Effects of antibiotics on microbial nitrogen cycling and N2O emissions: A review.

Sulfonamides, quinolones, tetracyclines, and macrolides are the most prevalent classes of antibiotics used in both medical treatment and agriculture. The misuse of antibiotics leads to their extensive dissemination in the environment. These antibiotics can modify the structure and functionality of microbial communities, consequently impacting microbial-mediated nitrogen cycling processes including nitrification, denitrification, and anammox. They can change the relative abundance of nirK/norB contributing to the emission of nitrous oxide, a potent greenhouse gas. This review provides a comprehensive examination of the presence of these four antibiotic classes across different environmental matrices and synthesizes current knowledge of their effects on the nitrogen cycle, including the underlying mechanisms. Such an overview is crucial for understanding the ecological impacts of antibiotics and for guiding future research directions. The presence of antibiotics in the environment varies widely, with significant differences in concentration and type across various settings. We conducted a comprehensive review of over 70 research articles that compare various aspects including processes, antibiotics, concentration ranges, microbial sources, experimental methods, and mechanisms of influence. Antibiotics can either inhibit, have no effect, or even stimulate nitrification, denitrification, and anammox, depending on the experimental conditions. The influence of antibiotics on the nitrogen cycle is characterized by dose-dependent responses, primarily inhibiting nitrification, denitrification, and anammox. This is achieved through alterations in microbial community composition and diversity, carbon source utilization, enzyme activities, electron transfer chain function, and the abundance of specific functional enzymes and antibiotic resistance genes. These alterations can lead to diminished removal of reactive nitrogen and heightened nitrous oxide emissions, potentially exacerbating the greenhouse effect and related environmental issues. Future research should consider diverse reaction mechanisms and expand the scope to investigate the combined effects of multiple antibiotics, as well as their interactions with heavy metals and other chemicals or organisms.

Reliability-Guided Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation.

This article aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only initialized with sparse target scribbles for inference but also trained by sparse scribble annotations. Thus, the annotation burdens for both initialization and training can be substantially lightened. The difficulties of scribble-supervised VOS lie in two aspects: 1) it demands a strong reasoning ability to carefully segment the target given only a sparse initial target scribble and 2) it necessitates learning dense prediction from sparse scribble annotations during training, requiring powerful learning capability. In this work, we propose a reliability-guided hierarchical memory network (RHMNet) for this task, which segments the target in a stepwise expanding strategy w.r.t. the memory reliability level. To be specific, RHMNet maintains a reliability-guided memory bank. It first uses the high-reliability memory to locate the region with high reliability belonging to the target, i.e., highly similar to the initial target scribble. Then, it expands the located high-reliability region to the entire target conditioned on the region itself and all existing memories. In addition, we propose a scribble-supervised learning mechanism to facilitate the model learning for dense prediction. It exploits the pixel-level relations within a single frame and the instance-level variations across multiple frames to take full advantage of the scribble annotations in sequence training samples. The favorable performance on four popular benchmarks demonstrates that our method is promising. Our project is available at: https://github.com/mkg1204/RHMNet-for-SSVOS.

Reduction of Tumor Growth with RNA-Targeting Treatment of the NAB2-STAT6 Fusion Transcript in Solitary Fibrous Tumor Models.

Solitary fibrous tumor (SFT) is a rare soft-tissue sarcoma. This nonhereditary cancer is the result of an environmental intrachromosomal gene fusion between NAB2 and STAT6 on chromosome 12, which fuses the activation domain of STAT6 with the repression domain of NAB2. Currently there is not an approved chemotherapy regimen for SFTs. The best response on available pharmaceuticals is a partial response or stable disease for several months. The purpose of this study is to investigate the potential of RNA-based therapies for the treatment of SFTs. Specifically, in vitro SFT cell models were engineered to harbor the characteristic NAB2-STAT6 fusion using the CRISPR/SpCas9 system. Cell migration as well as multiple cancer-related signaling pathways were increased in the engineered cells as compared to the fusion-absent parent cells. The SFT cell models were then used for evaluating the targeting efficacies of NAB2-STAT6 fusion-specific antisense oligonucleotides (ASOs) and CRISPR/CasRx systems. Our results showed that fusion specific ASO treatments caused a 58% reduction in expression of fusion transcripts and a 22% reduction in cell proliferation after 72 h in vitro. Similarly, the AAV2-mediated CRISPR/CasRx system led to a 59% reduction in fusion transcript expressions in vitro, and a 55% reduction in xenograft growth after 29 days ex vivo.

Cu-doped La0.5Sr0.5CoO3-δ perovskite as a highly efficient and durable electrocatalyst for hydrogen evolution.

