ATRA regulates myoblast differentiation and fusion through the RARα/Pitx2 signaling pathway, causing abnormal development of PFMs in ARM fetal rats.

Anorectal malformation (ARM) is the most common congenital digestive tract anomaly in newborns, and children with ARM often have varying degrees of underdevelopment of the pelvic floor muscles (PFMs). To explore the effects of RARα and Pitx2 on the development of rat PFMs, we constructed a rat ARM animal model using all-trans retinoic acid (ATRA), and verified the expression of RARα and Pitx2 in the PFMs of fetal rats. Additionally, we used rat myoblasts (L6 cells) to investigate the regulatory roles of RARα and Pitx2 in skeletal muscle myoblast differentiation and their interactions. The results indicated a significant decrease in the expression of RARα and Pitx2 in the PFMs of fetal rats with ARM. ATRA can also decrease the expression of RARα and Pitx2 in the L6 cells, while affecting the differentiation and fusion of L6 cells. Knocking down RARα in L6 cells reduced the expression of Pitx2, MYOD1, MYMK, and decreased myogenic activity in L6 cells. When RARα is activated, the decreased expression of Pitx2, MYOD1, and MYMK and myogenic differentiation can be restored to different extents. At the same time, increasing or inhibiting the expression of Pitx2 can counteract the effects of knocking down RARα and activating RARα respectively. These results indicate that Pitx2 may be downstream of the transcription factor RARα, mediating the effects of ATRA on the development of fetal rat PFMs.

GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework.

Particle-based Variational Inference (ParVI) methods have been widely adopted in deep Bayesian inference tasks such as Bayesian neural networks or Gaussian Processes, owing to their efficiency in generating high-quality samples given the score of the target distribution. Typically, ParVI methods evolve a weighted-particle system by approximating the first-order Wasserstein gradient flow to reduce the dissimilarity between the particle system's empirical distribution and the target distribution. Recent advancements in ParVI have explored sophisticated gradient flows to obtain refined particle systems with either accelerated position updates or dynamic weight adjustments. In this paper, we introduce the semi-Hamiltonian gradient flow on a novel Information-Fisher-Rao space, known as the SHIFR flow, and propose the first ParVI framework that possesses both accelerated position update and dynamical weight adjustment simultaneously, named the General Accelerated Dynamic-Weight Particle-based Variational Inference (GAD-PVI) framework. GAD-PVI is compatible with different dissimilarities between the empirical distribution and the target distribution, as well as different approximation approaches to gradient flow. Moreover, when the appropriate dissimilarity is selected, GAD-PVI is also suitable for obtaining high-quality samples even when analytical scores cannot be obtained. Experiments conducted under both the score-based tasks and sample-based tasks demonstrate the faster convergence and reduced approximation error of GAD-PVI methods over the state-of-the-art.

Effect of remediation reagents on bacterial composition and ecological function in black-odorous water sediments.

Black-odorous urban water bodies and sediments pose a serious environmental problem. In this study, we conducted microcosm batch experiments to investigate the effect of remediation reagents (magnesium hydroxide and calcium nitrate) on native bacterial communities and their ecological functions in the black-odorous sediment of urban water. The dominant phyla (Proteobacteria, Actinobacteria, Chloroflexi, and Planctomycetes) and classes (Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, Actinobacteria, Anaerolineae, and Planctomycetia) were determined under calcium nitrate and magnesium hydroxide treatments. Functional groups related to aerobic metabolism, including aerobic chemoheterotrophy, dark sulfide oxidation, and correlated dominant genera (Thiobacillus, Lysobacter, Gp16, and Gaiella) became more abundant under calcium nitrate treatment, whereas functional genes potentially involved in dissimilatory sulfate reduction became less abundant. The relative abundance of chloroplasts, fermentation, and correlated genera (Desulfomonile and unclassified Cyanobacteria) decreased under magnesium hydroxide treatment. Overall, these results indicated that calcium nitrate addition improved hypoxia-related reducing conditions in the sediment and promoted aerobic chemoheterotrophy.

Stress-responses of microbial population and activity in activated sludge under long-term ciprofloxacin exposure.

In this study, the effects of ciprofloxacin on activated sludge were evaluated based on the microbial community and metabolic characteristics. The results indicated that the metabolism of chemical oxygen demand (COD) and nitrogen were inhibited with ciprofloxacin at mg/L level compared to the control experiment, and the concentration of ciprofloxacin was slightly decreased. High-throughput sequencing (HTS) results showed that ciprofloxacin greatly shaped the microbial communities in activated sludge, especially for the Nitrospirae phylum and Nitrospira genus. High concentrations of ciprofloxacin stimulated the enrichment of Zoogloea, thus reducing the stability of the activated sludge. Moreover, quinolone resistance proteins in Aeromonas were enriched, which demonstrates their competitive advantage in these enrichment incubations. Finally, the functional profiles were predicted through Tax4Fun, which revealed the adaption to microbes in activated sludge to the ciprofloxacin selective pressure. This work demonstrates the influence of ciprofloxacin on the activated sludge process, and can provide a useful reference for the assessment of the ecological security of ciprofloxacin.

