Variety Of Architectures Research Articles

Cortical morphology variations during the menstrual cycle in individuals with and without premenstrual dysphoric disorder

BackgroundPremenstrual dysphoric disorder (PMDD) is hypothesized to stem from maladaptive neural sensitivity to ovarian steroid hormone fluctuations. Recently, we found thinner cortices in individuals with PMDD, compared to healthy controls, during the symptomatic phase. Here, we aimed at investigating whether such differences illustrate state-like characteristics specific to the symptomatic phase, or trait-like features defining PMDD. MethodsPatients and controls were scanned using structural magnetic resonance imaging during the mid-follicular and late-luteal phase of the menstrual cycle. Group-by-phase interaction effects on cortical architecture metrics (cortical thickness, gyrification index, cortical complexity, and sulcal depth) were assessed using surface-based morphometry. ResultsIndependently of menstrual cycle phase, a main effect of diagnostic group on surface metrics was found, primarily illustrating thinner cortices (0.3 < Cohen's d > 1.1) and lower gyrification indices (0.4 < Cohen's d > 1.0) in patients compared to controls. Furthermore, menstrual cycle-specific effects were detected across all participants, depicting a decrease in cortical thickness (0.4 < Cohen's d > 1.7) and region-dependent changes in cortical folding metrics (0.4 < Cohen's d > 2.2) from the mid-follicular to the late luteal phase. LimitationsSmall effects (d = 0.3) require a larger sample size to be accurately characterized. ConclusionsThese findings provide initial evidence of trait-like cortical characteristics of the brain of individuals with premenstrual dysphoric disorder, together with indications of menstrual cycle-related variations in cortical architecture in patients and controls. Further investigations exploring whether these differences constitute stable vulnerability markers or develop over the years may help understand PMDD etiology.

Classification of architectural and MEP BIM objects for building performance evaluation

Numerous factors in current workflows reduce the completeness, accuracy, and reliability of the information exchanged between Building Information Modelling (BIM) authoring tools and building performance simulation and analysis software. Automated classification of BIM objects could ameliorate most of the interoperability problems. However, architectural, structural, mechanical, electrical, and plumbing (MEP), and other building objects have a wide range of shapes, features, and properties, which makes identifying them reliably and accurately with any single AI tool difficult. This study explored methods and techniques for automated classification of architectural and MEP BIM objects that can significantly reduce or eliminate the manual effort required to correct data exchange and interoperability issues in pre-processing building models for performance evaluation. A series of ensemble models using a stacking mechanism of four classification tools (semantic keyword search, rule-based inferencing, machine learning using object geometry features, and deep learning using visual shape features) were implemented and tested. A BIM object dataset with 3,410 instances belonging to 40 classes was compiled for training and testing the models. The dataset contains diverse architecture and MEP objects with high-quality selected instances and is available for public use. We found that an ensemble learning approach that exploits the various types of information available in instance models may exhibit superior object classification performance than any individual tool. The ensemble model applying all four tools achieved 91.0 % accuracy (F1 score 0.88), suggesting that automated classification using ensemble learning is an effective strategy.

Abstract 148: Multiomic sequencing reveals diversity in clonal landscape and genomic alterations in a lung metastatic PDX osteosarcoma model

