Supersymmetry of Tensionless Rotating Strings in AdS_5 x S^5, and Nearly-BPS Operators

It is shown that a class of rotating strings in AdS_5 x S^5 with SO(6) angular momenta (J,J',J') preserve 1/8-supersymmetry for large J,J', in which limit they are effectively tensionless; when J=0, supersymmetry is enhanced to 1/4. These results imply that recent checks of the AdS/CFT correspondence actually test a nearly-BPS sector.Comment: 12 pages, no figures; v2: new section on CFT operators and new references added, discussion section and acknowledgements modified, abstract rephrashe

IMPLEMENTATION OF A HIGH-EFFICIENT ELEMENTARY HEATER IN THE CIRCUIT OF AN EXISTING EVAPORATOR WITH THE PURPOSE OF SAVING HEATING STEAM

he paper gives a brief description of the method for producing alumina (Al2O3) by the Bayer method.This work was supported by the Russian Foundation for Basic Research (grant # 19-53-55002)

Lead content and isotopic composition in submound and recent soils of the Volga upland

Literature data on the historical reconstructions of the atmospheric lead deposition in Europe and the isotopic composition of the ores that are potential sources of the anthropogenic lead in the atmospheric deposition in the lower Volga steppes during different time periods have been compiled. The effect of the increasing anthropogenic lead deposition recorded since the Bronze Age on the level of soil contamination has been investigated. For the first time paleosol buried under a burial mound of the Bronze Age has been used as a reference point to assess of the current contamination level. The contents and isotopic compositions of the mobile and total lead have been determined in submound paleosols of different ages and their recent remote and roadside analogues. An increase in the content and fraction of the mobile lead and a shift of its isotopic composition toward less radiogenic values (typical for lead from the recent anthropogenic sources) has been revealed when going from a Bronze-Age paleosol to a recent soil. In the Bronze-Age soil, the isotopic composition of the mobile lead is inherited from the parent rock to a greater extent than in the modern soils, where the lead is enriched with the less radiogenic component. The effect of the anthropogenic component is traced in the analysis of the mobile lead, but it is barely visible for the total lead. An exception is provided by the recent roadside soils characterized by increased contents and the significantly less radiogenic isotopic composition of the mobile and total lead

DEVELOPMENT OF A HEAT EXCHANGER CONSTRUCTION FOR HEATING ALUMINUM SOLUTION WITH A BATTERY ASSEMBLY

The paper outlines the main reasons for the inlay of heat exchange equipment during heating of the aluminate solution. Considered methods of reducing scale formation on heat transfer surfaces. The design of the newly developed heat exchanger is described.В работе изложены основные причины забивки теплообменного оборудования при подогреве алюминатного раствора. Рассмотрены меры для снижения накипевыделения на теплообменных поверхностях. Описано конструктивное исполнение вновь разработанного теплообменника

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology.Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials

Online Volunteering as an Object of Research: Essential Characteristics and Typology of Practice

Волонтерство и онлайн-волонтерство имеют большое значение для решения огромного спектра задач. Следовательно, количество научно-исследовательских работ по этой тематике значительно растет. В рамках исследования, где объектом является онлайн-волонтерство необходимо определится с терминологией. В статье анализируются различные авторские научные подходы к пониманию онлайн-волонтерства и выделяются его специфические черты. Автором формируется собственное интегративное определение данного социального явления. Особое внимание уделяется типовым практикам виртуального волонтерства.Volunteering and online volunteering are important for solving a huge range of tasks. Consequently, the number of research papers on this topic is growing significantly. As part of the study, where the object is online volunteering, it is necessary to determine the terminology. The article analyzes various author's scientific approaches to understanding online volunteering and highlights its specific features. The author forms his own integrative definition of this social phenomenon. Special attention is paid to the typical practices of virtual volunteering