Effects of bis (2-butoxyethyl) phthalate on adrenocortical function in male rats in puberty partially via down-regulating NR5A1/NR4A1/NR4A2 pathways.

Phthalates may interfere with the biosynthesis of steroid hormones in the adrenal cortex. Bis (2-butoxyethyl) phthalate (BBOP) is a phthalate containing oxygen atoms in the alcohol moiety. In this study, 35-day-old male Sprague-Dawley rats were daily gavaged with BBOP (0, 10, 100, 250, and 500 mg/kg body weight) for 21 days. BBOP did not affect the weight of body and adrenal glands. BBOP significantly reduced serum corticosterone levels at 250 and 500 mg/kg, and lowered aldosterone level at 500 mg/kg without affecting adrenocorticotropic hormone. BBOP did not alter the thickness of the adrenal cortex. BBOP significantly down-regulated the expression of steroidogenesis-related genes (Scarb1, Star, Cyp11a1, Cyp21, Cyp11b1, Cyp11b2, Nr5a1, Nr4a1, and Nr4a2) and proteins, and antioxidant enzymes (Sod1, Sod2, Gpx1, and Cat) and their proteins, while up-regulating the expression of Mc2r and Agtr1a at various doses. BBOP reduced the phosphorylation of AKT1, AKT2, and ERK1/2, as well as the levels of SIRT1 and PGC1α without affecting the phosphorylation of AMPK. BBOP significantly induced the production of reactive oxygen species and apoptosis rate in H295R cells at 100 µM and higher after 24 h of treatment. In conclusion, male rats exposed to BBOP in puberty have significant reduction of steroid biosynthesis with a potential mechanism that is involved in the decrease in the phosphorylation of AKT1, AKT2, ERK1/2, as well as SIRT1 and PGC1α and increase in ROS.

ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide Measurement.

Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage. Many applications of network management can benefit from a function that can find large flows efficiently, such as congestion control by dynamically scheduling large flows, caching of forwarding table entries, and network capacity planning. However, the current network-wide measurements neglect the diversity of different flows as they treat large flows and small flows equally. In this paper, we present a mechanism of accuracy-aware collaborative monitoring (ACM) to improve the measurement accuracies of large flows in network-wide measurements at the flow level. The structure of the sketch is an approximate counting algorithm, and a high-measurement accuracy can be achieved by merging the results from multiple monitors with sketches, which is termed as collaborative monitoring. The core idea of our method is to allocate more monitors to large flows and achieve the load balance to provide accuracy-aware monitoring. We modeled our problem as an integer-linear programming problem, which is NP-hard. Thus, we propose an approximation algorithm, named the improved longest processing time algorithm (iLPTA); we proved that its approximation ratio is (12+nl). We propose a two-stage online distribution algorithm (TODA). Moreover, we proved that its approximation ratio is (1+nl-1). The iLPTA is an offline approximation algorithm used to assign monitors for each flow, which prove the validity and feasibility of the core idea. The TODA is an online algorithm that attempts to achieve the load balance by selecting the monitor with the smallest load to a large flow. Our extensional experiment results verify the effectiveness of our proposed algorithms.

In utero exposure to dipentyl phthalate disrupts fetal and adult Leydig cell development.

Phthalates as plasticizers are widely used in many consumer products. Dipentyl phthalate (DPeP) is one of phthalates. However, there are currently few data on whether DPeP exposure affects rat Leydig cell development. In this study, we investigated the effects of in utero DPeP exposure on Leydig cell development in the testes of male newborn and adult rats. From gestational days 14 to 21, Sprague-Dawley pregnant rats were gavaged vehicle (corn oil, control) or DPeP (10, 50, 100, and 500 mg/kg body weight/day). Testosterone and the expression of Leydig cell genes and proteins in the testis at birth and at postnatal day 56 were examined. DPeP dose-dependently reduced serum testosterone levels of male offspring at birth and at postnatal day 56 at 100 and 500 mg/kg and lowered serum luteinizing hormone levels at adult males at ≥10 mg/kg when compared with the control. In addition, DPeP increased number of fetal Leydig cells by inducing their proliferation but down-regulated the expression of Lhcgr, Scarb1, Star, Cyp11a1, Hsd3b1, Cyp17a1, Hsd17b3, and Insl3 in fetal Leydig cells per se. DPeP reduced number of adult Leydig cells by inducing cell apoptosis and down-regulated the expression of Lhcgr and Star in adult Leydig cells at postnatal day 56. DPeP lowered SIRT1 and BCL2 levels in the testis of adult rats. In conclusion, DPeP adversely affects both fetal and adult Leydig cell development after in utero exposure.

Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning.

Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms.

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

NCDN is a Potential Biomarker and Therapeutic Target for Glioblastoma.

Background: Glioblastoma (GBM) is a type of central nervous system malignancy. In our study, we determined the effect of NCDN in GBM patients through The Cancer Genome Atlas (TCGA) data analysis, and studied the effects of NCDN on GBM cell function to estimate its potential as a therapeutic target. Methods: Gene expression profiles of glioblastoma cohort were acquired from TCGA database and analyzed to look for central genes that may serve as GBM therapeutic targets. Then the cell function of NCDN in glioblastoma cell was explored through in vitro cell experiments. Results: Through gene ontology (GO) analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis, we identified three key genes (NCDN, PAK1 and SPRYD3) associated with poor prognosis in glioblastoma. In vitro experiments showed impaired cell migration, apoptosis, and cell cycle arrest in NCDN knockdown cells. Conclusion: NCDN affects the progress and prognosis of glioblastoma by promoting cell migration and inhibiting apoptosis.

Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

Effects of metformin on Sonic hedgehog subgroup medulloblastoma progression: In vitro and in vivo studies.

Metformin is a first-line drug for type 2 diabetes, and its anticancer effects have also been widely studied in recent years. The Sonic hedgehog (Shh) signaling pathway is involved in the initiation and progression of medulloblastoma. In order to develop a new treatment strategy for medulloblastoma (MB), this study investigated the inhibitory effect of metformin on MB and the underlying mechanism of metformin on the Shh signaling pathway. The effect of metformin on proliferation was evaluated by the cell counting kit-8 (CCK-8) test and colony formation experiment. The effect of metformin on metastasis was assessed by the scratch-wound assay and transwell invasion assay. Cell cycle and apoptosis were evaluated by flow cytometry, and the associated proteins were examined by western blotting. The mRNA and protein expression levels related to the Shh pathway were measured by quantitative PCR, western blotting, and immunofluorescence staining. The xenograft murine model was carried out to evaluate the anticancer effect of metformin on medulloblastoma in vivo. Metformin inhibited proliferation and metastasis of the Shh subgroup MB cell line, and the inhibitory effect on proliferation was related to apoptosis and the block of the cell cycle at the G0/G1 phase. Animal experiments showed that metformin inhibits medulloblastoma growth in vivo. Moreover, metformin decreased mRNA and protein expression levels of the Shh pathway, and this effect was reversed by the AMP-activated protein kinase (AMPK) siRNA. Furthermore, the pro-apoptotic and cell cycle arrest effects of metformin on Daoy cells could be reversed by the Shh pathway activators. Our findings demonstrated that metformin could inhibit medulloblastoma progression in vitro and in vivo, and this effect was associated with AMPK-mediated inhibition of the Shh signaling pathway in vitro studies.

Effects of bis(2-butoxyethyl) phthalate exposure in utero on the development of fetal Leydig cells in rats.

Phthalates are plasticizers widely found in the environment. They are potential endocrine disruptors. Bis(2-butoxyethyl) phthalate (BBOP) is a unique phthalate that contains oxygen atoms in the carbon backbone. Little is known about its reproductive and developmental toxicity. The objective of this study was to determine the effect of BBOP on fetal Leydig cell development after in utero exposure to rats. Sprague Dawley pregnant dams were randomly allocated into 6 groups, and were gavaged with BBOP (0, 10, 100, 250, 500, and 1000 mg/kg body weight/day) from gestational day (GD) 14-21. Seven of the 8 dams in the 1000 mg/kg BBOP group died before giving birth. Twelve of the 20 dams in the 500 mg/kg BBOP group had whole litter loss. BBOP significantly reduced the body weight of dams and male offspring and serum testosterone level and anogenital distance of male fetus on GD 21 at 500 mg/kg. BBOP markedly increased fetal Leydig cell proliferation and number at 500 mg/kg while inducing their abnormal aggregation at 250 and 500 mg/kg. BBOP down-regulated the expression of Lhcgr, Scarb1, Star, Cyp11a1, Hsd3b1, Cyp17a1, Hsd17b3, Insl3, and Nr5a1 at various doses while up-regulating the expression of Sertoli cell gene Fshr and Sox9. The phosphorylation of AKT1, AKT2, and ERK1/2 was also markedly reduced by BBOP. In conclusion, BBOP in utero exposure can disrupt fetal Leydig cell development, possibly via the mechanism that may include inhibiting the phosphorylation of AKT1, AKT2, and ERK1/2.