Studies of tropical fruit ripening using three different spectroscopic techniques.

ABSTRACT. We present a noninvasive method to study fruit ripening. The method is based on the combination of reflectance and fluorescence spectroscopies, as well as gas in scattering media absorption spectroscopy (GASMAS). Chlorophyll and oxygen are two of the most important constituents in the fruit ripening process. Reflectance and fluorescence spectroscopies were used to quantify the changes of chlorophyll and other chromophores. GASMAS, based on tunable diode laser absorption spectroscopy, was used to measure free molecular oxygen in the fruit tissue at 760 nm, based on the fact that the free gases have much narrower spectral imprints than those of solid materials. The fruit maturation and ripening processes can be followed by studying the changes of chlorophyll and oxygen contents with these three techniques

TensorIR: An Abstraction for Automatic Tensorized Program Optimization

Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present TensorIR, a compiler abstraction for optimizing programs with these tensor computation primitives. TensorIR generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first-class citizen. Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives. Experimental results show that TensorIR compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state-of-art hand-optimized systems across platforms.Comment: Accepted to ASPLOS 202

Effects of 8-Year Nitrogen and Phosphorus Treatments on the Ecophysiological Traits of Two Key Species on Tibetan Plateau

Understanding how nitrogen (N) and/or phosphorus (P) addition affects plants carbon- and water- related ecophysiological characteristics is essential for predicting the global change impact on the alpine meadow ecosystem structure and function in carbon and water cycling. The Qinghai-Tibetan Plateau (QTP) with the largest alpine meadow in the world is regarded as the third pole in the earth and has been experiencing increased atmospheric N deposition. In this project, we focused on two key species (Elymus dahuricus and Gentiana straminea) of the alpine meadow on the Tibetan Plateau and investigated the variability of photosynthetic and stomatal responses to 8-year N and/or P treatments through field measurements and modeling. We measured photosynthesis- and gs-response curves to generate parameter estimates from individual leaves with two widely used stomatal models (the BWB model and MED model) for validation of growth and ecosystem models and to elucidate the physiological basis for observed differences in productivity and WUE. We assessed WUE by means of gas exchange measurements (WUEi) and stable carbon isotope composition (Δ13C) to get the intrinsic and integrated estimates of WUE of the two species. P and N+P treatments, but not N, improved the photosynthetic capacity (Anet and Vcmax) for both species. Stomatal functions including instaneous measurements of stomatal conductance, intrinsic water-use efficiency and stomatal slope parameters of the two widely used stomatal models were altered by the addition of P or N+P treatment, but the impact varied across years and species. The inconsistent responses across species suggest that an understanding of photosynthetic, stomatal functions and water-use should be evaluated on species separately. WUE estimated by Δ13C values had a positive relationship with Anet and gs and a negative relationship with WUEi. Our findings should be useful for understanding the underlying mechanisms of the response of alpine plants growth and alpine meadow ecosystem to global change

Povezanost polimorfizma pojedinačnog nukleotida gena ARID4A i kvalitete sperme kineskog vodenog bivola

