IMPACT OF COASTAL WETLAND RESTORATION STRATEGIES IN THE CHONGMING DONGTAN WETLANDS, CHINA: WATERBIRD COMMUNITY COMPOSITION AS AN INDICATOR

This paper aims to evaluate the success of coastal wetland restoration by quantifying the waterbird community composition at three restored sites and on one natural coastal wetland, which served as a reference site, from September 2011 to May 2012 in the Chongming Dongtan wetlands in China. The Shannon–Wiener diversity index was calculated to describe habitat diversity in the four study sites. Significant differences in habitat heterogeneity and species group diversity, richness, and waterbird density were observed in the sites, but a significant difference among three seasons was observed only in the waterbird density. Significant interactions between site and season were noted for species group diversity, richness, and waterbird density. The densities of four dominant waterbird groups exhibited significant differences in the four sites, and the density of Anatidae and Ardeidae exhibited significant differences among three seasons. Significant interactions were noted between site and season for the densities of Charadriidae, Anatidae, and Ardeidae. In conclusion, the restored coastal wetlands served as a suitable habitat for waterbirds to some extent, although not all restored wetlands were used equally by waterbirds. The restored wetlands with higher habitat heterogeneity supported a greater abundance of waterbirds. However, the same restored wetland was not used equally by waterbirds among different seasons. Multi-functional restored wetlands could be created for different seasons to attract a diverse group of waterbirds to forage and roost in the coastal wetlands of Yangtze River during their migration from Australia to Siberia

Volume Transfer: A New Design Concept for Fabric-Based Pneumatic Exosuits

The fabric-based pneumatic exosuit is now a hot research topic because it is lighter and softer than traditional exoskeletons. Existing research focused more on the mechanical properties of the exosuit (e.g., torque and speed), but less on its wearability (e.g., appearance and comfort). This work presents a new design concept for fabric-based pneumatic exosuits Volume Transfer, which means transferring the volume of pneumatic actuators beyond the garments profile to the inside. This allows for a concealed appearance and a larger stress area while maintaining adequate torques. In order to verify this concept, we develop a fabric-based pneumatic exosuit for knee extension assistance. Its profile is only 26mm and its stress area wraps around almost half of the leg. We use a mathematical model and simulation to determine the parameters of the exosuit, avoiding multiple iterations of the prototype. Experiment results show that the exosuit can generate a torque of 7.6Nm at a pressure of 90kPa and produce a significant reduction in the electromyography activity of the knee extensor muscles. We believe that Volume Transfer could be utilized prevalently in future fabric-based pneumatic exosuit designs to achieve a significant improvement in wearability

ACETest: Automated Constraint Extraction for Testing Deep Learning Operators

Deep learning (DL) applications are prevalent nowadays as they can help with multiple tasks. DL libraries are essential for building DL applications. Furthermore, DL operators are the important building blocks of the DL libraries, that compute the multi-dimensional data (tensors). Therefore, bugs in DL operators can have great impacts. Testing is a practical approach for detecting bugs in DL operators. In order to test DL operators effectively, it is essential that the test cases pass the input validity check and are able to reach the core function logic of the operators. Hence, extracting the input validation constraints is required for generating high-quality test cases. Existing techniques rely on either human effort or documentation of DL library APIs to extract the constraints. They cannot extract complex constraints and the extracted constraints may differ from the actual code implementation. To address the challenge, we propose ACETest, a technique to automatically extract input validation constraints from the code to build valid yet diverse test cases which can effectively unveil bugs in the core function logic of DL operators. For this purpose, ACETest can automatically identify the input validation code in DL operators, extract the related constraints and generate test cases according to the constraints. The experimental results on popular DL libraries, TensorFlow and PyTorch, demonstrate that ACETest can extract constraints with higher quality than state-of-the-art (SOTA) techniques. Moreover, ACETest is capable of extracting 96.4% more constraints and detecting 1.95 to 55 times more bugs than SOTA techniques. In total, we have used ACETest to detect 108 previously unknown bugs on TensorFlow and PyTorch, with 87 of them confirmed by the developers. Lastly, five of the bugs were assigned with CVE IDs due to their security impacts.Comment: Accepted by ISSTA 202