Unsupervised Domain Adaptation With Class-Aware Memory Alignment.

Unsupervised domain adaptation (UDA) is to make predictions on unlabeled target domain by learning the knowledge from a label-rich source domain. In practice, existing UDA approaches mainly focus on minimizing the discrepancy between different domains by mini-batch training, where only a few instances are accessible at each iteration. Due to the randomness of sampling, such a batch-level alignment pattern is unstable and may lead to misalignment. To alleviate this risk, we propose class-aware memory alignment (CMA) that models the distributions of the two domains by two auxiliary class-aware memories and performs domain adaptation on these predefined memories. CMA is designed with two distinct characteristics: class-aware memories that create two symmetrical class-aware distributions for different domains and two reliability-based filtering strategies that enhance the reliability of the constructed memory. We further design a unified memory-based loss to jointly improve the transferability and discriminability of features in the memories. State-of-the-art (SOTA) comparisons and careful ablation studies show the effectiveness of our proposed CMA.

Seasonal variations in spatial distribution, mobilization kinetic and toxicity risk of arsenic in sediments of Lake Taihu, China.

This study investigated seasonal variations in spatial distribution, mobilization kinetic and toxicity risk of arsenic (As) in sediments of three representative ecological lakes in Lake Taihu. Results suggested that the bioavailability and mobility of As in sediments depended on the lake ecological types and seasonal changes. At the algal-type zones and macrophyte-type zones, elevated As concentrations were observed in April and July, while these occurred at the transition areas in July and October. The diffusion flux of soluble As ranged from 0.03 to 3.03 ng/cm2/d, indicating sediments acted as a source of As. Reductive dissolution of As-bearing iron/manganese-oxides was the key driver of sediment As remobilization. However, labile S(-II) caused by the degradations of algae and macrophytes buffered sediment As release at the algal-type and macrophyte-type zones. Furthermore, the resupply ratio was less than 1 at three ecological lakes, indicating the resupply As capacity of sediment solid phase was partially sustained case. The risk quotient values were higher than 1 at the algal-type zones and transition areas in July, thereby, the adverse effects of As should not be ignored. This suggested that it is urgently need to be specifically monitored and managed for As contamination in sediments across multi-ecological lakes.

Elastic Knowledge Distillation by Learning From Recollection.

Model performance can be further improved with the extra guidance apart from the one-hot ground truth. To achieve it, recently proposed recollection-based methods utilize the valuable information contained in the past training history and derive a "recollection" from it to provide data-driven prior to guide the training. In this article, we focus on two fundamental aspects of this method, i.e., recollection construction and recollection utilization. Specifically, to meet the various demands of models with different capacities and at different training periods, we propose to construct a set of recollections with diverse distributions from the same training history. After that, all the recollections collaborate together to provide guidance, which is adaptive to different model capacities, as well as different training periods, according to our similarity-based elastic knowledge distillation (KD) algorithm. Without any external prior to guide the training, our method achieves a significant performance gain and outperforms the methods of the same category, even as well as KD with well-trained teacher. Extensive experiments and further analysis are conducted to demonstrate the effectiveness of our method.

Memory-Efficient Class-Incremental Learning for Image Classification.

With the memory-resource-limited constraints, class-incremental learning (CIL) usually suffers from the "catastrophic forgetting" problem when updating the joint classification model on the arrival of newly added classes. To cope with the forgetting problem, many CIL methods transfer the knowledge of old classes by preserving some exemplar samples into the size-constrained memory buffer. To utilize the memory buffer more efficiently, we propose to keep more auxiliary low-fidelity exemplar samples, rather than the original real-high-fidelity exemplar samples. Such a memory-efficient exemplar preserving scheme makes the old-class knowledge transfer more effective. However, the low-fidelity exemplar samples are often distributed in a different domain away from that of the original exemplar samples, that is, a domain shift. To alleviate this problem, we propose a duplet learning scheme that seeks to construct domain-compatible feature extractors and classifiers, which greatly narrows down the above domain gap. As a result, these low-fidelity auxiliary exemplar samples have the ability to moderately replace the original exemplar samples with a lower memory cost. In addition, we present a robust classifier adaptation scheme, which further refines the biased classifier (learned with the samples containing distillation label knowledge about old classes) with the help of the samples of pure true class labels. Experimental results demonstrate the effectiveness of this work against the state-of-the-art approaches. We will release the code, baselines, and training statistics for all models to facilitate future research.

Epoch-Evolving Gaussian Process Guided Learning for Classification.

