Expression of fatty acid and lipid biosynthetic genes in developing endosperm of Jatropha curcas

BACKGROUND: Temporal and spatial expression of fatty acid and lipid biosynthetic genes are associated with the accumulation of storage lipids in the seeds of oil plants. In jatropha (Jatropha curcas L.), a potential biofuel plant, the storage lipids are mainly synthesized and accumulated in the endosperm of seeds. Although the fatty acid and lipid biosynthetic genes in jatropha have been identified, the expression of these genes at different developing stages of endosperm has not been systemically investigated. RESULTS: Transmission electron microscopy study revealed that the oil body formation in developing endosperm of jatropha seeds initially appeared at 28 days after fertilization (DAF), was actively developed at 42 DAF and reached to the maximum number and size at 56 DAF. Sixty-eight genes that encode enzymes, proteins or their subunits involved in fatty acid and lipid biosynthesis were identified from a normalized cDNA library of jatropha developing endosperm. Gene expression with quantitative reverse-transcription polymerase chain reaction analysis demonstrated that the 68 genes could be collectively grouped into five categories based on the patterns of relative expression of the genes during endosperm development. Category I has 47 genes and they displayed a bell-shaped expression pattern with the peak expression at 28 or 42 DAF, but low expression at 14 and 56 DAF. Category II contains 8 genes and expression of the 8 genes was constantly increased from 14 to 56 DAF. Category III comprises of 2 genes and both genes were constitutively expressed throughout endosperm development. Category IV has 9 genes and they showed a high expression at 14 and 28 DAF, but a decreased expression from 42 to 56 DAF. Category V consists of 2 genes and both genes showed a medium expression at 14 DAF, the lowest expression at 28 or 42 DAF, and the highest expression at 56 DAF. In addition, genes encoding enzymes or proteins with similar function were differentially expressed during endosperm development. CONCLUSION: The formation of oil bodies in jatropha endosperm is developmentally regulated. The expression of the majority of fatty acid and lipid biosynthetic genes is highly consistent with the development of oil bodies and endosperm in jatropha seeds, while the genes encoding enzymes with similar function may be differentially expressed during endosperm development. These results not only provide the initial information on spatial and temporal expression of fatty acid and lipid biosynthetic genes in jatropha developing endosperm, but are also valuable to identify the rate-limiting genes for storage lipid biosynthesis and accumulation during seed development

An improved method for RNA isolation and cDNA library construction from immature seeds of Jatropha curcas L

<p>Abstract</p> <p>Background</p> <p>RNA quality and quantity is sometimes unsuitable for cDNA library construction, from plant seeds rich in oil, polysaccharides and other secondary metabolites. Seeds of jatropha (<it>Jatropha curcas </it>L.) are rich in fatty acids/lipids, storage proteins, polysaccharides, and a number of other secondary metabolites that could either bind and/or co-precipitate with RNA, making it unsuitable for downstream applications. Existing RNA isolation methods and commercial kits often fail to deliver high-quality total RNA from immature jatropha seeds for poly(A)⁺RNA purification and cDNA synthesis.</p> <p>Findings</p> <p>A protocol has been developed for isolating good quality total RNA from immature jatropha seeds, whereby a combination of the CTAB based RNA extraction method and a silica column of a commercial plant RNA extraction kit is used. The extraction time was reduced from two days to about 3 hours and the RNA was suitable for poly(A)⁺RNA purification, cDNA synthesis, cDNA library construction, RT-PCR, and Northern hybridization. Based on sequence information from selected clones and amplified PCR product, the cDNA library seems to be a good source of full-length jatropha genes. The method was equally effective for isolating RNA from mustard and rice seeds.</p> <p>Conclusions</p> <p>This is a simple CTAB + silica column method to extract high quality RNA from oil rich immature jatropha seeds that is suitable for several downstream applications. This method takes less time for RNA extraction and is equally effective for other tissues where the quality and quantity of RNA is highly interfered by the presence of fatty acids, polysaccharides and polyphenols.</p

Li2O-Reinforced Solid Electrolyte Interphase on Three-Dimensional Sponges for Dendrite-Free Lithium Deposition

