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The new advances in various experimental techniques that provide complementary information about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. Here we propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes, for which both Hi-C and FISH data are available, demonstrate that GEM-FISH can outperform previous chromosome structure modeling methods and accurately capture the higher order spatial features of chromosome conformations. Moreover, our reconstructed 3D models of chromosomes revealed interesting patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation. Methodological advances have increased our understanding of chromatin structure through chromosome conformation capture techniques and high resolution imaging, but integration of these datasets is challenging. Here the authors propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model.

A low-input Hi-C method is used to show that chromatin organization is markedly relaxed in pre-implantation mouse embryos after fertilization and that the subsequent maturation of 3D chromatin architecture is surprisingly slow. Early development chromatin reorganization In mammals, chromatin undergoes reorganization after fertilization, but little is known about the molecular basis for reprogramming of higher-order chromatin structure. Here, Wei Xie and colleagues have developed a low-input Hi-C approach, which they apply to examine chromatin organization in mouse oocytes and preimplantation embryos. They find that chromatin has markedly reduced higher-order structure for both parental genomes after fertilization. Topological associated domain boundaries and chromatin compartments start to emerge in zygotes but the subsequent maturation of three-dimensional chromatin architecture is surprisingly slow. In mammals, chromatin organization undergoes drastic reprogramming after fertilization 1 . However, the three-dimensional structure of chromatin and its reprogramming in preimplantation development remain poorly understood. Here, by developing a low-input Hi-C (genome-wide chromosome conformation capture) approach, we examined the reprogramming of chromatin organization during early development in mice. We found that oocytes in metaphase II show homogeneous chromatin folding that lacks detectable topologically associating domains (TADs) and chromatin compartments. Strikingly, chromatin shows greatly diminished higher-order structure after fertilization. Unexpectedly, the subsequent establishment of chromatin organization is a prolonged process that extends through preimplantation development, as characterized by slow consolidation of TADs and segregation of chromatin compartments. The two sets of parental chromosomes are spatially separated from each other and display distinct compartmentalization in zygotes. Such allele separation and allelic compartmentalization can be found as late as the 8-cell stage. Finally, we show that chromatin compaction in preimplantation embryos can partially proceed in the absence of zygotic transcription and is a multi-level hierarchical process. Taken together, our data suggest that chromatin may exist in a markedly relaxed state after fertilization, followed by progressive maturation of higher-order chromatin architecture during early development.

Fluorescence polarization microscopy images both the intensity and orientation of fluorescent dipoles and plays a vital role in studying molecular structures and dynamics of bio-complexes. However, current techniques remain difficult to resolve the dipole assemblies on subcellular structures and their dynamics in living cells at super-resolution level. Here we report polarized structured illumination microscopy (pSIM), which achieves super-resolution imaging of dipoles by interpreting the dipoles in spatio-angular hyperspace. We demonstrate the application of pSIM on a series of biological filamentous systems, such as cytoskeleton networks and λ-DNA, and report the dynamics of short actin sliding across a myosin-coated surface. Further, pSIM reveals the side-by-side organization of the actin ring structures in the membrane-associated periodic skeleton of hippocampal neurons and images the dipole dynamics of green fluorescent protein-labeled microtubules in live U2OS cells. pSIM applies directly to a large variety of commercial and home-built SIM systems with various imaging modality. Polarization microscopy has been combined with single-molecule localization, but it’s often limited in either speed or resolution. Here the authors present polarized Structured Illumination Microscopy (pSIM), a method that uses polarized laser excitation to measure dye orientation during fast super-resolution live cell imaging.

In the early stages of computer software information system development, requirements errors can lead to software system failure and performance degradation and even cause huge security incidents. Traditional requirements verification methods are inefficient and susceptible to human factors when dealing with complex software requirements. Rapid prototyping is an effective requirement validation method, but the generated prototype does not contain any data, and the traditional method requires domain experts to write the data manually, which is time consuming and complicated. In this study, an automatic software prototype data generation method, InitialGPT, is proposed, which automatically generates requirements-compliant prototype data by interacting with users through a requirements model to improve the efficiency and accuracy of requirements validation. We designed a framework containing a prompt generation template, a data generation model, a data evaluation model, and multiple prototype data tools, and validated it on four real-world software system cases. The results show that the approach improves the efficiency of requirements validation by a factor of 7.02, and generates data of similar quality to those written manually, but at a more advantageous cost and efficiency, demonstrating its potential for application in the computer software industry.

The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module’s accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation. Methods that analyse heterogeneity and compare tissue microenvironments using spatial omics data are challenging to develop. Here, the authors present SOTIP, a method that can perform spatial heterogeneity, spatial domain, and differential microenvironment analyses across multiple spatial omics modalities.