The conventional mini-batch gradient descent algorithms are usually trapped in the local batch-level distribution information, resulting in the ``zig-zag'' effect in the learning process. To characterize the correlation information between the batch-level distribution and the global data distribution, we propose a novel learning scheme called epoch-evolving Gaussian process guided learning (GPGL) to encode the global data distribution information in a non-parametric way. Upon a set of class-aware anchor samples, our GP model is built to estimate the class distribution for each sample in mini-batch through label propagation from the anchor samples to the batch samples. The class distribution, also named the context label, is provided as a complement for the ground-truth one-hot label. Such a class distribution structure has a smooth property and usually carries a rich body of contextual information that is capable of speeding up the convergence process. With the guidance of the context label and ground-truth label, the GPGL scheme provides a more efficient optimization through updating the model parameters with a triangle consistency loss. Furthermore, our GPGL scheme can be generalized and naturally applied to the current deep models, outperforming the state-of-the-art optimization methods on six benchmark datasets.

What and Where: Learn to Plug Adapters via NAS for Multidomain Learning.

As an important and challenging problem, multidomain learning (MDL) typically seeks a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and structure design are handcrafted and fixed for all domains before model learning, resulting in learning inflexibility and computational intensiveness. With this motivation, we propose to learn a data-driven adapter plugging strategy with neural architecture search (NAS), which automatically determines where to plug for those adapter modules. Furthermore, we propose an NAS-adapter module for adapter structure design in an NAS-driven learning scheme, which automatically discovers effective adapter module structures for different domains. Experimental results demonstrate the effectiveness of our MDL model against existing approaches under the conditions of comparable performance.

MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning.

As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge. In this paper, we concentrate on this '`slow vs. fast'' (SvF) dilemma to determine which knowledge components to be updated in a slow fashion or a fast fashion, and thereby balance old-knowledge preservation and new-knowledge adaptation. We propose a multi-grained SvF learning strategy to cope with the SvF dilemma from two different grains: intra-space (within the same feature space) and inter-space (between two different feature spaces). The proposed strategy designs a novel frequency-aware regularization to boost the intra-space SvF capability, and meanwhile develops a new feature space composition operation to enhance the inter-space SvF learning performance. With the multi-grained SvF learning strategy, our method outperforms the state-of-the-art approaches by a large margin.

Compositional changes of sedimentary microbes in the Yangtze River Estuary and their roles in the biochemical cycle.

Due to the geographical circumstances, the Yangtze River Estuary (YRE) and the adjacent East China Sea are extensively influenced by both anthropogenic activities and environmental factors. To reveal the responses of microbes in surface sediment to environmental factors and their contributions to the biogeochemical cycle in this area, surface sediment and overlying water samples were collected at 21 stations from the estuary to the coastal region. Water and sediment parameters were determined, and 16S rRNA genes of microbes in sediment samples were sequenced using high throughput sequencing technology. The results indicated that ocean currents, sediment density (SD), nutrients, sulfate (SO42-), and salinity were the key factors shaping the microbial communities. Coastal microbes were affected mainly by SD, whereas anthropogenic discharge might have been responsible for a decrease in indigenous microbial diversity in the ocean. Due to the anthropogenic discharge, the most representative bacteria in the nearshore were aerobic and chemoheterotrophic bacteria, including ammonia-oxidizing bacteria, nitrite-oxidizing bacteria, denitrifying bacteria, and polyphosphate accumulating organisms. In the offshore, anaerobic bacteria, thermophilic bacteria, halophilic bacteria, sulfate-reducing bacteria, and sulfide oxidizing bacteria were the dominant bacteria, and these were characterized by strong solidarity and cooperative properties within the malnourished environment. In summary, these results provide a new perspective for revealing the biogeochemical significance of the bacterial lineages in the YRE, as well as constructive guidance for the management of the marginal sea ecosystems in distinct regions.

Towards the comprehensive water quality control in Lake Taihu: Correlating chlorphyll a and water quality parameters with generalized additive model.

In this study, the generalized additive model (GAM) was used to analyze seasonal monitoring data from Lake Taihu, collected from 2010 to 2014, with the aim to explore the correlation between chlorophyll a (Chla) and other water quality parameters. The selected optimal multivariable GAM could effectively explain the concentration variation of Chla occurring during each season, and the interpretation degree followed the order: summer > autumn > spring > winter. The fitting results indicated that the concentration variation of Chla could reflect that of biochemical oxygen demand and chemical oxygen demand in all seasons. In addition, the total phosphorus showed strong ability to explain the concentration change of Chla in spring and summer, as the growth of algae would be affected when the concentration of phosphorus shifted high or low. Nitrogen showed strong ability to explain the variations in Chla concentration in autumn. The conclusions of the optimal multivariable GAM could provide decision basis for the eutrophication control. In other words, the prevention of eutrophication outbreaks could be carried out via the targeted control of key water pollutants. According to these results, the concentration of Chla was higher in northern and western lake during summer and autumn, the management should focus on nutrient input of adjacent rivers.

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models.

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (CODMn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.