Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China.

Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were conducted at seven sampling sites along the Yangtze River Estuary (YRE) during summer, autumn, and winter 2021 to analyze the concentrations of seven heavy metals (As, Cd, Cr, Pb, Cu, Ni, Zn) in water and surface sediments. The order of heavy metal concentrations in water samples from highest to lowest was Zn > As > Cu > Ni > Cr > Pb > Cd, while that in surface sediments samples was Zn > Cr > As > Ni > Pb > Cu > Cd. Human health risk assessment of the heavy metals in water samples indicated a chronic and carcinogenic risk associated with As. The risks of heavy metals in surface sediments were evaluated using the geo-accumulation index (Igeo) and potential ecological risk index (RI). Among the seven heavy metals, As and Cd were highly polluted, with Cd being the main contributor to potential ecological risks. Principal component analysis (PCA) was employed to identify the sources of the different heavy metals, revealing that As originated primarily from anthropogenic emissions, while Cd was primarily from atmospheric deposition. To further analyze the influence of water quality indicators on heavy metal pollution, an artificial neural network (ANN) model was utilized. A modified model was proposed, incorporating biochemical parameters to predict the level of heavy metal pollution, achieving an accuracy of 95.1%. This accuracy was 22.5% higher than that of the traditional model and particularly effective in predicting the maximum 20% of values. Results in this paper highlight the pollution of As and Cd along the YRE, and the proposed model provides valuable information for estimating heavy metal pollution in estuarine water environments, facilitating pollution prevention efforts.

A Seawater Salinity Sensor Based on Optimized Long Period Fiber Grating in the Dispersion Turning Point.

Variations of seawater salinity often cause ocean internal waves, water masses and stratification, which affect the stability of the ocean environment. Therefore, the study of seawater salinity is significant for the prediction of changes in the ocean environment. However, existing methods for measuring seawater salinity generally have the disadvantages of low sensitivity and low accuracy. In this work, we proposed a seawater salinity sensor based on long period fiber grating (LPFG) in the dispersion turning point (DTP), which has demonstrated the possibility to fabricate LPFG with a shorter grating period by CO2 laser in a thin single mode fiber (SMF) of 80 µm cladding diameter without etching. For obtaining higher sensitivity that could meet the measurement requirement in practice, the proposed sensor was optimized by combining etching cladding and DTP. After the LPFG working near DTP was fabricated by a CO2 laser, the cladding diameter was reduced to 57.14 µm for making cladding mode LP1,7 work near DTP by hydrofluoric acid (HF) solutions. The experimental results have demonstrated that a sensitivity of 0.571 nm/‱ can be achieved when the salinity increases from 5.001‱ to 39.996‱, and the sensor shows good repeatability and stability. Based on its excellent performance, the optimized LPFG is a prospective sensor to monitor seawater salinity in real time. Meanwhile, a low-cost way was provided to make LPFG work near DTP instead of ultraviolet exposure and femtosecond laser writing.

A novel Rickettsia species closely related to Rickettsia felis in Anopheles mosquitoes from Yingkou City, Northeast China.

Mosquitoes are generally recognized as the most important vector of many zoonotic pathogens. In this study, seven mosquitoes species were identified (Anopheles pullus, Anopheles sinensis, Anopheles lesteri, Anopheles kleini, Ochlerotatus dorsalis, Aedes koreicus and Culex inatomii) in samples collected from Yingkou City, Liaoning Province, Northeastern China. A novel Rickettsia species was detected in Anopheles sinensis (two of 71, 2.82%) and Anopheles pullus (one of 106, 0.94%) mosquitoes. Genetic analysis indicated that the rrs and ompB genes have highest 99.60% and 97.88%-98.14% identities to Rickettsia felis, an emerging human pathogen of global concern mainly harboured by fleas, mosquitoes and booklice. The gltA sequences of these strains have 99.72% of nucleotide similarity with Rickettsia endosymbiont of Medetera jacula. The groEL sequences have 98.37% similarity to both Rickettsia tillamookensis and Rickettsia australis. The htrA sequences have 98.77% similarity to Rickettsia lusitaniae. In the phylogenetic tree based on concatenated nucleotide sequences of rrs, gltA, groEL, ompB and htrA genes, these strains are closely related to R. felis. Herein, we name it 'Candidatus Rickettsia yingkouensis'. Its human pathogenicity to humans and animals is still to be determined.

Effect of blocked sediments on flow characteristics and associated backwater effect in drainage pipes.

The study of hydraulic characteristics of water flow affected by blocked sediment is vital to assess discharge capacity in pipes with sediment sand to understand the process of pollutant and fine sand aggregation. The aim of this paper is to study the blocking effect of permeable sediments on non-full flow in circular pipe and to analyze hydraulic characteristics with backwater. Experiments and numerical simulations were performed and the porous media model is used to simulate the flow in permeable blocked sediments. To evaluate the degree of congestion, the backwater ratio ß is proposed. The backwater ratio is positively proportional to blocked sediment height and inversely proportional to blocked sediment particle size and flow value. Cross-sectional velocity in the backwater zone decreases significantly and tends to be uniform as backwater ratio increases. Furthermore, the development of secondary flows and velocity distributions influenced by backwater height is discussed. The dimensionless shear velocity in backwater zone shows an exponential decrease as the backwater ratio increases, which greatly increases the possibility of further sediment deposition.

Forward volume scattering function (0.03°-60°) measured using an oblique-incidence particle sizer.

