Detection-driven exposure-correction network for nighttime drone-view object detection.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset

Gene Deletion in Barley Mediated by LTR-retrotransposon BARE

A poly-row branched spike (prbs) barley mutant was obtained from soaking a two-rowed barley inflorescence in a solution of maize genomic DNA. Positional cloning and sequencing demonstrated that the prbs mutant resulted from a 28 kb deletion including the inflorescence architecture gene HvRA2. Sequence annotation revealed that the HvRA2 gene is flanked by two LTR (long terminal repeat) retrotransposons (BARE) sharing 89% sequence identity. A recombination between the integrase (IN) gene regions of the two BARE copies resulted in the formation of an intact BARE and loss of HvRA2. No maize DNA was detected in the recombination region although the flanking sequences of HvRA2 gene showed over 73% of sequence identity with repetitive sequences on 10 maize chromosomes. It is still unknown whether the interaction of retrotransposons between barley and maize has resulted in the recombination observed in the present study.Peer reviewe

Point Process Analysis of Noise in Early Invertebrate Vision

Noise is a prevalent and sometimes even dominant aspect of many biological processes. While many natural systems have adapted to attenuate or even usefully integrate noise, the variability it introduces often still delimits the achievable precision across biological functions. This is particularly so for visual phototransduction, the process responsible for converting photons of light into usable electrical signals (quantum bumps). Here, randomness of both the photon inputs (regarded as extrinsic noise) and the conversion process (intrinsic noise) are seen as two distinct, independent and significant limitations on visual reliability. Past research has attempted to quantify the relative effects of these noise sources by using approximate methods that do not fully account for the discrete, point process and time ordered nature of the problem. As a result the conclusions drawn from these different approaches have led to inconsistent expositions of phototransduction noise performance. This paper provides a fresh and complete analysis of the relative impact of intrinsic and extrinsic noise in invertebrate phototransduction using minimum mean squared error reconstruction techniques based on Bayesian point process (Snyder) filters. An integrate-fire based algorithm is developed to reliably estimate photon times from quantum bumps and Snyder filters are then used to causally estimate random light intensities both at the front and back end of the phototransduction cascade. Comparison of these estimates reveals that the dominant noise source transitions from extrinsic to intrinsic as light intensity increases. By extending the filtering techniques to account for delays, it is further found that among the intrinsic noise components, which include bump latency (mean delay and jitter) and shape (amplitude and width) variance, it is the mean delay that is critical to noise performance. Consequently, if one wants to increase visual fidelity, reducing the photoconversion lag is much more important than improving the regularity of the electrical signal.This work was supported by the Gates Cambridge Trust (PhD studentship for research) https://www.gatescambridge.org/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Phytoplankton Composition and Their Related Factors in Five Different Lakes in China: Implications for Lake Management

In this paper, two trophic lakes: Lake Taihu and Lake Yanghe, and three alpine lakes: Lake Qinghai, Lake Keluke, and Lake Tuosu, were investigated to discover the connections between environmental factors and the phytoplankton community in lakes with differences in trophic levels and climatic conditions. Three seasonal data, including water quality and phytoplankton, were collected from the five lakes. The results demonstrated clear differences in water parameters and phytoplankton compositions in different lakes. The phytoplankton was dominated by Bacillariophyta, followed by Cyanobacteria and Chlorophyta in Lake Qinghai, Lake Keluke, and Lake Tuosu. It was dominated by Cyanobacteria (followed by Chlorophyta and Bacillariophyta in Lake Yanghe) and Cyanobacteria (followed by Chlorophyta and Cryptophyta in Lake Taihu). The temperature was an essential factor favoring the growth of Cyanobacteria, Chlorophyta, and Bacillariophyta, especially Cyanobacteria and Chlorophyta. The pH had significantly negative relationships with Cyanobacteria, Chlorophyta, and Bacillariophyta. Particularly, a high pH might be a strong and negative factor for phytoplankton growth in alpine lakes. A high salinity was also an adverse factor for phytoplankton. Those results could provide fundamental information about the phytoplankton community and their correlated factors in the alpine lakes of the Tibetan Plateau, contributing to the protection and management of alpine lakes