ARID4A (AT-rich interaction domain 4A) is closely related to animal sperm quality traits. In the present study, the association between ARID4A gene polymorphisms of Chinese water buffalo (Bubalus bubalis) with sperm quality traits was examined, including ejaculate volume, sperm concentration, post-thaw sperm motility, and sperm abnormality of buffalo semen. Seven single-nucleotide polymorphisms (SNPs) of ARID4A gene were detected in 156 Chinese water buffaloes by Sanger sequencing and identifying overlap peaks. Among the SNPs, six were associated with at least one sperm quality trait. In brief, g.21192G>C, g.21285C>G, and g.21364A>G could be used as potential markers for selecting semen with low sperm abnormality, high ejaculate volume, sperm concentration, and sperm motility. Furthermore, 10 haplotypes (H1: -CTCGG, H2: GTGGCA, H3: GCGGCA, H4: GCTGCA, H5: GCTCGA, H6: GTGGGG, H7: GCTCCG, H8: -CGGGA, H9: GCGGCG, and H10: GTTGCA) were formed by the six SNPs through linkage disequilibrium analysis, and then 14 different combined haplotypes were collected. Correlation analysis showed that the combined H1H2 haplotype had the highest genotype frequency. Notably, the combined H1H2 haplotype had low sperm concentration, low sperm motility, and high sperm abnormality. The combined H2H3 haplotype could be used as a potential molecular marker for selecting semen with high sperm motility. In general, we illustrated a significant correlation between SNPs in ARID4A and sperm quality traits of Chinese water buffalo, which may be useful in the marker-assisted selection of buffalo breeding. This study was the first to analyze the genetic polymorphisms of ARID4A and association with sperm qualities of Chinese buffalo.Gen ARID4A (engl AT-rich interaction domain 4A) usko je povezan s kvalitetom sperme. U ovom je radu istraživana povezanost polimorfizma gena ARID4A u kineskih vodenih bivola (Bubalus bubalis) s kvalitetom sperme, uključujući volumen ejakulata, koncentraciju sperme, pokretljivost spermija nakon odmrzavanja i abnormalnost spermija u sjemenu bivola. U 156 kineskih vodenih bivola otkriveno je sedam polimorfizama pojedinačnog nukleotida (SNPs) gena ARID4A Sangerovim sekvenciranjem i identifikacijom preklopljenih vrhova. Među SNP-ovima njih je šest bilo povezano s barem jednim svojstvom kvalitete spermija. Ukratko, g. 21192G>C, g. 21285C>G i g. 21364A>G mogu se upotrijebiti kao potencijalni markeri za selekciju sjemena s niskom abnormalnošću spermija, većim volumenom ejakulata, većom koncentracijom i pokretljivošću spermija. Nadalje, šest SNP-ova formiralo je 10 haplotipova (H1: -CTCGG, H2: GTGGCA, H3: GCGGCA, H4: GCTGCA, H5: GCTCGA, H6: GTGGGG, H7: GCTCCG, H8: -CGGGA, H9: GCGGCG i H10: GTTGCA) analizom povezanosti nepodudarnosti te je ustanovljeno 14 različitih kombiniranih haplotipova. Analiza korelacije pokazala je da kombinirani haplotip H1H2 ima najveću učestalost. Kombinirani haplotip H1H2 imao je najmanju koncentraciju sperme, slabu pokretljivost seprmija i znatnu abnormalnost spermija. Kombinirani haplotip H2H3 može se upotrijebiti kao potencijalni molekularni marker za odabir sjemena s većom pokretljivošću. Općenito je pokazana znakovita korelacija između SNP-ova u ARID4A i kvalitete sperme kineskog vodenog bivola, što može biti korisno u selekciji bivola potpomognutoj markerima. Ovo je prvo istraživanje koje je analiziralo genske polimorfizme ARID4A i njihovu povezanost s kvalitetom sjemena kineskih vodenih bivola

PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

With the emergence of a spectrum of high-end mobile devices, many applications that formerly required desktop-level computation capability are being transferred to these devices. However, executing the inference of Deep Neural Networks (DNNs) is still challenging considering high computation and storage demands, specifically, if real-time performance with high accuracy is needed. Weight pruning of DNNs is proposed, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained, accurate, but not hardware friendly; structured pruning is coarse-grained, hardware-efficient, but with higher accuracy loss. In this paper, we introduce a new dimension, fine-grained pruning patterns inside the coarse-grained structures, revealing a previously unknown point in design space. With the higher accuracy enabled by fine-grained pruning patterns, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency. In other words, our method achieves the best of both worlds, and is desirable across theory/algorithm, compiler, and hardware levels. The proposed PatDNN is an end-to-end framework to efficiently execute DNN on mobile devices with the help of a novel model compression technique (pattern-based pruning based on extended ADMM solution framework) and a set of thorough architecture-aware compiler- and code generation-based optimizations (filter kernel reordering, compressed weight storage, register load redundancy elimination, and parameter auto-tuning). Evaluation results demonstrate that PatDNN outperforms three state-of-the-art end-to-end DNN frameworks, TensorFlow Lite, TVM, and Alibaba Mobile Neural Network with speedup up to 44.5x, 11.4x, and 7.1x, respectively, with no accuracy compromise. Real-time inference of representative large-scale DNNs (e.g., VGG-16, ResNet-50) can be achieved using mobile devices.Comment: To be published in the Proceedings of Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 20

