Biochemical versus stomatal acclimation of dynamic photosynthetic gas exchange to elevated CO2 in three horticultural species with contrasting stomatal morphology.

Understanding photosynthetic acclimation to elevated CO2 (eCO2) is important for predicting plant physiology and optimizing management decisions under global climate change, but is underexplored in important horticultural crops. We grew three crops differing in stomatal density-namely chrysanthemum, tomato, and cucumber-at near-ambient CO2 (450 µmol mol-1) and eCO2 (900 µmol mol-1) for 6 weeks. Steady-state and dynamic photosynthetic and stomatal conductance (gs) responses were quantified by gas exchange measurements. Opening and closure of individual stomata were imaged in situ, using a novel custom-made microscope. The three crop species acclimated to eCO2 with very different strategies: Cucumber (with the highest stomatal density) acclimated to eCO2 mostly via dynamic gs responses, whereas chrysanthemum (with the lowest stomatal density) acclimated to eCO2 mostly via photosynthetic biochemistry. Tomato exhibited acclimation in both photosynthesis and gs kinetics. eCO2 acclimation in individual stomatal pore movement increased rates of pore aperture changes in chrysanthemum, but such acclimation responses resulted in no changes in gs responses. Although eCO2 acclimation occurred in all three crops, photosynthesis under fluctuating irradiance was hardly affected. Our study stresses the importance of quantifying eCO2 acclimatory responses at different integration levels to understand photosynthetic performance under future eCO2 environments.

SAMCF: Adaptive global style alignment and multi-color spaces fusion for joint optic cup and disc segmentation.

The optic cup (OC) and optic disc (OD) are two critical structures in retinal fundus images, and their relative positions and sizes are essential for effectively diagnosing eye diseases. With the success of deep learning in computer vision, deep learning-based segmentation models have been widely used for joint optic cup and disc segmentation. However, there are three prominent issues that impact the segmentation performance. First, significant differences among datasets collecting from various institutions, protocols, and devices lead to performance degradation of models. Second, we find that images with only RGB information struggle to counteract the interference caused by brightness variations, affecting color representation capability. Finally, existing methods typically ignored the edge perception, facing the challenges in obtaining clear and smooth edge segmentation results. To address these drawbacks, we propose a novel framework based on Style Alignment and Multi-Color Fusion (SAMCF) for joint OC and OD segmentation. Initially, we introduce a domain generalization method to generate uniformly styled images without damaged image content for mitigating domain shift issues. Next, based on multiple color spaces, we propose a feature extraction and fusion network aiming to handle brightness variation interference and improve color representation capability. Lastly, an edge aware loss is designed to generate fine edge segmentation results. Our experiments conducted on three public datasets, DGS, RIM, and REFUGE, demonstrate that our proposed SAMCF achieves superior performance to existing state-of-the-art methods. Moreover, SAMCF exhibits remarkable generalization ability across multiple retinal fundus image datasets, showcasing its outstanding generality.

The Concept of Neuromuscular Repatterning in Dancers: A Systematic Review.

Repatterning is a term that can be used in different fields, including genetics, molecular biology, neurology, psychology, or rehabilitation. Our aim is to identify the key concept of neuromuscular repatterning in somatic training programmes for dancers. A systematic search of eight databases was conducted using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. The Quality Assessment Tool for Quantitative Studies and the Oxford Levels of Evidence scales were used. The search yielded 1218 results, of which 5 met the inclusion criteria. Five studies (n = 5) were related to psychosomatic health (n = 5), two studies highlighted integration and inter-articular connectivity in movement (n = 2), four studies investigated the neurological component of alignment and efficiency in dance practice (n = 4), and two studies investigated self-confidence (n = 2). Five studies (n = 5) used imagery based on the anatomical and physiological experience of body systems as the main analytical method. Four studies (n = 4) used developmental movement through Bartenieff fundamentals as the main technique for this methodology. Developmental movement and imagery are two methodologies strongly connected to the concept of neuromuscular repatterning in somatic training programmes for dancers. The acquisition of further quantitative experimental or quasi-experimental studies is warranted to better define the level of improvement or impact of neuromuscular repatterning in dancers.

