β-n-oxalyl-l-α, β -diaminopropionic acid (β -odap) content in lathyrus sativus: The integration of nitrogen and sulfur metabolism through β -cyanoalanine synthase

Grass pea (Lathyrus sativus L.) is an important legume crop grown mainly in South Asia and Sub-Saharan Africa. This underutilized legume can withstand harsh environmental conditions including drought and flooding. During drought-induced famines, this protein-rich legume serves as a food source for poor farmers when other crops fail under harsh environmental conditions; however, its use is limited because of the presence of an endogenous neurotoxic nonprotein amino acid β-N-oxalyl-l-α,β-diaminopropionic acid (β-ODAP). Long-term consumption of Lathyrus and β-ODAP is linked to lathyrism, which is a degenerative motor neuron syndrome. Pharmacological studies indicate that nutritional deficiencies in methionine and cysteine may aggravate the neurotoxicity of β-ODAP. The biosynthetic pathway leading to the production of β-ODAP is poorly understood, but is linked to sulfur metabolism. To date, only a limited number of studies have been conducted in grass pea on the sulfur assimilatory enzymes and how these enzymes regulate the biosynthesis of β-ODAP. Here, we review the current knowledge on the role of sulfur metabolism in grass pea and its contribution to β-ODAP biosynthesis. Unraveling the fundamental steps and regulation of β-ODAP biosynthesis in grass pea will be vital for the development of improved varieties of this underutilized legume

An ancestral NB-LRR with duplicated 3'UTRs confers stripe rust resistance in wheat and barley.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a global threat to wheat production. Aegilops tauschii, one of the wheat progenitors, carries the YrAS2388 locus for resistance to Pst on chromosome 4DS. We reveal that YrAS2388 encodes a typical nucleotide oligomerization domain-like receptor (NLR). The Pst-resistant allele YrAS2388R has duplicated 3' untranslated regions and is characterized by alternative splicing in the nucleotide-binding domain. Mutation of the YrAS2388R allele disrupts its resistance to Pst in synthetic hexaploid wheat; transgenic plants with YrAS2388R show resistance to eleven Pst races in common wheat and one race of P. striiformis f. sp. hordei in barley. The YrAS2388R allele occurs only in Ae. tauschii and the Ae. tauschii-derived synthetic wheat; it is absent in 100% (n = 461) of common wheat lines tested. The cloning of YrAS2388R will facilitate breeding for stripe rust resistance in wheat and other Triticeae species

FLOWERING LOCUS T2 regulates spike development and fertility in temperate cereals

FLOWERING LOCUS T2 (FT2) is the closest paralog of the FT1 flowering gene in the temperate grasses. Here we show that overexpression of FT2 in Brachypodium distachyon and barley results in precocious flowering and reduced spikelet number, while down-regulation by RNA interference results in delayed flowering and a reduced percentage of filled florets. Similarly, truncation mutations of FT2 homeologs in tetraploid wheat delayed flowering (2-4 d) and reduced fertility. The wheat ft2 mutants also showed a significant increase in the number of spikelets per spike, with a longer spike development period potentially contributing to the delayed heading time. In the wheat leaves, FT2 was expressed later than FT1, suggesting a relatively smaller role for FT2 in the initiation of the reproductive phase. FT2 transcripts were detected in the shoot apical meristem and increased during early spike development. Transversal sections of the developing spike showed the highest FT2 transcript levels in the distal part, where new spikelets are formed. Our results suggest that, in wheat, FT2 plays an important role in spike development and fertility and a limited role in the timing of the transition between the vegetative and reproductive shoot apical meristem

Multiple linear epitopes (B-cell, CTL and Th) of JEV expressed in recombinant MVA as multiple epitope vaccine induces a protective immune response

Epitope-based vaccination might play an important role in the protective immunity against Japanese encephalitis virus (JEV) infection. The purpose of the study is to evaluate the immune characteristics of recombinant MVA carrying multi-epitope gene of JEV (rMVA-mep). The synthetic gene containing critical epitopes (B-cell, CTL and Th) of JEV was cloned into the eukaryotic expression vector pGEM-K1L, and the rMVA-mep was prepared. BALB/c mice were immunized with different dosages of purified rMVA-mep and the immune responses were determined in the form of protective response against JEV, antibodies titers (IgG1 and IgG2a), spleen cell lymphocyte proliferation, and the levels of interferon-γ and interleukin-4 cytokines. The results showed that live rMVA-mep elicited strongly immune responses in dose-dependent manner, and the highest level of immune responses was observed from the groups immunized with 107 TCID50 rMVA-mep among the experimental three concentrations. There were almost no difference of cytokines and neutralizing antibody titers among 107 TCID50 rMVA-mep, recombinant ED3 and inactivated JEV vaccine. It was noteworthy that rMVA-mep vaccination potentiates the Th1 and Th2-type immune responses in dose-dependent manner, and was sufficient to protect the mice survival against lethal JEV challenge. These findings demonstrated that rMVA-mep can produce adequate humoral and cellular immune responses, and protection in mice, which suggested that rMVA-mep might be an attractive candidate vaccine for preventing JEV infection