3D Face Arbitrary Style Transfer

Style transfer of 3D faces has gained more and more attention. However, previous methods mainly use images of artistic faces for style transfer while ignoring arbitrary style images such as abstract paintings. To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST). To begin with, FDST employs a 3D decoupling module to separate facial geometry and texture. Then we propose a style fusion strategy for facial geometry. Subsequently, we design an optimization-based DDSG mechanism for textures that can guide the style transfer by two style images. Besides the normal style image input, DDSG can utilize the original face input as another style input as the face prior. By this means, high-quality face arbitrary style transfer results can be obtained. Furthermore, FDST can be applied in many downstream tasks, including region-controllable style transfer, high-fidelity face texture reconstruction, large-pose face reconstruction, and artistic face reconstruction. Comprehensive quantitative and qualitative results show that our method can achieve comparable performance. All source codes and pre-trained weights will be released to the public

Analysis of Tumor Metabolism Reveals Mitochondrial Glucose Oxidation in Genetically Diverse Human Glioblastomas in the Mouse Brain In Vivo

SummaryDysregulated metabolism is a hallmark of cancer cell lines, but little is known about the fate of glucose and other nutrients in tumors growing in their native microenvironment. To study tumor metabolism in vivo, we used an orthotopic mouse model of primary human glioblastoma (GBM). We infused 13C-labeled nutrients into mice bearing three independent GBM lines, each with a distinct set of mutations. All three lines displayed glycolysis, as expected for aggressive tumors. They also displayed unexpected metabolic complexity, oxidizing glucose via pyruvate dehydrogenase and the citric acid cycle, and using glucose to supply anaplerosis and other biosynthetic activities. Comparing the tumors to surrounding brain revealed obvious metabolic differences, notably the accumulation of a large glutamine pool within the tumors. Many of these same activities were conserved in cells cultured ex vivo from the tumors. Thus GBM cells utilize mitochondrial glucose oxidation during aggressive tumor growth in vivo

Functional characterization of genetic variants in NPC1L1 supports the sequencing extremes strategy to identify complex trait genes

Resequencing genes in individuals at extremes of the population distribution constitutes a powerful and efficient strategy to identify sequence variants associated with complex traits. An excess of sequence variants at one extreme relative to the other that is not due to chance or to population stratification constitutes evidence for genetic association and implies the presence of functionally significant sequence variants. Recently, we reported that non-synonymous sequence variants in Niemann–Pick type C1-like 1 (NPC1L1), an intestinal cholesterol transporter, were significantly more common among individuals with low cholesterol absorption than in those with high cholesterol absorption. To determine whether sequence variations identified in individuals with low cholesterol absorption affect protein function, we performed studies in cultured cells and in families. Expression of the mutant proteins in Chinese hamster ovarian-K1 cells revealed that a majority (14 of 20) of the variants identified in low absorbers were associated with very low levels of NPC1L1 protein. In two extended families, mean cholesterol absorption levels, as measured using stable isotopes, were significantly lower in family members with the sequence variants than in those without the variant. These data indicate that the excess of sequence variations in individuals with extreme phenotypes reflects an enrichment of functionally significant variants. These findings are consistent with in silico predictions that some sequence variations found in healthy individuals are as deleterious to protein function as mutations that, in other genes, cause monogenic diseases. Such sequence variations may explain a significant fraction of quantitative phenotypic variation in humans