Cloud file sharing (CFS) has become one of the important tools for enterprises to reduce technology operating costs and improve their competitiveness. Due to the untrustworthy cloud service provider, access control and security issues for sensitive data have been key problems to be addressed. Current solutions to these issues are largely related to the traditional public key cryptography, access control encryption or attribute-based encryption based on the bilinear mapping. The rapid technological advances in quantum algorithms and quantum computers make us consider the transition from the tradtional cryptographic primitives to the post-quantum counterparts. In response to these problems, we propose a lattice-based Ciphertext-Policy Attribute-Based Encryption(CP-ABE) scheme, which is designed based on the ring learing with error problem, so it is more efficient than that designed based on the learing with error problem. In our scheme, the indirect revocation and binary tree-based data structure are introduced to achieve efficient user revocation and dynamic management of user groups. At the same time, in order to further improve the efficiency of the scheme and realize file sharing across enterprises, the scheme also allows multiple authorities to jointly set up system parameters and manage distribute keys. Furthermore, by re-randomizing the user’s private key and update key, we achieve decryption key exposure resistance(DKER) in the scheme. We provide a formal security model and a series of security experiments, which show that our scheme is secure under chosen-plaintext attacks. Experimental simulations and evaluation analyses demonstrate the high efficiency and practicality of our scheme.

The orientation of fluorophores can reveal crucial information about the structure and dynamics of their associated subcellular organelles. Despite significant progress in super-resolution, fluorescence polarization microscopy remains limited to unique samples with relatively strong polarization modulation and not applicable to the weak polarization signals in samples due to the excessive background noise. Here we apply optical lock-in detection to amplify the weak polarization modulation with super-resolution. This novel technique, termed optical lock-in detection super-resolution dipole orientation mapping (OLID-SDOM), could achieve a maximum of 100 frames per second and rapid extraction of 2D orientation, and distinguish distance up to 50 nm, making it suitable for monitoring structural dynamics concerning orientation changes in vivo. OLID-SDOM was employed to explore the universal anisotropy of a large variety of GFP-tagged subcellular organelles, including mitochondria, lysosome, Golgi, endosome, etc. We found that OUF (Orientation Uniformity Factor) of OLID-SDOM can be specific for different subcellular organelles, indicating that the anisotropy was related to the function of the organelles, and OUF can potentially be an indicator to distinguish normal and abnormal cells (even cancer cells). Furthermore, dual-color super-resolution OLID-SDOM imaging of lysosomes and actins demonstrates its potential in studying dynamic molecular interactions. The subtle anisotropy changes of expanding and shrinking dendritic spines in live neurons were observed with real-time OLID-SDOM. Revealing previously unobservable fluorescence anisotropy in various samples and indicating their underlying dynamic molecular structural changes, OLID-SDOM expands the toolkit for live cell research.

Fusarium oxysporum is a destructive plant pathogen with robust survival mechanisms, complicating control efforts. This study aimed to develop nanoformulated fungicides, screen antagonistic bacteria, and evaluate their combined efficacy. A novel self-emulsifying nanoemulsion (DZW) was formulated using zein and benzaldehyde-modified wheat gluten (BgWG) as carriers for dimethachlon (DTN). The preparation process optimized material ratios and emulsification techniques. Concurrently, antagonistic bacterial strains against F. oxysporum were screened via the plate standoff method, identifying Bacillus velezensis MLY71 as both antagonistic and compatible with DTN. The DZW nanoemulsion achieved a particle size of 93.22 nm, an encapsulation efficiency (EE) of 90.57%, and a DTN loading capacity (LC) of 67.09%, with sustained release over 96 h. The combination of DTN (0.04 mg·mL⁻¹) and B. velezensis MLY71 (1 × 10⁴ CFU·mL⁻¹) achieved a 76.66% inhibition rate against F. oxysporum MLY127, 1.71 times greater than DTN alone, indicating significant synergy. At a DTN concentration of 0.20 mg·mL⁻¹, the combination of DZW and MLY71 showed a synergy coefficient of 1.33. This synergy was also observed in soil environments, indicating its adaptability for controlling soil-borne pathogens. As sustainable management continues to gain attention in agricultural disease control, this study offers a promising strategy for achieving higher efficacy with the same fungicide dose or satisfactory control with reduced fungicide application. The excellent drug-loading performance of BgWG also expanded the applications of the wheat by-product gluten.

RNA polymerase II (Pol II) apparatuses are compartmentalized into transcriptional clusters. Whether protein factors control these clusters remains unknown. In this study, we find that the A TPase- a ssociated with diverse cellular a ctivities (AAA + ) ATPase RUVBL2 co-occupies promoters with Pol II and various transcription factors. RUVBL2 interacts with unphosphorylated Pol II in chromatin to promote RPB1 carboxy-terminal domain (CTD) clustering and transcription initiation. Rapid depletion of RUVBL2 leads to a decrease in the number of Pol II clusters and inhibits nascent RNA synthesis, and tethering RUVBL2 to an active promoter enhances Pol II clustering at the promoter. We also identify target genes that are directly linked to the RUVBL2-Pol II axis. Many of these genes are hallmarks of cancers and encode proteins with diverse cellular functions. Our results demonstrate an emerging activity for RUVBL2 in regulating Pol II cluster formation in the nucleus. RNA polymerase II (Pol II) transcription factories play a central role in gene expression and 3D chromatin organization. Here, the authors demonstrate that RUVBL2 directly regulates Pol II clustering at active gene promoters.

The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.