutritional and medicinal value. Lycium ruthenicum is a close relative of Lycium barbarum but differs significantly in size, color, flavor and nutritional composition. To date, the metabolic differences between the fruits of the two wolfberry varieties and the genetic basis behind them are unclear. Here, we compared metabolome and transcriptome data of two kinds of wolfberry fruits at five stages of development. Metabolome results show that amino acids, vitamins and flavonoids had the same accumulation pattern in various developmental stages of fruit but that Lycium ruthenicum accumulated more metabolites than Lycium barbarum during the same developmental stage, including L-glutamate, L-proline, L-serine, abscisic acid (ABA), sucrose, thiamine, naringenin and quercetin. Based on the metabolite and gene networks, many key genes that may be involved in the flavonoid synthesis pathway in wolfberry were identified, including PAL, C4H, 4CL, CHS, CHI, F3H, F3’H and FLS. The expression of these genes was significantly higher in Lycium ruthenicum than in Lycium barbarum, indicating that the difference in the expression of these genes was the main reason for the variation in flavonoid accumulation between Lycium barbarum and Lycium ruthenicum. Taken together, our results reveal the genetic basis of the difference in metabolomics between Lycium barbarum and Lycium ruthenicum and provide new insights into the flavonoid synthesis of wolfberry.

Telomerase activity is elevated in most cancer cells and is required for telomere length maintenance and immortalization of cancer cells. Glucose metabolic reprogramming is a hallmark of cancer and accompanied with increased expression of key metabolic enzymes. Whether these enzymes influence telomerase activity and cell immortalization remains unclear. In the current study, we screened metabolic enzymes using telomerase activity assay and identified lactate dehydrogenase B (LDHB) as a regulator of telomerase activity. Sodium lactate and sodium pyruvate did not influence telomerase activity, indicating LDHB regulates telomerase activity independent of its metabolism regulating function. Further studies revealed that LDHB directly interacted with TERT and regulated the interaction between TERT and TERC. Additionally, long-term knockdown of LDHB inhibited cancer cell growth and induced cell senescence in vitro and in vivo. Higher LDHB expression was detected in pancreatic cancer tissues compared with that in adjacent normal tissues and expression of LDHB correlated negatively with prognosis. Thus, we identified LDHB as the first glucose metabolic enzyme contributing to telomerase activity and pancreatic cancer cell immortalization.

Data chunking algorithms divide data into several small data chunks in a certain way, thus transforming the operation of data into the one of multiple small data chunks. Data chunking algorithms have been widely used in duplicate data detection, parallel computing and other fields, but it is seldom used in data incremental synchronization. Aiming at the characteristics of incremental data synchronization, this paper proposes a novel data chunking algorithm. By dividing two data that need synchronization into small data chunks, comparing the contents of these small data chunks, different ones are the incremental data that need to be found. The new algorithm determines to set a cut-point based on the number of 1 contained in the binary format of all bytes in an interval. Thus it improves the resistance against the byte shifting problem at the expense of the chunk size stability, which makes it more suitable for the incremental data synchronization. Comparing this algorithm with several known classical or state of art algorithms, experiments show that the incremental data found by this algorithm can be reduced by 32%~57% compared to the others with same changes between two data. The experimental results based on real-world datasets show that PCI improves the calculation speed of classic Rsync algorithm up to 70%, however, with a drawback of increasing the Transmission compression rate up to 11.8%.

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.

Vessel big data play a significant role in understanding vessel behaviors and thus facilitating the prosperity of waterway transportation. However, relevant research regarding vessel trajectory recognition in a broad range of narrow channels still lacks, especially using VITS data. The major objective of this paper is to conduct vessel trajectory analysis based on the novel VITS data and examine its availability in inland waterway vessel transportation. An alternate aim is to develop a more comprehensive framework to extract the vessel trajectory of multiple narrow waterways. This paper utilized vessel trajectory information of multiple narrow channels belonging to Yangtze River captured by VITS. Four compression algorithms were conducted. Additionally, the performances of three clustering approaches were evaluated. Speed distribution analysis was also implemented. The results indicated that slide window (SW) algorithm outperforms its other counterparts. Relative to DBSCAN, K-means and hierarchical clustering analysis (HCA) tend to be more capable of balanced classification. This paper is the first to utilize VITS data in vessel trajectory feature extraction and can potentially provide useful insight for vessel trajectory extraction in multiple narrow channels.

