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Flood regulation service (FRS) is essential for a sustainable landscape and human society. It is not only subject to land use changes, but also affected by engineered measures (e.g., dam construction). However, the comprehensive impacts of the two major driving factors are still to be elucidated. To fill the research gap, this paper aims to evaluate the response of basin-scale FRS supply and demand to land use changes and dam construction. A case study in the Yangtze River Basin, China, shows that the 2020 FRS supply is mainly from land uses (84.85%) rather than dams (15.15%), with the biggest contributions from water bodies (rivers and lakes, 30.00%) and forests (29.35%). Between 1980 and 2020, the total FRS supply has increased by 10.50%, from 1.48 × 1010 m3 to 1.64 × 1010 m3. The net increase mainly comes from dam construction (84.65%) rather than land use changes (15.35%). Regarding the land use changes, the developed transfer matrix shows the ecosystem FRS flows and indicates that both the land use change amount and direction play a key role, e.g., the declines in farmlands have increased ecosystem FRS supplies when transiting into forest lands (by 3.68 × 108 m3) and water bodies (by 1.37 × 109 m3), while have decreased the ecosystem FRS supplies by −1.96 × 107 m3 with the transition into construction lands. These findings could shed light on the divergent roles between land-based measures and engineered measures in managing flood risk.

BackgroundGemcitabine (GEM) is a second-line anticancer drug of choice for some colorectal cancer (CRC) patients, and GEM inability to be commonly available in the clinic due to the lack of clarity of the exact action targets.MethodsThe half maximal inhibitory concentration (IC50) of GEM treatment for 42 CRC cell lines were accessed from the Genomics of Drug sensitivity in Cancer (GDSC) database. High-throughput sequencing data of CRC patients were captured in The Cancer Genome Atlas (TCGA) and Weighted correlation network analysis (WGCNA) was conducted. Pearson correlations were derived for GEM potency-related genes. Differential analysis was conducted in the TCGA cohort to obtain CRC development-related genes (CDRGs), and univariate COX model analysis was performed on CDRGs overlapping with GEM potency-related genes to obtain CDRGs affecting CRC prognosis. Hub genes affecting GEM potency were identified by Spearman correlation.ResultsCALB2 and GPX3 were identified as potential targets for GEM treatment of CRC via prognostic analysis, which we also observed to be elevated with elevated clinical stage in CRC patients. The enhanced expression of CALB2 and GPX3 genes identified in the pathway analysis might inhibit the body metabolism as well as activate immune and inflammation related pathways. In addition, we found that CALB2 and GPX3 could also be considered as prognostic biomarkers in pan-cancer. Finally, we found that CALB2 and GPX3 were remarkably associated with the drug sensitivity of MG-132, Dasatinib, Shikonin, Midostaurin, MS-275, and Z-LNle-CHO, which were expected to be the drugs of choice for GEM combination.ConclusionCALB2 and GPX3 represent prognostic biomarkers for CRC and they might be potential action targets for GEM. Our study offered innovative ideas for GEM administration strategies.

Flood vulnerability is the key to understanding and assessing flood risk. However, analyzing flood vulnerability requires sophisticated data, which is usually not available in reality. With the widespread use of big data in cities today, it is possible to quickly obtain building parameters in cities on a large scale, thus offering the possibility to study the risk flooding poses to urban buildings. To fill this research gap, taking Shanghai as an example, this study developed a new research framework to assess urban vulnerability based on vulnerability curves and online data of residential buildings. First, detailed information about residential buildings was prepared via web crawlers. Second, the cleaned residential building information fed a support vector machine (SVM) algorithm to classify the buildings into four flood vulnerability levels that represented the vulnerability curves of the four building types. Third, the buildings of different levels were given vulnerability scores by accumulating the depth–damage ratios across the possible range of flood depth. Further, combined with the unit price of houses, flood risk was assessed for residential buildings. The results showed that the F1-score for the classification of buildings was about 80%. The flood vulnerability scores were higher in both the urban center and the surrounding areas and lower between them. Since 1990, the majority of residential buildings in Shanghai have switched from masonry–concrete structures to steel–concrete structures, greatly reducing the vulnerability to floods. The risk assessment showed decreasing risk trend from the center outward, with the highest risk at the junction of the Huangpu, Jing’an and Xuhui districts. Therefore, this framework can not only identify the flood vulnerability patterns but also provide a clue for revealing the flood risk of residential buildings. With real estate data becoming increasingly accessible, this method can be widely applied to other cities to facilitate flood vulnerability and risk assessment.

An ecosystem’s flood regulation service (EFRS) is important for alleviating flood risk. Both the EFRS and the relationship between EFRS supply and demand may change across flood return periods. However, most studies only considered the EFRS under a single flood return period. In this regard, a new framework is proposed that integrates multiple flood return periods to assess the expected annual EFRS supply and demand. It also considers the upstream EFRS supply flowing from the headwaters toward the downstream. A case study in the Yangtze River Basin, China, shows that the EFRS demand increases dramatically from 25- to 200-year return periods, while the EFRS supplies remain relatively constant. In terms of the expected annual EFRSs that integrate the four return-period EFRS demands and local supplies, 47 (or 43.12%) sub-basins are found to be unsatisfied (the demand is larger than the supply). This number could be reduced to 17 (or 15.60%) sub-basins if the upstream EFRS supply is considered in comparing the EFRS supply and demand, implying the importance of a catchment-wide EFRS management, particularly protecting the upstream forests, rivers and lakes, grasslands, as well as properly regulating the dams. The proposed framework could be readily applied in other basins and shed light on a comprehensive understanding and management of the EFRSs.

