Towards artificial general intelligence with hybrid Tianjic chip architecture.

There are two general approaches to developing artificial general intelligence (AGI)1: computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms2-8, retarding the development of AGI. A general platform that could support the prevailing computer-science-based artificial neural networks as well as neuroscience-inspired models and algorithms is highly desirable. Here we present the Tianjic chip, which integrates the two approaches to provide a hybrid, synergistic platform. The Tianjic chip adopts a many-core architecture, reconfigurable building blocks and a streamlined dataflow with hybrid coding schemes, and can not only accommodate computer-science-based machine-learning algorithms, but also easily implement brain-inspired circuits and several coding schemes. Using just one chip, we demonstrate the simultaneous processing of versatile algorithms and models in an unmanned bicycle system, realizing real-time object detection, tracking, voice control, obstacle avoidance and balance control. Our study is expected to stimulate AGI development by paving the way to more generalized hardware platforms.

Exon shuffling potentiates a diverse repertoire of brown algal NB-ARC-TPR candidate immune receptor proteins via alternative splicing.

Like other organisms, brown algae are subject to diseases caused by bacteria, fungi, and viruses. Brown algal immunity mechanisms are not well characterized; however, there is evidence suggesting that pathogen receptors exist in brown algae. One key protein family likely associated with brown algal innate immunity possesses an NB-ARC domain analogous to innate immune proteins in plants and animals. In this study, we conducted an extensive survey of NB-ARC genes in brown algae and obtained insights into the domain organization and evolutionary history of the encoded proteins. Our data show that brown algae possess an ancient NB-ARC-tetratricopeptide repeat (NB-TPR) domain architecture. We identified an N-terminal effector domain, the four-helix bundle, which was not previously found associated with NB-ARC domains. The phylogenetic tree including NB-ARC domains from all kingdoms of life suggests the three clades of brown algal NB-TPRs are likely monophyletic, whereas their TPRs seem to have distinct origins. One group of TPRs exhibit intense exon shuffling, with various alternative splicing and diversifying selection acting on them, suggesting exon shuffling is an important mechanism for evolving ligand-binding specificities. The reconciliation of gene duplication and loss events of the NB-ARC genes reveals that more independent gene gains than losses have occurred during brown algal evolution, and that tandem duplication has played a major role in the expansion of NB-ARC genes. Our results substantially enhance our understanding of the evolutionary history and exon shuffling mechanisms of the candidate innate immune repertoire of brown algae.

Endowing Porphyrinic Metal-Organic Frameworks with High Stability by a Linker Desymmetrization Strategy.

The fabricating of metal-organic frameworks (MOFs) that integrate high stability and functionality remains a long-term pursuit yet a great challenge. Herein, we develop a linker desymmetrization strategy to construct highly stable porphyrinic MOFs, namely, USTC-9 (USTC represents the University of Science and Technology of China), presenting the same topological structure as the well-known PCN-600 that readily loses crystallinity in air or upon conventional activation. For USTC-9, the involved porphyrinic linker (TmCPP-M) with carboxylate groups located in the meta-position presents a chair-shaped conformation with lower C2h symmetry than that (D4h) of the common porphyrinic carboxylate (TCPP) linker in PCN-600. As a result, the wrinkled and interlocked linker arrangements collectively contribute to the remarkable stability of USTC-9. Given the high stability and porosity as well as Lewis acidity, USTC-9(Fe) demonstrates its excellent performance toward catalytic CO2 cycloaddition with diverse epoxides at moderate temperature and atmospheric pressure.

Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring.

A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

Switchable hydrophilic solvent-based dispersive liquid-liquid microextraction coupled with high-performance liquid chromatography for the determination of four types of sulfonylurea herbicides in soils.