Abstract Introduction: Osteosarcoma is a primary malignant bone tumor characterized by the production of spindle cells resulting in immature bone formation. The lung is the most frequent site of metastatic disease and relapse occurs in more than 30 % of patients. Recent results show that relapse is due to a subset of cells with different phenotypic and genetic signatures conferring advantage to drive progression and drug resistance within the intra-tumorally heterogeneous population. Thus, developing a defined in vivo model that can identify the rare genetic subpopulation and recapitulate clonal evolution is crucial. In this study, we used whole exome, barcode and RNA sequencing to characterize the landscape of genomic alterations and also track and identify the clonal subpopulations, clonal and genetic drivers of lungs metastasis. Methods: We injected barcoded PDX (OS17) cells intravenously in ten SCID mice to track the clonal subpopulation of cells that metastasize to the lungs. Metastasized lung nodules were collected following death or euthanasia. All samples collected were snap frozen in liquid nitrogen and stored at -80°C. DNA and RNA was extracted and analyzed by PCR, NGS, WES and RNA sequencing to map clonality, mutational and evolutionary profiles. Results: Four (40%) of the mice developed lung metastases with three mice having multiple metastatic lung nodules. Surgery to remove the metastatic nodules was performed 216 ± 55.5 days following tumor cell injection. Nine metastatic lung nodules from three mice were used for sequencing and analysis. A Shannon-Weaver and Jaccard similarity index show a diversity in clonal architecture between the lung nodules. A total of 15,394 somatic variants was identified including 12,547 single nucleotide variants (SNV), 4054 synonymous, 2662 missense, 28 nonsense mutations and 1639 somatic indels. The SNVs accounted for 81% of all variants followed by CNV 11 % and indels 8% respectively. Synonymous and missense mutations were the most frequently observed in all samples. Copy number loss was two-fold compared to copy number gain across all tumors. Furthermore, the deletions span longer genomic distances compared to amplifications. The mutational spectrum showed that the highest substitutions were C&amp;gt;T/G&amp;gt;A followed by T&amp;gt;C/A&amp;gt;G; the least substitutions were T&amp;gt;A/A&amp;gt;T. Also, we identified a large set of genes with genomic aberrations. These include amplifications in VEGF, RB1, RUNX, PARP4, ICAM3, EGFR, BRCA2, COLA6A1, COLA6A2, CCND3, CDKN2D etc. Conclusions: In the current model, we identified multiple tumorigenic seeding clones and the diverse set of genes mutated in osteosarcoma. Further analysis of the mutational signature resulted in the identification of potential genes that act as drivers in the lung metastatic process. Acknowledgments: Swim Across America, the Foster Foundation and the Barbara Epstein Foundation. Citation Format: Sylvester Jusu, Wendong Zhang, Zhongting Zhang, Xu Zhaohui, Sankaranarayanan Kannan, Yifei Wang, Zhou Xin, Yi Yanhua, Michael Roth, Jonathan Gill, Richard Gorlick. Multiomic sequencing reveals diversity in clonal landscape and genomic alterations in a lung metastatic PDX osteosarcoma model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 148.

Abstract 1596: Disrupting local immunosuppression by combined myCAF/myeloid targeting in pancreatic cancer

Abstract Immunotherapy has revolutionized clinical care for many patients, but some cancers, such as pancreatic ductal adenocarcinoma (PDAC) remain stubbornly resistant. Local immunosuppression (LIS) is one of the striking hallmarks of PDAC due to the combined effects of multiple different immunosuppressive cell types in the tumor microenvironment (TME). There is great need for an increased understanding of the cellular crosstalk within tumors to understand how stromal cells coordinate in their suppression of immune responses. Oncogenic KRAS activation in tumor cells promotes the invasion and proliferation of tumor-supporting stromal cells, while excluding cancer-targeted cytotoxic T cells. Prior attempts to reverse LIS in PDAC by targeting individual stromal cell populations have been unsuccessful, alluding to the contributions of multiple distinct cell types. Here we examine the interactions of cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs) and how they coordinate to suppress immune responses in the PDAC TME. We interrogate PDAC stromal cell biology by using targeted therapies to perturb various cell populations both in vivo using genetically engineered mouse models, and ex vivo using PDAC tumor explants. Explants are short-term slice cultures that enable experimental study of intact tumor sections with a full complement of cell types. Importantly, PDAC explants maintain their histopathological architecture and cellular diversity over time. This medium-throughput platform allows for testing of multiple drugs and mechanistic hypotheses in the native PDAC TME. We show in preliminary data that Smoothened inhibition (SMOi) decreases the proliferation and activity of myCAFs, but provokes the expansion of CD11b-positive myeloid cells in vivo. Thus, we hypothesize that LIS in PDAC is maintained by a balance between myCAFs and myeloid cells, preventing effective T cell invasion. Single cell RNA-seq data comparing ctrl vs. SMOi-treated murine PDAC elucidates stromal subpopulations involved in the LIS phenotype and guides the identification of myeloid subtypes emerging after SMOi. Strikingly, we demonstrated that simultaneous SMOi and targeting myeloid cells via anti-Gr1 or CCR1 inhibition significantly elevates cytotoxic T cell numbers within the TME. We are currently investigating whether the activity of these T cells may be further potentiated through combination with immunomodulatory agents. By testing various treatment combination in the same TME, we will identify the best synergistic effects for future immunotherapy approaches in human PDAC. In summary, we are elucidating the complex mechanism behind LIS in PDAC by employing our novel explant culture system alongside in vivo studies. We aim to develop a translatable regimen to neutralize LIS, reactivating the cytotoxic T cells in the tumor periphery to invade, proliferate, and attack cancer cells. Citation Format: Marie C. Hasselluhn, Dafydd Thomas, Lukas Vlahos, Alvaro Curiel-Garcia, Amanda R. Decker-Farrell, Tanner C. Dalton, Stephen A. Sastra, Carmine F. Palermo, Andrea Califano, Kenneth P. Olive. Disrupting local immunosuppression by combined myCAF/myeloid targeting in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1596.

