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With global warming and climate change, abiotic stresses often simultaneously occur. Combined salt and heat stress was a common phenomenon that was severe, particularly in arid/semi-arid lands. We aimed to reveal the systematic responsive mechanisms of tomato genotypes with different salt/heat susceptibilities to combined salt and heat stress. Morphological and physiological responses of salt-tolerant/sensitive and heat-tolerant/sensitive tomatoes at control, heat, salt and combined stress were investigated. Based on leaf Fv/Fm and H2O2 content, samples from tolerant genotype at the four treatments for 36 h were taken for transcriptomics and metabolomics. We found that plant height, dry weight and net photosynthetic rate decreased while leaf Na+ concentration increased in all four genotypes under salt and combined stress than control. Changes in physiological indicators such as photosynthetic parameters and defence enzyme activities in tomato under combined stress were regulated by the expression of relevant genes and the accumulation of key metabolites. We screened five key pathways in tomato responding to a combination of salt and heat stress, such as oxidative phosphorylation (map00190). Synergistic regulation at morphological, physiological, transcriptional and metabolic levels in tomato plants was induced by combined stress. Heat stress was considered as a dominant stressor for tomato plants under the current combined stress. The oxidative phosphorylation pathway played a key role in tomato in response to combined stress, where tapped key genes (e.g. alternative oxidase, Aox1a) need further functional analysis. Our study will provide a valuable resource important for studying stress combination and improving tomato tolerance.

The concept of Digital Twin (DT) is increasingly applied to systems on different levels of abstraction across domains, to support monitoring, analysis, diagnosis, decision making and automated control. Whilst the interest in applying DT is growing, the definition of DT is unclear, neither is there a clear pathway to develop DT to fully realise its capacities. In this paper, we revise the concept of DT and its categorisation. We propose a DT maturity matrix, based on which we propose a model-based DT development methodology. We also discuss how model-based tools can be used to support the methodology and present our own supporting tool. We report our preliminary findings with a discussion on a case study, in which we use our proposed methodology and our supporting tool to develop an extensible DT platform for the assurance of Electrical and Electronics systems of space launch vehicles.

MTL is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to STL, MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including CV, NLP, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for ZSL, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://github.com/junfish/Awesome-Multitask-Learning., Comment: 60 figures, 116 pages, 500+ references

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Nonetheless, the risk factors such as economic risk, political risk, and financial risk have their respective pros and cons in various economic and financial investigations. Yet, the influence of these risk factors on sustainable venture capital is hardly studied in the existing literature. In this sense, the present research tends to investigate the influence of these risks on sustainable venture capital while considering the role of human capital in the US economy. This study uses novel time series approaches using quarterly data from 2006Q1 to 2020Q4. The estimated results validated each variable’s stationarity and cointegration between the study variables. The asymmetric data distribution leads to the employment of a novel method of moment quantile regression, which illustrates the positive association between economic risk, political risk, human capital, and sustainable venture capital. On the contrary, financial risk is found adversely affecting the sustainable venture capital in the country. The robustness of the model is examined by employing the bootstrap quantile regression. This study suggests minimizing economic, political, and financial risks and increasing investment in human capital to encourage sustainable venture capital.

Jet precession has previously been proposed to explain the apparently repeating features in the light curves of a few gamma-ray bursts (GRBs). In this {\it Letter}, we further apply the precession model to a bright GRB 220408B by examining both its temporal and spectral consistency with the predictions of the model. As one of the recently confirmed GRBs observed by our GRID CubeSat mission, GRB 220408B is noteworthy as it exhibits three apparently similar emission episodes. Furthermore, the similarities are reinforced by their strong temporal correlations and similar features in terms of spectral evolution and spectral lags. Our analysis demonstrates that these features can be well explained by the modulated emission of a Fast-Rise-Exponential-Decay (FRED) shape light curve intrinsically produced by a precessing jet with a precession period of $18.4 \pm 0.2$ seconds, a nutation period of $11.1 \pm 0.2$ seconds and viewed off-axis. This study provides a straightforward explanation for the complex yet similar multi-episode GRB light curves., Comment: 11 pages, 3 tables, 8 figures

Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine. At the same time, the growing availability of biomedical data, coupled with the advancements in modern multi-modal multi-task AI techniques, has paved the way for the emergence of generalist biomedical AI solutions. These solutions hold the potential to interpret different medical modalities and produce expressive outputs such as free-text reports or disease diagnosis. Here, we propose BiomedGPT, the first open-source and generalist visual language AI for diverse biomedical tasks. BiomedGPT achieved 16 state-of-the-art results across five clinically significant tasks on 26 datasets. Notably, it outperformed OpenAI's GPT-4 with vision (GPT-4V) in radiology human evaluation and surpassed Google's Med-PaLM M (12B) in breast cancer diagnosis and medical visual question answering. Moreover, BiomedGPT facilitates zero-shot transfer learning, greatly enhancing its utility as a biomedical assistant, similar to ChatGPT. Our method demonstrates effective training with diverse datasets can lead to more practical biomedical AI., Comment: under submission