In this study, switchable hydrophilic solvent-based dispersive liquid-liquid microextraction coupled with high-performance liquid chromatography was developed for the determination of four sulfonylurea herbicides in soils. For the first time, the sample pretreatment was achieved due to the similar acid-base status of sulfonylurea herbicides and switchable hydrophilic solvent. In the extraction step, sulfonylurea herbicides were extracted as anions and transferred to an alkaline solution with switchable hydrophilic solvent anions. In the concentration step, two types of anions were transformed to their molecular state after the aqueous solution was acidified. In addition, the dispersion and microextraction processes were completed efficiently with the simultaneous formation of analytes and extractants. The factors affecting the extraction performance were optimized. Under the optimized conditions, good linearity was observed for each herbicide with correlation coefficients ranging from 0.9952 to 0.9978. The limits of detection were in the range of 0.1-0.2 µg/g. Moreover, the relative recoveries of the sulfonylurea herbicides at spiking levels of 0.5, 1, and 1.5 µg/g in soil samples were between 75 and 111% (relative standard deviations: 0.4-11.4%). Therefore, the proposed method in this study could be successfully applied to the analysis of four types of sulfonylurea herbicides in soil samples.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. OBJECTIVE: The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. METHODS: Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. RESULTS: A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non-logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. CONCLUSIONS: Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Integrative analysis of chloroplast DNA methylation in a marine alga-Saccharina japonica.

KEY MESSAGE: We applied an integrative approach using multiple methods to verify cytosine methylation in the chloroplast DNA of the multicellular brown alga Saccharina japonica. Cytosine DNA methylation is a heritable process which plays important roles in regulating development throughout the life cycle of an organism. Although methylation of nuclear DNA has been studied extensively, little is known about the state and role of DNA methylation in chloroplast genomes, especially in marine algae. Here, we have applied an integrated approach encompassing whole-genome bisulfite sequencing, methylated DNA immunoprecipitation, gene co-expression networks and photophysiological analyses to provide evidence for the role of chloroplast DNA methylation in a marine alga, the multicellular brown alga Saccharina japonica. Although the overall methylation level was relatively low in the chloroplast genome of S. japonica, gametophytes exhibited higher methylation levels than sporophytes. Gene-specific bisulfite-cloning sequencing provided additional evidence for the methylation of key photosynthetic genes. Many of them were highly expressed in sporophytes whereas genes involved in transcription, translation and biosynthesis were strongly expressed in gametophytes. Nucleus-encoded photosynthesis genes were co-expressed with their chloroplast-encoded counterparts potentially contributing to the higher photosynthetic performance in sporophytes compared to gametophytes where these co-expression networks were less pronounced. A nucleus-encoded DNA methyltransferase of the DNMT2 family is assumed to be responsible for the methylation of the chloroplast genome because it is predicted to possess a plastid transit peptide.

Recent progress of inertial microfluidic-based cell separation.

Cell separation has consistently been a pivotal technology of sample preparation in biomedical research. Compared with conventional bulky cell separation technologies applied in the clinic, cell separation based on microfluidics can accurately manipulate the displacement of liquid or cells at the microscale, which has great potential in point-of-care testing (POCT) applications due to small device size, low cost, low sample consumption, and high operating accuracy. Among various microfluidic cell separation technologies, inertial microfluidics has attracted great attention due to its simple structure and high throughput. In recent years, many researchers have explored the principles and applications of inertial microfluidics and developed different channel structures, including straight channels, curved channels, and multistage channels. However, the recently developed multistage channels have not been discussed and classified in detail compared with more widely discussed straight and curved channels. Therefore, in this review, a comprehensive and detailed review of recent progress in the multistage channel is presented. According to the channel structure, the inertial microfluidic separation technology is divided into (i) straight channel, (ii) curved channel, (iii) composite channel, and (iv) integrated device. The structural development of straight and curved channels is discussed in detail. And based on straight and curved channels, the multistage cell separation structures are reviewed, with a special focus on a variety of latest structures and related innovations of composite and integrated channels. Finally, the future prospects for the existing challenges in the development of inertial microfluidic cell separation technology are presented.

Elevated CO2 affects kelp nutrient quality: A case study of Saccharina japonica from CO2 -enriched coastal mesocosm systems.