Construction of ethyl methane sulfonate mutant library in G. arboreum and rapid identification of mutant genes via repeated re-sequencing

Cotton (Gossypium) is the leading economic crop and provides most of renewable spinning fibers for the worldwide textile industry. The diploid Asiatic cotton (G. arboreum), with spinnable fiber and relatively simple genome, is an ideal model for cotton functional genomics and fiber biology. In this study, we constructed an ethyl methane sulfonate (EMS) mutant library of Asiatic cotton. Stable mutants with various phenotypic variations in leaf, flower, fiber and plant architecture were obtained in M1 generation. Genome-wide mutation analysis revealed high-density mutations distributed evenly on whole genome. Using repeated re-sequencing of M1 mutant plants, the candidate genes of 10 out of 15 mutants were identified. The functions of GaYUC4 and GaPDX1, candidate genes for bar blade and yellow leaf vein, respectively, were further confirmed using virus induced gene silencing in Asiatic and/or upland cottons. Our work paved way for expansion of Asiatic cotton mutant library and subsequent applications in cotton functional genomics and cotton breeding.

The inheritance of alternative nest architectural traditions in stingless bees

The transmission of complex behavior and culture in humans has long been attributed to advanced forms of social learning,1,2 which play a crucial role in our technological advancement.3 While similar phenomena of behavioral traditions and cultural inheritance have been observed in animals,1,2,4,5,6 including in primates,7 whales,8 birds,9 and even insects,10 the underlying mechanisms enabling the persistence of such animal traditions, particularly in insects, are less well understood. This study introduces pioneering evidence of enduring architectural traditions in the stingless bee Scaptotrigona depilis, which are maintained without any evidence for social learning. We demonstrate that S.depilis exhibits two distinct nest architectures, comprising either helicoidal or flat, stacked horizontal combs, which are transmitted across generations through stigmergy11,12,13,14,15,16,17-an environmental feedback mechanism whereby the presence of the existing comb structures guides subsequent construction behaviors-thereby leading to a form of environmental inheritance.18,19,20 Cross-fostering experiments further show that genetic factors or prior experience does not drive the observed variation in nest architecture. Moreover, the experimental introduction of corkscrew dislocations within the combs prompted helicoidal building, confirming the use of stigmergic building rules. At a theoretical level, we establish that the long-term equilibrium of building in the helicoidal pattern fits with the expectations of a two-state Markov chain model. Overall, our findings provide compelling evidence for the persistence of behavioral traditions in an insect, based on a simple mechanism of environmental inheritance and stigmergic interactions, without requiring any sophisticated learning mechanism, thereby expanding our understanding of how traditions can be maintained in non-human species.