In the data backup system, to reduce the bandwidth and processing time overhead caused by full backup technology during data synchronization between backups and source data, incremental backup technology is emerging as the focus of academic and industrial research. It is key but poorly-solved to find the incremental data between backups and source data for incremental backup technology. To find out the incremental data during the backup process, here, in this paper, we propose a novel content-defined chunking algorithm. The source data and backup data are chunked into some small chunks in the same way with the variable length. Then, by comparing whether a chunk of source data is different from any of the chunks in backup data, we can evaluate whether the chunk of source data is incremental data. By experiments, the chunking algorithm in this paper is compared to other ones which are the classical or state-of-the-art algorithms. The experimental results show that the incremental data found by this algorithm can be reduced by 13%-34% compared to the others with the same chunk throughput.

Despite the broad application of advanced fleet management systems (FMSs) in third-party logistics (3PL) companies, there is a marginally limited understanding of how to employ them to enhance transport energy efficiency. In a case study of a Chinese 3PL company, this paper analyzed data obtained from the online FMS to assess drivers’ awareness of fuel-saving factors. A questionnaire was primarily designed to investigate the drivers’ awareness of fuel-saving factors based on the reliability and validity test. Then, Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was utilized to explore the intrinsic impacts of various factors on fuel consumption with the outputs providing the evaluation basis for individual awareness of the drivers. The results show a significant deviation in the driver’s awareness of fuel-saving factors, among which the three indicators of engine speed, idling condition, and rolling without engine load are seriously underestimated, while the indicators related to the environment are seriously overestimated due to social expectations. In addition, the average speed was found to be the most important fuel-saving indicator besides the load. Based on these findings, this paper recommends that the 3PL companies choose a route with more freeways when planning, and the mileage should be controlled within 800 km as far as possible.

Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver’s distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver’s distracted behavior recognition systems. The driver’s posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver’s real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver’s microscopic behavior to establish a more comprehensive proactive surveillance system.

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers.

With increasingly prominent environmental problems, controlling automobile exhaust has become essential to the environment. The fuel consumption of transportation is the critical factor that determines exhaust gas. By analyzing the naturalistic driving data of heavy-duty diesel trucks (HDDTs), this paper explored the influence of engine technical state, road features, weather, and temperature conditions on fuel consumption during driving. The detailed process is as follows: Firstly, we collected 1153 naturalistic driving data from 34 HDDTs and made a specific analysis and summary description of the data; secondly, by establishing a binary Logistic regression model, we quantitatively explored the influence of significant factors on the fuel consumption; meanwhile, based on quantitative analysis of factor’s effectiveness, this research used several machine learning algorithms (back-propagation neural network, decision tree, and random forest) to build fuel consumption predictors, and compared the prediction performance of different algorithms. The results showed that the prediction accuracy of the decision tree, back-propagation (BP) neural network, and random forest is 81.38%, 83.98%, and 86.58%, respectively. The random forest showed the best performance in predicting. The conclusions can assist transportation companies in formulating driving training strategies and contribute to reducing energy consumption and emissions.

Single-walled carbon nanotubes offer exciting optoelectronic applications but generally suffer from low quantum yields. Here, Luo et al. demonstrate that coupling nanotubes to plasmonic antennas can lead to large Purcell enhancement and corresponding increase in quantum yield as well as plasmonic thermometry at the single molecule level.