Kelps provide critical services for coastal food chains and ecosystem, and they are important food source for some segments of human population. Despite their ecological importance, little is known about long-term impacts of elevated CO2 (eCO2 ) on nutrient metabolites in kelps and the underlying regulation mechanisms. In this study, the kelp Saccharina japonica was cultured in CO2 -enriched coastal mesocosm systems for up to 3 months. We found that, although eCO2 significantly increased the growth rate, carbon concentrations, and C/N ratio of S. japonica, and it had no effect on total nitrogen and protein contents at the end of cultivation period. Meanwhile, it decreased the lipid, magnesium, sodium, and calcium content and changed the amino acid and fatty acid composition. Combining the genome-wide transcriptomic and metabolic evidence, we obtained a system-level understanding of metabolic response of S. japonica to eCO2 . The unique ornithine-urea cycle (OUC) and aspartate-argininosuccinate shunt (AAS), coupled with TCA cycle, balanced the carbon and nitrogen metabolism under eCO2 by providing carbon skeleton for amino acid synthesis and reduced power for nitrogen assimilation. This research provides a major advance in the understanding of kelp nutrient metabolic mechanism in the context of global climate change, and such CO2 -induced shifts in nutritional value may induce changes in the structure and stability of marine trophic webs and affect the quality of human nutrition resources.

High-Precision Lensless Microscope on a Chip Based on In-Line Holographic Imaging.

The lensless on-chip microscope is an emerging technology in the recent decade that can realize the imaging and analysis of biological samples with a wide field-of-view without huge optical devices and any lenses. Because of its small size, low cost, and being easy to hold and operate, it can be used as an alternative tool for large microscopes in resource-poor or remote areas, which is of great significance for the diagnosis, treatment, and prevention of diseases. To improve the low-resolution characteristics of the existing lensless shadow imaging systems and to meet the high-resolution needs of point-of-care testing, here, we propose a high-precision on-chip microscope based on in-line holographic technology. We demonstrated the ability of the iterative phase recovery algorithm to recover sample information and evaluated it with image quality evaluation algorithms with or without reference. The results showed that the resolution of the holographic image after iterative phase recovery is 1.41 times that of traditional shadow imaging. Moreover, we used machine learning tools to identify and count the mixed samples of mouse ascites tumor cells and micro-particles that were iterative phase recovered. The results showed that the on-chip cell counter had high-precision counting characteristics as compared with manual counting of the microscope reference image. Therefore, the proposed high-precision lensless microscope on a chip based on in-line holographic imaging provides one promising solution for future point-of-care testing (POCT).

Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.

The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient's immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021, 21, 512.

The authors wish to make the following correction to their paper [...].

Single-base methylome profiling of the giant kelp Saccharina japonica reveals significant differences in DNA methylation to microalgae and plants.

Brown algae have convergently evolved plant-like body plans and reproductive cycles, which in plants are controlled by differential DNA methylation. This contribution provides the first single-base methylome profiles of haploid gametophytes and diploid sporophytes of a multicellular alga. Although only c. 1.4% of cytosines in Saccharina japonica were methylated mainly at CHH sites and characterized by 5-methylcytosine (5mC), there were significant differences between life-cycle stages. DNA methyltransferase 2 (DNMT2), known to efficiently catalyze tRNA methylation, is assumed to methylate the genome of S. japonica in the structural context of tRNAs as the genome does not encode any other DNA methyltransferases. Circular and long noncoding RNA genes were the most strongly methylated regulatory elements in S. japonica. Differential expression of genes was negatively correlated with DNA methylation with the highest methylation levels measured in both haploid gametophytes. Hypomethylated and highly expressed genes in diploid sporophytes included genes involved in morphogenesis and halogen metabolism. The data herein provide evidence that cytosine methylation, although occurring at a low level, is significantly contributing to the formation of different life-cycle stages, tissue differentiation and metabolism in brown algae.

Diversity and evolution of cytochromes P450 in stramenopiles.