Sesame (Sesamum indicum L.) is a significantly lucrative cash crop for millions of small-holder farmers. Its seeds are an important source of a highly appreciated vegetable oil globally and two clinically essential antioxidant lignans, sesamin and sesamolin. Accordingly, many countries import millions of tons of sesame seed every year. The demand for lignan-rich sesame seeds has been increasing in recent years due to the continuous discovery of several pharmacological attributes of sesamin and sesamolin. To meet this demand, the sesame breeder's primary objective is to release sesame cultivars that are enriched in oil and lignans. Thus, it is necessary to summarize the information related to the sesamin and sesamolin contents in sesame in order to promote the joint efforts of specialized research teams on this important oilseed crop. In this article, we present the current knowledge on the sesamin and sesamolin contents in S. indicum L. with respect to the updated biosynthesis pathway, associated markers, governing loci, available variability in sesame germplasm, the in planta potential roles of these compounds in sesame, and the newly discovered pharmacological attributes. In addition, we propose and discuss some required studies that might facilitate genomics-assisted breeding of high lignan content sesame varieties.

Winter wheat is sensitive to heat stress, which highlights the need for adaptation to hightemperature events to ensure high and stable yields. This study aimed to investigate the chlorophyll fluorescence response of eight wheat varieties from southeastern Europe to heat stress. Heat stress was applied during anthesis and mid-grain filling at 35/25 °C and 38/28 °C day/night for seven days, respectively. The results showed no significant differences in Fv/Fm values between wheat plants grown under control and heat stress during anthesis on the first, second, fifth, and seventh stress days. This absence of high-temperature effect during anthesis indicates that temperatures of 35/25 °C for seven days are insufficient to differentiate wheat variety reactions based on Fv/Fm measurements. However, when temperatures were increased to 38/28 °C during mid-grain filling, a significant variation in Fv/Fm values among the studied wheat varieties was observed. NS 40S and NS Ilina showed a notably slower reduction in Fv/Fm over time under heat stress treatment at mid-grain filling, exhibiting the highest Fv/Fm values on the seventh day. These varieties can be valuable sources of heat stress tolerance and should be considered for further breeding activities under the conditions of southeastern Europe.

With global climate change, the frequency and intensity of waterlogging events are increasing due to frequent and heavy precipitation. Little is known however about the response of plants to repeated waterlogging stress events. The aim is to clarify physiological regulation mechanisms of tomato plants under repeated waterlogging stress, and whether Trichoderma harzianum can alleviate waterlogging injury. We identified two genotypes of tomato, ‘MIX-002’ and ‘LA4440’, as waterlogging tolerant and sensitive genotypes, respectively, based on plant biomass accumulation. The two tomato genotypes were subjected to a waterlogging priming treatment for 2 days (excess water for 1 cm above substrate surface) followed by a recovery stage for 2 days, and then a second waterlogging stress for 5 days (excess water for 1 cm above substrate surface) followed by a second recovery stage for 3 days. Leaf physiological, plant growth parameters, and the expression of five key genes were investigated. We found that the two genotypes responded differently to waterlogging priming and stress in terms of photosynthesis, reactive oxygen species (ROS), and osmotic regulatory mechanisms. Waterlogging stress significantly increased H2O2 content of ‘MIX-002’, while that of ‘LA4440’ had no significant change. Under waterlogging stress, photosynthesis of the two genotypes treated with waterlogging priming returned to the control level. However, Trichoderma harzianum treatment during the second recovery stage did not show positive mitigative effects. The plants of ‘LA4440’ with priming showed lower peroxidase (POD) activity and proline content but higher H2O2 content than that without priming under waterlogging stress. Under waterlogging stress with priming as compared to without priming, SODCC2 was downregulated in two tomatoes, and AGR2 and X92888 were upregulated in ‘MIX-002’ but downregulated in ‘LA4440’. Overall, the two tomato genotypes exhibited d

With global climate change, the frequency and intensity of waterlogging events are increasing due to frequent and heavy precipitation. Little is known however about the response of plants to repeated waterlogging stress events. The aim is to clarify physiological regulation mechanisms of tomato plants under repeated waterlogging stress, and whether Trichoderma harzianum can alleviate waterlogging injury. We identified two genotypes of tomato, ‘MIX-002’ and ‘LA4440’, as waterlogging tolerant and sensitive genotypes, respectively, based on plant biomass accumulation. The two tomato genotypes were subjected to a waterlogging priming treatment for 2 days (excess water for 1 cm above substrate surface) followed by a recovery stage for 2 days, and then a second waterlogging stress for 5 days (excess water for 1 cm above substrate surface) followed by a second recovery stage for 3 days. Leaf physiological, plant growth parameters, and the expression of five key genes were investigated. We found that the two genotypes responded differently to waterlogging priming and stress in terms of photosynthesis, reactive oxygen species (ROS), and osmotic regulatory mechanisms. Waterlogging stress significantly increased H2O2 content of ‘MIX-002’, while that of ‘LA4440’ had no significant change. Under waterlogging stress, photosynthesis of the two genotypes treated with waterlogging priming returned to the control level. However, Trichoderma harzianum treatment during the second recovery stage did not show positive mitigative effects. The plants of ‘LA4440’ with priming showed lower peroxidase (POD) activity and proline content but higher H2O2 content than that without priming under waterlogging stress. Under waterlogging stress with priming as compared to without priming, SODCC2 was downregulated in two tomatoes, and AGR2 and X92888 were upregulated in ‘MIX-002’ but downregulated in ‘LA4440’. Overall, the two tomato genotypes exhibited d

