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"Yu, Rong"
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Tracks of a panda
A mother panda teaches her cub how to survive in their mountain habitat but as the sound of villagers clearing the forest approaches, she knows they must look for a new home.
Book
Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal
Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models.
•We review the effects of anesthesia on neurovascular coupling and brain circuit function.•Anesthesia decreases brain metabolism and perturbs other physiological processes.•Neural excitability and neurovascular coupling are altered by anesthesia.•Brain circuit function are changed by anesthesia relative to the awake animal.•Techniques for imaging in awake rodents are presented.
Journal Article
Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning
Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores' spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.
Journal Article
طائر في السحاب
هذا الكتاب مخصص للأطفال يستهدف الطفولة المبكرة وتعمل علي اسثمار الطفل في بناء المهارات المختلفة المرتبطة بالخيال والأبتكار وقوة الشخصية والبحث عن حلول إبداعية ويستمد الطفل الكثير من العلم والمعرفة والمعلومات من المنهج السلوكي التربوي رائع يعلم الطفل كيف يستخلص من مشكلاته وكيف يبني شخصيته بشكل مميز ويعطي المربي حلولا لحل مشكلات أبنه تعنيه عن تجاوز الأزمة وإنهائها.
BOOK
Image generator for tabular data based on non-Euclidean metrics for CNN-based classification
Tabular data is the predominant format for statistical analysis and machine learning across domains such as finance, biomedicine, and environmental sciences. However, conventional methods often face challenges when dealing with high dimensionality and complex nonlinear relationships. In contrast, deep learning models, particularly Convolutional Neural Networks (CNNs), are well-suited for automatic feature extraction and achieve high predictive accuracy, but are primarily designed for image-based inputs. This study presents a comparative evaluation of non-Euclidean distance metrics within the Image Generator for Tabular Data (IGTD) framework, which transforms tabular data into image representations for CNN-based classification. While the original IGTD relies on Euclidean distance, we extend the framework to adopt alternative metrics, including one minus correlation, Geodesic distance, Jensen-Shannon distance, Wasserstein distance, and Tropical distance. These metrics are designed to better capture complex, nonlinear relationships among features. Through systematic experiments on both simulated and real-world genomics datasets, we compare the performance of each distance metric in terms of classification accuracy and structural fidelity of the generated images. The results demonstrate that non-Euclidean metrics can significantly improve the effectiveness of CNN-based classification on tabular data. By enabling a more accurate encoding of feature relationships, this approach broadens the applicability of CNNs and offers a flexible, interpretable solution for high-dimensional, structured data across disciplines.
Journal Article
Consistent responses of soil microbial taxonomic and functional attributes to mercury pollution across China
Background
The ecological consequences of mercury (Hg) pollution—one of the major pollutants worldwide—on microbial taxonomic and functional attributes remain poorly understood and largely unexplored. Using soils from two typical Hg-impacted regions across China, here, we evaluated the role of Hg pollution in regulating bacterial abundance, diversity, and co-occurrence network. We also investigated the associations between Hg contents and the relative abundance of microbial functional genes by analyzing the soil metagenomes from a subset of those sites.
Results
We found that soil Hg largely influenced the taxonomic and functional attributes of microbial communities in the two studied regions. In general, Hg pollution was negatively related to bacterial abundance, but positively related to the diversity of bacteria in two separate regions. We also found some consistent associations between soil Hg contents and the community composition of bacteria. For example, soil total Hg content was positively related to the relative abundance of
Firmicutes
and
Bacteroidetes
in both paddy and upland soils. In contrast, the methylmercury (MeHg) concentration was negatively correlated to the relative abundance of
Nitrospirae
in the two types of soils. Increases in soil Hg pollution correlated with drastic changes in the relative abundance of ecological clusters within the co-occurrence network of bacterial communities for the two regions. Using metagenomic data, we were also able to detect the effect of Hg pollution on multiple functional genes relevant to key soil processes such as element cycles and Hg transformations (e.g., methylation and reduction).
