Thousands Of Domains Research Articles

Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems

Traditional numerical discretization-based solvers of partial differential equations (PDEs) are fundamentally agnostic to domains, boundary conditions and coefficients. In contrast, machine learnt solvers have a limited generalizability across these elements of boundary value problems. This is strongly true in the case of surrogate models that are typically trained on direct numerical simulations of PDEs applied to one specific boundary value problem. In a departure from this direct approach, the label-free machine learning of solvers is centered on a loss function that does not use computed field solutions as labels. Instead, the PDE and boundary conditions are directly incorporated in residual form to express the loss function during training. However, their generalization across boundary conditions is limited and they remain strongly domain-dependent. Here, we present a framework that generalizes across domains, boundary conditions and coefficients while simultaneously learning the PDE in weak form. Our work explores the ability of convolutional neural network (CNN)-based encoder–decoder architectures to learn to solve a PDE in greater generality than its restriction to a particular boundary value problem. In this first Communication, we take the canonical path through elliptic PDEs and focus on steady-state diffusion, linear and nonlinear elasticity. Importantly, the learning happens independently of any labeled field data from either experiments or direct numerical solutions. We develop probabilistic CNNs in the Bayesian setting using variational inference. Extensive results for these problem classes demonstrate the framework’s ability to learn PDE solvers that generalize across hundreds of thousands of domains, boundary conditions and coefficients, including extrapolation beyond the learning regime. Once trained, the machine learning solvers are orders of magnitude faster than discretization-based solvers. They therefore have relevance to high-throughput solutions of PDEs on varied domains, boundary conditions and coefficients, such as for inverse modeling, optimization, design and decision-making. We place our work in the context of other recent machine learning solvers of PDEs including continuous operator learning frameworks, as well as make comparisons of performance where possible. Finally, we note extensions to transfer learning, active learning and reinforcement learning.

High-Throughput Discovery and Characterization of Human Transcriptional Effectors

Thousands of proteins localize to the nucleus; however, it remains unclear which contain transcriptional effectors. Here, we develop HT-recruit, a pooled assay where protein libraries are recruited to a reporter, and their transcriptional effects are measured by sequencing. Using this approach, we measure gene silencing and activation for thousands of domains. We find a relationship between repressor function and evolutionary age for the KRAB domains, discover that Homeodomain repressor strength is collinear with Hox genetic organization, and identify activities for several domains of unknown function. Deep mutational scanning of the CRISPRi KRAB maps the co-repressor binding surface and identifies substitutions that improve stability/silencing. By tiling 238 proteins, we find repressors as short as ten amino acids. Finally, we report new activator domains, including a divergent KRAB. These results provide a resource of 600 human proteins containing effectors and demonstrate a scalable strategy for assigning functions to protein domains.

CYBER RISKS IN THE MUNICIPAL ECONOMY DURING THE PANDEMIC: DAMAGES AND THE STRUGGLE FOR CYBER SECURITY

As a result of remote control and automation, the urban infrastructure becomes extremely vulnerable to intrusions, attacks, human errors, accidents that are growing. Due to the concentration of local and global computer networks, systems and software, the “cyber risk” of the municipal economy is multiplicative, which makes it systemic and international. Its essence is manifested both at the national and global levels through the impact on business, municipal and state authorities. Today, the existing pandemic contributes to an increase in the number of cyberattacks, which indicates an even greater cyber vulnerability of municipal administrations and public authorities. Coronavirus COVID-19 has become a tool for hacker attacks on users and enterprises. The purpose of the article is to determine, based on a systematic analysis of the new cyber risks of the municipal economy that arose during the pandemic, to formulate our own views on the classification and methods of counteracting municipal organizations and enterprises. According to experts, since the beginning of this year, thousands of domains associated with coronavirus have been registered in the world. This number also includes sites of various hacker groups that offer information about coronavirus, masks, or quick treatment methods. In fact, these phishing sites are used by hackers to extort money or steal confidential, as well as commercial information. The main types of attacks that increase the threat and actually create a "cyber infection" are noted. Based on the material of this article and the experience of the authors, measures are proposed that ensure the safety of municipal enterprises, which should be carried out in the first place. Strict measures in IT management during a crisis are also recognized as undeniable and necessary. Such as help from cyber experts and help for cyber experts; preservation of evidence of intrusion, staff training, accumulation of experience to accelerate progress in work. Remote work during a pandemic can make it difficult for IT staff to monitor cyber risks, since many of these risks go beyond the financial or technical capabilities of municipalities. Therefore, based on these proposals for the safety of municipal enterprises, an integrated approach to cyber risks is proposed, including an emergency response plan. Based on international experience, the possibility of insuring municipal enterprises and organizations against potential losses associated with cyber attacks by hackers, as well as to eliminate the consequences of these attacks, is also noted. Keywords: cyber-security, cyber-risk, municipal economy, “phishing”, pandemic, Covid-19

Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering

One of the tasks Law Enforcement Agencies are responsible for is to find evidence of criminal activities in the Darknet. However, visiting thousands of domains to locate visual information containing illicit acts manually requires a considerable amount of time and human resources. To support this task, in this paper, we explore the automatic classification of images uploaded to Tor darknet.Unfortunately, the foreground objects on such images are not always presented standalone, without background, due to the environmental conditions. To address this challenge on the digital investigation of Tor darknet visual content, we propose to classify automatically only relevant parts of the image combining saliency maps, i.e. to select the regions with the most salient information, with Bag of Visual Words (BoVW). We introduce Semantic Attention Keypoint Filtering (SAKF), a filtering strategy that removes non-significant features at a pixel level that mainly do not belong to the object of interest or foreground. We assessed SAKF on seven publicly available datasets, obtaining from 1.64 to 15.73 points higher accuracies than the method set as the baseline, i.e. BoVW using dense SIFT (Scale-Invariant Feature Transform) descriptors. We also compared SAKF filtering performance against the deep features extracted from two well-known Convolutional Neural Network (CNN) architectures, namely MobileNet and ResNet50.Experimental results reveal the effectiveness of the proposed approach and highlight that the use of automatic image classification could be advantageous to support daily Law Enforcement Agencies investigations on Tor darknet.

Perfectly Ordered Patterns Formed by a Heterogeneous Nucleation Process of Block Copolymer Self-Assembly Under Rectangular Confinement.

The heterogeneous nucleation process during the phase separation of binary blends of the AB diblock and the C homopolymer induced by rectangular confinement is studied by cell dynamics simulation based on the time-dependent Ginzburg-Landau theory. The main goal is to yield large-scale ordered hexagonal patterns by tailoring the surface potentials of the sidewalls. Our study reveals a crucial condition to induce the desired heterogeneous nucleation process in which the nucleated domain grains grow and merge into a defect-free pattern. Specifically, nucleations are induced simultaneously by two parallel sidewalls with a strong surface potential, whereas the spontaneous nucleation and the heterogeneous nucleation at the other two walls with a weak surface potential are suppressed. Moreover, the confinement effect of the other two walls can ensure that the two rows of nucleated domains have correlated positions. Importantly, we find that the ordering process under the crucial condition exhibits a high tolerance to the rectangular sizes. Only a few defects in thousands of domains are occasionally caused that are observed to be annihilated in a short-annealing time via various mechanisms. This study may provide a facile route to prepare large-scale ordered patterns via a simple rectangular confinement.

A systematic survey of the Cys2His2 zinc finger DNA-binding landscape.

Cys2His2 zinc fingers (C2H2-ZFs) comprise the largest class of metazoan DNA-binding domains. Despite this domain's well-defined DNA-recognition interface, and its successful use in the design of chimeric proteins capable of targeting genomic regions of interest, much remains unknown about its DNA-binding landscape. To help bridge this gap in fundamental knowledge and to provide a resource for design-oriented applications, we screened large synthetic protein libraries to select binding C2H2-ZF domains for each possible three base pair target. The resulting data consist of >160 000 unique domain–DNA interactions and comprise the most comprehensive investigation of C2H2-ZF DNA-binding interactions to date. An integrated analysis of these independent screens yielded DNA-binding profiles for tens of thousands of domains and led to the successful design and prediction of C2H2-ZF DNA-binding specificities. Computational analyses uncovered important aspects of C2H2-ZF domain–DNA interactions, including the roles of within-finger context and domain position on base recognition. We observed the existence of numerous distinct binding strategies for each possible three base pair target and an apparent balance between affinity and specificity of binding. In sum, our comprehensive data help elucidate the complex binding landscape of C2H2-ZF domains and provide a foundation for efforts to determine, predict and engineer their DNA-binding specificities.

Protein Domains of Unknown Function Are Essential in Bacteria

ABSTRACTMore than 20% of all protein domains are currently annotated as “domains of unknown function” (DUFs). About 2,700 DUFs are found in bacteria compared with just over 1,500 in eukaryotes. Over 800 DUFs are shared between bacteria and eukaryotes, and about 300 of these are also present in archaea. A total of 2,786 bacterial Pfam domains even occur in animals, including 320 DUFs. Evolutionary conservation suggests that many of these DUFs are important. Here we show that 355 essential proteins in 16 model bacterial species contain 238 DUFs, most of which represent single-domain proteins, clearly establishing the biological essentiality of DUFs. We suggest that experimental research should focus on conserved and essential DUFs (eDUFs) for functional analysis given their important function and wide taxonomic distribution, including bacterial pathogens.

Fragment and Conquer: From Structure to Complexes to Function

In this issue of Structure, Shumilin and colleagues show the power of metabolite screening by means of X-ray crystallography to link an orphan protein domain with an orphan biochemical function. This result paves the way for large-scale functional annotation and sets new objectives for structural genomics.