The Clinical and Genetic Features of Co-occurring Epilepsy and Autism Spectrum Disorder in Chinese Children

There is still no comprehensive description of the general population regarding clinical features and genetic etiology for co-occurring epilepsy and autism spectrum disorder (ASD) in Chinese children. This study was a retrospective study of children diagnosed with epilepsy and ASD from January 1st, 2015, to May 1st, 2018, at the Children's Hospital of Fudan University. A total of 117 patients met the inclusion criteria, and 103 subjects were eligible. Among them, 88 underwent genetic testing, and 47 children (53.4%) were identified as having pathogenic or likely pathogenic variants: 39 had single gene mutations (83.0%, 39/47), and eight had copy number variants (17.0%, 8/47), with SCN1A (14.9%, 7/47) and MECP2 (10.6%, 5/47) gene mutations being the most common. Mutations in other genes encoding voltage-gated ion channels including SCN2A, CACNA1A, CACNA1H, CACNA1D, and KCNQ2 were also common, but the number of individual cases for each gene was small. Epilepsy syndrome and epilepsy-associated syndrome were more common (P = 0.014), and higher rates of poly-therapy (P = 0.01) were used in the positive genetic test group than in the negative group. There were no statistically significant differences in drug-refractory epilepsy, ASD severity, or intellectual disability between the positive genetic test group and the negative genetic group. These data strongly indicate the need for ASD screening in children with epilepsy with voltage-gated ion channel gene variants for better diagnosis and early intervention

Multi-omics profiling reveals resource allocation and acclimation strategies to temperature changes in a marine dinoflagellate

Temperature is a critical environmental factor that affects the cell growth of dinoflagellates and bloom formation. To date, the molecular mechanisms underlying the physiological responses to temperature variations are poorly understood. Here, we applied quantitative proteomic and untargeted metabolomic approaches to investigate protein and metabolite expression profiles of a bloom-forming dinoflagellate Prorocentrum shikokuense at different temperatures. Of the four temperatures (19, 22, 25, and 28°C) investigated, P. shikokuense at 25°C exhibited the maximal cell growth rate and maximum quantum efficiency of photosystem II (Fv/Fm) value. The levels of particulate organic carbon (POC) and nitrogen (PON) decreased with increasing temperature, while the POC/PON ratio increased and peaked at 25°C. Proteomic analysis showed proteins related to photoreaction, light harvesting, and protein homeostasis were highly expressed at 28°C when cells were under moderate heat stress. Metabolomic analysis further confirmed reallocated amino acids and soluble sugars at this temperature. Both omic analyses showed glutathione metabolism that scavenges the excess reactive oxygen species, and transcription and lipid biosynthesis that compensate for the low translation efficiency and plasma membrane fluidity were largely upregulated at suboptimal temperature. Higher accumulations of glutathione, glutarate semialdehyde, and 5-KETE at 19°C implied their important roles in low-temperature acclimation. The strikingly active nitrate reduction and nitrogen flux into asparagine, glutamine, and aspartic acid at 19°C indicated these three amino acids may serve as nitrogen storage pools and help cells cope with low temperature. Our study provides insights into the effects of temperature on dinoflagellate resource allocation and advances our knowledge of dinoflagellate bloom formation in marine environments

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis

IntroductionThe comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection).MethodsIn this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis.ResultsWe have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037–3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021–1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction.DiscussionOur findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19