Host Identity Determines the Bacterial and Fungal Community and Network Structures in the Phyllosphere of Plant Species in a Temperate Steppe

Elucidating the plant−microbiome network is of importance in understanding species coexistence in natural ecosystems. The phyllosphere, which is the aerial parts of terrestrial plants, is inhabited by diverse microbes. However, few studies have focused on the phyllosphere microbiome and plant-microbiome network in temperate grasslands. In this study, we explored the diversity, community structure, and network architecture of phyllosphere bacteria and fungi in 19 plant species native to the temperate grassland in Inner Mongolia, China. We obtained 3,313 and 758 phyllosphere bacterial and fungal operational taxonomic units, respectively, and found that the bacterial community was dominated by Proteobacteria, Actinobacteriota, and Firmicutes, whereas the fungal community was dominated by Ascomycota and Basidiomycota. Plant identity exerted significant impacts on α-diversities of both bacterial and fungal communities. The composition of bacterial and fungal communities differed among plant species. Plant identity had a greater effect on fungal than bacterial communities. Both bacterial and fungal network structures were characterized by specialized, modular, lowly connected, and no nested properties. The plant−fungal network had a high level of specification, modularity, antinestedness, and connectance compared with the plant−bacterial network. Our results suggest more intimate relationships between plants and phyllosphere fungi than between plants and phyllosphere bacteria and also that the phyllosphere fungal community in the temperate grassland ecosystem is more resistant to environmental disturbance than the phyllosphere bacterial community. These findings may contribute to our understanding of the mechanisms by which species coexist and communities stabilize in grassland ecosystems.

Three-Dimensional Imaging of the Enteric Nervous System in Human Pediatric Colon Reveals New Features of Hirschsprung’s Disease

Hirschsprung's disease is defined by the absence of the enteric nervous system (ENS) from the distal bowel. Primary treatment is "pull-through" surgery to remove bowel that lacks ENS, with reanastomosis of "normal" bowel near the anal verge. Problems after pull-through are common, and some may be due to retained hypoganglionic bowel (ie, low ENS density). Testing this hypothesis has been difficult because counting enteric neurons in tissue sections is unreliable, even for experts. Tissue clearing and 3-dimensional imaging provide better data about ENS structure than sectioning. Regions from 11 human colons and 1 ileal specimen resected during Hirschsprung's disease pull-through surgery were cleared, stained with antibodies to visualize the ENS, and imaged by confocal microscopy. Control distal colon from people with no known bowel problems were similarly cleared, stained, and imaged. Quantitative analyses of human colon, ranging from 3 days to 60 years old, suggest age-dependent changes in the myenteric plexus area, ENS ganglion area, percentage of myenteric plexus occupied by ganglia, neurons/mm2, and neuron Feret's diameter. Neuron counting using 3-dimensional images was highly reproducible. High ENS density in neonatal colon allowed reliable neuron counts using 500-μm2× 500-μm2 regions (36-fold smaller than in adults). Hirschsprung's samples varied 8-fold in proximal margin enteric neuron density and had diverse ENS architecture in resected bowel. Tissue clearing and 3-dimensional imaging provide more reliable information about ENS structure than tissue sections. ENS structure changes during childhood. Three-dimensional ENS anatomy may provide new insight into human bowel motility disorders, including Hirschsprung's disease.

Metal-Organic Framework Induced Stabilization of Proteins in Polymeric Nanoparticles.

