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Cell detection and classification is a key technique for disease diagnosis, but conventional methods such as optical microscopy and flow cytometry have limitations in terms of field-of-view (FOV), throughput, cost, size, and operation complexity. Lensless holographic imaging is a promising alternative that offers large FOV, rich information content, and simple structure. However, its performance on cell detection and classification still needs to be improved. In this paper, we propose an intelligent cell detection system based on lensless holographic imaging and deep learning. Our system uses unstained cells suspended in solution as samples and employs a threshold segmentation-based auto-focusing algorithm to determine the optimal focusing distance for each imaging session. We also use a deep learning-based object detection neural network to classify different types of cells from the focused holographic images without the need for cell segmentation. We demonstrated the performance of our system using four cell detection tasks: tumor cells vs. polystyrene microspheres (77.6% accuracy), different tumor cells (80.1% accuracy), red blood cells vs. white blood cells (78.1% accuracy), white blood cell subtypes (88% accuracy), which showed that our system achieved high accuracy with label-free, portable, intelligent, and fast cell detection capabilities. It has potential applications in the miniaturized cell detection field.

The reliability of the model-based decomposition result, which is seldom investigated, is a key factor in determining whether decomposition parameters can be effectively applied to polarimetric synthetic aperture radar (PolSAR) applications. In this paper, a type of polarimetric parameter for evaluating the reliability of model-based decomposition result is proposed. It originates from the degree of correlation between the scattering models. The main idea is whether the measured power can be assigned to the corresponding scattering model. The more the measured power that can be allocated to the corresponding scattering model, the more reliable the decomposition result. The C-band Radarsat-2, L-band UAVSAR, X-band TerraSAR-X, and L-band E-SAR PolSAR data are selected for experiments. The results show that the type of polarimetric parameter can effectively represent the reliability of the decomposition result. The reliabilities of surface and double-bounce scattering are high in the ocean and orthogonal buildings, respectively. For oriented buildings, the reliability of the decomposition result is lower owing to the overestimation of volume scattering. For forested areas, reliability is generally higher if volume scattering is not overestimated. Furthermore, the results show that the reliability of soil moisture retrieval is positively correlated with surface scattering reliability.

The weighted average current control strategy has attracted much attention because of its lower-order effect on grid-connected inverters. However, the system reverse resonant peak caused by digital control delay causes the traditional WAC control to fail, and the inverter’s adaptation range to the grid impedance under the weak power grid is reduced. In view of this, this article does not start from the point of view of reducing the order of WAC control, but re-examines it from the perspective of grid-side current stability through the well-known capacitive current feedback active damping control method. The analysis shows that the traditional undamped WAC control method has sufficient stability margin under strong grid conditions, while the grid-side current will be in a critical stable state under the capacitive current feedback active damping (CCFAD) control method. It is easy to cause the harmonic resonance of the grid-connected inverter. Furthermore, an improved WAC control strategy is proposed, which enhances the damping effect and robustness of the system by introducing a compensation function in the feedback channel of the machine-side current and the grid-side current. Finally, experiments verify the effectiveness of the proposed control method.

Abstract Plectropomus leopardus, as known as leopard coral grouper, is a valuable marine fish that has gradually been bred artificially. To promote future conservation, molecular breeding, and comparative studies, we generated an improved high-quality chromosomal-level genome assembly of leopard coral grouper using Nanopore long-reads, Illumina short reads, and the Hi-C sequencing data. The draft genome is 849.74 Mb with 45 contigs and N50 of 35.59 Mb. Finally, a total of 846.49 Mb corresponding to 99.6% of the contig sequences was anchored to 24 pseudo-chromosomes using Hi-C technology. A final set of 25,965 genes is annotated after manual curation of the predicted gene models, and BUSCO analysis yielded a completeness score of 99.5%. This study significantly improves the utility of the grouper genome and provided a reference for the study of molecular breeding, genomics and biology in this species.

Genome miniaturization drives key evolutionary innovations of adaptive traits in vertebrates, such as the flight evolution of birds. However, whether similar evolutionary processes exist in invertebrates remains poorly understood. Derived from the second-largest animal phylum, scallops are a special group of bivalve molluscs and acquire the evolutionary novelty of the swimming lifestyle, providing excellent models for investigating the coordinated genome and lifestyle evolution. Here, we show for the first time that genome sizes of scallops exhibit a generally negative correlation with locomotion activity. To elucidate the co-evolution of genome size and swimming lifestyle, we focus on the Asian moon scallop (Amusium pleuronectes) that possesses the smallest known scallop genome while being among scallops with the highest swimming activity. Whole-genome sequencing of A. pleuronectes reveals highly conserved chromosomal macrosynteny and microsynteny, suggestive of a highly contracted but not degenerated genome. Genome reduction of A. pleuronectes is facilitated by significant inactivation of transposable elements, leading to reduced gene length, elevated expression of genes involved in energy-producing pathways, and decreased copy numbers and expression levels of biomineralization-related genes. Similar evolutionary changes of relevant pathways are also observed for bird genome reduction with flight evolution. The striking mimicry of genome miniaturization underlying the evolution of bird flight and scallop swimming unveils the potentially common, pivotal role of genome size fluctuation in the evolution of novel lifestyles in the animal kingdom.