Corrigendum: Variation of photosynthetic induction in major horticultural crops is mostly driven by differences in stomatal traits.

[This corrects the article DOI: 10.3389/fpls.2022.860229.].

Dual-path multi-scale context dense aggregation network for retinal vessel segmentation.

There has been steady progress in the field of deep learning-based blood vessel segmentation. However, several challenging issues still continue to limit its progress, including inadequate sample sizes, the neglect of contextual information, and the loss of microvascular details. To address these limitations, we propose a dual-path deep learning framework for blood vessel segmentation. In our framework, the fundus images are divided into concentric patches with different scales to alleviate the overfitting problem. Then, a Multi-scale Context Dense Aggregation Network (MCDAU-Net) is proposed to accurately extract the blood vessel boundaries from these patches. In MCDAU-Net, a Cascaded Dilated Spatial Pyramid Pooling (CDSPP) module is designed and incorporated into intermediate layers of the model, enhancing the receptive field and producing feature maps enriched with contextual information. To improve segmentation performance for low-contrast vessels, we propose an InceptionConv (IConv) module, which can explore deeper semantic features and suppress the propagation of non-vessel information. Furthermore, we design a Multi-scale Adaptive Feature Aggregation (MAFA) module to fuse the multi-scale feature by assigning adaptive weight coefficients to different feature maps through skip connections. Finally, to explore the complementary contextual information and enhance the continuity of microvascular structures, a fusion module is designed to combine the segmentation results obtained from patches of different sizes, achieving fine microvascular segmentation performance. In order to assess the effectiveness of our approach, we conducted evaluations on three widely-used public datasets: DRIVE, CHASE-DB1, and STARE. Our findings reveal a remarkable advancement over the current state-of-the-art (SOTA) techniques, with the mean values of Se and F1 scores being an increase of 7.9% and 4.7%, respectively. The code is available at https://github.com/bai101315/MCDAU-Net.

C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation.

Glaucoma is a leading cause of worldwide blindness and visual impairment, making early screening and diagnosis is crucial to prevent vision loss. Cup-to-Disk Ratio (CDR) evaluation serves as a widely applied approach for effective glaucoma screening. At present, deep learning methods have exhibited outstanding performance in optic disk (OD) and optic cup (OC) segmentation and maturely deployed in CAD system. However, owning to the complexity of clinical data, these techniques could be constrained. Therefore, an original Coarse-to-Fine Transformer Network (C2FTFNet) is designed to segment OD and OC jointly , which is composed of two stages. In the coarse stage, to eliminate the effects of irrelevant organization on the segmented OC and OD regions, we employ U-Net and Circular Hough Transform (CHT) to segment the Region of Interest (ROI) of OD. Meanwhile, a TransUnet3+ model is designed in the fine segmentation stage to extract the OC and OD regions more accurately from ROI. In this model, to alleviate the limitation of the receptive field caused by traditional convolutional methods, a Transformer module is introduced into the backbone to capture long-distance dependent features for retaining more global information. Then, a Multi-Scale Dense Skip Connection (MSDC) module is proposed to fuse the low-level and high-level features from different layers for reducing the semantic gap among different level features. Comprehensive experiments conducted on DRIONS-DB, Drishti-GS, and REFUGE datasets validate the superior effectiveness of the proposed C2FTFNet compared to existing state-of-the-art approaches.

Exploring the mechanism of JiGuCao capsule formula on treating hepatitis B virus infection via network pharmacology analysis and in vivo/vitro experiment verification.

