Floristic changes and environmental drivers of soil fungi and archaea in different salt-tolerant plant communities in the intertidal habitat of coastal wetlands.

Microorganisms are crucial elements of terrestrial ecosystems, which play significant roles in improving soil physicochemical properties, providing plant growth nutrients, degrading toxic and harmful chemicals, and biogeochemical cycling. Variations in the types and quantities of root exudates among different plants greatly alter soil physicochemical properties and result in variations in the diversity, structure, and function of soil microorganisms. Not much is understood about the differences of soil fungi and archaea communities for different plant communities in coastal wetlands, and their response mechanisms to environmental changes. In this study, fungal and archaea communities in soils of Suaeda salsa, Phragmites australis, and Spartina alterniflora in the intertidal habitat of coastal wetlands were selected for research. Soil fungi and archaea were analyzed for diversity, community structure, and function using high throughput ITS and 16S rRNA gene sequencing. The study revealed significant differences in fungi and archaea's diversity and community structure in the rhizosphere soil of three plant communities. At the same time, there is no significant difference in the functional groups. SOM, TP, AP, MC, EC and SOM, TN, TP, AP, MC, EC are the primary environmental determinants affecting changes in soil fungal and archaeal communities, respectively. Variations in the diversity, community structure, and ecological functions of fungi and archaea can be used as indicators characterizing the impact of external disturbances on the soil environment, providing a theoretical foundation for the effective utilization of soil microbial resources, thereby achieving the goal of environmental protection and health promotion.

End-to-End Signal Classification in Signed Cumulative Distribution Transform Space.

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sample using a nearest local subspace (NLS) search algorithm in SCDT domain. Experiments show that the proposed method provides high accuracy classification results while being computationally cheap, data efficient, and robust to out-of-distribution samples with respect to the existing end-to-end classification methods. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit.

Diet Diversity of the Fluviatile Masu Salmon, Oncorhynchus masou (Brevoort 1856) Revealed via Gastrointestinal Environmental DNA Metabarcoding and Morphological Identification of Contents.

Masu salmon, Oncorhynchus masou (Brevoort 1856), a commercially important fish species endemic to the North Pacific Ocean, attained national second-level protected animal status in China in 2021. Despite this recognition, knowledge about the trophic ecology of this fish remains limited. This study investigated the diet diversity of fluviatile Masu salmon in the Mijiang River, China, utilizing the gastrointestinal tract environmental DNA (GITeDNA) metabarcoding and morphological identification. The results revealed a diverse prey composition, ranging from terrestrial and aquatic invertebrates to small fishes. The fluviatile Masu salmon in general consumed noteworthily more aquatic prey than terrestrial prey. There were much more prey taxa and a higher diet diversity detected by GITeDNA metabarcoding than by morphological identification. GITeDNA metabarcoding showed that larger and older Masu salmon consumed significantly more terrestrial insects than aquatic prey species did, with 7366 verses 5012 sequences in the group of ≥20 cm, 9098 verses 4743 sequences in the group of ≥100 g and 11,540 verses 729 sequences in the group of age 3+. GITeDNA metabarcoding also showed size- and age-related diet diversity, indicating that the dietary niche breadth and trophic diversity of larger and older Masu salmon increased with food resources expanding to more terrestrial prey. Terrestrial invertebrates of riparian habitats play a vital role in the diet of fluviatile Masu salmon, especially larger individuals, highlighting their importance in connecting aquatic and terrestrial food webs. Conservation plans should prioritize the protection and restoration of riparian habitats. This study advocates the combined use of GITeDNA metabarcoding and morphological observation for a comprehensive understanding of fish diet diversity.

Effects of Aquatic Plant Coverage on Diversity and Resource Use Efficiency of Phytoplankton in Urban Wetlands: A Case Study in Jinan, China.