Depression and health outcomes: An umbrella review of systematic reviews and meta-analyses of observational studies

BACKGROUND: Currently, most studies of depression are limited to a single disease endpoint. AIMS: This study aimed to conduct an umbrella review to comprehensively assess the association between depression and health outcomes. METHOD: Until December 17, 2024, we conducted a systematic search of systematic reviews and meta-analyses in PubMed, Embase, and Web of Science. We reanalyzed the summary effects and 95% confidence intervals for each study using random models. We assessed the methodological quality and evidence quality of the research with A Measurement Tool to Assess Systematic Reviews 2 and Grade of Recommendations, Assessment, Development and Evaluation, classifying studies into four categories based on evidence classification criteria. RESULTS: We selected a total of 72 articles from 27,150 resulting in 114 meta-analyses and 109 health outcomes. Depression exposure was associated with 23 mortality, 21 cardiovascular outcomes, 15 offspring outcomes, 9cancer outcomes, 9 neurological outcomes, 5 endocrine outcomes, 5 dental outcomes, 3 digestive outcomes, and 19 other health outcomes. Moderate-quality evidence linked depression to specific mortality in bladder cancer (Class IV), all-cause mortality in myocardial infarction (Class III), mortality within 2 years of initial assessment in coronary artery disease (Class IV), major adverse cardiovascular events after percutaneous coronary intervention (Class III), irritable bowel syndrome (insignificant), fear of falling (Class III), and frailty (Class III). CONCLUSIONS: Depression has a significant impact on health outcomes, primarily mortality and cardiovascular outcomes. However, more definitive conclusions still require randomized controlled trials or prospective studies for validation

Machine learning in diagnosing middle ear disorders using tympanic membrane images : a meta-analysis

OBJECTIVE : To systematically evaluate the development of Machine Learning (ML) models and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) using Tympanic Membrane (TM) images. METHODS : PubMed, EMBASE, CINAHL, and CENTRAL were searched up until November 30, 2021. Studies on the development of ML approaches for diagnosing MED using TM images were selected according to the inclusion criteria. PRISMA guidelines were followed with study design, analysis method, and outcomes extracted. Sensitivity, specificity, and area under the curve (AUC) were used to summarize the performance metrics of the meta-analysis. Risk of Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool in combination with the Prediction Model Risk of Bias Assessment Tool. RESULTS : Sixteen studies were included, encompassing 20254 TM images (7025 normal TM and 13229 MED). The sample size ranged from 45 to 6066 per study. The accuracy of the 25 included ML approaches ranged from 76.00% to 98.26%. Eleven studies (68.8%) were rated as having a low risk of bias, with the reference standard as the major domain of high risk of bias (37.5%). Sensitivity and specificity were 93% (95% CI, 90%–95%) and 85% (95% CI, 82%–88%), respectively. The AUC of total TM images was 94% (95% CI, 91%–96%). The greater AUC was found using otoendoscopic images than otoscopic images. CONCLUSIONS : ML approaches perform robustly in distinguishing between normal ears and MED, however, it is proposed that a standardized TM image acquisition and annotation protocol should be developed.NIHR, Sêr Cymru III Enhancing Competitiveness Infrastructure Award, Great Britain Sasakawa Foundation, Cardiff Metropolitan University Research Innovation Award, and The Global Academies Research and Innovation Development Fund, National Natural Science Foundation of China, Guizhou Provincial Science and Technology Projects and Global Academies and Santandar 2021 Fellowship Award.https://onlinelibrary.wiley.com/journal/15314995am2024Electrical, Electronic and Computer EngineeringSpeech-Language Pathology and AudiologySDG-03:Good heatlh and well-beingSDG-09: Industry, innovation and infrastructur

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis

In Situ High Temperature Synthesis of Single-Component Metallic Nanoparticles

Nanoparticles (NPs) dispersed within a conductive host are essential for a range of applications including electrochemical energy storage, catalysis, and energetic devices. However, manufacturing high quality NPs in an efficient manner remains a challenge, especially due to agglomeration during assembly processes. Here we report a rapid thermal shock method to in situ synthesize well-dispersed NPs on a conductive fiber matrix using metal precursor salts. The temperature of the carbon nanofibers (CNFs) coated with metal salts was ramped from room temperature to 2000 K in 5 ms, which corresponds to a rate of 400,000 K/s. Metal salts decompose rapidly at such high temperatures and nucleate into metallic nanoparticles during the rapid cooling step (cooling rate of 100,000 K/s). The high temperature duration plays a critical role in the size and distribution of the nanoparticles: the faster the process is, the smaller the nanoparticles are, and the narrower the size distribution is. We also demonstrated that the peak temperature of thermal shock can reach 3000 K, much higher than the decomposition temperature of many salts, which ensures the possibility of synthesizing various types of nanoparticles. This universal, in situ, high temperature thermal shock method offers considerable potential for the bulk synthesis of unagglomerated nanoparticles stabilized within a matrix