Thermal discharge-created increasing temperatures alter the bacterioplankton composition and functional redundancy

Additional file 1: Table S1. Measurements of geochemical factors of the 10 sampling sites. Table S2. Pearson correlations between seawater temperature and biogeochemical variables. Figure S1. Pearson correlations between seawater temperature and bacterial abundance (A), DNA yield (a proxy for microbial biomass) (B), and grazing rate (C). Figure S2. Multivariate regression tree (MRT) of bacterial diversity associated with driving biogeochemical factors

ROR-γ drives androgen receptor expression and represents a therapeutic target in castration-resistant prostate cancer.

The androgen receptor (AR) is overexpressed and hyperactivated in human castration-resistant prostate cancer (CRPC). However, the determinants of AR overexpression in CRPC are poorly defined. Here we show that retinoic acid receptor-related orphan receptor γ (ROR-γ) is overexpressed and amplified in metastatic CRPC tumors, and that ROR-γ drives AR expression in the tumors. ROR-γ recruits nuclear receptor coactivator 1 and 3 (NCOA1 and NCOA3, also known as SRC-1 and SRC-3) to an AR-ROR response element (RORE) to stimulate AR gene transcription. ROR-γ antagonists suppress the expression of both AR and its variant AR-V7 in prostate cancer (PCa) cell lines and tumors. ROR-γ antagonists also markedly diminish genome-wide AR binding, H3K27ac abundance and expression of the AR target gene network. Finally, ROR-γ antagonists suppressed tumor growth in multiple AR-expressing, but not AR-negative, xenograft PCa models, and they effectively sensitized CRPC tumors to enzalutamide, without overt toxicity, in mice. Taken together, these results establish ROR-γ as a key player in CRPC by acting upstream of AR and as a potential therapeutic target for advanced PCa

Assessment of the feasibility and coverage of a modified universal hearing screening protocol for use with newborn babies of migrant workers in Beijing

BACKGROUND: Although migrant workers account for the majority of newborns in Beijing, their children are less likely to undergo appropriate universal newborn hearing screening/rescreening (UNHS) than newborns of local non-migrant residents. We hypothesised that this was at least in part due to the inadequacy of the UNHS protocol currently employed for newborn babies, and therefore aimed to modify the protocol to specifically reflect the needs of the migrant population. METHODS: A total of 10,983 healthy babies born to migrant mothers between January 2007 and December 2009 at a Beijing public hospital were investigated for hearing abnormalities according to a modified UNHS protocol. This incorporated two additional/optional otoacoustic emissions (OAE) tests at 24–48 hours and 2 months after birth. Infants not passing a screening test were referred to the next test, until any hearing loss was confirmed by the auditory brainstem response (ABR) test. RESULTS: A total of 98.91% (10983/11104) of all newborn children underwent the initial OAE test, of which 27.22% (2990/10983) failed the test. 1712 of the failed babies underwent the second inpatient OAE test, with739 failing again; thus significantly decreasing the overall positive rate for abnormal hearing from 27.22% to 18.36% ([2990–973 /10983)]; p = 0). Overall, 1147(56.87%) babies underwent the outpatient OAE test again after1-month, of whom 228 failed and were referred for the second outpatient OAE test (i.e. 2.08% (228/10983) referral rate at 1month of age). 141 of these infants underwent the referral test, of whom 103 (73.05%) tested positive again and were referred for a final ABR test for hearing loss (i.e. final referral rate of 1.73% ([228-38/10983] at 2 months of age). Only 54 infants attended the ABR test and 35 (0.32% of the original cohort tested) were diagnosed with abnormal hearing. CONCLUSIONS: Our study shows that it is feasible and practical to achieve high coverage rates for screening hearing loss and decrease the referral rates in newborn babies of migrant workers, using a modification of the currently employed UNHS protocol

A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index can be calculated

Changes in Nitric Oxide Level and Thickness Index of Synovial Fluid in Osteoarthritis Patients following Intraarticular Injection of Sodium Hyaluronate