In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets from 11 modalities (e.g. Microscopy, Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver, lung, etc.). Our datasets encompass a comprehensive range of medical inquiries, including sixteen distinct question types. Throughout our evaluations, we devised textual prompts for GPT-4V, directing it to synergize visual and textual information. The experiments with accuracy score conclude that the current version of GPT-4V is not recommended for real-world diagnostics due to its unreliable and suboptimal accuracy in responding to diagnostic medical questions. In addition, we delineate seven unique facets of GPT-4V's behavior in medical VQA, highlighting its constraints within this complex arena. The complete details of our evaluation cases are accessible at https://github.com/ZhilingYan/GPT4V-Medical-Report.

The mechanism of environmental factors in Kashin-Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin-responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin-responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63, p = 0.011). Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG (additive model: OR = 0.46, p = 0.012; dominant model: OR = 0.45, p = 0.049; recessive model: OR = 0.18, p = 0.018), rs1871054 of ADAM12 (dominant model: OR = 2.19, p = 0.022), rs1800796 of IL6 (dominant model: OR = 0.30, p = 0.003), rs6494629 of SMAD3 (additive model: OR = 0.65, p = 0.019; dominant model: OR = 0.52, p = 0.012), and rs4789936 of TIMP2 (recessive model: OR = 5.90, p = 0.024). Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.

The mechanism of environmental factors in Kashin-Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin-responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin-responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63, p = 0.011). Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG (additive model: OR = 0.46, p = 0.012; dominant model: OR = 0.45, p = 0.049; recessive model: OR = 0.18, p = 0.018), rs1871054 of ADAM12 (dominant model: OR = 2.19, p = 0.022), rs1800796 of IL6 (dominant model: OR = 0.30, p = 0.003), rs6494629 of SMAD3 (additive model: OR = 0.65, p = 0.019; dominant model: OR = 0.52, p = 0.012), and rs4789936 of TIMP2 (recessive model: OR = 5.90, p = 0.024). Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.

The mechanism of environmental factors in Kashin-Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin-responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin-responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63, p = 0.011). Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG (additive model: OR = 0.46, p = 0.012; dominant model: OR = 0.45, p = 0.049; recessive model: OR = 0.18, p = 0.018), rs1871054 of ADAM12 (dominant model: OR = 2.19, p = 0.022), rs1800796 of IL6 (dominant model: OR = 0.30, p = 0.003), rs6494629 of SMAD3 (additive model: OR = 0.65, p = 0.019; dominant model: OR = 0.52, p = 0.012), and rs4789936 of TIMP2 (recessive model: OR = 5.90, p = 0.024). Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.

Background: The adverse effects of climate change on crop production are constraining breeders to develop high-quality environmentally stable varieties. Hence, efforts are being made to identify key genes that could be targeted for enhancing crop tolerance to environmental stresses. ERF transcription factors play an important role in various abiotic stresses in plants. However, the roles of the ERF family in abiotic stresses tolerance are still largely unknown in sesame, the “queen” of oilseed crops. Results: In total, 114 sesame ERF genes (SiERFs) were identified and characterized. 96.49% of the SiERFs were distributed unevenly on the 16 linkage groups of the sesame genome. The phylogenetic analysis with the Arabidopsis ERFs (AtERFs) subdivided SiERF subfamily proteins into 11 subgroups (Groups I to X; and VI-L). Genes in the same subgroup exhibited similar structure and conserved motifs. Evolutionary analysis showed that the expansion of ERF genes in sesame was mainly induced by whole-genome duplication events. Moreover, cis-acting elements analysis showed that SiERFs are mostly involved in environmental responses. Gene expression profiles analysis revealed that 59 and 26 SiERFs are highly stimulated under drought and waterlogging stress, respectively. In addition, qRT-PCR analyses indicated that most of SiERFs are also significantly up-regulated under osmotic, submerge, ABA, and ACC stresses. Among them, SiERF23 and SiERF54 were the most induced by both the abiotic stresses, suggesting their potential for targeted improvement of sesame response to multiple abiotic stresses. Conclusion: This study provides a comprehensive understanding of the structure, classification, evolution, and abiotic stresses response of ERF genes in sesame. Moreover, it offers valuable gene resources for functional characterization towards enhancing sesame tolerance to multiple abiotic stresses.

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups., Comment: 5 pages, 3 figures, conference

Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality apparent diffusion coefficient (ADC) maps. Methods: A deep learning method was developed to generate accurate ADC maps from accelerated DWI data acquired with the Rad-DW-SE method. The deep learning method integrates convolutional neural networks (CNNs) with vision transformers to generate high quality ADC maps from accelerated DWI data, regularized by a monoexponential ADC model fitting term. A model was trained on DWI data of 147 mice and evaluated on DWI data of 36 mice, with acceleration factors of 4x and 8x compared to the original acquisition parameters. We have made our code publicly available at GitHub: https://github.com/ymli39/DeepADC-Net-Learning-Apparent-Diffusion-Coefficient-Maps, and our dataset can be downloaded at https://pennpancreaticcancerimagingresource.github.io/data.html. Results: Ablation studies and experimental results have demonstrated that the proposed deep learning model generates higher quality ADC maps from accelerated DWI data than alternative deep learning methods under comparison when their performance is quantified in whole images as well as in regions of interest, including tumors, kidneys, and muscles. Conclusions: The deep learning method with integrated CNNs and transformers provides an effective means to accurately compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method., Comment: Accepted by Magnetic Resonance in Medicine