Developing protein confinement platforms is an attractive research area that not only promotes protein delivery but also can result in artificial environment mimicking of the cellular one, impacting both the controlled release of proteins and the fundamental protein biophysics. Polymeric nanoparticles (PNPs) are attractive platforms to confine proteins due to their superior biocompatibility, low cytotoxicity, and controllable release under external stimuli. However, loading proteins into PNPs can be challenging due to the potential protein structural perturbation upon contacting the interior of PNPs. In this work, we developed a novel approach to encapsulate proteins in PNPs with the assistance of the zeolitic imidazolate framework (ZIF). Here, ZIF offers an additional protection layer to the target protein by forming the protein@ZIF composite via aqueous-phase cocrystallization. We demonstrated our platform using a model protein, lysozyme, and a widely studied PNP composed of poly(ethylene glycol)-poly(lactic-co-glycolic acid) (PEG-PLGA). A comprehensive study via standard loading and release tests as well as various spectroscopic techniques was carried out on lysozyme loaded onto PEG-PLGA with and without ZIF protection. As compared with the direct protein encapsulation, an additional layer with ZIF prior to loading offered enhanced loading capacity, reduced leaching, especially in the initial stage, led to slower release kinetics, and reduced secondary structural perturbation. Meanwhile, the function, cytotoxicity, and cellular uptake of proteins encapsulated within the ZIF-bound systems are decent. Our results demonstrated the use of ZIF in assisting in protein encapsulation in PNPs and established the basis for developing more sophisticated protein encapsulation platforms using a combination of materials of diverse molecular architectures and disciplines. As such, we anticipate that the protein-encapsulated ZIF systems will serve as future polymer protein confinement and delivery platforms for both fundamental biophysics and biochemistry research and biomedical applications where protein delivery is needed to support therapeutics and/or nutrients.

Plant root mechanisms and their effects on carbon and nutrient accumulation in desert ecosystems under changes in land use and climate.

Deserts represent key carbon reservoirs, yet as these systems are threatened this has implications for biodiversity and climate change. This review focuses on how these changes affect desert ecosystems, particularly plant root systems and their impact on carbon and mineral nutrient stocks. Desert plants have diverse root architectures shaped by water acquisition strategies, affecting plant biomass and overall carbon and nutrient stocks. Climate change can disrupt desert plant communities, with droughts impacting both shallow and deep-rooted plants as groundwater levels fluctuate. Vegetation management practices, like grazing, significantly influence plant communities, soil composition, root microorganisms, biomass, and nutrient stocks. Shallow-rooted plants are particularly susceptible to climate change and human interference. To safeguard desert ecosystems, understanding root architecture and deep soil layers is crucial. Implementing strategic management practices such as reducing grazing pressure, maintaining moderate harvesting levels, and adopting moderate fertilization can help preserve plant-soil systems. Employing socio-ecological approaches for community restoration enhances carbon and nutrient retention, limits desert expansion, and reduces CO2 emissions. This review underscores the importance of investigating belowground plant processes and their role in shaping desert landscapes, emphasizing the urgent need for a comprehensive understanding of desert ecosystems.

Revised Architecture and Two New Super-Earths in the HD 134606 Planetary System

Multiplanet systems exhibit a diversity of architectures that diverge from the solar system and contribute to the topic of exoplanet demographics. Radial velocity (RV) surveys form a crucial component of exoplanet surveys, as their long observational baselines allow for searches for more distant planetary orbits. This work provides a significantly revised architecture for the multiplanet system HD 134606 using both HARPS and UCLES RVs. We confirm the presence of previously reported planets b, c, and d with periods of 12.0897−0.0018+0.0019 , 58.947−0.054+0.056 , and 958.7−5.9+6.3 days and masses of 9.14−0.63+0.65 , 11.0 ± 1, and 44.5 ± 2.9 Earth masses, respectively, with the planet d orbit significantly revised to over double that originally reported. We report two newly detected super-Earths, e and f, with periods of 4.31943−0.00068+0.00075 and 26.9−0.017+0.019 days and masses of 2.31−0.35+0.36 and 5.52−0.73+0.74 Earth masses, respectively. In addition, we identify a linear trend in the RV time series, and the cause of this acceleration is deemed to be a newly detected massive companion with a very long orbital period. HD 134606 now displays four low-mass planets in a compact region near the star, one gas giant further out in the habitable zone, an additional companion in the outer regime, and a low-mass M dwarf stellar companion at large separation, making it an intriguing target for system formation/evolution studies. The location of planet d in the habitable zone proves to be an exciting candidate for future space-based direct imaging missions, whereas continued RV observations of this system are recommended for understanding the nature of the massive, long-period companion.

Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions. Predominant methodologies either delineate a single dataset distribution into training, validation, and testing subsets or merge datasets from diverse conditions or distributions before their division into the subsets. Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions, evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions, and assessing their performance in entirely distinct settings through three experiments. By evaluating diverse network architectures and training approaches (finetuning versus feature extraction), testing various architectures, employing different training strategies, and amalgamating data, we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1, conducted in a uniform environment, accuracy ranged from 80% to 100% across all models and training strategies, with finetune mode achieving a superior performance of 94% to 99.9% compared to the feature extraction mode at 80% to 92.96%. Experiment 2 underscored a significant performance decline, with accuracy figures between 25% and 60%, primarily at 40%, when the origin of the test data deviated from the train and validation sets. Experiment 3, spotlighting dataset and distribution amalgamation, yielded promising accuracy metrics, notably a peak of 99.6% for ResNet in finetuning mode to a low of 69.9% for InceptionV3 in feature extraction mode. These pivotal findings emphasize that merging data from diverse distributions, coupled with finetuned training on advanced architectures like ResNet and MobileNet, markedly enhances performance, contrasting with the relatively lower performance exhibited by simpler networks like AlexNet. Our results suggest that embracing data diversity and flexible training methodologies are crucial for optimizing weed classification models when disparate data distributions are available. This study gives a practical alternative for treating diverse datasets with real-world agricultural variances.

Metal Ions Can Modulate the Self-Assembly and Activity of Catalytic Peptide Amyloids.

Rational design of peptides has become a powerful tool to produce self-assembled nanostructures with the ability to catalyze different chemical reactions, paving the way to develop minimalistic enzyme-like nanomaterials. Catalytic amyloid-like assemblies have emerged among the most versatile and active, but they often require additional factors for activity. Elucidating how these factors influence the structure and activity is key for the design. Here, we showed that biologically relevant metal ions can guide and modulate the self-assembly of a small peptide into diverse amyloid architectures. The morphology and catalytic activity of the resulting fibrils were tuned by the specific metal ion decorating the surface, whereas X-ray structural analysis of the amyloids showed ion-dependent shape sizes. Molecular dynamics simulations showed that the metals can strongly affect the local conformational space, which can trigger major rearrangements of the fibrils. Our results demonstrate that the conformational landscape of catalytic amyloids is broad and tunable by external factors, which can be critical for future design strategies.

Studies on Genetic Divergence Analysis in Black Gram (Vigna mungo L. Hepper) Genotypes

In order to select superior genotypes for the creation of genetic stock for hybridization program or the introduction of a crop variety, plant breeders can assist from genetic diversity both within and between plant species. Mahalanobis D2 Statistics was used to conduct the current study on Genetic Divergence and clustering pattern utilizing twenty eight different genotypes of Black gram. The experiment was conducted in Randomized Block Design with three replications at Plant Breeding Farm, Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University, Chidambaram, Tamil Nadu, India. Nine different biometrical characteristics were observed and documented viz., plant height at maturity (PH), pod length (LP), number of branches per plant (NBPP), number of clusters per plant (NCPP), number of pods per cluster (NPPC), number of pods per plant (NPPP), number of seeds per pod (NSPP), 100 seed weight (SW), and seed yield per plant (SY). All of these characters have a significant degree of variability, according to an analysis of variance. Mahalanobis (D2) static revealed considerable genetic diversity among the genotypes. Genotypes were grouped into 6 clusters. Cluster VI was the largest including 11 genotypes. The intra-cluster distance for cluster IV is the maximum and the lowest intra-cluster distance was reported for cluster II. The highest inter-cluster distance was revealed between cluster I and III and then between cluster II and III. This demonstrates unambiguously that the genotypes in this cluster exhibit a wide range of genetic variation and may be useful in a black gramme hybridization scheme to increase production. Between clusters I and II, the minimal inter-cluster distance was discovered. The percentage that each character contributed to the overall divergence was as follows: the number of pods per plant (50.997), the greater seed output (50.997), and the 100 seed weight (29.456). (8.121). It is proposed that crossings between parents from the most divergent clusters should show the highest level of heterosis as well as the greatest degree of genetic architectural variability.