BackgroundsDiabetic retinopathy (DR) is a common diabetic ocular disease characterized by retinal ganglion cell (RGC) changes. An abnormal environment, hyperglycemia, may progressively alter the structure and function of RGCs, which is a primary pathological feature of retinal neurodegeneration in DR. Accumulated studies confirmed autophagy and senescence play a vital role in DR; however, the underlying mechanisms need to be clarified.MethodsThis study included the microarray expression profiling dataset GSE60436 from Gene Expression Omnibus (GEO) to conduct the bioinformatics analysis. The R software was used to identify autophagy-related genes (ARGs) that were differentially expressed in fibrovascular membranes (FVMs) and normal retinas. Co-expression and tissue-specific expression were elicited for the filtered genes. The genes were then analyzed by ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). R28 cells were cultured with high glucose, detected by reverse transcription-quantitative (RT-qPCR) and stained by apoptosis kit.ResultsIn the retina, 31 differentially expressed ARGs (24 up-regulated genes) were discovered and enriched. The enrichment results revealed that differentially expressed ARGs were significantly enriched in autophagy, apoptosis, aging, and neural function. Four hub genes (i.e., TP53, CASP1, CCL2, and CASP1) were significantly up-regulated. Upregulation of cellular autophagy and apoptosis level was detected in the hyperglycemia model in vitro.ConclusionsOur results provide evidence for the autophagy and cellular senescence mechanisms involved in retinal hyperglycemia injury, and the protective function of autophagy is limited. Further study may favour understanding the disease progression and neuroprotection of DR.

Existing volume scattering models play an important role in interpreting the scattering characteristics of vegetation canopies. A common assumption of these models is that a vegetation canopy consists of one type of particle. However, since a vegetation canopy comprises boughs, twigs, and leaves (or needles), they tend to form scatterers with various shapes, resulting in differing scattering processes. In this article, we propose a new volume scattering model. The proposed scattering model uses an isotropic plate, isotropic dihedral, and dipole particles to model vegetation scatterers. Different preferred orientation angles characterize the latter two types of particles. The proposed method is validated with AIRSAR and ALOS2/PALSAR2 datasets. The results show that the proposed model provides us an opportunity to extract refined scattering processes of vegetation canopies. In addition, it can be found that the proposed model shows potential in vegetation classification and biomass estimation, resulting from modeling a vegetation canopy as various types of particles.

In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-world scenarios. We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. (1) We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. (2) We explore the best practice of prompt tuning with large-scale PLMs. Compared with conventional fine-tuning, prompt tuning significantly reduces the number of task-specific parameters. (3) We implement a new inference toolkit, namely infmoe, for using large-scale PLMs with limited computational resources. Based on our cost-effective pipeline, we pre-train two models: an encoder-decoder bilingual model with 11 billion parameters (CPM-2) and its corresponding MoE version with 198 billion parameters. In our experiments, we compare CPM-2 with mT5 on downstream tasks. Experimental results show that CPM-2 has excellent general language intelligence. Moreover, we validate the efficiency of infmoe when conducting inference of large-scale models having tens of billions of parameters on a single GPU. All source code and model parameters are available at https://github.com/TsinghuaAI/CPM.

BackgroundGestational 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. ObjectiveThe 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. MethodsFour 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. ResultsA 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

The hydrolysis of sugar-containing compounds by glycoside hydrolases (GHs) plays essential roles in many major biological processes, but to date our systematic understanding of the functional diversity and evolution of GH families remains largely limited to a few well-studied terrestrial animals. Molluscs represent the largest marine phylum in the animal kingdom, and many of them are herbivorous that utilize algae as a main nutritional source, making them good subjects for studying the functional diversity and adaptive evolution of GH families. In the present study, we conducted genome-wide identification and functional and evolutionary analysis of all GH families across major molluscan lineages. We revealed that the remarkable expansion of the GH9, GH10, GH18 and GH20 families and the wide adoption of carbohydrate-binding modules in molluscan expanded GH families likely contributed to the efficient hydrolysis of marine algal polysaccharides and were involved in the consolidation of molluscan algae-feeding habits. Gene expression and network analysis revealed the hepatopancreas as the main organ for the prominent expression of approximately half of the GH families (well corresponding to the digestive roles of the hepatopancreas) and key or hub GHs in the coexpression gene network with potentially diverse functionalities. We also revealed the evolutionary signs of differential expansion and functional divergence of the GH family, which possibly contributed to lineage-specific adaptation. Systematic analysis of GH families at both genomic and transcriptomic levels provides important clues for understanding the functional divergence and evolution of GH gene families in molluscs in relation to their algae-feeding biology.