The frequency of waterlogging episodes has increased due to unpredictable and intense rainfalls. However, less is known about waterlogging memory and its interaction with other climate change events, such as elevated CO2 concentration (e[CO2]). This study investigated the combined effects of e[CO2] and two rounds of waterlogging stress on the growth of cultivated tomato (Solanum lycopersicum) and wild tomato (S. pimpinellifolium). The aim is to elucidate the interaction between genotypes and environmental factors and thereby to improve crop resilience to climate change. We found that two rounds of treatments appeared to induce different acclimation strategies of the two tomato genotypes. S. pimpinellifolium responded more negatively to the first-time waterlogging than S. lycopersicum, as indicated by decreased photosynthesis and biomass loss. Nevertheless, the two genotypes respond similarly when waterlogging stress recurred, showing that they could maintain a higher leaf photosynthesis compared to single stress, especially for the wild genotype. This showed that waterlogging priming played a positive role in stress memory in both tomato genotypes. Multivariate analysis showed that waterlogging played a dominant role when combined with [CO2] for both the cultivated and wild tomato genotypes. This work will benefit agricultural production strategies by pinpointing the positive effects of e[CO2] and waterlogging memory.

The frequency of waterlogging episodes has increased due to unpredictable and intense rainfalls. However, less is known about waterlogging memory and its interaction with other climate change events, such as elevated CO2 concentration (e[CO2]). This study investigated the combined effects of e[CO2] and two rounds of waterlogging stress on the growth of cultivated tomato (Solanum lycopersicum) and wild tomato (S. pimpinellifolium). The aim is to elucidate the interaction between genotypes and environmental factors and thereby to improve crop resilience to climate change. We found that two rounds of treatments appeared to induce different acclimation strategies of the two tomato genotypes. S. pimpinellifolium responded more negatively to the first-time waterlogging than S. lycopersicum, as indicated by decreased photosynthesis and biomass loss. Nevertheless, the two genotypes respond similarly when waterlogging stress recurred, showing that they could maintain a higher leaf photosynthesis compared to single stress, especially for the wild genotype. This showed that waterlogging priming played a positive role in stress memory in both tomato genotypes. Multivariate analysis showed that waterlogging played a dominant role when combined with [CO2] for both the cultivated and wild tomato genotypes. This work will benefit agricultural production strategies by pinpointing the positive effects of e[CO2] and waterlogging memory.

The mechanism of environmental factors in Kashin-Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin-responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin-responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63, p = 0.011). Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG (additive model: OR = 0.46, p = 0.012; dominant model: OR = 0.45, p = 0.049; recessive model: OR = 0.18, p = 0.018), rs1871054 of ADAM12 (dominant model: OR = 2.19, p = 0.022), rs1800796 of IL6 (dominant model: OR = 0.30, p = 0.003), rs6494629 of SMAD3 (additive model: OR = 0.65, p = 0.019; dominant model: OR = 0.52, p = 0.012), and rs4789936 of TIMP2 (recessive model: OR = 5.90, p = 0.024). Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.

The mechanism of environmental factors in Kashin-Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin-responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin-responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63, p = 0.011). Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG (additive model: OR = 0.46, p = 0.012; dominant model: OR = 0.45, p = 0.049; recessive model: OR = 0.18, p = 0.018), rs1871054 of ADAM12 (dominant model: OR = 2.19, p = 0.022), rs1800796 of IL6 (dominant model: OR = 0.30, p = 0.003), rs6494629 of SMAD3 (additive model: OR = 0.65, p = 0.019; dominant model: OR = 0.52, p = 0.012), and rs4789936 of TIMP2 (recessive model: OR = 5.90, p = 0.024). Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.

Chondrocytes are the key target cells of the cartilage degeneration that occurs in Kashin-Beck disease (KBD) and osteoarthritis (OA). However, the heterogeneity of articular cartilage cell types present in KBD and OA patients and healthy controls is still unknown, which has prevented the study of the pathophysiology of the mechanisms underlying the roles of different populations of chondrocytes in the processes leading to KBD and OA. Here, we aimed to identify the transcriptional programmes and all major cell populations in patients with KBD, patients with OA and healthy controls to identify the markers that discriminate among chondrocytes in these three groups. Single-cell RNA sequencing was performed to identify chondrocyte populations and their gene signatures in KBD, OA and healthy cells to investigate their differences as related to the pathogenetic mechanisms of these two osteochondral diseases. We performed immunohistochemistry and quantitative reverse-transcription PCR (qRT-PCR) assays to validate the markers for chondrocyte population. Ten clusters were labelled by cell type according to the expression of previously described markers, and one novel population was identified according to the expression of a new set of markers. The homeostatic and mitochondrial chondrocyte populations, which were identified by the expression of the unknown markers MT1X and MT2A and MT-ND1 and MT-ATP6, were markedly expanded in KBD. The regulatory chondrocyte population, identified by the expression of CHI3L1, was markedly expanded in OA. Our study allows us to better understand the heterogeneity of chondrocytes in KBD and OA and provides new evidence of differences in the pathogenetic mechanisms between these two diseases.