The JiGuCao capsule formula (JCF) has demonstrated promising curative effects in treating chronic hepatitis B (CHB) in clinical trials. Here, we aimed to investigate JCF's function and mechanism in diseases related to the hepatitis B virus (HBV). We used mass spectrometry (MS) to identify the active metabolites of JCF and established the HBV replication mouse model by hydrodynamically injecting HBV replication plasmids into the mice's tail vein. Liposomes were used to transfect the plasmids into the cells. The CCK-8 kit identified cell viability. We detected the levels of HBV s antigen (HBsAg) and HBV e antigen (HBeAg) by the quantitative determination kits. qRT-PCR and Western blot were used to detect the genes' expression. The key pathways and key genes related to JCF on CHB treatment were obtained by network pharmacological analysis. Our results showed that JCF accelerated the elimination of HBsAg in mice. JCF and its medicated serum inhibited HBV replication and proliferation of HBV-replicating hepatoma cells in vitro. And the key targets of JCF in treating CHB were CASP3, CXCL8, EGFR, HSPA8, IL6, MDM2, MMP9, NR3C1, PTGS2, and VEGFA. Furthermore, these key targets were related to pathways in cancer, hepatitis B, microRNAs in cancer, PI3K-Akt signaling, and proteoglycans in cancer pathways. Finally, Cholic Acid, Deoxycholic Acid, and 3', 4', 7-Trihydroxyflavone were the main active metabolites of JCF that we obtained. JCF employed its active metabolites to perform an anti-HBV effect and prevent the development of HBV-related diseases.

A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes.

The conversion of supplemental greenhouse light energy into biomass is not always optimal. Recent trends in global energy prices and discussions on climate change highlight the need to reduce our energy footprint associated with the use of supplemental light in greenhouse crop production. This can be achieved by implementing "smart" lighting regimens which in turn rely on a good understanding of how fluctuating light influences photosynthetic physiology. Here, a simple fit-for-purpose dynamic model is presented. It accurately predicts net leaf photosynthesis under natural fluctuating light. It comprises two ordinary differential equations predicting: 1) the total stomatal conductance to CO2 diffusion and 2) the CO2 concentration inside a leaf. It contains elements of the Farquhar-von Caemmerer-Berry model and the successful incorporation of this model suggests that for tomato (Solanum lycopersicum L.), it is sufficient to assume that Rubisco remains activated despite rapid fluctuations in irradiance. Furthermore, predictions of the net photosynthetic rate under both 400ppm and enriched 800ppm ambient CO2 concentrations indicate a strong correlation between the dynamic rate of photosynthesis and the rate of electron transport. Finally, we are able to indicate whether dynamic photosynthesis is Rubisco or electron transport rate limited.

Ankle Active Range of Motion as an Essential Factor of Footwork Technique in the Prevention of Overuse Injuries in Flamenco Dancers / Rango de movimiento activo del tobillo como factor esencial de la técnica de zapateado en la prevención de lesiones por sobreuso en bailarinas de flamenco

Flamenco dance is a performing art which is based on footwork technique where the foot and ankle play an important role.The objective of this study was to investigate the effect of ankle active range of motion on external load and its efficacy as apredictor during a flamenco footwork technique, with consideration of accelerometer positions and dance proficiency. Twelveflamenco dancers composed of 6 professional and 6 amateurs participated voluntarily in this study for whom no significantdifferences were detected regarding age, mass or height. Participants completed a 15-second Zap-3 footwork test at a speedof 160 bpm (beats per minute), 180 bpm and as fast as they could. Triaxial accelerometers were positioned at the dominantankle, 5th lumbar vertebrae and 7th cervical vertebrae to calculate accumulated PlayerLoad and uniaxial PlayerLoad of the3 planes (anteroposterior, mediolateral and vertical) for each speed level. Percentage contributions were also calculated. Theeffect of dorsiflexion on the external load of the dominant ankle of both professional and amateur dancers existed only in theanteroposterior axis while dorsiflexion was related to the external load at the 7th cervical vertebrae and only amateurs wereaffected. Plantarflexion only affected the uniaxial contribution of the vertical-axis of professional dancers. These programswould be applied to develop a technique feedback system for the flamenco dancer to follow their own model with respectto the ideal. This would allow intervention in the prevention of overuse injuries in flamenco dance artists.(AU)