With the acceleration of urbanization, biodiversity and ecosystem functions of urban wetlands are facing serious challenges. The loss of aquatic plants in urban wetlands is becoming more frequent and intense due to human activities; nevertheless, the effects of aquatic plants on wetland ecosystems have received less attention. Therefore, we conducted field investigations across 10 urban wetlands in Jinan, Shandong Province, as a case in North China to examine the relationships between aquatic plant coverage and phytoplankton diversity, as well as resource use efficiency (RUE) in urban wetlands. Multivariate regression and partial least squares structural equation modeling (PLS-SEM) were used to analyze the water quality, phytoplankton diversity, and RUE. The results demonstrate that the increase in aquatic plant coverage significantly reduced the concentration of total nitrogen and suspended solids' concentrations and significantly increased the phytoplankton diversity (e.g., species richness and functional diversity). The aquatic plant coverage significantly affected the composition of phytoplankton functional groups; for example, functional groups that had adapted to still-water and low-light conditions became dominant. Furthermore, the increase in phytoplankton diversity improved phytoplankton RUE, highlighting the importance of aquatic plants in maintaining wetland ecosystem functions. This study may provide a scientific basis for the management strategy of aquatic plants in urban wetlands, emphasizing the key role of appropriate aquatic plant cover in maintaining the ecological stability and ecosystem service functions of wetlands.

Homogenization of Functional Diversity of Rotifer Communities in Relation to Eutrophication in an Urban River of North China.

Rapid urbanization has triggered nutrient loading, which will inevitably lead to the eutrophication of water bodies and further affect the structure of aquatic populations. At present, eutrophication is a significant challenge for urban aquatic ecosystems. However, we still know little about the correlation between eutrophication in urban rivers and the composition of aquatic functional groups. The effects of urban river eutrophication on rotifer communities were investigated using an annual field survey of the Jinan section of the Xiaoqing River, a typical urban river in northern China. Using functional diversity (FD) and beta diversity, the spatiotemporal variation of the aquatic biological functional groups regime along stretches subject to different eutrophication was investigated. The functional evenness (FEve) and functional divergence (FDiv) decreased significantly with the increment of the trophic level index. Functional diversity exhibits an extremely low level across functional groups, with the richness difference (RichDiff) being an important component. The results indicate that eutrophication led to the homogenization of rotifer communities. This can be attributed to the functional homogenization of the rotifer community in the Jinan section of the Xiaoqing River. The observed homogenization may be due to widely distributed species complementing the ecological niche space. Our findings provide valuable information on the conservation of the urban river under the threat of eutrophication caused by high-intensity human activities.

Approach to Quantify Eye Movements to Augment Stroke Diagnosis With a Non-Calibrated Eye-Tracker.

Automated eye-tracking technology could enhance diagnosis for many neurological diseases, including stroke. Current literature focuses on gaze estimation through a form of calibration. However, patients with neuro-ocular abnormalities may have difficulty completing a calibration procedure due to inattention or other neurological deficits. OBJECTIVE: We investigated 1) the need for calibration to measure eye movement symmetry in healthy controls and 2) the potential of eye movement symmetry to distinguish between healthy controls and patients. METHODS: We analyzed fixations, smooth pursuits, saccades, and conjugacy measured by a Spearman correlation coefficient and utilized a linear mixed-effects model to estimate the effect of calibration. RESULTS: Healthy participants (n = 18) did not differ in correlations between calibrated and non-calibrated conditions for all tests. The calibration condition did not improve the linear mixed effects model (log-likelihood ratio test p = 0.426) in predicting correlation coefficients. Interestingly, the patient group (n = 17) differed in correlations for the DOT (0.844 [95% CI 0.602, 0.920] vs. 0.98 [95% CI 0.976, 0.985]), H (0.903 [95% CI 0.746, 0.958] vs. 0.979 [95% CI 0.971, 0.986]), and OKN (0.898 [95% CI 0.785, 0.958] vs. 0.993 [95% CI 0.987, 0.996]) tests compared to healthy controls along the x-axis. These differences were not observed along the y-axis. SIGNIFICANCE: This study suggests that automated eye tracking can be deployed without calibration to measure eye movement symmetry. It may be a good discriminator between normal and abnormal eye movement symmetry. Validation of these findings in larger populations is required.

Predator-mediated diversity of stream fish assemblages in a boreal river basin, China.