Variations of the NodB Architecture Are Attuned to Functional Specificities into and beyond the Carbohydrate Esterase Family 4.

Enzymes of the carbohydrate esterase family 4 (CE4) deacetylate a broad range of substrates, including linear, branched and mesh-like polysaccharides. Although they are enzymes of variable amino acid sequence length, they all comprise the conserved catalytic domain NodB. NodB carries the metal binding and active site residues and is characterized by a set of conserved sequence motifs, which are linked to the deacetylation activity. Besides a non-structured, flexible peptide of variable length that precedes NodB, several members of the CE4 family contain additional domains whose function or contribution to substrate specificity are not efficiently characterized. Evidence suggests that CE4 family members comprising solely the NodB domain have developed features linked to a variety of substrate specificities. To understand the NodB-based substrate diversity within the CE4 family, we perform a comparative analysis of all NodB domains structurally characterized so far. We show that amino acid sequence variations, topology diversities and excursions away from the framework structure give rise to different NodB domain classes associated with different substrate specificities and particular functions within and beyond the CE4 family. Our work reveals a link between specific NodB domain characteristics and substrate recognition. Thus, the details of the fold are clarified, and the structural basis of its variations is deciphered and associated with function. The conclusions of this work are also used to make predictions and propose specific functions for biochemically/enzymatically uncharacterized NodB-containing proteins, which have generally been considered as putative CE4 deacetylases. We show that some of them probably belong to different enzymatic families.

Hybrid Bonding for Ultra-High-Density Interconnect

Abstract Hybrid bonding is the technology for interchip ultrahigh-density interconnect at pitch smaller than 10 μm. The feasibility at wafer-to-wafer level bonding with bond pad pitch of sub-0.5 μm has been demonstrated with scaling limitations under exploration beyond sub-0.4 μm. The heterogeneous integration of chiplets often requires die-to-wafer hybrid bonding for diverse chip stacking architectures. This overview emphasis on some main issues associated with hybrid bonding extending to die-to-wafer level. The hybrid bond pad structure design is a critical factor affecting sensitivity to overlay accuracy, copper recess or protrusion requirements, and performances. Cases of hybrid bonding schemes and pad structure designs are summarized and analyzed. Performance assessment and characterization methods are briefly overviewed. The scalability of pad pitch is addressed by analyzing the recent literature reports. Challenges of managing singulated dies for die-to-wafer bonding with direct placement or collective die-to-wafer bonding schemes under exploration are addressed. Nonetheless, industry collaboration for manufacturing equipment development and industry standards on handling chiplets from different technology nodes and different factories are highlighted.

Reciprocal regulation by Elm1 and Gin4 controls septin hourglass assembly and remodeling.

The septin cytoskeleton is extensively regulated by posttranslational modifications, such as phosphorylation, to achieve the diversity of architectures including rings, hourglasses, and gauzes. While many of the phosphorylation events of septins have been extensively studied in the budding yeast Saccharomyces cerevisiae, the regulation of the kinases involved remains poorly understood. Here, we show that two septin-associated kinases, the LKB1/PAR-4-related kinase Elm1 and the Nim1/PAR-1-related kinase Gin4, regulate each other at two discrete points of the cell cycle. During bud emergence, Gin4 targets Elm1 to the bud neck via direct binding and phosphorylation to control septin hourglass assembly and stability. During mitosis, Elm1 maintains Gin4 localization via direct binding and phosphorylation to enable timely remodeling of the septin hourglass into a double ring. This mutual control between Gin4 and Elm1 ensures that septin architecture is assembled and remodeled in a temporally controlled manner to perform distinct functions during the cell cycle.