El baile flamenco es un arte en el que el zapateado tiene un papel muy relevante. El objetivo de este estudio fue investigar elefecto del rango de movimiento activo del tobillo sobre la carga externa y su eficacia como predictor durante la realizaciónde un zapateado flamenco, en función de las posiciones del acelerómetro y el dominio técnico de los participantes. Un totalde doce bailaoras de flamenco, 6 profesionales y 6 amateurs, participaron voluntariamente en este estudio y en los que nose encontraron diferencias significativas respecto a edad, peso o estatura. Los participantes realizaron un test de zapateadodenominado Zap-3 durante 15 segundos a una velocidad de 160 pulsos por minuto, 180 y tan rápido como pudieron. Secolocaron acelerómetros triaxiales en el tobillo del pie dominante, en la 5ª vértebra lumbar y en la 7ª vértebra cervical paracalcular la PlayerLoad acumulada y la PlayerLoad uniaxial de los 3 planos (anteroposterior, medio-lateral y vertical) en funciónde cada nivel de velocidad, así como sus contribuciones porcentuales. Solamente se ha encontrado relación entre la flexióndorsal del tobillo dominante y la carga externa en el eje anteroposterior, tanto en profesionales como amateurs, mientras quea nivel de la 7ª vértebra cervical sólo se ha encontrado relación entre la dorsiflexión y la carga externa en el grupo de amateurs.Respecto a la flexión plantar solo se ha encontrado relación a nivel uniaxial con el eje vertical de los bailarines profesionales.Estos programas podrían servir de ayuda a desarrollar un sistema de retroalimentación de la técnica para que el practicantede baile flamenco pueda seguir su propio modelo respecto al ideal. Esto permitiría intervenir en la prevención de las lesionespor sobreuso en los artistas de baile flamenco.(AU)

Machine learning versus crop growth models: an ally, not a rival.

The rapid increases of the global population and climate change pose major challenges to a sustainable production of food to meet consumer demands. Process-based models (PBMs) have long been used in agricultural crop production for predicting yield and understanding the environmental regulation of plant physiological processes and its consequences for crop growth and development. In recent years, with the increasing use of sensor and communication technologies for data acquisition in agriculture, machine learning (ML) has become a popular tool in yield prediction (especially on a large scale) and phenotyping. Both PBMs and ML are frequently used in studies on major challenges in crop production and each has its own advantages and drawbacks. We propose to combine PBMs and ML given their intrinsic complementarity, to develop knowledge- and data-driven modelling (KDDM) with high prediction accuracy as well as good interpretability. Parallel, serial and modular structures are three main modes can be adopted to develop KDDM for agricultural applications. The KDDM approach helps to simplify model parameterization by making use of sensor data and improves the accuracy of yield prediction. Furthermore, the KDDM approach has great potential to expand the boundary of current crop models to allow upscaling towards a farm, regional or global level and downscaling to the gene-to-cell level. The KDDM approach is a promising way of combining simulation models in agriculture with the fast developments in data science while mechanisms of many genetic and physiological processes are still under investigation, especially at the nexus of increasing food production, mitigating climate change and achieving sustainability.

Identification of immune cell function in breast cancer by integrating multiple single-cell data.

Breast cancer has now become the most commonly diagnosed cancer worldwide. It is a highly complex and heterogeneous disease that comprises distinct histological features and treatment response. With the development of molecular biology and immunology, immunotherapy has become a new field of breast cancer treatment. Identifying cell-type-specific genes critical to the immune microenvironment contributes to breast cancer treatment. Single-cell RNA sequencing (scRNA-seq) technology could serve as a powerful tool to analyze cellular genetic information at single-cell resolution and to uncover the gene expression status of each cell, thus allowing comprehensive assessment of intercellular heterogeneity. Because of the influence of sample size and sequencing depth, the specificity of genes in different cell types for breast cancer cannot be fully revealed. Therefore, the present study integrated two public breast cancer scRNA-seq datasets aiming to investigate the functions of different type of immune cells in tumor microenvironment. We identified total five significant differential expressed genes of B cells, T cells and macrophage and explored their functions and immune mechanisms in breast cancer. Finally, we performed functional annotation analyses using the top fifteen differentially expressed genes in each immune cell type to discover the immune-related pathways and gene ontology (GO) terms.