Aspergillus fumigatus is able to internalize into lung epithelial cells to escape from immune attack for further dissemination. We previously reported that gliotoxin, a major mycotoxin of A. fumigatus, promotes this internalization; however, the mechanism remained unclear. Here, we report that gliotoxin is able to induce cofilin phosphorylation in A549 type II human pneumocytes. Either too high or too low a level of cofilin phosphorylation blocked the gliotoxin-induced actin cytoskeleton rearrangement and A. fumigatus internalization. LIM domain kinase 1 (LIMK1) and its upstream small GTPases (Cdc42 and RhoA, but not Rac1) predominantly mediated the gliotoxin-induced cofilin phosphorylation and A. fumigatus internalization. Simultaneously, gliotoxin significantly stimulated an increase in cAMP; however, adding an antagonist of PKA did not block gliotoxin-induced A. fumigatus internalization. In vivo, exogenous gliotoxin helped gliotoxin synthesis deficient strain gliPΔ invade into the lung tissue and the lung fungal burden increased markedly in immunosuppressed mice. In conclusion, these data revealed a novel role of gliotoxin in inducing cofilin phosphorylation mostly through the Cdc42/RhoA-LIMK1 signaling pathway to promote actin cytoskeleton rearrangement and internalization of A. fumigatus into type II human pneumocytes.

This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and Fmeasure are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video.

Lung epithelial cells constitute the first defense line of host against the inhaled Aspergillus fumigatus; however, the transcriptional response of human alveolar type II epithelial cells was still unclear. Here we used RNA-Seq technology to assess the transcriptome profiles of A549 cells following direct interaction with conidia of A. fumigatus. The total number of identified genes was 19118. Compared with uninfected A549 cells, 459 genes were differentially expressed in cells co-incubated with conidia for 8 h, including 302 up-regulated genes and 157 down-regulated genes. GO and KEGG pathway enrichment analysis showed that most of the up-regulated genes were related to immune response, chemotaxis and inflammatory response and enriched in cytokine-cytokine receptor interaction, JAK-STAT and MAPK signaling pathways. The down-regulated genes were mainly enriched for terms associated with development, hemopoiesis and ion transport. Among them, EGR4 and HIST1H4J gene had the maximum of fold change in up-regulated and down-regulated genes, respectively. Fourteen up-regulated genes and three down-regulated genes were further validated and significant increase on expression of IL-6, IL-8 and TNF-α in A549 cells were confirmed by qRT-PCR during the interaction of A549 cells with A. fumigatus. Besides, western blot showed that expression of two proteins (ARC, EGR1) significantly increased in A549 cells during interaction with A. fumigatus conidia for 8h. Interference of endogenous expression of ARC or EGR1 protein in A549 cells reduced the internalization of A. fumigatus. These results provided important insights into dynamic changes of gene expression in lung epithelial cells, especially its strong immunological response against A. fumigatus infection.

Staphylococcus aureus belongs to one of the most common bacteria causing healthcare and community associated infections in China, but their molecular characterization has not been well studied. From May 2011 to June 2012, a total of 322 non-duplicate S. aureus isolates were consecutively collected from seven tertiary care hospitals in seven cities with distinct geographical locations in China, including 171 methicillin sensitive S. aureus (MSSA) and 151 MRSA isolates. All isolates were characterized by spa typing. The presence of virulence genes was tested by PCR. MRSA were further characterized by SCCmec typing. Seventy four and 16 spa types were identified among 168 MSSA and 150 MRSA, respectively. One spa type t030 accounted for 80.1% of all MRSA isolates, which was higher than previously reported, while spa-t037 accounted for only 4.0% of all MRSA isolates. The first six spa types (t309, t189, t034, t377, t078 and t091) accounted for about one third of all MSSA isolates. 121 of 151 MRSA isolates (80.1%) were identified as SCCmec type III. pvl gene was found in 32 MSSA (18.7%) and 5 MRSA (3.3%) isolates, with ST22-MSSA-t309 as the most commonly identified strain. Compared with non-epidemic MRSA clones, epidemic MRSA clones (corresponding to ST239) exhibited a lower susceptibility to rifampin, ciprofloxacin, gentamicin and trimethoprim-sulfamethoxazole, a higher prevalence of sea gene and a lower prevalence of seb, sec, seg, sei and tst genes. The increasing prevalence of multidrug resistant spa-t030 MRSA represents a major public health problem in China.