MAIN CONCLUSION: Comparative genomic analysis of cytochromes P450 revealed high diversification and dynamic changes in stramenopiles, associated with transcriptional responsiveness to various environmental stimuli. Comparative genomic and molecular evolution approaches were used to characterize cytochromes P450 (P450) diversity in stramenopiles. Phylogenetic analysis pointed to a high diversity of P450 in stramenopiles and identified three major clans. The CYP51 and CYP97 clans were present in brown algae, diatoms and Nannochloropsis gaditana, whereas the CYP5014 clan mainly includes oomycetes. Gene gain and loss patterns revealed that six CYP families-CYP51, CYP97, CYP5160, CYP5021, CYP5022, and CYP5165-predated the split of brown algae and diatoms. After they diverged, diatoms gained more CYP families, especially in the cold-adapted species Fragilariopsis cylindrus, in which eight new CYP families were found. Selection analysis revealed that the expanded CYP51 family in the brown alga Cladosiphon okamuranus exhibited a more relaxed selection constraint compared with those of other brown algae and diatoms. Our RNA-seq data further evidenced that most of P450s in Saccharina japonica are highly expressed in large sporophytes, which could potentially promote the large kelp formation in this developmental stage. A survey of Ectocarpus siliculosus and diatom transcriptomes showed that many P450s are responsive to stress, nutrient limitation or light quality, suggesting pivotal roles in detoxification or metabolic processes under adverse environmental conditions. The information provided in this study will be helpful in designing functional experiments and interpreting P450 roles in this particular lineage.

Seasonal expression of P450c17 and 5α-reductase-2 in the scented gland of male muskrats (Ondatra zibethicus).

Cytochrome P450 17A1 (P450c17) is the key enzyme required for the production of androgenic sex steroids by converting progestogens to androgens. 5α-reductases are enzymes that convert testosterone (T) to dihydrotestosterone (DHT), which has a greater affinity for androgen receptors (AR) and stronger action than T. Our previous studies revealed that the scented glands of male muskrats expressed AR during the breeding and nonbreeding seasons. To further seek evidence of the activities of androgens in scented glands, the expression patterns of P450c17 and 5α-reductase 2 were investigated in the scented glands of male muskrats during the breeding and nonbreeding seasons. The weight and size of scented glands in the breeding season were significantly higher than those of the nonbreeding season. Immunohistochemical data showed that P450c17 and 5α-reductase 2 were presented in the glandular cells and epithelial cells of scented glands in both the seasons. The protein and mRNA expression of P450c17 and 5α-reductase 2 were significantly higher in the scented gland during the breeding season than those during the nonbreeding season. In addition, the levels of DHT and T in the scented gland were remarkably higher during the breeding season. Taken together, these results suggested that the scented glands of male muskrats were capable of locally synthesizing T and DHT, and T and DHT might play an important role in the scented glandular function via an autocrine or paracrine manner.

Seasonal changes of androgen receptor, estrogen receptors and aromatase expression in the hippocampus of the wild male ground squirrels (Citellus dauricus Brandt).

The wild ground squirrel is a typical seasonal breeder whose annual life cycle can be roughly divided into the breeding season, the post-breeding season and hibernation. Our previous study has reported the seasonal changes in the expressions of androgen receptor (AR), estrogen receptors α and ß (ERα and ERß), and aromatase cytochrome P450 (P450arom) in the hypothalamus of male wild ground squirrels. To further seek evidence of seasonal expression of steroid hormone receptors and steroid hormone synthases in other brain regions, we investigated the protein and mRNA expressions of AR, ERα, ERß and P450arom in the hippocampus of the male wild ground squirrels during these different reproductive periods. Histological observation showed that the number of pyramidal cells in Cornu Ammonis 1 (CA1) increased in the breeding season. Both protein and mRNA of AR, ERα, ERß and P450arom were present in CA1 and CA3 of all seasons. There was significant increment in the immune-signal intensity and mRNA level of AR and ERα during the pre-hibernation, whereas those of ERß and P450arom were higher during the post-breeding season. In addition, the profile of plasma testosterone concentration showed the nadir in the post-breeding season, a marked elevation in the pre-hibernation, and the summit in the breeding season. These findings suggested that the hippocampus may be a direct target of androgen and estrogen; androgen may play important regulatory roles through its receptor and/or the aromatized estrogen in the hippocampus of the wild male ground squirrels.

Pan-evolutionary and regulatory genome architecture delineated by an integrated macro- and microsynteny approach.