Network pharmacology and in vitro experiments-based strategy to investigate the mechanisms of KangXianYiAi formula for hepatitis B virus-related hepatocellular carcinoma.

The Chinese traditional medicine KangXianYiAi formula (KXYA) is used to treat hepatic disease in the clinic. Here we aim to confirm the therapeutic effects and explore the pharmacological mechanisms of KXYA on hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). We first collected and analyzed clinical data of 40 chronic hepatitis B (CHB) patients with precancerous liver lesions under KXYA treatment. Then, the cell viability, migration, cell cycle, and apoptosis of HepAD38 cells with KXYA treatment were examined. Next, we performed network pharmacological analysis based on database mining to obtain the key target pathways and genes of KXYA treatment on HBV-related HCC. We finally analyzed the expression of the key genes between normal and HBV-related HCC tissues in databases and measured the mRNA expression of the key genes in HepAD38 cells after KXYA treatment. The KXYA treatment could reduce the liver nodule size of CHB patients, suppress the proliferation and migration capabilities, and promote apoptosis of HepAD38 cells. The key pathways of KXYA on HBV-related HCC were Cancer, Hepatitis B, Viral carcinogenesis, Focal adhesion, and PI3K-Akt signaling, and KXYA treatment could regulate the expression of the key genes including HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1. The KXYA exhibited a curative effect via inhibiting proliferation, migration, and promoting apoptosis of HBV-related HCC and the pharmacological mechanism was related to the regulation of the expression of HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1.

External Load of Flamenco Zap-3 Footwork Test: Use of PlayerLoad Concept with Triaxial Accelerometry.

The intense footwork required in flamenco dance may result in pain and injury. This study aimed to quantify the external load of the flamenco Zapateado-3 (Zap-3) footwork via triaxial accelerometry in the form of PlayerLoad (PL), comparing the difference in external loads at the fifth lumbar vertebra (L5), the seventh cervical vertebra (C7) and the dominant ankle (DA), and to explore whether the speed, position, axis and proficiency level of the flamenco dancer affected the external load. Twelve flamenco dancers, divided into professional and amateur groups, completed a 15-s Zap-3 footwork routine at different speeds. Triaxial accelerometry sensors were positioned at the DA, L5 and C7 and were utilized to calculate the total PlayerLoad (PLTOTAL), uniaxial PlayerLoad (PLUNI) and uniaxial contributions (PL%). For both PLTOTAL and PLUNI, this study identified significant effects of speed and position (p < 0.001), as well as the interaction between speed and position (p ≤ 0.001), and at the DA, values were significantly higher (p < 0.001) than those at C7 and L5. Significant single axis and group effects (p < 0.001) and effects of the interactions between the position and a single axis and the group and speed (p ≤ 0.001) were also identified for PLUNI. Medial-lateral PL% represented a larger contribution compared with anterior-posterior PL% and vertical PL% (p < 0.001). A significant interaction effect of position and PL% (p < 0.001) also existed. In conclusion, the Zap-3 footwork produced a significant external load at different positions, and it was affected by speed, axis and the proficiency level of the flamenco dancer. Although the ankle bears the most external load when dancing the flamenco, some external load caused by significant vibrations is also borne by the lumbar and cervical vertebrae.

Variation of Photosynthetic Induction in Major Horticultural Crops Is Mostly Driven by Differences in Stomatal Traits.