Purpose: To monitor the changes in nitric oxide levels and synovium thickness index in synovial fluid following intra-articular injection of sodium hyaluronate.Methods: One hundred patients diagnosed with osteoarthritis of the knee from April 2014 to January 2015 in The Third Hospital of Jinan, Jinan, Shandong, China were selected and categorized into three phases; namely, mild, moderate and severe. Patients received a 20 mL sodium hyaluronate injection into the articular cavity of the knee once per week for 15 weeks, with continuous observation. The Visual Analogue Scale (VAS) and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) scores were recorded after five weeks. A total of 56 patients (78 knees) remaining in serious condition after 5 weeks were divided into mild, moderate, and severe groups and treated with sodium hyaluronate once a week. Internationally reorganized VAS and WOMAC scores were adopted as clinical observation indices to indicate the curative effect of sodium hyaluronate among the 56 patients after 15 weeks of treatment. The conditions of the patients in the two phases were compared.Results: After 5 weeks of treatment, treatment effective rate in the mild, moderate and severe groups was 72.92, 66.10 and 28.57 %, respectively, with an overall effective rate of 78 %. After 15 weeks of treatment, treatment effective rate in mild, moderate, and severe groups was 96.77, 95.45 and 66.67 %, respectively, with an overall effective rate of 67.95 %.Conclusions: Clinically curative effect of sodium hyaluronate is significant for mild and moderate phase patients after intra-articular injection of sodium hyaluronate, while the effect is insignificant in severe patients. Thus, sodium hyaluronate can effectively improve nitric oxide levels in synovial fluid, reduce synovium thickness, enhances articular cavity lubrication and effectively alleviates disease severity.Keywords: Osteoarthritis, Knee, Intra-articular injection, Sodium hyaluronate, Nitric oxide, Synovium thickness, WOMA

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks

Gossip Consensus Algorithm Based on Time-Varying Influence Factors and Weakly Connected Graph for Opinion Evolution in Social Networks

We provide a new gossip algorithm to investigate the problem of opinion consensus with the time-varying influence factors and weakly connected graph among multiple agents. What is more, we discuss not only the effect of the time-varying factors and the randomized topological structure but also the spread of misinformation and communication constrains described by probabilistic quantized communication in the social network. Under the underlying weakly connected graph, we first denote that all opinion states converge to a stochastic consensus almost surely; that is, our algorithm indeed achieves the consensus with probability one. Furthermore, our results show that the mean of all the opinion states converges to the average of the initial states when time-varying influence factors satisfy some conditions. Finally, we give a result about the square mean error between the dynamic opinion states and the benchmark without quantized communication

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning

Equipping a deep model the abaility of few-shot learning, i.e., learning quickly from only few examples, is a core challenge for artificial intelligence. Gradient-based meta-learning approaches effectively address the challenge by learning how to learn novel tasks. Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (i.e., its hyperparameters), while the inner-loop process leverage it to optimize a task-specific model by using only few labeled data. Although these existing methods have shown superior performance, the outer-loop process requires calculating second-order derivatives along the inner optimization path, which imposes considerable memory burdens and the risk of vanishing gradients. Drawing inspiration from recent progress of diffusion models, we find that the inner-loop gradient descent process can be actually viewed as a reverse process (i.e., denoising) of diffusion where the target of denoising is model weights but the origin data. Based on this fact, in this paper, we propose to model the gradient descent optimizer as a diffusion model and then present a novel task-conditional diffusion-based meta-learning, called MetaDiff, that effectively models the optimization process of model weights from Gaussion noises to target weights in a denoising manner. Thanks to the training efficiency of diffusion models, our MetaDiff do not need to differentiate through the inner-loop path such that the memory burdens and the risk of vanishing gradients can be effectvely alleviated. Experiment results show that our MetaDiff outperforms the state-of-the-art gradient-based meta-learning family in few-shot learning tasks.Comment: Accepted by AAAI 202

Resonant Andreev reflections in superconductor-carbon-nanotube devices

Resonant Andreev reflection through superconductor-carbon-nanotube devices was investigated theoretically with a focus on the superconducting proximity effect. Consistent with a recent experiment, we find that for high transparency devices on-resonance, the Andreev current is characterized by a large value and a resistance dip; low-transparency off-resonance devices give the opposite result. We also give evidence that the observed low-temperature transport anomaly may be a natural result of Andreev reflection process