Search Results (122)

South Africa’s updated Nationally Determined Contribution sets a 2030 emissions ceiling of 420 MtCO₂. Using historical data from 2000 to 2023 and projections to 2050, this study identifies optimal decarbonization pathways by integrating logistic decay model, linear programming, genetic algorithm optimization (Decay–LP–GA), and Pareto analysis. A prior study notes that 2023 emissions lie substantially below the NDC ceiling, leaving 239.1 MtCO₂ of unused carbon space. However, the present study finds that optimized pathways transcend rather than utilize this space, achieving 67–85% emissions below 2023 levels. Notably, aggressive near-term coal phase-out increases 2050 emissions by disrupting efficiency investment, indicating that timing governs long-term outcomes. The optimal strategy therefore prioritizes slow near-term coal reduction (0.69% annually) to allow front-loaded efficiency gains (4.46% annually) through 2035, followed by accelerated phase-out to achieve 96% reduction by 2050. This sequencing reduces cumulative emissions by 8.0% (300 MtCO₂) relative to the LP minimum. The Pareto frontier spans 51.5–92.6 MtCO₂ in 2030 and 52.8–64.2 MtCO₂ in 2050, with all Pareto-optimal solutions requiring coal shares below 40% by 2030, conflicting with Integrated Resource Plan constraints. Consequently, maintaining a ≥40% coal floor raises minimum feasible emissions to 101.5 MtCO₂, generating a 22.8–49.4 MtCO₂ feasibility gap. The findings show that optimal strategy is not to use its remaining carbon space, but to render the 420 MtCO₂ target redundant through front-loaded efficiency gains and strategically timed coal phase-out, providing clear direction for sustainable climate finance. Full article

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production. Full article

Genomic selection (GS) provides an effective approach to accelerating genetic gain in dairy goats, but the prediction performance is strongly influenced by the statistical model, marker density, phenotype adjustment strategy, and biological architecture of the target trait. In this study, dairy goat populations comprising Xinong Saanen and Saanen dairy goats from major production regions in China were used to evaluate genomic prediction for milk yield (MY), milk fat percentage (MFP), and milk protein percentage (MPP). Genotypes from 1034 dairy goats were generated using low-coverage whole-genome sequencing (lcWGS), imputed to improve genotype completeness and accuracy; a high-quality chip-based dataset was also constructed from previously developed 25K single-nucleotide polymorphism (SNP) chip loci. Conventional genomic best linear unbiased prediction (GBLUP) models, Bayesian regression models, and machine learning algorithms were compared using 10-fold cross-validation. Bayesian models showed clear trait-specific advantages, with BayesB improving MFP prediction by approximately 12.9% relative to GBLUP under the 25K chip-based strategy. Among machine learning methods, gradient boosting models performed strongly; extreme gradient boosting (XGBoost) improved the prediction accuracy for MY, MFP, and MPP by 14.3%, 17.9%, and 18.5%, respectively, relative to GBLUP under the chip-based strategy. Incorporating genome-wide association study (GWAS)-derived prior information and selection signature priors further improved the prediction accuracy, particularly for milk composition traits. Overall, the results indicate that genomic prediction in dairy goats can be optimized by matching models, genotyping platforms, and prior biological information to the genetic characteristics of the target trait. Full article