A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead of model decomposition methods when there are similar scattering models for a decomposition scheme. It is difficult to separate the polarization power of similar scattering mechanisms reasonably and robustly. Therefore, in this paper, we first analyze the necessity of orthogonality between scattering models. Following this, we propose two mutually orthogonal rank-1 scattering models, which can degenerate to mostly current scattering models. The orthogonality and adaptability of scattering models are considered in the model derivation. Simulated PolSAR data, real ALOS-2/PALSAR-2, GF-3, and E-SAR data are selected to validate the proposed method. As shown by the results, first, the proposed method enhances the double-bounce scattering contribution in urban areas and maintains the volume-scattering contribution in vegetation areas because it separates the polarization power of different scattering mechanisms more reasonably. Second, the proposed method is powerful in the robust interpretation of different ground targets, resulting from the orthogonality of scattering models. These two characteristics of orthogonal scattering models are expected to play a positive role in large-scale applications, especially in land-cover classification.

As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. A chunk spans a particular time interval and covers a set of vertices in a graph. We propose chunk layout and chunk splitting designs to achieve the desired efficiency and the abovementioned goals. First, by carefully choosing the time split policy, Auxo achieves linear complexity in both space usage and query time. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. Third, Auxo optimizes the data layout inside chunks, thereby significantly improving the performance of traverse-based graph queries. Experimental evaluation showed that Auxo achieved 2.9× to 12.1× improvement for global queries, and 1.7× to 2.7× improvement for local queries, as compared with state-of-the-art open-source solutions.

Spermidine, a natural polyamine, exists in almost all human tissues, exhibiting broad properties like anti-aging, autophagy induction, anti-inflammation, anti-oxidation, cell proliferation activation, and ion channel regulation. Considering that spermidine is already present in human nutrition, recent studies targeting supplementing exogenous sources of this polyamine appear feasible. The protective role of spermidine in various systems has been illuminated in the literature, while recent progress of spermidine administration in ocular diseases remains to be clarified. This study shows the current landscape of studies on spermidine and its potential to become a promising therapeutic agent to treat ocular diseases: glaucoma, optic nerve injury, age-related macular degeneration (AMD), cataracts, dry eye syndrome, and bacterial keratitis. It also has the potential to become a potent biomarker to predict keratoconus (KC), cataracts, uveitis, glaucoma, proliferative diabetic retinopathy (PDR), proliferative vitreoretinopathy (PVR), and retinopathy of prematurity (ROP). We also summarize the routes of administration and the effects of spermidine at different doses.

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

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).

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.

Lipoxygenase (LOX) plays important roles in fatty acid oxidation and lipid mediator biosynthesis. In this study, we give first insights into brown algal LOX evolution. Whole genome searches revealed four, three, and eleven LOXs in Ectocarpus siliculosus, Cladosiphon okamuranus, and Saccharina japonica, respectively. In phylogenetic analyses, LOXs from brown algae form a robust clade with those from prokaryotes, suggesting an ancestral origin and slow evolution. Brown algal LOXs were divided into two clades, C1 and C2 in a phylogenetic tree. Compared to the two species of Ectocarpales, LOX gene expansion occurred in the kelp S. japonica through tandem duplication and segmental duplication. Selection pressure analysis showed that LOX genes in brown algae have undergone strong purifying selection, while the selective constraint in the C2 clade was more relaxed than that in the C1 clade. Furthermore, within each clade, LOXs of S. japonica evolved under more relaxed selection constraints than E. siliculosus and C. okamuranus. Structural modeling showed that unlike LOXs of plants and animals, which contain a β barrel in the N-terminal part of the protein, LOXs in brown algae fold into a single domain. Analysis of previously published transcriptomic data showed that LOXs in E. siliculosus are responsive to hyposaline, hypersaline, oxidative, and copper stresses. Moreover, clear divergence of expression patterns was observed among different life stages, as well as between duplicate gene pairs. In E. siliculosus, all four LOXs are male-biased in immature gametophytes, and mature gametophytes showed significantly higher LOX mRNA levels than immature gametophytes and sporophytes. In S. japonica, however, our RNA-Seq data showed that most LOXs are highly expressed in sporophytes. Even the most recently duplicated gene pairs showed divergent expression patterns, suggesting that functional divergence has likely occurred since LOX genes duplicated, which potentially contributes to the production of various oxylipins in brown algae.