There has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSNs) have become the premier choice in many research communities and industry. Many state-of-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSNs. The transmission speed is constrained by the big file size of video data and fixed bandwidth allocation in constant routing paths. In this paper, we present a CamShift based algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which each sensor node is able to dynamically select the node for the next hop with the highest potential bandwidth capacity to resume communication. Key to this strategy is that each node merely maintains two parameters that contain its historical bandwidth varying trend and then predict its near future bandwidth capacity. Then, the forwarding node selects the next hop with the highest potential bandwidth capacity. Simulations demonstrate that our approach significantly increases the data received by the sink node and decreases the delay on video transmission in Wireless Multimedia Sensor Network environments.

In wireless sensor networks, an important issue of geographic routing is “local minimum” problem, which is caused by a “hole” that blocks the greedy forwarding process. Existing geographic routing algorithms use perimeter routing strategies to find a long detour path when such a situation occurs. To avoid the long detour path, recent research focuses on detecting the hole in advance, then the nodes located on the boundary of the hole advertise the hole information to the nodes near the hole. Hence the long detour path can be avoided in future routing. We propose a heuristic hole detecting algorithm which identifies the hole easily and quickly and then propose a representation of hole no matter what the shape of the hole is. In addition, we quantitatively figure out the areas in the vicinity of the hole that need to be announced the hole information. With such information, a new routing scheme with two landmarks was developed. Simulation results illustrate that our approach can achieve better performance in terms of the average length and number of hops in routing paths. Simulation also shows that our approach introduces very small computational complexity.

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds.

Conventional wearable sensors are mainly used to detect the physiological and activity information of individuals who wear them, but fail to perceive the information of the surrounding environment. This paper presents a wearable thermal sensing system to detect and perceive the information of surrounding human subjects. The proposed system is developed based on a pyroelectric infrared sensor. Such a sensor system aims to provide surrounding information to blind people and people with weak visual capability to help them adapt to the environment and avoid collision. In order to achieve this goal, a low-cost, low-data-throughput binary sampling and analyzing scheme is proposed. We also developed a conditioning sensing circuit with a low-noise signal amplifier and programmable system on chip (PSoC) to adjust the amplification gain. Three statistical features in information space are extracted to recognize static humans and human scenarios in indoor environments. The results demonstrate that the proposed wearable thermal sensing system and binary statistical analysis method are efficient in static human detection and human scenario perception.

Introduction: The aim of this study is to investigate changes in TNF-related apoptosis-inducing ligand (TRAIL) and gamma interferon-induced protein 10 (IP-10) after COVID-19 vaccination in pregnant women and to explore their association with neutralizing antibody (Nab) inhibition. Methods: The study evaluated 93 pregnant women who had previously received two (n=21), three (n=55) or four (n=17) doses of COVID-19 vaccine. Also we evaluated maternal blood samples that were collected during childbirth. The levels of TRAIL, IP-10 and Nab inhibition were measured using enzyme-linked immunosorbent assays (ELISA). Results and discussion: Our study revealed four-dose group resulted in lower TRAIL levels when compared to the two-dose and three-dose groups (4.78 vs. 16.07 vs. 21.61 pg/ml, p = 0.014). The two-dose group had reduced IP-10 levels than the three-dose cohort (111.49 vs. 147.89 pg/ml, p=0.013), with no significant variation compared to the four-dose group. In addition, the four-dose group showed stronger Nab inhibition against specific strains (BA.2 and BA.5) than the three-dose group. A positive correlation was observed between TRAIL and IP-10 in the two-dose group, while this relationship was not found in other dose groups or between TRAIL/IP-10 and Nab inhibition. As the doses of the COVID-19 vaccine increase, the levels of TRAIL and IP-10 generally increase, only by the fourth dose, the group previously vaccinated with AZD1222 showed lower TRAIL but higher IP-10. Despite these changes, more doses of the vaccine consistently reinforced Nab inhibition, apparently without any relation to TRAIL and IP-10 levels. The variation may indicate the induction of immunological memory in vaccinated mothers, which justifies further research in the future. [ABSTRACT FROM AUTHOR]

Ischemia-reperfusion injury (IRI) is inevitable in kidney transplantations and, as a complex pathophysiological process, it can be greatly impacted by ferroptosis and immune inflammation. Our study aimed to identify the biomarkers of renal IRI (RIRI) and elucidate their relationship with immune infiltration. In this study, the GSE148420 database was used as a training set to analyze differential genes and overlap them with ferroptosis-related genes to identify hub genes using a protein-protein interaction (PPI) network, the least absolute shrinkage and selection operator (LASSO), and random forest algorithm (RFA). We verified the hub gene and ferroptosis-related phenotypes in a verification set and animal experiments involving unilateral IRI with contralateral nephrectomy in rats. Gene set enrichment analysis (GSEA) of single genes was conducted according to the hub gene to predict related endogenous RNAs (ceRNAs) and drugs to establish a network. Finally, we used the Cibersort to analyze immunological infiltration and conducted Spearman's correlation analysis. We identified 5456 differential genes and obtained 26 ferroptosis-related differentially expressed genes. Through PPI, LASSO, and RFA, Hmox1 was identified as the only hub gene and its expression levels were verified using verification sets. In animal experiments, Hmox1 was verified as a key biomarker. GSEA of single genes revealed the seven most related pathways, and the ceRNAs network included 138 mRNAs and miRNAs. We predicted 11 related drugs and their three-dimensional structural maps. Thus, Hmox1 was identified as a key biomarker and regulator of ferroptosis in RIRI and its regulation of ferroptosis was closely related to immune infiltration. [ABSTRACT FROM AUTHOR]