Predator-prey interactions are critical for understanding species composition and community assembly; however, there is still limited research on whether and how the prey species composition or community assembly in natural communities are mediated by predators. To address this question, we performed a field investigation to examine the influence of the presence of Lutra lutra on the diversity of fish communities of the Hunchun River Basin, Jilin Province, China. Our results indicate that L. lutra, as a potential umbrella species and generalist predator in the stream ecosystem, promotes the coexistence of a vast variety of fish taxa, which emphasizes the importance of top-down control in the ecological community. We suggest that L. lutra regulates the fish community assembly likely through the stochastic process. Although this was a pilot study regarding predator-prey interactions, the results highlight the effects of predators on the prey community assembly, and emphasize the role of predators on the maintenance of biodiversity and ecosystem function. Future conservation decisions involving ecosystem biodiversity should require the inclusion of predation intensity. The inclusion of scientific research and protection of umbrella species would thus constitute an additional and important step in biodiversity conservation.

Invariance encoding in sliced-Wasserstein space for image classification with limited training data.

Deep convolutional neural networks (CNNs) are broadly considered to be state-of-the-art generic end-to-end image classification systems. However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective. Rather than using a data augmentation strategy to encode invariances as typically done in machine learning, here we propose to mathematically augment a nearest subspace classification model in sliced-Wasserstein space by exploiting certain mathematical properties of the Radon Cumulative Distribution Transform (R-CDT), a recently introduced image transform. We demonstrate that for a particular type of learning problem, our mathematical solution has advantages over data augmentation with deep CNNs in terms of classification accuracy and computational complexity, and is particularly effective under a limited training data setting. The method is simple, effective, computationally efficient, non-iterative, and requires no parameters to be tuned. Python code implementing our method is available at https://github.com/rohdelab/mathematical augmentation. Our method is integrated as a part of the software package PyTransKit, which is available at https://github.com/rohdelab/PyTransKit.

[Impact of land use pattern on water quality under different riparian buffer zone scales in Gaya River Basin, Northeast China]. / 不同河岸缓冲区尺度下土地利用方式对嘎呀河流域水质的影响.

River water quality is influenced by land use and landscape distribution patterns. Quantifying the relationship between land use, landscape pattern and water quality factor at different riparian buffer zone scales is of great significance for rational land use planning and water quality improvement. Based on water quality data from 91 sites in May 2021 in the Gaya River Basin, we analyzed the spatial characteristics of land use types and landscape patterns at the riparian buffer zone scales. With redundancy analysis (RDA) and generalized additive models (GAM), we examined the effects of land use and landscape patterns on river water quality. The results showed that water quality was primarily impacted by total nitrogen (TN). Farmland was the dominant land use type at riparian buffer zone of 50, 100 and 500 m. The sampling sites were classified into farmland dominant group and farmland other group. Forest was dominant at riparian buffer zone of 1000, 1500, 2000 m, and the sampling sites were classified into forest dominant group and forest other group. 100 m riparian buffer zone was the strongest scale in the Gaya River, and 1000 m was the second. Land use types in the forest dominant group were closely related with electrical conductivity, dissolved oxygen, phosphate, permanganate index and ammonium (NH4+-N) of water. NH4+-N was positively correlated with proportion of forest and farmland area. Phosphate was significantly affected by Shannon diversity index (SHDI). SHDI and largest patch index (LPI) was the key landscape indices affecting permanganate index. TN was significantly impacted by area proportion of forest, grassland and LPI in farmland dominant group, showing decreasing trend with the area proportion of forest increasing from 8% to 40%. Total suspended solids in farmland other group were significantly correlated with proportion of farmland area, while negatively correlated with proportion of forest area. Water quality in the Gaya River was mainly affected by proportion of forest area, followed by proportion of farmland area. The combined effects of LPI, SHDI and other land use types played an important role in affecting water quality.

Examination of the potential roles of insulin-like peptide receptor in regulating the growth of Manila clam Ruditapes philippinarum.

The role of insulin/insulin-like growth factor (IGF) signaling pathway in the growth regulation of marine invertebrates has not been fully studied. In this study, the economically important species Ruditapes philippinarum was sacrificed to clarify the role of IGF system in the growth regulation of R. philippinarum by real-time quantitative PCR. Systematic bioinformatics analysis can identify the major genes of IGF signaling pathway and insulin-like peptide receptor (ILPR) - mediated signaling pathway in R. philippinarum. The expression levels of IGF and its downstream signaling pathway genes in larger clams were significantly higher than those in small clams, indicating that they were involved in the growth regulation of R. philippinarum. These results suggest that IGF signaling pathway and ILPR mediated signaling pathway to regulate the growth of R. philippinarum.

Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19.

A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. However, the underlying process of COVID-19-associated microvascular thrombosis remains elusive due to the lack of tools to statistically examine platelet aggregation (i.e., the initiation of microthrombus formation) in detail. Here we report the landscape of circulating platelet aggregates in COVID-19 obtained by massive single-cell image-based profiling and temporal monitoring of the blood of COVID-19 patients (n = 110). Surprisingly, our analysis of the big image data shows the anomalous presence of excessive platelet aggregates in nearly 90% of all COVID-19 patients. Furthermore, results indicate strong links between the concentration of platelet aggregates and the severity, mortality, respiratory condition, and vascular endothelial dysfunction level of COVID-19 patients.

Dormant plasticity of rotifer diapausing eggs in response to predator kairomones.

In freshwater ecosystems, hatching strategy of diapausing eggs (DEs) under predation risk has important ecological implication for zooplankters. Although kairomones released by predators can induce phenotypic responses of prey, hatching patterns of DEs in response to kairomones have received contradictory conclusions in zooplankters. Maternal environment may also affect hatching strategy of DEs during predator-prey interactions. We used classical Brachionus calyciflorus-Asplanchna models to determine the timing and proportion of DE hatching in association with parental and embryonic exposure to kairomones. Results obtained from two Brachionus clones supported the hypothesis that DEs could detect Asplanchna kairomones and adjust hatching patterns. DEs showed early and synchronous hatching patterns in the environment with kairomones. Data also supported the prediction that DEs could gain information about predators from maternal environments and adjusted their hatching pattern in response to the presence of kairomones. Compared with DEs from Brachionus mothers not exposed to kairomones, DEs produced by mothers that were experienced with kairomones attained a higher hatching rate when both of them hatched in the environment either with or without kairomones. Our results suggest that DEs of B. calyciflorus possess dormant plasticity to defend against predation from Asplanchna, which may be regulated by maternal environmental effects during sexual life cycles.

Molecular mechanisms of wound healing and regeneration of siphon in the Manila clam Ruditapes philippinarum revealed by transcriptomic analysis.

Ruditapes philippinarum is an economically important marine shellfish aquaculture species, and it has the ability to regenerate its siphons. To gain a greater understanding of the molecular mechanisms at work during siphon regeneration and to provide evidence for morphological regeneration, we examined transcriptome responses of siphon tissue of R. philippinarum during regeneration and observed regenerative siphons under the stereomicroscope. The overall process of siphon regeneration was dissected based on the morphological changes of siphon and the identification of up-regulated key differentially expressed genes (DEGs). The protein biosynthesis and metabolism played important roles in wound healing and siphon regeneration of R. philippinarum. Transcriptomic analysis identified the Wnt and TGF-ß signaling pathways by focusing on the function and expression pattern of genes in these pathways during siphon regeneration. In addition, we carried out a genome-wide identification and phylogenetic analysis of TGF-ß superfamily in R. philippinarum. The expression profiles of the TGF-ß superfamily genes were analyzed in eight adult tissues (adductor muscle, mantle, foot, gill, siphon, digestive gland, gonad, and labial palp) and regenerative siphon. This study shed new light on the process of morphological regeneration and regenerative mechanism of siphon of R. philippinarum.

A unified framework for herbivore-to-producer biomass ratio reveals the relative influence of four ecological factors.

The biomass ratio of herbivores to primary producers reflects the structure of a community. Four primary factors have been proposed to affect this ratio, including production rate, defense traits and nutrient contents of producers, and predation by carnivores. However, identifying the joint effects of these factors across natural communities has been elusive, in part because of the lack of a framework for examining their effects simultaneously. Here, we develop a framework based on Lotka-Volterra equations for examining the effects of these factors on the biomass ratio. We then utilize it to test if these factors simultaneously affect the biomass ratio of freshwater plankton communities. We found that all four factors contributed significantly to the biomass ratio, with carnivore abundance having the greatest effect, followed by producer stoichiometric nutrient content. Thus, the present framework should be useful for examining the multiple factors shaping various types of communities, both aquatic and terrestrial.

Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.

We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method - utilizing a nearest-subspace algorithm in the R-CDT space - is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at [1].

Cell Image Classification: A Comparative Overview.

Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. The article also reviews three main approaches for cell image classification most often used: numerical feature extraction, end-to-end classification with neural networks (NNs), and transport-based morphometry (TBM). In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. The results computed using four publicly available datasets show that numerical features tend to carry the best discriminative information for most of the classification tasks. Results also show that NN-based methods produce state-of-the-art results in the dataset that contains a relatively large number of training samples. Data augmentation or the choice of a more recently reported architecture does not necessarily improve the classification performance of NNs in the datasets with limited number of training samples. If understanding and visualization are desired aspects, TBM methods can offer the ability to invert classification functions, and thus can aid in the interpretation of results. These and other comparison outcomes are discussed with the aim of clarifying the advantages and disadvantages of each method. © 2020 International Society for Advancement of Cytometry.

Facial Weakness Analysis and Quantification of Static Images.

Facial weakness is a symptom commonly associated to lack of facial muscle control due to neurological injury. Several diseases are associated with facial weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if accurate enough can improve treatment by enabling faster patient triage and recovery progress monitoring. Most of the existing facial weakness detection approaches from static images are based on facial landmarks from which geometric features can be calculated. Landmark-based methods, however, can suffer from inaccuracies in face landmarks localization. In this study, We also experimentally evaluate the performance of several feature extraction methods for measuring facial weakness, including the landmark-based features, as well as intensity-based features on a neurologist-certified dataset that comprises 186 images of normal, 125 images of left facial weakness, and 126 images of right facial weakness. We demonstrate that, for the application of facial weakness detection from single (static) images, approaches that incorporate the Histogram of Oriented Gradients (HoG) features tend to be more accurate.

Multi-omics analysis reveals niche and fitness differences in typical denitrification microbial aggregations.

Suspended floc and fixed biofilm are two commonly applied strategies for heterotrophic denitrification in wastewater treatment. These two strategies use different carbon sources and reside within different ecological niches for microbial aggregation, which were hypothesized to show distinct microbial structures and metabolic fitness. We surveyed three floc reactors and three biofilm reactors for denitrification and determined if there were distinct microbial aggregations. Multiple molecular omics approaches were used to determine the microbial community composition, co-occurrence network and metabolic pathways. Proteobacteria was the dominating and most active phylum among all samples. Carbon source played an important role in shaping the microbial community composition while the distribution of functional protein was largely influenced by salinity. We found that the topological network features had different ecological patterns and that the microorganisms in the biofilm reactors had more nodes but less interactions than those in floc reactors. The large niche differences in the biofilm reactors explained the observed high microbial diversity, functional redundancy and resulting high system stability. We also observed a lower proportion of denitrifiers and higher resistance to oxygen and salinity perturbation in the biofilm reactors than the floc reactors. Our findings support our hypothesis that niche differences caused a distinct microbial structure and increased microbial ecology distribution, which has the potential to improve system efficiency and stability.

Effect of stock density on the microbial community in biofloc water and Pacific white shrimp (Litopenaeus vannamei) gut microbiota.

Biofloc technology is an efficient approach for intensive shrimp culture. However, the extent to which this process can influence the composition of intestinal microbial community is still unknown. Here, we surveyed the shrimp intestinal bacteria as well as the floc water from three biofloc systems with different stock densities. Our study revealed a similar variation trend in phylum taxonomy level between floc bacteria and gut microbiota. Microbial community varied notably in floc water from different stock densities, while a core genus with dominating relative abundance was detected in gut samples. Extensive variation was discovered in gut microbiota, but still clustered into groups according to stock density. Our results indicated that shrimp intestinal microbiota as well as bacteria aggregated in flocs assembled into distinct communities from different stock densities, and the intestinal communities were more similar with the surrounding environment as the increase of stock density and resulting high floc biomass. The high stock density changed the core gut microbiota by reducing the relative abundance of Paracoccus and increasing that of Nocardioides, which may negatively influence shrimp performance. Therefore, this study helps us to understand further bacteria and host interactions in biofloc system.