SARS-CoV-2 infection causes dopaminergic neuron senescence

COVID-19 patients commonly present with signs of central nervous system and/or peripheral nervous system dysfunction. Here, we show that midbrain dopamine (DA) neurons derived from human pluripotent stem cells (hPSCs) are selectively susceptible and permissive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. SARS-CoV-2 infection of DA neurons triggers an inflammatory and cellular senescence response. High-throughput screening in hPSC-derived DA neurons identified several FDA-approved drugs that can rescue the cellular senescence phenotype by preventing SARS-CoV-2 infection. We also identified the inflammatory and cellular senescence signature and low levels of SARS-CoV-2 transcripts in human substantia nigra tissue of COVID-19 patients. Furthermore, we observed reduced numbers of neuromelanin+ and tyrosine-hydroxylase (TH)+ DA neurons and fibers in a cohort of severe COVID-19 patients. Our findings demonstrate that hPSC-derived DA neurons are susceptible to SARS-CoV-2, identify candidate neuroprotective drugs for COVID-19 patients, and suggest the need for careful, long-term monitoring of neurological problems in COVID-19 patients.</p

SARS-CoV-2 infection causes dopaminergic neuron senescence

COVID-19 patients commonly present with signs of central nervous system and/or peripheral nervous system dysfunction. Here, we show that midbrain dopamine (DA) neurons derived from human pluripotent stem cells (hPSCs) are selectively susceptible and permissive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. SARS-CoV-2 infection of DA neurons triggers an inflammatory and cellular senescence response. High-throughput screening in hPSC-derived DA neurons identified several FDA-approved drugs that can rescue the cellular senescence phenotype by preventing SARS-CoV-2 infection. We also identified the inflammatory and cellular senescence signature and low levels of SARS-CoV-2 transcripts in human substantia nigra tissue of COVID-19 patients. Furthermore, we observed reduced numbers of neuromelanin+ and tyrosine-hydroxylase (TH)+ DA neurons and fibers in a cohort of severe COVID-19 patients. Our findings demonstrate that hPSC-derived DA neurons are susceptible to SARS-CoV-2, identify candidate neuroprotective drugs for COVID-19 patients, and suggest the need for careful, long-term monitoring of neurological problems in COVID-19 patients.</p

Mutations in mitochondrial enzyme GPT2 cause metabolic dysfunction and neurological disease with developmental and progressive features

Mutations that cause neurological phenotypes are highly informative with regard to mechanisms governing human brain function and disease. We report autosomal recessive mutations in the enzyme glutamate pyruvate transaminase 2 (GPT2) in large kindreds initially ascertained for intellectual and developmental disability (IDD). GPT2 [also known as alanine transaminase 2 (ALT2)] is one of two related transaminases that catalyze the reversible addition of an amino group from glutamate to pyruvate, yielding alanine and α-ketoglutarate. In addition to IDD, all affected individuals show postnatal microcephaly and ∼80% of those followed over time show progressive motor symptoms, a spastic paraplegia. Homozygous nonsense p.Arg404* and missense p.Pro272Leu mutations are shown biochemically to be loss of function. The GPT2 gene demonstrates increasing expression in brain in the early postnatal period, and GPT2 protein localizes to mitochondria. Akin to the human phenotype, Gpt2-null mice exhibit reduced brain growth. Through metabolomics and direct isotope tracing experiments, we find a number of metabolic abnormalities associated with loss of Gpt2. These include defects in amino acid metabolism such as low alanine levels and elevated essential amino acids. Also, we find defects in anaplerosis, the metabolic process involved in replenishing TCA cycle intermediates. Finally, mutant brains demonstrate misregulated metabolites in pathways implicated in neuroprotective mechanisms previously associated with neurodegenerative disorders. Overall, our data reveal an important role for the GPT2 enzyme in mitochondrial metabolism with relevance to developmental as well as potentially to neurodegenerative mechanisms.National Institute of Neurological Diseases and Stroke (U.S.) (R01NS035129)United States. National Institutes of Health (R21TW008223)National Cancer Institute (U.S.) (R01CA157996