Lithium (Li) metal, with ultra-high theoretical capacity and low electrochemical potential, is the ultimate anode for next-generation Li metal batteries. However, the undesirable Li dendrite growth usually results in severe safety hazards and low Coulombic efficiency. In this work, we design a three-dimensional CuO@Cu submicron wire sponge current collector with high mechanical strength SEI layer dominated by Li2O during electrochemical reaction process. The 3D CuO@Cu current collector realizes an enhanced CE of above 91% for an ultrahigh current of 10 mA cm−2 after 100 cycles, and yields decent cycle stability at 5 C for the full cell. The exceptional performances of CuO@Cu submicron wire sponge current collector hold promise for further development of the next-generation metal-based batteries

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way

Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, LOMO and AdaLomo. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.Comment: To appear at EMNLP 2023 Demo; Code is available at https://github.com/OpenLMLab/colli

Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing

The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. The software has been made publicly available at https://github.com/MaciejMazurowski/osteoarthritis-classification

Tenofovir alafenamide versus entecavir for treating hepatitis B virus-related acute-on-chronic liver failure: real-world study

Background and aimsReal-world data regarding hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) patients receiving tenofovir alafenamide (TAF) as an antiviral drug are limited. Hence, we evaluated the efficacy and kidney safety of TAF among this population.MethodsA total of 272 HBV-related ACLF patients hospitalized at Xiangya Hospital of Central South University were enrolled in this retrospective research. All patients received antiviral therapy with TAF (n = 100) or ETV (n = 172) and comprehensive medical treatments.ResultsThrough 1:1 propensity score matching, 100 patients were finally included in each group. At week 48, the survival rates without transplantation of the TAF group and ETV group were 76.00 and 58.00%, separately (P = 0.007). After 4 weeks of treatment, the TAF treatment group exhibited a significantly decline in HBV DNA viral load (P = 0.029). The mean estimated glomerular filtration rate was apparently improved in the TAF group compared with the ETV group (TAF 5.98 ± 14.46 vs. ETV 1.18 ± 18.07 ml/min/1.73 m2) (P &lt; 0.05). There were 6 patients in TAF group and 21 patients in ETV group with chronic kidney disease (CKD) stage progression ≥ 1. By contrast, the ETV treatment group has a greater risk of renal function progression in CKD 1 stage patients (P &lt; 0.05).ConclusionThis real-world clinical study showed that TAF is more effective than ETV in reducing viral load and improving survival rate in HBV-ACLF patients and the risk of renal function decline is lower.Clinical trial registrationhttps://ClinicalTrials.gov, identifier NCT05453448

FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs

This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs). At its core are two innovative models: SenseVoice, which handles multilingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, which facilitates natural speech generation with control over multiple languages, timbre, speaking style, and speaker identity. SenseVoice-Small delivers exceptionally low-latency ASR for 5 languages, and SenseVoice-Large supports high-precision ASR for over 50 languages, while CosyVoice excels in multi-lingual voice generation, zero-shot in-context learning, cross-lingual voice cloning, and instruction-following capabilities. The models related to SenseVoice and CosyVoice have been open-sourced on Modelscope and Huggingface, along with the corresponding training, inference, and fine-tuning codes released on GitHub. By integrating these models with LLMs, FunAudioLLM enables applications such as speech-to-speech translation, emotional voice chat, interactive podcasts, and expressive audiobook narration, thereby pushing the boundaries of voice interaction technology. Demos are available at https://fun-audio-llm.github.io, and the code can be accessed at https://github.com/FunAudioLLM.Comment: Work in progress. Authors are listed in alphabetical order by family nam

A First Generation Microsatellite- and SNP-Based Linkage Map of Jatropha

Jatropha curcas is a potential plant species for biodiesel production. However, its seed yield is too low for profitable production of biodiesel. To improve the productivity, genetic improvement through breeding is essential. A linkage map is an important component in molecular breeding. We established a first-generation linkage map using a mapping panel containing two backcross populations with 93 progeny. We mapped 506 markers (216 microsatellites and 290 SNPs from ESTs) onto 11 linkage groups. The total length of the map was 1440.9 cM with an average marker space of 2.8 cM. Blasting of 222 Jatropha ESTs containing polymorphic SSR or SNP markers against EST-databases revealed that 91.0%, 86.5% and 79.2% of Jatropha ESTs were homologous to counterparts in castor bean, poplar and Arabidopsis respectively. Mapping 192 orthologous markers to the assembled whole genome sequence of Arabidopsis thaliana identified 38 syntenic blocks and revealed that small linkage blocks were well conserved, but often shuffled. The first generation linkage map and the data of comparative mapping could lay a solid foundation for QTL mapping of agronomic traits, marker-assisted breeding and cloning genes responsible for phenotypic variation