The preparation of a high-efficiency and durable electrocatalyst for the alkaline hydrogen evolution reaction (HER) is essential for realizing renewable energy technologies. Herein, a series of La0.5Sr0.5CoO3-δ perovskites with different amounts of Cu cations substituting at B-sites were fabricated for the HER. Specifically, the optimized La0.5Sr0.5Co0.8Cu0.2O3-δ (LSCCu0.2) exhibits a significantly enhanced electrocatalytic activity with an ultralow overpotential of 154 mV at 10 mA cm-2 in 1.0 M KOH, which is reduced by 125 mV compared with that of pristine La0.5Sr0.5CoO3-δ (LSC, 279 mV). It also delivers a robust durability with no obvious degradation after 150 h. Impressively, the HER activity of LSCCu0.2 is superior to that of commercial Pt/C at large current densities (>270 mA cm-2). XPS analysis indicates that Co2+ ions replaced by an appropriate amount of Cu2+ ions can increase the proportion of Co3+ and generate high content of oxygen vacancies in LSC, which leads to an increased electrochemically active surface area, thereby greatly facilitating the HER. This work offers a simple way for the rational design of cost-effective and highly efficient catalysts, which may be extended to other Co-based perovskite oxides for the alkaline HER.

Remnant of the late Permian superplume that generated the Siberian Traps inferred from geomagnetic data.

Mantle plumes have played a key role in tectonic events such as continental break-up and large magmatic events since at least the formation of Gondwana. However, as their signatures on Earth's surface, many of large igneous provinces have disappeared into the mantle during Earth's long-term evolution, meaning that plume remnants in the mantle are crucial in advancing mantle plume theory and accurately reconstructing Earth history. Here we present an electrical conductivity model for North Asia constructed from geomagnetic data. The model shows a large high-electrical-conductivity anomaly in the mantle transition zone beneath the Siberian Traps at the time of their eruption that we interpret to be a thermal anomaly with trace amounts of melt. This anomaly lies almost directly over an isolated low-seismic-wave-velocity anomaly known as the Perm anomaly. The spatial correlation of our anomaly with the Siberian Traps suggests that it represents a remnant of a superplume that was generated from the Perm anomaly. This plume was responsible for the late Permian Siberian large igneous province. The model strengthens the validity of the mantle plume hypothesis.

Adaptive ensemble perception tracking.

Recently, tracking models based on bounding box regression (such as region proposal networks), built on the Siamese network, have attracted much attention. Despite their promising performance, these trackers are less effective in perceiving the target information in the following two aspects. First, existing regression models cannot take a global view of a large-scale target since the effective receptive field of a neuron is too small to cover the target with a large scale. Second, the neurons with a fixed receptive field (RF) size in these models cannot adapt to the scale and aspect ratio changes of the target. In this paper, we propose an adaptive ensemble perception tracking framework to address these issues. Specifically, we first construct a per-pixel prediction model, which predicts the target state at each pixel of the correlated feature. On top of the per-pixel prediction model, we then develop a confidence-guided ensemble prediction mechanism. The ensemble mechanism adaptively fuses the predictions of multiple pixels with the guidance of confidence maps, which enlarges the perception range and enhances the adaptive perception ability at the object-level. In addition, we introduce a receptive field adaption model to enhance the adaptive perception ability at the neuron-level, which adjusts the RF by adaptively integrating the features with different RFs. Extensive experimental results on the VOT2018, VOT2016, UAV123, LaSOT, and TC128 datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and speed.

Learning dual-margin model for visual tracking.

Existing trackers usually exploit robust features or online updating mechanisms to deal with target variations which is a key challenge in visual tracking. However, the features being robust to variations remain little spatial information, and existing online updating methods are prone to overfitting. In this paper, we propose a dual-margin model for robust and accurate visual tracking. The dual-margin model comprises an intra-object margin between different target appearances and an inter-object margin between the target and the background. The proposed method is able to not only distinguish the target from the background but also perceive the target changes, which tracks target appearance changing and facilitates accurate target state estimation. In addition, to exploit rich off-line video data and learn general rules of target appearance variations, we train the dual-margin model on a large off-line video dataset. We perform tracking under a Siamese framework using the constructed appearance set as templates. The proposed method achieves accurate and robust tracking performance on five public datasets while running in real-time. The favorable performance against the state-of-the-art methods demonstrates the effectiveness of the proposed algorithm.

Dual-regression model for visual tracking.

Existing regression based tracking methods built on correlation filter model or convolution model do not take both accuracy and robustness into account at the same time. In this paper, we propose a dual-regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. The convolutional module trained in a classification manner with hard negative mining ensures the discriminative ability of the proposed tracker, which facilitates the handling of several challenging problems, such as drastic deformation, distractors, and complicated backgrounds. The correlation filter component built on the shallow features with fine-grained features enables accurate localization. By fusing these two branches in a coarse-to-fine manner, the proposed dual-regression tracking framework achieves a robust and accurate tracking performance. Extensive experiments on the OTB2013, OTB2015, and VOT2015 datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.