Multicomponent Stress–Strength Model Based on Generalized Progressive Hybrid Censoring Scheme: A Statistical Analysis

The statistical inference of the reliability and parameters of the stress–strength model has received great attention in the field of reliability analysis. When following the generalized progressive hybrid censoring (GPHC) scheme, it is important to discuss the point estimate and interval estimate of the reliability of the multicomponent stress–strength (MSS) model, in which the stress and the strength variables are derived from different distributions by assuming that stress follows the Chen distribution and that strength follows the Gompertz distribution. In the present study, the Newton–Raphson method was adopted to derive the maximum likelihood estimation (MLE) of the model parameters, and the corresponding asymptotic distribution was adopted to construct the asymptotic confidence interval (ACI). Subsequently, the exact confidence interval (ECI) of the parameters was calculated. A hybrid Markov chain Monte Carlo (MCMC) method was adopted to determine the approximate Bayesian estimation (BE) of the unknown parameters and the high posterior density credible interval (HPDCI). A simulation study with the actual dataset was conducted for the BEs with squared error loss function (SELF) and the MLEs of the model parameters and reliability, comparing the bias and mean squares errors (MSE). In addition, the three interval estimates were compared in terms of the average interval length (AIL) and coverage probability (CP)

A Systematic Investigation into the Environmental Fate of Microcystins and The Potential Risk: Study in Lake Taihu

A systematic investigation was conducted in Lake Taihu in autumn of 2013 and 2014, in order to understand the environmental fate of microcystins (MCs) and evaluate the health risk from MCs. Samples of water, algal cells, macrophytes, shrimps and fish were taken to detect MCs by HPLC-MS/MS after solid phase extraction. Widespread MC contamination in water, algal cells, macrophytes, shrimps and fish was found in Lake Taihu. The ubiquitous presence of MCs in water, algal cells and biota was found in 100% of samples. MC accumulation was in the order of primary producer > tertiary consumer > secondary consumer > primary consumer. The highest levels of MCs in macrophytes, shrimps and fish tissue were found in Potamogeton maackianus, Exopalaemon modestus, and Hyporhamphus intermedius, respectively. The MCs level in shrimps and the tissues of three fish species, Neosalanx tangkahkeii taihuensis, Coilia ectenes and silver carp, was closely linked to their dietary exposure. Ceratophyllum demersum L. was an ideal plant for introduction into lakes to protect against Microcystis blooms and MCs, due to its ability to absorb nutrients, accumulate large amounts of MCs and tolerate these toxins compared to other macrophytes. The average daily intakes (ADIs) of MCs for Exopalaemon modestus and three fish species, Coilia ectenes, Hyporhamphus intermedius and Carassius carassius, were all above the tolerable daily intakes (TDI) set by the World Health Organization (WHO), implying there existed potential threats to human health

Recent Advances in Woven Spacer Fabric Sandwich Composite Panels: A Review

Because of the advantageous characteristics of strong integrity, lightweight, high performance, and various designs, woven spacer fabric (WSF) and its composite are extensively used in construction, traffic, and aerospace, among other fields. This paper first describes the WSF structure, including core yarns and cross-linking, and then discusses the influence of the processing parameters, among angle of the wall decisive the failure mode on the plate properties. Moreover, we summarize the molding and filling technology of WSF composite sandwich panels and discuss the process order, resulting in a significant effect on the stiffness of the sandwich composite plate; the current processing is mostly hand lay-up technology. In addition, we introduce the core and matrix material of the sandwich composite plate, which are mainly polyurethane (PU) foam and epoxy resin (70% of matrix material), respectively. Finally, the mechanical properties of WSF composite sandwich panels are summarized, including bending, compression, impact, shear, and peel properties. Factors influencing the mechanical properties are analyzed to provide a theoretical basis for future plate design and preparation