Conclusions
Together, our study provides solid evidence that Hg pollution has predictable and significant effects on multiple taxonomic and functional attributes including bacterial abundance, diversity, and the relative abundance of ecological clusters and functional genes. Our results suggest an increase in soil Hg pollution linked to human activities will lead to predictable shifts in the taxonomic and functional attributes in the Hg-impacted areas, with potential implications for sustainable management of agricultural ecosystems and elsewhere.
Journal Article
Carbon content and climate variability drive global soil bacterial diversity patterns
Despite the vital role of microorganisms for ecosystem functioning and human welfare, our understanding of their global diversity and biogeographical patterns lags significantly behind that of plants and animals. We conducted a meta-analysis including ~600 soil samples from all continents to evaluate the biogeographical patterns and drivers of bacterial diversity in terrestrial ecosystems at the global scale. Similar to what has been found with plants and animals, the diversity of soil bacteria in the Southern Hemisphere decreased from the equator to Antarctica. However, soil bacteria showed similar levels of diversity across the Northern Hemisphere. The composition of bacterial communities followed dissimilar patterns between hemispheres, as the Southern and Northern Hemispheres were dominated by Actinobacteria and Acidobacteria, respectively. However, Proteobacteria was co-dominant in both hemispheres. Moreover, we found a decrease in soil bacterial diversity with altitude. Climatic features (e.g., high diurnal temperature range and low temperature) were correlated with the lower diversity found at high elevations, but geographical gradients in soil total carbon and species turnover were important drivers of the observed latitudinal patterns. We thus found both parallels and differences in the biogeographical patterns of aboveground vs. soil bacterial diversity. Our findings support previous studies that highlighted soil pH, spatial influence, and organic matter as important drivers of bacterial diversity and composition. Furthermore, our results provide a novel integrative view of how climate and soil factors influence soil bacterial diversity at the global scale, which is critical to improve ecosystem and earth system simulation models and for formulating sustainable ecosystem management and conservation policies.
Journal Article
Soil biodiversity and function under global change
Soil organisms represent the most abundant and diverse organisms on the planet and support almost every ecosystem function we know, and thus impact our daily lives. Some of these impacts have been well-documented, such as the role of soil organisms in regulating soil fertility and carbon sequestration; processes that have direct implications for essential ecosystem services including food security and climate change mitigation. Moreover, soil biodiversity also plays a critical role in supporting other aspects from One Health—the combined health of humans, animals, and the environment—to the conservation of historic structures such as monuments. Unfortunately, soil biodiversity is also highly vulnerable to a growing number of stressors associated with global environmental change. Understanding how and when soil biodiversity supports these functions, and how it will adapt to changing environmental conditions, is crucial for conserving soils and maintaining soil processes for future generations. In this Essay, we discuss the fundamental importance of soil biodiversity for supporting multiple ecosystem services and One Health, and further highlight essential knowledge gaps that need to be addressed to conserve soil biodiversity for the next generations.
Journal Article
Unveiling the charge transfer dynamics steered by built-in electric fields in BiOBr photocatalysts
Construction of internal electric fields (IEFs) is crucial to realize efficient charge separation for charge-induced redox reactions, such as water splitting and CO
2
reduction. However, a quantitative understanding of the charge transfer dynamics modulated by IEFs remains elusive. Here, electron microscopy study unveils that the non-equilibrium photo-excited electrons are collectively steered by two contiguous IEFs within binary (001)/(200) facet junctions of BiOBr platelets, and they exhibit characteristic Gaussian distribution profiles on reduction facets by using metal co-catalysts as probes. An analytical model justifies the Gaussian curve and allows us to measure the diffusion length and drift distance of electrons. The charge separation efficiency, as well as photocatalytic performances, are maximized when the platelet size is about twice the drift distance, either by tailoring particle dimensions or tuning IEF-dependent drift distances. The work offers great flexibility for precisely constructing high-performance particulate photocatalysts by understanding charge transfer dynamics.
While internal electric fields alter charge-separation dynamics in solar-to-chemical conversions, a greater understanding of such processes is necessary. Here, authors analyze charge transfer dynamics modulated by built-in electric fields and identify carrier drift distances as a critical parameter.
Journal Article