Reviewerly/exHarmony huggingface dataset:
Abstract 5408: AI powered-platform to predict gene modifications from prostate and breast cancer whole slide images

Abstract There is little published evidence of predicting cancer genotypes directly from tissue histology, especially for breast and prostate cancers. Artificial intelligence (AI) enables discriminating and extracting morphological features reflective of the underlying genomic alterations at visual and subvisual levels. We have built a morphology-based and AI-powered platform for cancer genotyping, risk stratification and outcome prediction that addresses the needs for treatment decision-making in a cost effective and timely fashion. A cohort of 390 prostate and 742 invasive breast cancer patients with known molecular status of key genes, such as TP53, PIK3CA, MYC, ERBB2, TMPRSS2-ERG and PTEN from The Cancer Genome Atlas were included in this study. Hematoxylin and eosin (H&amp;E) stained whole slide images (WSI) of the cancer tissue sections were available at 20x or 40x. The WSI from the two cancer cohorts were split 2:1 into a training and test dataset, respectively. Our platform involved two different deep Convolutional Neural Network (DCNN) architectures. The platform first divided each WSI into multiple tiles. Each tile was then analyzed using a DCNN that graded the tile and generated a high dimensional vector to provide a mathematical representation of the morphology. The combination of high dimensional vectors across the WSI was then fed into a second DCNN that generated a morphological score, which predicted whether the gene under consideration was wild type or modified. Our platform has achieved 70 - 80% accuracy as defined by the Area under the Curve for the receiver operating characteristics curves for the genetic markers on the test cohorts (Table 1). Our platform can predict genotypes/molecular alterations directly from H&amp;E stained WSI with high accuracy. This technology presents a novel, practical and cost-effective approach for cancer molecular classification and risk stratification, enabling timely and optimal treatment decision-making for positive clinical outcomes. Genotype Prediction for Breast Cancer (BrCa) and Prostate Cancer (PCa) from WSI Cohort Molecular Biomarker Training Dataset (n) Test Dataset (n) Test ROC AUC Score (%) Gene Modification/Loss Intact/Wild type Gene Modification/Loss Intact/Wild type BrCa TP53 196 302 96 149 80 PIK3CA 153 344 75 170 74 MYC 68 414 33 204 77 ERBB2 66 416 32 205 78 PCa TMPRSS2-ERG 104 157 51 78 70 PTEN 57 201 28 99 73 Citation Format: Wei Huang, Parag Jain, Chensu Xie, Hassan Muhammad, Hirak Basu, George Wilding, Rajat Roy. AI powered-platform to predict gene modifications from prostate and breast cancer whole slide images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5408.
Humanization of the continuity of modern society social reorganization on the basis of the idea of recreating the USSR in the students' mind

Introduction. The relevance of the problem of humanizing the continuity of the social reorganization of modern society on the basis of the idea of recreating the USSR in students' mind is due to rapid changes in the political climate and many challenges of our time that require prompt and immediate solutions. Theoretical analysis. The purpose of the article is to reveal the process of updating the worldview of students on the basis of humanistic identification in the process of interaction of the social progress and regression. Destructive factors of development and radical and palliative mechanisms of successive identification are highlighted. The lines of social progress are analyzed on the basis of the identification methodology. Empirical analysis made it possible to confirm the theoretical provisions obtained during the survey. Conclusion. Multicultural orientation of vocational education requires humanization of the principles of social reorganization of modern Russia. This process can be used most effectively through the reconstruction of the USSR image in the views of students in modern forms and establishment of a successive connection with the past. At the same time, the development of social progress is carried out in the space of a new freedom, the restriction of which is a mechanism for the revival of accountability in the course of a return to genuine historical memory and modernization of society.
DATA SCIENCE: EXPLORING EFFECTIVE STRATEGIES AND OPERATIONS FOR MAXIMIZING INSIGHTS AND IMPACT

Data science has become a critical component of decision-making across many industries.With the proliferation of data and the increasing availability of powerful tools and technologies, organizations can gain valuable insights from their data to drive better decision-making, improve operational efficiencies, and achieve their strategic goals.However, to fully realize the potential of data science, organizations must develop effective strategies and operations that can maximize insights and impact.This research paper explores the key strategies and operations that organizations can use to achieve success in data science, including data collection and preparation, modeling and analysis, visualization and communication, and governance and ethical considerations.Through a comprehensive review of the literature and case studies of successful data science initiatives, this paper provides insights into best practices that can help organizations develop effective data science strategies and operations that can lead to significant business impact.
Adaptive basis sets for practical quantum computing