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.

Chondrocytes are the key target cells of the cartilage degeneration that occurs in Kashin-Beck disease (KBD) and osteoarthritis (OA). However, the heterogeneity of articular cartilage cell types present in KBD and OA patients and healthy controls is still unknown, which has prevented the study of the pathophysiology of the mechanisms underlying the roles of different populations of chondrocytes in the processes leading to KBD and OA. Here, we aimed to identify the transcriptional programmes and all major cell populations in patients with KBD, patients with OA and healthy controls to identify the markers that discriminate among chondrocytes in these three groups. Single-cell RNA sequencing was performed to identify chondrocyte populations and their gene signatures in KBD, OA and healthy cells to investigate their differences as related to the pathogenetic mechanisms of these two osteochondral diseases. We performed immunohistochemistry and quantitative reverse-transcription PCR (qRT-PCR) assays to validate the markers for chondrocyte population. Ten clusters were labelled by cell type according to the expression of previously described markers, and one novel population was identified according to the expression of a new set of markers. The homeostatic and mitochondrial chondrocyte populations, which were identified by the expression of the unknown markers MT1X and MT2A and MT-ND1 and MT-ATP6, were markedly expanded in KBD. The regulatory chondrocyte population, identified by the expression of CHI3L1, was markedly expanded in OA. Our study allows us to better understand the heterogeneity of chondrocytes in KBD and OA and provides new evidence of differences in the pathogenetic mechanisms between these two diseases.

Chondrocytes are the key target cells of the cartilage degeneration that occurs in Kashin-Beck disease (KBD) and osteoarthritis (OA). However, the heterogeneity of articular cartilage cell types present in KBD and OA patients and healthy controls is still unknown, which has prevented the study of the pathophysiology of the mechanisms underlying the roles of different populations of chondrocytes in the processes leading to KBD and OA. Here, we aimed to identify the transcriptional programmes and all major cell populations in patients with KBD, patients with OA and healthy controls to identify the markers that discriminate among chondrocytes in these three groups. Single-cell RNA sequencing was performed to identify chondrocyte populations and their gene signatures in KBD, OA and healthy cells to investigate their differences as related to the pathogenetic mechanisms of these two osteochondral diseases. We performed immunohistochemistry and quantitative reverse-transcription PCR (qRT-PCR) assays to validate the markers for chondrocyte population. Ten clusters were labelled by cell type according to the expression of previously described markers, and one novel population was identified according to the expression of a new set of markers. The homeostatic and mitochondrial chondrocyte populations, which were identified by the expression of the unknown markers MT1X and MT2A and MT-ND1 and MT-ATP6, were markedly expanded in KBD. The regulatory chondrocyte population, identified by the expression of CHI3L1, was markedly expanded in OA. Our study allows us to better understand the heterogeneity of chondrocytes in KBD and OA and provides new evidence of differences in the pathogenetic mechanisms between these two diseases.

Chondrocytes are the key target cells of the cartilage degeneration that occurs in Kashin-Beck disease (KBD) and osteoarthritis (OA). However, the heterogeneity of articular cartilage cell types present in KBD and OA patients and healthy controls is still unknown, which has prevented the study of the pathophysiology of the mechanisms underlying the roles of different populations of chondrocytes in the processes leading to KBD and OA. Here, we aimed to identify the transcriptional programmes and all major cell populations in patients with KBD, patients with OA and healthy controls to identify the markers that discriminate among chondrocytes in these three groups. Single-cell RNA sequencing was performed to identify chondrocyte populations and their gene signatures in KBD, OA and healthy cells to investigate their differences as related to the pathogenetic mechanisms of these two osteochondral diseases. We performed immunohistochemistry and quantitative reverse-transcription PCR (qRT-PCR) assays to validate the markers for chondrocyte population. Ten clusters were labelled by cell type according to the expression of previously described markers, and one novel population was identified according to the expression of a new set of markers. The homeostatic and mitochondrial chondrocyte populations, which were identified by the expression of the unknown markers MT1X and MT2A and MT-ND1 and MT-ATP6, were markedly expanded in KBD. The regulatory chondrocyte population, identified by the expression of CHI3L1, was markedly expanded in OA. Our study allows us to better understand the heterogeneity of chondrocytes in KBD and OA and provides new evidence of differences in the pathogenetic mechanisms between these two diseases.