Under natural conditions, irradiance frequently fluctuates, causing net photosynthesis rate (A) to respond slowly and reducing the yields. We quantified the genotypic variation of photosynthetic induction in 19 genotypes among the following six horticultural crops: basil, chrysanthemum, cucumber, lettuce, tomato, and rose. Kinetics of photosynthetic induction and the stomatal opening were measured by exposing shade-adapted leaves (50 µmol m-2 s-1) to a high irradiance (1000 µmol m-2 s-1) until A reached a steady state. Rubisco activation rate was estimated by the kinetics of carboxylation capacity, which was quantified using dynamic A vs. [CO2] curves. Generally, variations in photosynthetic induction kinetics were larger between crops and smaller between cultivars of the same crop. Time until reaching 20-90% of full A induction varied by 40-60% across genotypes, and this was driven by a variation in the stomatal opening rather than Rubisco activation kinetics. Stomatal conductance kinetics were partly determined by differences in the stomatal size and density; species with densely packed, smaller stomata (e.g., cucumber) tended to open their stomata faster, adapting stomatal conductance more rapidly and efficiently than species with larger but fewer stomata (e.g., chrysanthemum). We conclude that manipulating stomatal traits may speed up photosynthetic induction and growth of horticultural crops under natural irradiance fluctuations.

A multi-network integration approach for measuring disease similarity based on ncRNA regulation and heterogeneous information.

BACKGROUND: Measuring similarity between complex diseases has significant implications for revealing the pathogenesis of diseases and development in the domain of biomedicine. It has been consentaneous that functional associations between disease-related genes and semantic associations can be applied to calculate disease similarity. Currently, more and more studies have demonstrated the profound involvement of non-coding RNA in the regulation of genome organization and gene expression. Thus, taking ncRNA into account can be useful in measuring disease similarities. However, existing methods ignore the regulation functions of ncRNA in biological process. In this study, we proposed a novel deep-learning method to deduce disease similarity. RESULTS: In this article, we proposed a novel method, ImpAESim, a framework integrating multiple networks embedding to learn compact feature representations and disease similarity calculation. We first utilize three different disease-related information networks to build up a heterogeneous network, after a network diffusion process, RWR, a compact feature learning model composed of classic Auto Encoder (AE) and improved AE model is proposed to extract constraints and low-dimensional feature representations. We finally obtain an accurate and low-dimensional feature representation of diseases, then we employed the cosine distance as the measurement of disease similarity. CONCLUSION: ImpAESim focuses on extracting a low-dimensional vector representation of features based on ncRNA regulation, and gene-gene interaction network. Our method can significantly reduce the calculation bias resulted from the sparse disease associations which are derived from semantic associations.

Towards greenhouse cultivation of Artemisia annua: The application of LEDs in regulating plant growth and secondary metabolism.

Artemisinin is a sesquiterpene lactone produced in glandular trichomes of Artemisia annua, and is extensively used in the treatment of malaria. Growth and secondary metabolism of A. annua are strongly regulated by environmental conditions, causing unstable supply and quality of raw materials from field grown plants. This study aimed to bring A. annua into greenhouse cultivation and to increase artemisinin production by manipulating greenhouse light environment using LEDs. A. annua plants were grown in a greenhouse compartment for five weeks in vegetative stage with either supplemental photosynthetically active radiation (PAR) (blue, green, red or white) or supplemental radiation outside PAR wavelength (far-red, UV-B or both). The colour of supplemental PAR hardly affected plant morphology and biomass, except that supplemental green decreased plant biomass by 15% (both fresh and dry mass) compared to supplemental white. Supplemental far-red increased final plant height by 23% whereas it decreased leaf area, plant fresh and dry weight by 30%, 17% and 7%, respectively, compared to the treatment without supplemental radiation. Supplemental UV-B decreased plant leaf area and dry weight (both by 7%). Interestingly, supplemental green and UV-B increased leaf glandular trichome density by 11% and 9%, respectively. However, concentrations of artemisinin, arteannuin B, dihydroartemisinic acid and artemisinic acid only exhibited marginal differences between the light treatments. There were no interactive effects of far-red and UV-B on plant biomass, morphology, trichome density and secondary metabolite concentrations. Our results illustrate the potential of applying light treatments in greenhouse production of A. annua to increase trichome density in vegetative stage. However, the trade-off between light effects on plant growth and trichome initiation needs to be considered. Moreover, the underlying mechanisms of light spectrum regulation on artemisinin biosynthesis need further clarification to enhance artemisinin yield in greenhouse production of A. annua.

DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data.

Endocrinology is the study focusing on hormones and their actions. Hormones are known as chemical messengers, released into the blood, that exert functions through receptors to make an influence in the target cell. The capacity of the mammalian organism to perform as a whole unit is made possible based on two principal control mechanisms, the nervous system and the endocrine system. The endocrine system is essential in regulating growth and development, tissue function, metabolism, and reproductive processes. Endocrine diseases such as diabetes mellitus, Grave's disease, polycystic ovary syndrome, and insulin-like growth factor I deficiency (IGFI deficiency) are classical endocrine diseases. Endocrine dysfunction is also an increasing factor of morbidity in cancer and other dangerous diseases in humans. Thus, it is essential to understand the diseases from their genetic level in order to recognize more pathogenic genes and make a great effort in understanding the pathologies of endocrine diseases. In this study, we proposed a deep learning method named DeepGP based on graph convolutional network and convolutional neural network for prioritizing susceptible genes of five endocrine diseases. To test the performance of our method, we performed 10-cross-validations on an integrated reported dataset; DeepGP obtained a performance of the area under the curve of ∼83% and area under the precision-recall curve of ∼65%. We found that type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) share most of their associated genes; therefore, we should pay more attention to the rest of the genes related to T1DM and T2DM, respectively, which could help in understanding the pathogenesis and pathologies of these diseases.

A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels.

Background and Aims: Chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) levels are at risk of disease progression. Currently, liver biopsy is suggested to identify this population. We aimed to establish a non-invasive diagnostic model to identify patients with significant liver inflammation. Method: A total of 504 CHB patients who had undergone liver biopsy with normal ALT levels were randomized into a training set (n = 310) and a validation set (n = 194). Independent variables were analyzed by stepwise logistic regression analysis. After the predictive model for diagnosing significant inflammation (Scheuer's system, G ≥ 2) was established, a nomogram was generated. Discrimination and calibration aspects of the model were measured using the area under the receiver operating characteristic curve (AUC) and assessment of a calibration curve. Clinical significance was evaluated by decision curve analysis (DCA). Result: The model was composed of 4 variables: aspartate aminotransferase (AST) levels, γ-glutamyl transpeptidase (GGT) levels, hepatitis B surface antigen (HBsAg) levels, and platelet (PLT) counts. Good discrimination and calibration of the model were observed in the training and validation sets (AUC = 0.87 and 0.86, respectively). The best cutoff point for the model was 0.12, where the specificity was 83.43%, the sensitivity was 77.42%, and the positive likelihood and negative likelihood ratios were 4.67 and 0.27, respectively. The model's predictive capability was superior to that of each single indicator. Conclusion: This study provides a non-invasive approach for predicting significant liver inflammation in CHB patients with normal ALT. Nomograms may help to identify target patients to allow timely initiation of antiviral treatment.

Light from below matters: Quantifying the consequences of responses to far-red light reflected upwards for plant performance in heterogeneous canopies.

In vegetation stands, plants receive red to far-red ratio (R:FR) signals of varying strength from all directions. However, plant responses to variations in R:FR reflected from below have been largely ignored despite their potential consequences for plant performance. Using a heterogeneous rose canopy, which consists of bent shoots down in the canopy and vertically growing upright shoots, we quantified upward far-red reflection by bent shoots and its consequences for upright shoot architecture. With a three-dimensional plant model, we assessed consequences of responses to R:FR from below for plant photosynthesis. Bent shoots reflected substantially more far-red than red light, causing reduced R:FR in light reflected upwards. Leaf inclination angles increased in upright shoots which received low R:FR reflected from below. The increased leaf angle led to an increase in simulated plant photosynthesis only when this low R:FR was reflected off their own bent shoots and not when it reflected off neighbour bent shoots. We conclude that plant response to R:FR from below is an under-explored phenomenon which may have contrasting consequences for plant performance depending on the type of vegetation or crop system. The responses are beneficial for performance only when R:FR is reflected by lower foliage of the same plants.