The forward volume scattering function (VSF) is an inherent optical property important in ocean lidar and underwater imaging and communication. The scattered power within 60° contains >90% of total scattered power, making it essential for determining the asymmetry parameter g. Thus, the new oblique-incidence-design Bettersize BT-3000 particle sizer was utilized to measure forward VSF (0.03°-60°) synchronously. A double-exponential model was then used to construct the full-angle-range VSF (0°-180°). The g value calculated therefrom had an uncertainty of <1%. Calibration was implemented using 11-µm beads alone, and the BT-3000's performance was validated.

A Variable Clock Underlies Internally Generated Hippocampal Sequences.

Humans have the ability to store and retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of male rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-associated population events known as sharp-wave ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further indicate that a similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a log-normal distribution. This variability increases with experience despite the decline in SWR rates that occurs as environments become more familiar. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for storing experiences with various degrees of specificity.SIGNIFICANCE STATEMENT One of the most remarkable properties of memory is its flexibility: the brain can retrieve stored representations at varying levels of detail where, for example, we can begin with a memory of an entire extended event and then zoom in on a particular episode. The neural mechanisms that support this flexibility are not understood. Here we show that hippocampal sharp-wave ripples, which mark the times of memory replay and are important for memory storage, have a highly variable temporal structure that is well suited to support the storage of memories at different levels of detail.

Survival benefit of pure dose-dense chemotherapy in breast cancer: a meta-analysis of randomized controlled trials.

BACKGROUND: Dose-dense chemotherapy is a widely accepted regimen for high-risk breast cancer patients. However, conflicting survival benefits of pure dose-dense chemotherapy have been reported in different randomized controlled trials (RCTs). This meta-analysis aimed to further assess the efficacy and safety of pure dose-dense chemotherapy in breast cancer. METHODS: A literature search of electronic databases and websites was performed to identify phase III RCTs reporting the efficacy and toxicity of pure dose-dense chemotherapy. The endpoints of interest were overall survival (OS), disease-free survival (DFS), and toxicities. The hazard ratios (HRs) of death and recurrence and the odds ratios (ORs) of adverse events were estimated and pooled. RESULTS: Seven studies (five trials) were eligible, encompassing a total of 9851 patients. Patients treated with dose-dense chemotherapy obtained better DFS (HR = 0.83; 95% CI 0.75-0.91; p = 0.0001) than those treated with the conventional schedule, while OS benefit of dose-dense chemotherapy was less impressive (HR = 0.86; 95% CI 0.73-1.02; p = 0.08). However, significant OS benefit was observed in node-positive patients (HR = 0.77; 95% CI 0.66-0.90; p = 0.001). The incidence of anemia, pain, and transaminase elevation was higher in the dose-dense chemotherapy arm. CONCLUSIONS: Dose-dense chemotherapy leads to better prognosis; these findings suggest that it may be a potentially preferred treatment for breast cancer patients, particularly for women with lymph node involvement. However, more RCTs are warranted to better define the best candidates for dose-dense chemotherapy.

Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process.

We consider the task of learning the structure of the graph underlying a mutually-exciting multivariate Hawkes process in the high-dimensional setting. We propose a simple and computationally inexpensive edge screening approach. Under a subset of the assumptions required for penalized estimation approaches to recover the graph, this edge screening approach has the sure screening property: with high probability, the screened edge set is a superset of the true edge set. Furthermore, the screened edge set is relatively small. We illustrate the performance of this new edge screening approach in simulation studies.

Network Reconstruction From High-Dimensional Ordinary Differential Equations.

We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.

Distribution-free tests of independence in high dimensions.

We consider the testing of mutual independence among all entries in a [Formula: see text]-dimensional random vector based on [Formula: see text] independent observations. We study two families of distribution-free test statistics, which include Kendall's tau and Spearman's rho as important examples. We show that under the null hypothesis the test statistics of these two families converge weakly to Gumbel distributions, and we propose tests that control the Type I error in the high-dimensional setting where [Formula: see text]. We further show that the two tests are rate-optimal in terms of power against sparse alternatives and that they outperform competitors in simulations, especially when [Formula: see text] is large.

Deep Ranking for Person Re-Identification via Joint Representation Learning.

This paper proposes a novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems. Although a variety of powerful algorithms have been presented in the past few years, most of them usually focus on designing hand-crafted features and learning metrics either individually or sequentially. Different from previous works, we formulate a unified deep ranking framework that jointly tackles both of these key components to maximize their strengths. We start from the principle that the correct match of the probe image should be positioned in the top rank within the whole gallery set. An effective learning-to-rank algorithm is proposed to minimize the cost corresponding to the ranking disorders of the gallery. The ranking model is solved with a deep convolutional neural network (CNN) that builds the relation between input image pairs and their similarity scores through joint representation learning directly from raw image pixels. The proposed framework allows us to get rid of feature engineering and does not rely on any assumption. An extensive comparative evaluation is given, demonstrating that our approach significantly outperforms all the state-of-the-art approaches, including both traditional and CNN-based methods on the challenging VIPeR, CUHK-01, and CAVIAR4REID datasets. In addition, our approach has better ability to generalize across datasets without fine-tuning.

Selection and estimation for mixed graphical models.

We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, we investigate the selection of edges between nodes whose conditional distributions take different parametric forms, and show that efficiency can be gained if edge estimates obtained from the regressions of particular nodes are used to reconstruct the graph. These results are illustrated with examples of Gaussian, Bernoulli, Poisson and exponential distributions. Our theoretical findings are corroborated by evidence from simulation studies.