Abiotic stress due to climate change with continuous rise of atmospheric CO2 concentration is predicted to cause severe changes to crop productivity. Thus, research into wheat cultivars, capable of maintaining yield under limiting conditions is necessary. The aim of this study was to investigate the physiological responses of spring wheat to individual and combined drought- and heat events and their interaction with CO2 concentration. Two heat sensitive (LM19, KU10) and two heat tolerant (LM62, GN5) genotypes were selected and grown under ambient (400 ppm, aCO2) and elevated (800 ppm, eCO2) CO2 concentrations. At the tillering stage, the wheat plants were subjected to different treatments: control, progressive drought, heat and combined drought and heat stress. Our results showed that eCO2 mitigated the negative impact of the moderate stress in all genotypes. However, no distinctive responses were observed in some of the measured parameters between heat sensitive and tolerant genotypes. All genotypes grown at eCO2 had significantly higher net photosynthetic rates and maintained maximum quantum efficiency of PSII photochemistry under heat and combined stress compared to aCO2. Under heat and combined stress, the chlorophyll a:b ratios decreased only in heat tolerant genotypes at eCO2 compared to the control. Furthermore, the heat tolerant genotypes grown at eCO2 showed an increased glucose and fructose contents and a decreased sucrose content under combined stress compared to aCO2. These findings provide new insights into the underlying mechanisms of different genotypic responses to combined abiotic stresses at eCO2 that differ from the response to individual stresses.

Protein crosslinks occur endogenously such as modifications by ubiquitin-like proteins for signaling, or exogenously through genetically encoded chemical crosslinkers (GECX) for studying elusive protein-protein interactions. However, it remains challenging to identify these protein crosslinks efficiently at the proteomic scale. Herein, software OpenUaa is developed for identifying protein crosslinks generated by genetically encoded unnatural amino acids and endogenous protein conjugation. OpenUaa features inclusive and open search capability, dramatically improving identification sensitivity and coverage. Integrating GECX with OpenUaa, the direct interactome of thioredoxin is identified in Escherichia coli cells, yielding 289 crosslinked peptides and corresponding to 205 direct binding protein of thioredoxin. These identified direct binders provide evidence for thioredoxin's regulation of redox state and mitochondria energy metabolism. When identifying endogenous conjugation of small ubiquitin-like modifier (SUMO), OpenUaa also markedly improves coverage of SUMOylated peptides by ≈92%, revealing new SUMO targets. GECX-OpenUaa will enable efficient identification of direct interactomes of various proteins in live cells.

Chondrocytes are the key target cells of the cartilage degeneration that occurs in Kashin-Beck disease (KBD) and osteoarthritis (OA). However, the heterogeneity of articular cartilage cell types present in KBD and OA patients and healthy controls is still unknown, which has prevented the study of the pathophysiology of the mechanisms underlying the roles of different populations of chondrocytes in the processes leading to KBD and OA. Here, we aimed to identify the transcriptional programmes and all major cell populations in patients with KBD, patients with OA and healthy controls to identify the markers that discriminate among chondrocytes in these three groups. Single-cell RNA sequencing was performed to identify chondrocyte populations and their gene signatures in KBD, OA and healthy cells to investigate their differences as related to the pathogenetic mechanisms of these two osteochondral diseases. We performed immunohistochemistry and quantitative reverse-transcription PCR (qRT-PCR) assays to validate the markers for chondrocyte population. Ten clusters were labelled by cell type according to the expression of previously described markers, and one novel population was identified according to the expression of a new set of markers. The homeostatic and mitochondrial chondrocyte populations, which were identified by the expression of the unknown markers MT1X and MT2A and MT-ND1 and MT-ATP6, were markedly expanded in KBD. The regulatory chondrocyte population, identified by the expression of CHI3L1, was markedly expanded in OA. Our study allows us to better understand the heterogeneity of chondrocytes in KBD and OA and provides new evidence of differences in the pathogenetic mechanisms between these two diseases.

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.

High temperature decreases winter wheat grain yield by reducing the grain number and grain weight. The effect of heat stress on spike grain distribution and weight of individual grains within spike and spikelets was less studied. Our aim is to identify influence of high temperatures during different phenological stages on spike grain distribution and weight and to explore genotypic variation of the studied wheat cultivars. Within this study, a controlled experiment was conducted with 12 different winter wheat cultivars under heat stress at anthesis and mid-grain filling stage. The results showed that spike grain weight, thousand-grain weight and grain number per spike decreased moderately in treatments with individual heat stress at anthesis and mid-grain filling period, respectively, which decreased severely in the multiple heat stressed plants at both stages compared with the control treatment. Heat stress decreased number of spikelets with grains. Grain weight at the G1, G2 and G3 positions had a positive relationship with spike grain weight. Among the studied Serbian wheat cultivars Subotičanka and Renesansa were identified as the most heat tolerant and sensitive, respectively. Heat tolerance of the studied cultivars should be based on the cultivar capacity to retain higher grain weight, and to maintain production of distal spikelet grains.

Due to climate change, multiple heat events are expected to be an additional limiting factor that will adversely affect wheat production. The study aimed to analyze the physiological response to heat stress in four winter wheat cultivars at different physiological stages under greenhouse conditions during 2019. The net photosynthetic rate, stomatal conductance, chlorophyll index, maximum quantum efficiency of photosystem II, fructose, glucose and sucrose content, grain yield per plant, grain weight and number of grains per plant were analyzed in wheat cultivars under short periods of heat stress at anthesis and mid-grain filling, and combined stress at anthesis and mid-grain filling. The results of the study indicated that heat stress modified the photosynthesis-related and grain yield–related traits. Moreover, heat stress caused a decrease of sucrose content, while fructose and glucose content increased. Heat stress had more pronounced effects on the photosynthetic parameters and grain yield during grain filling than during anthesis. A significant variation observed among cultivar responses to the negative impact of heat stress highlighted the fact that cultivars Pobeda and Gladius were more tolerant than Renesansa and Simonida. Different cultivar reactions to heat stress during anthesis and grain filling indicated the need to conduct further studies with wheat cultivars of different origin in order to identify additional sources of tolerance.

