Machine learning models predicts risk of proliferative lupus nephritis

ObjectiveThis study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe.MethodsThis study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers.ResultsInvolving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835–0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN.ConclusionThis study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy

The plastidial retrograde signal methyl erythritol cyclopyrophosphate is a regulator of salicylic acid and jasmonic acid crosstalk.

The exquisite harmony between hormones and their corresponding signaling pathways is central to prioritizing plant responses to simultaneous and/or successive environmental trepidations. The crosstalk between jasmonic acid (JA) and salicylic acid (SA) is an established effective mechanism that optimizes and tailors plant adaptive responses. However, the underlying regulatory modules of this crosstalk are largely unknown. Global transcriptomic analyses of mutant plants (ceh1) with elevated levels of the stress-induced plastidial retrograde signaling metabolite 2-C-methyl-D-erythritol cyclopyrophosphate (MEcPP) revealed robustly induced JA marker genes, expected to be suppressed by the presence of constitutively high SA levels in the mutant background. Analyses of a range of genotypes with varying SA and MEcPP levels established the selective role of MEcPP-mediated signal(s) in induction of JA-responsive genes in the presence of elevated SA. Metabolic profiling revealed the presence of high levels of the JA precursor 12-oxo-phytodienoic acid (OPDA), but near wild type levels of JA in the ceh1 mutant plants. Analyses of coronatine-insensitive 1 (coi1)/ceh1 double mutant plants confirmed that the MEcPP-mediated induction is JA receptor COI1 dependent, potentially through elevated OPDA. These findings identify MEcPP as a previously unrecognized central regulatory module that induces JA-responsive genes in the presence of high SA, thereby staging a multifaceted plant response within the environmental context

Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions

Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era

Measurement and Spatial-Temporal Evolution Characteristics of Low-Carbon Cities with High-Quality Development: The Case Study of the Yangtze River Economic Belt, China

Carrying out measurements of low-carbon city development levels and exploring their core driving factors are focuses of attention in the field of building sustainable low-carbon cities (LCC). Previous studies have mainly focused on the national or provincial level, ignoring the problem of heterogeneity among different cities, and the consideration of the influencing factors of low-carbon cities has not been comprehensive enough. Given this, the authors of this paper selected 107 cities in the Yangtze River Economic Belt from 2006 to 2019, constructed a general comprehensive index system for measuring the high-quality development level of low-carbon cities at the prefecture-level city level, and explored the spatial and temporal evolution trends and core drivers of the high-quality development level of low-carbon cities in the Yangtze River Economic Belt using the CRITIC–VIKOR method and an ensemble learning algorithm. The empirical results showed that most of the cities in the Yangtze River Economic Belt showed an overall upward trend in the level of high-quality development and a certain degree of “central collapse” in the spatial distribution. In addition, this paper further confirms that industrial structure is the most central driver of low-carbon urban development, the importance of urban carbon emissions and the level of science and technology innovation are gradually increasing, and a certain aggregation effect is formed in space that has led to a significant urban “siphon effect”. These results provide new evidence on the spatial and temporal evolution of the high-quality development of low-carbon cities in China and can help authorities formulate more targeted policies and strategic plans to enhance the high-quality development of low-carbon cities

Photoactivated UVR8-COP1 Module Determines Photomorphogenic UV-B Signaling Output in <i>Arabidopsis</i>

<div><p>In <i>Arabidopsis</i>, ultraviolet (UV)-B-induced photomorphogenesis is initiated by a unique photoreceptor UV RESISTANCE LOCUS 8 (UVR8) which utilizes its tryptophan residues as internal chromophore to sense UV-B. As a result of UV-B light perception, the UVR8 homodimer shaped by its arginine residues undergoes a conformational switch of monomerization. Then UVR8 associates with the CONSTITUTIVELY PHOTOMORPHOGENIC 1-SUPPRESSOR OF PHYA (COP1-SPA) core complex(es) that is released from the CULLIN 4-DAMAGED DNA BINDING PROTEIN 1 (CUL4-DDB1) E3 apparatus. This association, in turn, causes COP1 to convert from a repressor to a promoter of photomorphogenesis. It is not fully understood, however, regarding the biological significance of light-absorbing and dimer-stabilizing residues for UVR8 activity in photomorphogenic UV-B signaling. Here, we take advantage of transgenic UVR8 variants to demonstrate that two light-absorbing tryptophans, W233 and W285, and two dimer-stabilizing arginines, R286 and R338, play pivotal roles in UV-B-induced photomorphogenesis. Mutation of each residue results in alterations in UV-B light perception, UVR8 monomerization and UVR8-COP1 association in response to photomorphogenic UV-B. We also identify and functionally characterize two constitutively active UVR8 variants, UVR8^W285A and UVR8^R338A, whose photobiological activities are enhanced by the repression of CUL4, a negative regulator in this pathway. Based on our molecular and biochemical evidence, we propose that the UVR8-COP1 affinity in plants critically determines the photomorphogenic UV-B signal transduction coupling with UVR8-mediated UV-B light perception.</p></div

UVR8^W285A and UVR8R^338A display constitutive activity in darkness.

<p>(A) Phenotypes and relative hypocotyl length of 4-day-old seedlings of indicated genotypes grown under −UV-B and +UV-B. Data are shown as mean ± SD; n>30. (B) Phenotypes of 4-day-old dark-grown seedlings. Data are shown as mean ± SD; n>30. (C) qRT-PCR analysis of light-regulated gene expression in 4-day-old dark-grown seedlings of indicated genotypes. Data are shown as mean ± SD; n = 3. (D) Phenotypes and hypocotyl length of 4-day-old dark-grown seedlings of indicated genotypes. Data are shown as mean ± SD; n>30. **, p<0.01. Student's t test.</p

Polycrystal Li2ZnTi3O8/C anode with lotus seedpod structure for high-performance lithium storage

Lotus-seedpod structured Li2ZnTi3O8/C (P-LZTO) microspheres obtained by the molten salt method are reported for the first time. The received phase-pure Li2ZnTi3O8 nanoparticles are inserted into the carbon matrix homogeneously to form a Lotus-seedpod structure, as confirmed by the morphological and structural measurements. As the anode for lithium-ion batteries, the P-LZTO material demonstrates excellent electrochemical performance with a high rate capacity of 193.2 mAh g-1 at 5 A g-1 and long-term cyclic stability up to 300 cycles at 1 A g-1. After even 300 cyclings, the P-LZTO particles can maintain their morphological and structural integrity. The superior electrochemical performances have arisen from the unique structure where the polycrystalline structure is beneficial for shorting the lithium-ion diffusion path, while the well-encapsulated carbon matrix can not only enhance the electronic conductivity of the composite but also alleviate the stress anisotropy during lithiation/delithiation process, leading to well-preserved particles

UVR8 variants display altered interaction with COP1 in yeast.

<p>(A) Conformational status of wild-type and mutated UVR8 proteins in yeast. Total proteins of yeast expressing LexA fused wild-type and mutated UVR8 were extracted and incubated under −UV-B and +UV-B for 20 min. Protein samples without heat denaturation were assayed in SDS-PAGE and immunoblot analysis by anti-LexA antibody. Staining by Coomassie brilliant blue (CBB) is shown as a loading control. The asterisks indicate unspecific degradation products. (B) Interaction of wild-type and mutated UVR8 proteins with COP1 in yeast two-hybrid assays. Transformants in the respective combinations were incubated under −UV-B and +UV-B for 16 h.</p