Three near-complete genome assemblies reveal substantial centromere dynamics from diploid to tetraploid in Brachypodium genus

BackgroundCentromeres are critical for maintaining genomic stability in eukaryotes, and their turnover shapes genome architectures and drives karyotype evolution. However, the co-evolution of centromeres from different species in allopolyploids over millions of years remains largely unknown.ResultsHere, we generate three near-complete genome assemblies, a tetraploid Brachypodium hybridum and its two diploid ancestors, Brachypodium distachyon and Brachypodium stacei. We detect high degrees of sequence, structural, and epigenetic variations of centromeres at base-pair resolution between closely related Brachypodium genomes, indicating the appearance and accumulation of species-specific centromere repeats from a common origin during evolution. We also find that centromere homogenization is accompanied by local satellite repeats bursting and retrotransposon purging, and the frequency of retrotransposon invasions drives the degree of interspecies centromere diversification. We further investigate the dynamics of centromeres during alloploidization process, and find that dramatic genetics and epigenetics architecture variations are associated with the turnover of centromeres between homologous chromosomal pairs from diploid to tetraploid. Additionally, our pangenomes analysis reveals the ongoing variations of satellite repeats and stable evolutionary homeostasis within centromeres among individuals of each Brachypodium genome with different polyploidy levels.ConclusionsOur results provide unprecedented information on the genomic, epigenomic, and functional diversity of highly repetitive DNA between closely related species and their allopolyploid genomes at both coarse and fine scale.

Complete genome assembly provides a high-quality skeleton for pan-NLRome construction in melon.

Melon (Cucumis melo L.), being under intensive domestication and selective breeding, displays an abundant phenotypic diversity. Wild germplasm with tolerance to stress represents an untapped genetic resource for discovery of disease-resistance genes. To comprehensively characterize resistance genes in melon, we generate a telomere-to-telomere (T2T) and gap-free genome of wild melon accession PI511890 (C. melo var. chito) with a total length of 375.0 Mb and a contig N50 of 31.24 Mb. The complete genome allows us to dissect genome architecture and identify resistance gene analogs. We construct a pan-NLRome using seven melon genomes, which include 208 variable and 18 core nucleotide-binding leucine-rich repeat receptors (NLRs). Multiple disease-related transcriptome analyses indicate that most up-regulated NLRs induced by pathogens are shell or cloud NLRs. The T2T gap-free assembly and the pan-NLRome not only serve as essential resources for genomic studies and molecular breeding of melon but also provide insights into the genome architecture and NLR diversity.

Leaf Architecture and Genome Size Variation of Durio zibethinus L. from Jelebu, Negeri Sembilan, Malaysia.

Durio zibethinus L. is known as the "king of fruit" in Malaysia. Meanwhile, Jelebu, Negeri Sembilan has always become the top choice district to visit for durian lover for its Durian Kampung Jelebu, which possessed good quality on par with top D. zibethinus clones such as D197 Musang King and D24. However, there is still lacking in taxonomic data of D. zibethinus especially from Jelebu. This study aimed to analyse the leaf architecture and genome size variations of selected D. zibethinus accessions from Jelebu. Five D. zibethinus accessions from Jelebu were examined. Thirty-seven parameters of gross leaf morphological characteristics and leaf venation pattern were observed and recorded for identification and classification of D. zibethinus accessions from Jelebu. Seven parameters have been recorded which are petiole length, petiole features, leaf size, leaf shape, leaf base shape, lower leaf surface colour and areolation could be used in differentiating between accessions. Results of this study showed the intraspecific variations existed among D. zibethinus accessions from Jelebu with a genome size varying between 1.7433 pg and 1.800 pg. In conclusion, data on leaf architecture and genome size variations from D. zibethinus accessions are beneficial for early plant identification and classification.