The transition toward carbon neutrality demands not only an understanding of the complex dynamics within energy–economy–environment (3E) systems but also the ability to strategically enhance their resilience against external shocks. Here, we introduce a bidirectional coupled framework that integrates macro-level supernetwork topology with micro-level agent-based modeling (ABM) to diagnose structural vulnerabilities and optimize systemic performance. Applied to 41 cities in the Yangtze River Delta (YRD) from 2010 to 2023, our framework reveals a persistent core–periphery spatial disparity in coupling coordination, underpinned by four distinct network layers—energy flow, economic linkage, environmental impact, and policy synergy—whose densities vary by an order of magnitude. Through coupled evolutionary simulations, we quantify system resilience as a tripartite metric of robustness, adaptability, and recovery, identifying a systemic structural weakness: a robust recovery capacity is offset by substantially lower robustness. To address this, we deploy a genetic algorithm to solve for optimal investment allocation under a budget constraint, demonstrating that a targeted, hub-centric strategy yields a higher marginal resilience gain than uniform distribution. Furthermore, embedding multi-agent reinforcement learning (MARL) and social learning into policy scenario simulations shows that unified environmental standards and a carbon-inclusive mechanism effectively eliminate regulatory arbitrage and accelerate low-carbon behavioral diffusion, improving system-wide robustness by 35% under extreme climate shocks. This work delivers a closed-loop, transferable analytical framework that transforms structural diagnosis into actionable optimization, offering a scientific basis for coordinated regional decarbonization strategies. Full article

Early identification of genetically superior animals is important for improving growth performance and accelerating genetic gain in swamp buffalo breeding programs. This study investigated the genetic relationships between growth and body structural traits and evaluated their potential use as early selection indicators in Thai swamp buffalo. Phenotypic records from 1034 animals and genotypic data from 462 buffaloes genotyped with 30,979 SNP markers were analyzed using weighted single-step genomic best linear unbiased prediction (WssGBLUP) and weighted single-step Genome-Wide Association Study (WssGWAS) approaches. Moderate to high heritability estimates were observed for growth traits (0.41–0.59), whereas body structural traits showed low to moderate heritability (0.08–0.27). Positive genetic correlations were identified between growth traits and several structural traits, particularly heart girth, hip height, and body depth. Principal component analysis identified two major components explaining 80.1% of the total phenotypic variation, with the first principal component (PC1) representing overall body size and skeletal development. PC1 also showed relatively high heritability (0.57), indicating its potential utility as a composite selection trait. Genome-wide association analysis identified significant SNPs and candidate genes associated with weaning weight and PC1, including ST6GALNAC5, EPHA6, SYN3, DDIT4, DNAJB12, BLCAP, and NNAT, which are involved in growth regulation, metabolism, cellular development, and stress-response pathways. These findings demonstrate that body structural traits are genetically associated with growth performance and may serve as effective early selection indicators in genomic breeding programs for Thai swamp buffalo. Full article

Selecting superior families at the seedling stage is crucial for accelerating genetic gain in sugarcane, yet systematic comparisons of selection methods remain limited. This study evaluated seven selection strategies: phenotypic check-based selection (Pheno), a three-trait combined index (CI3), Best Linear Unbiased Prediction (BLUP), Best Linear Unbiased Estimation (BLUE), tiered family selection (Tiered), logistic regression (LASSO), and the Multi-Trait Family Ideotype Distance Index (MFIDI). The experiment followed an augmented block design with four blocks, two check varieties, and included 125 test families comprising 10,955 seedlings. Using a combined index of standardized cane and sugar yields, families were classified as elite (top 20%), moderate (60%), and weak (bottom 20%). BLUP and BLUE rankings were consistent (Spearman’s ρ > 0.95, TCI = 88%, Jaccard = 0.79). Elite families showed median index values of 0.90 (BLUP) and 0.88 (BLUE) with wide interquartile ranges, whereas weak families had medians of −0.70 with narrow ranges. LASSO achieved excellent predictive performance: AUC = 0.95, accuracy = 0.92, sensitivity = 0.90, specificity = 0.94, identifying cane yield, sugar yield, and millable cane as key drivers. Agreement for inferior families was lower across methods (BCI ≤ 68%). BLUP with a multi-trait index proved most effective for discriminating elite families. Families F31 and F71 consistently ranked top. Combining selection approaches with agreement indices improves early-stage decisions for family selection in sugarcane breeding. Full article