Due to climate change, multiple heat events are expected to be an additional limiting factor that will adversely affect wheat production. The study aimed to analyze the physiological response to heat stress in four winter wheat cultivars at different physiological stages under greenhouse conditions during 2019. The net photosynthetic rate, stomatal conductance, chlorophyll index, maximum quantum efficiency of photosystem II, fructose, glucose and sucrose content, grain yield per plant, grain weight and number of grains per plant were analyzed in wheat cultivars under short periods of heat stress at anthesis and mid-grain filling, and combined stress at anthesis and mid-grain filling. The results of the study indicated that heat stress modified the photosynthesis-related and grain yield–related traits. Moreover, heat stress caused a decrease of sucrose content, while fructose and glucose content increased. Heat stress had more pronounced effects on the photosynthetic parameters and grain yield during grain filling than during anthesis. A significant variation observed among cultivar responses to the negative impact of heat stress highlighted the fact that cultivars Pobeda and Gladius were more tolerant than Renesansa and Simonida. Different cultivar reactions to heat stress during anthesis and grain filling indicated the need to conduct further studies with wheat cultivars of different origin in order to identify additional sources of tolerance.

Kashin-Beck disease (KBD) is a severe osteochondral disorder that may be driven by the interaction between genetic and environmental factors. We aimed to improve our understanding of the gut microbiota structure in KBD patients of different grades and the relationship between the gut microbiota and serum metabolites. Fecal and serum samples collected from KBD patients and normal controls (NCs) were used to characterize the gut microbiota using 16S rDNA gene and metabolomic sequencing via liquid chromatography-mass spectrometry (LC/MS). To identify whether gut microbial changes at the species level are associated with the genes or functions of the gut bacteria in the KBD patients, metagenomic sequencing of fecal samples from grade I KBD, grade II KBD and NC subjects was performed. The KBD group was characterized by elevated levels of Fusobacteria and Bacteroidetes. A total of 56 genera were identified to be significantly differentially abundant between the two groups. The genera Alloprevotella, Robinsoniella, Megamonas, and Escherichia_Shigella were more abundant in the KBD group. Consistent with the 16S rDNA analysis at the genus level, most of the differentially abundant species in KBD subjects belonged to the genus Prevotella according to metagenomic sequencing. Serum metabolomic analysis identified some differentially abundant metabolites among the grade I and II KBD and NC groups that were involved in lipid metabolism metabolic networks, such as that for unsaturated fatty acids and glycerophospholipids. Furthermore, we found that these differences in metabolite levels were associated with altered abundances of specific species. Our study provides a comprehensive landscape of the gut microbiota and metabolites in KBD patients and provides substantial evidence of a novel interplay between the gut microbiome and metabolome in KBD pathogenesis.

Chondrocytes are the key target cells of the cartilage degeneration that occurs in Kashin-Beck disease (KBD) and osteoarthritis (OA). However, the heterogeneity of articular cartilage cell types present in KBD and OA patients and healthy controls is still unknown, which has prevented the study of the pathophysiology of the mechanisms underlying the roles of different populations of chondrocytes in the processes leading to KBD and OA. Here, we aimed to identify the transcriptional programmes and all major cell populations in patients with KBD, patients with OA and healthy controls to identify the markers that discriminate among chondrocytes in these three groups. Single-cell RNA sequencing was performed to identify chondrocyte populations and their gene signatures in KBD, OA and healthy cells to investigate their differences as related to the pathogenetic mechanisms of these two osteochondral diseases. We performed immunohistochemistry and quantitative reverse-transcription PCR (qRT-PCR) assays to validate the markers for chondrocyte population. Ten clusters were labelled by cell type according to the expression of previously described markers, and one novel population was identified according to the expression of a new set of markers. The homeostatic and mitochondrial chondrocyte populations, which were identified by the expression of the unknown markers MT1X and MT2A and MT-ND1 and MT-ATP6, were markedly expanded in KBD. The regulatory chondrocyte population, identified by the expression of CHI3L1, was markedly expanded in OA. Our study allows us to better understand the heterogeneity of chondrocytes in KBD and OA and provides new evidence of differences in the pathogenetic mechanisms between these two diseases.

High temperature decreases winter wheat grain yield by reducing the grain number and grain weight. The effect of heat stress on spike grain distribution and weight of individual grains within spike and spikelets was less studied. Our aim is to identify influence of high temperatures during different phenological stages on spike grain distribution and weight and to explore genotypic variation of the studied wheat cultivars. Within this study, controlled experiment was conducted with 12 different winter wheat cultivars under heat stress at anthesis and mid-grain filling stage. The results showed that spike grain weight, thousand-grain weight and grain number per spike decreased moderately in treatments with individual heat stress at anthesis and mid-grain filling period, respectively, which decreased severely in the multiple heat stressed plants at both stages compared with the control treatment. Heat stress decreased number of spikelets with grains. Grain weight at the G1, G2 and G3 positions had positive relationship with spike grain weight. Among the studied Serbian wheat cultivars Suboticanka and Renesansa were identified as the most heat tolerant and sensitive, respectively. Heat tolerance of the studied cultivars should be based on the cultivar capacity to retain higher grain weight, and to maintain production of distal spikelet grains.