The forthcoming massive genome data generated by the Earth BioGenome Project will open up a new era of comparative genomics, for which genome synteny analysis provides an important framework. Profiling genome synteny represents an essential step in elucidating genome architecture, regulatory blocks/elements and their evolutionary history. Here we describe PanSyn, ( https://github.com/yhw320/PanSyn ), the most comprehensive and up-to-date genome synteny pipeline, providing step-by-step instructions and application examples to demonstrate its usage. PanSyn inherits both basic and advanced functions from existing popular tools, offering a user-friendly, highly customized approach for genome macrosynteny analysis and integrated pan-evolutionary and regulatory analysis of genome architecture, which are not yet available in public synteny software or tools. The advantages of PanSyn include: (i) advanced microsynteny analysis by functional profiling of microsynteny genes and associated regulatory elements; (ii) comprehensive macrosynteny analysis, including the inference of karyotype evolution from ancestors to extant species; and (iii) functional integration of microsynteny and macrosynteny for pan-evolutionary profiling of genome architecture and regulatory blocks, as well as integration with external functional genomics datasets from three- or four-dimensional genome and ENCODE projects. PanSyn requires basic knowledge of the Linux environment and Perl programming language and the ability to access a computer cluster, especially for large-scale genomic comparisons. Our protocol can be easily implemented by a competent graduate student or postdoc and takes several days to weeks to execute for dozens to hundreds of genomes. PanSyn provides yet the most comprehensive and powerful tool for integrated evolutionary and functional genomics.

Inhibiting Demetalation of Fe─N─C via Mn Sites for Efficient Oxygen Reduction Reaction in Zinc-Air Batteries.

Demetalation caused by the electrochemical dissolution of metallic Fe atoms is a major challenge for the practical application of Fe─N─C catalysts. Herein, an efficient single metallic Mn active site is constructed to improve the strength of the Fe─N bond, inhibiting the demetalation effect of Fe─N─C. Mn acts as an electron donor inducing more delocalized electrons to reduce the oxidation state of Fe by increasing the electron density, thereby enhancing the Fe─N bond and inhibiting the electrochemical dissolution of Fe. The oxygen reduction reaction pathway for the dissociation of Fe─Mn dual sites can overcome the high energy barriers to direct O─O bond dissociation and modulate the electronic states of Fe─N4 sites. The resulting FeMn─N─C exhibits excellent ORR activity with a high half-wave potential of 0.92 V in alkaline electrolytes. FeMn─N─C as a cathode catalyst for Zn-air batteries has a cycle stability of 700 h at 25 °C and a long cycle stability of more than 210 h under extremely cold conditions at -40 °C. These findings contribute to the development of efficient and stable metal-nitrogen-carbon catalysts for various energy devices.

Deciphering the population structure and genetic basis of growth traits from whole-genome resequencing of the leopard coral grouper ( Plectropomus leopardus).

The leopard coral grouper ( Plectropomus leopardus) is a species of significant economic importance. Although artificial cultivation of P. leopardus has thrived in recent decades, the advancement of selective breeding has been hindered by the lack of comprehensive population genomic data. In this study, we identified over 8.73 million single nucleotide polymorphisms (SNPs) through whole-genome resequencing of 326 individuals spanning six distinct groups. Furthermore, we categorized 226 individuals with high-coverage sequencing depth (≥14×) into eight clusters based on their genetic profiles and phylogenetic relationships. Notably, four of these clusters exhibited pronounced genetic differentiation compared with the other populations. To identify potentially advantageous loci for P. leopardus, we examined genomic regions exhibiting selective sweeps by analyzing the nucleotide diversity ( θπ) and fixation index ( F ST) in these four clusters. Using these high-coverage resequencing data, we successfully constructed the first haplotype reference panel specific to P. leopardus. This achievement holds promise for enabling high-quality, cost-effective imputation methods. Additionally, we combined low-coverage sequencing data with imputation techniques for a genome-wide association study, aiming to identify candidate SNP loci and genes associated with growth traits. A significant concentration of these genes was observed on chromosome 17, which is primarily involved in skeletal muscle and embryonic development and cell proliferation. Notably, our detailed investigation of growth-related SNPs across the eight clusters revealed that cluster 5 harbored the most promising candidate SNPs, showing potential for genetic selective breeding efforts. These findings provide a robust toolkit and valuable insights into the management of germplasm resources and genome-driven breeding initiatives targeting P. leopardus.