Monitoring genetic gain is critical for evaluating breeding program performance. This study assessed genetic trends in the Zambia national maize breeding program using historical data (2001–2017) from 2225 hybrids tested across years and locations. Best linear unbiased estimates (BLUEs) were calculated, and genetic trends were determined by regressing entry means on first-year testing data. Mean heritability was moderate for grain yield, plant height, and ear height, and high for anthesis and silking dates, indicating strong reliability for flowering traits. Significant positive genetic gains were observed for most traits except days to silking. Grain yield (GY) increased at 0.021 t ha⁻¹ per year (0.85% annually), reflecting progress but remaining below levels required to meet regional future production demands. Plant and ear height increased by more than 1.3 cm annually, suggesting directional selection for taller plant architecture. Grain texture declined by 1.28% per year, indicating a shift toward flint-type kernels. Anthesis date and ears per plant showed minimal genetic variation. Regression models explained more than 15% of the total variation in plant height, ear height, ear number, and grain texture, confirming consistent genetic progress. Although measurable gains were achieved, the study’s baseline indicates that accelerating yield improvement will require rapid-cycle breeding, enhanced trait heritability, modern breeding tools, and a strategic reallocation of resources to sustain long-term impact. Full article

Ensuring global food security under accelerating climate change requires transformative approaches to crop improvement that extend beyond the limits of traditional breeding and gene editing. While domestication and modern agriculture have delivered substantial gains in productivity, these advances often came at the cost of genetic diversity, stress resilience, and developmental plasticity. Plants, however, inherently exhibit remarkable flexibility in their morphology and development, as evidenced by the vast diversity of organ shapes, cell types, and adaptive responses that have evolved across lineages. This natural design space provides a foundation for reimagining plant architecture using synthetic biology. Recent advances in plant synthetic biology, including programmable transcription factors, CRISPR-based regulatory systems, synthetic gene circuits, orthogonal signalling pathways, and plant artificial chromosomes, now enable precise, modular, and environmentally responsive manipulation of developmental processes. These tools allow researchers to rewire hormone pathways, tune quantitative gene expression, integrate multiple environmental signals, and create novel regulatory modules that operate independently of endogenous networks. Beyond understanding plant development, these capabilities open avenues for engineering crops with dynamic architectures, enhanced plasticity, and improved resilience to complex and fluctuating stresses. In this review, we synthesise insights from natural diversity, developmental biology, and synthetic regulatory engineering to outline how plant architecture can be rationally redesigned. We argue that integrating synthetic biology with modern breeding and modelling frameworks will be essential for generating the next generation of programmable crops; i.e., varieties capable of sustaining productivity and stability in an era of unprecedented environmental and geopolitical changes. Full article

Duolang sheep, an indigenous breed of southern Xinjiang, are significant for their agricultural systems due to their adaptation to arid and semi-arid environments. This review integrates recent advancements in Duolang’s reproductive biology, genomic studies, and management strategies to address the breed’s reproductive efficiency under challenging ecological conditions. Reproductive traits such as puberty onset, estrous cycle characteristics, and seasonal breeding are influenced by complex genetic and several environmental factors. Numerous remarkable genomic findings highlight key loci related to fecundity, including the Booroola FecB mutation, as well as genes involved in steroidogenesis, folliculogenesis, and HPG axis regulation. Despite the genetic potential for increased prolificacy, Duolang sheep often exhibit low litter sizes, largely constrained by detrimental environmental factors and management practices. This review emphasizes the significance of integrating genetics, nutrition, and reproductive management to optimize productivity. Strategies such as nutritional flushing, hormone-based estrous synchronization, and selective breeding for increased litter size are discussed, with a focus on minimizing the risks associated with early puberty and lamb survival. Furthermore, the review explores the potential of genomic selection, marker-assisted breeding, and advanced reproductive technologies to enhance the breed’s performance. Finally, the review outlines future research directions, necessitating the development of genomic resources, precise breeding programs, and field trials on reproductive interventions to accelerate genetic gains in Duolang sheep. This integrated approach promises to improve reproductive outcomes, with implications for sustainable sheep production in Xinjiang and similar environments across the globe. Full article