Developing crops with improved root system is crucial in current global warming scenario. Underexploited crops are valuable reservoirs of unique genes that can be harnessed for the improvement of major crops. In this study, we performed genome-wide association studies on seven root traits in sesame (Sesamum indicum L.) and uncovered 409 significant signals, 19 quantitative trait loci containing 32 candidate genes. A peak SNP significantly associated with root number and root dry weight traits was located in the promoter of the gene named 'Big Root Biomass' (BRB), which was subsequently validated in a bi-parental population. BRB has no functional annotation and is restricted to the Lamiales order. We detected the presence of a novel motif 'AACACACAC' located in the 5'-UTR of BRB in single and duplicated copy in accessions with high and small root biomass, respectively. A strong expression level of BRB was negatively correlated with high root biomass, and this was attributed to the gene SiMYB181 which represses the activity of BRB by binding specifically to the single motif but not to the duplicated one. Curiously, the allele that enhanced BRB expression has been intensively selected by modern breeding. Overexpression of BRB in Arabidopsis modulates auxin pathway leading to reduced root biomass, improved yield parameters under normal growth conditions and increased drought stress sensitivity. Overall, BRB represents a solid gene model for improving the performance of sesame and other crops.

Major crops are generally sensitive to waterlogging, but our limited understanding of the waterlogging gene regulatory network hinders the efforts to develop waterlogging-tolerant cultivars. We generated high-resolution temporal transcriptome data from root of two contrasting sesame genotypes over a 48 h period waterlogging and drainage treatments. Three distinct chronological transcriptional phases were identified, including the earlywaterlogging, late-waterlogging and drainage responses. We identified 47 genes representing the core waterlogging-responsive genes. Waterlogging/drainage-induced transcriptional changes were mainly driven by ERF and WRKY transcription factors (TF). The major difference between the two genotypes resides in the early transcriptional phase. A chronological transcriptional network model predicting putative causal regulations between TFs and downstream waterlogging-responsive genes was constructed and some interactions were validated through yeast one-hybrid assay. Overall, this study unveils the architecture and dynamic regulation of the waterlogging/drainage response in a non-model crop and helps formulate new hypotheses on stress sensing, signaling and sophisticated adaptive responses.

Neuroinflammation play an important role in Alzheimer's disease pathogenesis. Advances in molecular imaging using positron emission tomography have provided insights into the time course of neuroinflammation and its relation with Alzheimer's disease central pathologies in patients and in animal disease models. Recent single-cell sequencing and transcriptomics indicate dynamic disease-associated microglia and astrocyte profiles in Alzheimer's disease. Mitochondrial 18-kDa translocator protein is the most widely investigated target for neuroinflammation imaging. New generation of translocator protein tracers with improved performance have been developed and evaluated along with tau and amyloid imaging for assessing the disease progression in Alzheimer's disease continuum. Given that translocator protein is not exclusively expressed in glia, alternative targets are under rapid development, such as monoamine oxidase B, matrix metalloproteinases, colony-stimulating factor 1 receptor, imidazoline-2 binding sites, cyclooxygenase, cannabinoid-2 receptor, purinergic P2X7 receptor, P2Y12 receptor, the fractalkine receptor, triggering receptor expressed on myeloid cells 2, and receptor for advanced glycation end products. Promising targets should demonstrate a higher specificity for cellular locations with exclusive expression in microglia or astrocyte and activation status (pro- or anti-inflammatory) with highly specific ligand to enable in vivo brain imaging. In this review, we summarised recent advances in the development of neuroinflammation imaging tracers and provided an outlook for promising targets in the future.

Background Extreme weather events are predicted to increase, such as combined heat and drought. The CO2 concentration ([CO2]) is predicted to approximately double by 2100. We aim to explore how tomato physiology, especially photosynthesis, is affected by combined heat and drought under elevated [CO2] (e [CO2]). Results Two genotypes, 'OuBei' ('OB', Solanum lycopersicum) and 'LA2093' (S. pimpinellifolium) were grown at a [CO2] (atmospheric [CO2], 400 ppm) and e [CO2] (800 ppm), respectively. The 27-days-old seedlings were treated at 1) a [CO2], 2) a [CO2] + combined stress, 3) e [CO2] and 4) e [CO2] + combined stress, followed by recovery. The P-N (net photosynthetic rate) increased at e [CO2] as compared with a [CO2] and combined stress inhibited the P-N. Combined stress decreased the F-v/F-m (maximum quantum efficiency of photosystem II) of 'OB' at e [CO2] and that of 'LA2093' in regardless of [CO2]. Genotypic difference was observed in the e [CO2] effect on the gas exchange, carbohydrate accumulation, pigment content and dry matter accumulation. Conclusions Short-term combined stress caused reversible damage on tomato while the e [CO2] alleviated the damage on photosynthesis. However, the e [CO2] cannot be always assumed have positive effects on plant growth during stress due to increased water consumption. This study provided insights into the physiological effects of e [CO2] on tomato growth under combined stress and contributed to tomato breeding and management under climate change.