This paper investigates the performance of a speed-dependent variable inerter in improving vehicle suspension performance. Unlike conventional and passive inerter suspensions with fixed mechanical properties, the proposed speed-dependent variable inerter allows continuous adjustment of inertance according to the relative acceleration between the sprung and unsprung masses, enabling variable inertance under changing driving speeds and road conditions. A quarter-vehicle model is employed to evaluate a conventional passive inerter and both a linearly and non-linearly increasing variable inerter system in series and parallel layouts. A multi-objective genetic algorithm simultaneously optimizes the suspension damping and variable inertance range with respect to ride comfort and road-holding ability. To further validate the simulations, the optimized systems are evaluated under step, random and sinusoidal road profiles. The results showed that a linearly increasing variable inerter, particularly in parallel configuration, offers the best compromise between ride comfort and road holding, achieving up to 4.94% improvement in ride comfort under a random road profile, outperforming conventional passive inerter and non-linearly increasing inerter suspensions, while maintaining acceptable tire–road contact. Performance improvements under step and sinusoidal road profiles were moderate, while more significant performance gains were observed under a random road profile due to the larger acceleration change induced, which led to larger inertance variation. These findings confirmed the potential of variable inerters as an alternative approach to vehicle suspension systems, due to their passive implementation, absence of control requirement and compatibility with compact suspension architectures. Full article

Proteasome inhibitors (PIs) are central to multiple myeloma (MM) therapy; however, resistance remains a major clinical challenge, particularly in relapsed/refractory disease. To identify functional mediators of carfilzomib (CFZ) resistance, we performed complementary gain-of-function CRISPR activation and pharmacological screening approaches. These unbiased strategies converged on the E3 ubiquitin ligase MDM2 as a modulator of PI response. MDM2 transactivation enhanced MM cell survival and accelerated recovery following CFZ exposure, supporting a causal role in proteotoxic stress tolerance. Pharmacologic inhibition of MDM2 with NVP-CGM097 synergized with CFZ across multiple PI-sensitive and PI-resistant MM cell lines, irrespective of TP53 status. Mechanistically, MDM2 inhibition induced p21 upregulation, cell-cycle arrest, and reduced c-MYC expression, accompanied by impaired activation of DNA damage response mediators. Genetic silencing of MDM2 phenocopied these effects and increased CFZ sensitivity. Importantly, the combination retained efficacy in MM–stromal co-culture models and in primary patient samples, including cases harboring del(17p), while sparing normal peripheral blood mononuclear cells. Collectively, these findings identify MDM2 as a functional driver of PI resistance and support combined MDM2 and proteasome inhibition as a rational therapeutic strategy in MM, including TP53-deficient contexts. Full article

Genotyping is a key step in molecular breeding. Due to its cost-effectiveness, accuracy, and flexibility, genotyping by target sequencing (GBTS) has become a preferred technology for medium-density genotyping. In this study, a new GBTS array for fresh edible maize was developed using resequencing data from 477 lines. The array contains 5759 SNPs evenly distributed across the maize genome, with average minor allele frequency (MAF) and polymorphism information content (PIC) values of 0.40 and 0.36, respectively. These SNPs are closely associated with 1566 functional genes. Cluster analysis of 198 maize lines based on the GBTS array was consistent with their pedigree relationships. Furthermore, 277 fresh waxy maize lines were genotyped and used for genomic selection analyses of hundred-kernel weight, kernel length, and kernel width. Comparative evaluation of different models indicated that Ridge Regression Best Linear Unbiased Prediction (rrBLUP) was the optimal model, with prediction accuracies of 0.33, 0.64, and 0.36, respectively. Additional analyses using different marker densities based on the rrBLUP model showed that prediction accuracy did not increase when the number of markers exceeded 2000, indicating that this array provides sufficient marker density for genetic analysis and genomic selection. Overall, this array provides a useful tool for genetic studies of fresh edible maize and facilitates the application of genomic selection in breeding programs. Full article

Preimplantation embryo genomic selection (eGS) enables selection prior to implantation and could accelerate genetic gain in cattle. A major hurdle is the limited DNA from embryo biopsies, requiring efficient whole-genome amplification (WGA) for accurate genomic analyses. However, alternative WGA methods and genotyping strategies have not been systematically compared in cattle. This study evaluated different methods for WGA (multiple displacement amplification (MDA) or multiple annealing and looping-based amplification cycles (MALBAC)) and for genotyping (single nucleotide polymorphism array (SNP-array), genotyping by targeted sequencing (GBTS), or whole-genome sequencing (WGS)) using 3-, 6-, and 9-cell bovine samples. MDA consistently outperformed MALBAC across various performance metrics, including amplification length, call rates, genome coverage (93.43–94.40% vs. 53.01–67.08%), and genotyping concordance (0.89–0.98 vs. 0.75–0.92). GBTS achieved the highest call rates, while SNP-array and GBTS showed excellent concordance and low error rates. WGS provided genome-wide data for precise aneuploidy detection. We further validated the workflow in trophectoderm biopsies and arrested embryos, generating reliable data for genomic evaluation, sex determination, and aneuploidy screening. MDA from ≥6 cells combined with GBTS or SNP-array showed a favorable balance of efficiency and accuracy for bovine eGS. This framework may facilitate the application of eGS in cattle breeding by enhancing selection intensity and accelerating genetic improvement. Full article

The study analyzed ten reproductive traits in three major commercial breeds—Yorkshire, Landrace, and Duroc—raised under uniform management. Genetic parameters were estimated using a repeatability animal model in ASReml, genome-wide association studies (GWAS) were performed with 46,358 post-QC SNPs using GCTA, and genomic prediction was evaluated with GBLUP. Heritability estimates were low to moderate, with gestation length (GL) highest (0.33–0.41). GWAS identified significant loci across breeds: in Yorkshire, 37 genome-wide significant SNPs across 17 SSCs for seven traits; in Landrace, 16 SNPs for TNB and one for NBW; and in Duroc, 31 SNPs across 12 SSCs (predominantly for TNB). Among these SNPs, CNC10042060, CNC10160995, and CNCB10003799 were consistently associated with TNB in both Yorkshire and Duroc pigs. Additionally, five SNPs, CNC10012965, CNC10042060, CNC10120451, CNCB10003799, and CNCB10007759, showed significant associations with NBW in Yorkshire and Landrace pigs. Candidate genes mapped within ±1 Mb of significant SNPs were enriched for biologically plausible pathways. Genomic prediction accuracies ranged from low to high depending on trait and breed, such as reaching 0.68 for GL in 39 Yorkshire and 0.59 in Landrace. These results delineate shared and breed-specific genetic architectures, provide actionable markers and candidate genes, and can accelerate genetic gains in commercial breeding programs. Full article

The discovery of apolipoprotein L1 (APOL1) risk polymorphisms has significantly changed our knowledge of kidney disease susceptibility and development in African American populations. Several non-diabetic kidney disorders, such as focal segmental glomerulosclerosis (FSGS), collapsing glomerulopathy, HIV-associated nephropathy (HIVAN), and accelerated chronic kidney disease (CKD) development, are significantly more likely to occur in people with two coding variations, G1 and G2. The significance of context-dependent pathogenic processes is highlighted by the poor penetrance and remarkable phenotypic variety of APOL1-associated kidney disease, despite its substantial impact. This review synthesizes current knowledge of APOL1 biology through a molecular framework, emphasizing gain-of-toxic-function effects of risk variants in podocytes, dysregulated ion fluxes, mitochondrial dysfunction, impaired proteostasis, and activation of innate immune and inflammatory signaling pathways. We describe how the well-recognized “second-hit” paradigm has a biological basis, driven by strong inducibility by interferons and immunological activation, as well as strict basal regulation of APOL1 expression. Lastly, we explore future approaches to precision nephrology and highlight translational advancements, such as APOL1 gene-silencing techniques. This review provides a mechanistic roadmap for translating APOL1 biology into targeted therapeutic strategies by integrating genetics, cell biology, immunology, and systems-level approaches. Full article