Abstract Electronic structure calculations on small systems such as H 2 , H 2 O, LiH, and BeH 2 with chemical accuracy are still a challenge for the current generation of noisy intermediate‐scale quantum (NISQ) devices. One of the reasons is that due to the device limitations, only minimal basis sets are commonly applied in quantum chemical calculations, which allows one to keep the number of qubits employed in the calculations at a minimum. However, the use of minimal basis sets leads to very large errors in the computed molecular energies as well as potential energy surface shapes. One way to increase the accuracy of electronic structure calculations is through the development of small basis sets better suited for quantum computing. In this work, we show that the use of adaptive basis sets, in which exponents and contraction coefficients depend on molecular structure, provides an easy way to dramatically improve the accuracy of quantum chemical calculations without the need to increase the basis set size and thus the number of qubits utilized in quantum circuits. As a proof of principle, we optimize an adaptive minimal basis set for quantum computing calculations on an H 2 molecule, in which exponents and contraction coefficients depend on the HH distance, and apply it to the generation of H 2 potential energy surface on IBM‐Q quantum devices. The adaptive minimal basis set reaches the accuracy of the double‐zeta basis sets, thus allowing one to perform double‐zeta quality calculations on quantum devices without the need to utilize twice as many qubits in simulations. This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.
Supplementary Figure 1 from A Pilot Study of Low-Cost, High-Resolution Microendoscopy as a Tool for Identifying Women with Cervical Precancer

&lt;p&gt;PDF file - 267K, Automated nuclear segmentation in HRME images acquired at the sites shown in Figures 2f and 2h of the main text. In the segmented overlays, (b) and (d), the user-defined region of interest (ROI) is enclosed by the green polygon. The algorithm identifies nuclei within the ROI based on image intensity, as described in the main text. Groups of pixels identified as nuclei are shown in red. Large clumps are outlined in yellow and excluded from the N/C ratio calculation&lt;/p&gt;
Probability in Physics—An Introductory Guide

Journal Article Probability in Physics – An Introductory Guide Get access Probability in Physics—An Introductory Guide by AndyLawrence, 1st edition, Springer Nature Switzerland AG. 2019. 39.99€. ISBN: 978-030-04542-5 Tobias Wand Tobias Wand WWU Münster, Institut für Theoretische Physik, Münster, Germany t_wand01@uni-muenster.de Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad039, https://doi.org/10.1093/jrsssa/qnad039 Published: 05 April 2023
Meta-e-learning: la aplicación del Metaverso en la educación online. Un análisis de la evolución del e-learning como propuesta de mejora en la etapa universitaria

La situación pandémica provocada por la COVID-19 ha acelerado, en algunos casos, y forzado en otros, la remodelación de multitud de modelos empresariales. En formación, evolución es sinónimo de nuevas formas de hacer llegar el conocimiento a los alumnos. La presencia del e-learning ya supone una revolución que ocurrió, que está presente, y que sigue incrementándose con el paso del tiempo. Sin embargo, aunque parezca que ya abarcamos todas las opciones posibles, nos olvidamos del escenario que ha cogido fuerza desde que la empresa Facebook anunció su cambio de nombre a Meta, potenciando lo que conocíamos como Second Life y que no deja de ser un escenario de realidad virtual (VR) donde se potencian las grandes ventajas de la formación online y donde existe la posibilidad de incorporar tecnologías inmersivas como la realidad aumentada (AR) mediante el uso de un avatar en Metaverso y la aplicación de tecnologías que permiten obtener una serie de recursos académicos muy próximos a la realidad. En el presente trabajo repasamos las ventajas del e-learning evaluando las opciones que nos permite el metaverso con el fin de definir una vía formativa que puede ofrecer una experiencia distinta, mucho más cercana a la realidad, creativa, potente y motivadora respecto a la forma actual, y que podría aplicarse en estudios superiores: el Meta-e-learning (MeL).------------------------------------------------------------------------------The pandemic situation caused by COVID-19 has accelerated, in some cases, and forced in others, the remodeling of many business models. In training, evolution is synonymous with new ways of getting knowledge to students. The presence of e-learning already represents a revolution that has occurred, is present, and continues to increase over time. However, although it seems that we have already covered all the possible options, we are forgetting the scenario that has gained strength since the company Facebook announced its name change to Meta, promoting what we knew as Second Life and that is still a scenario of virtual reality (VR) where the great advantages of online training are enhanced and where there is the possibility of incorporating immersive technologies such as augmented reality (AR) through the use of an avatar in the Metaverse and the application of technologies that allow obtaining a series of academic resources very close to reality. In this paper we review the advantages of e-learning evaluating the options that the metaverse allows us in order to define a training path that can offer a different experience, much closer to reality, creative, powerful and motivating compared to the way current, and that could be applied in higher studies: Meta-e-learning (MeL).
Research on DSG-DSMM Evaluation Model of Data Security Based on Entropy Method and BP Neural Network and Its Application

From the perspective of data security evaluation, based on the data security governance framework and data security capability maturity model, combined with the network security level protection system 2.0 standard, this paper establishes a general data security theoretical model DSG-DSMM, and on this basis, establishes a data security evaluation index system. By introducing entropy method and BP neural network Kunming Longhui Technology Co., Ltd. calculated the data security degree as an example, and found that the data environment security open forest index has the largest weight value in the data security evaluation index, indicating that different local governments attach great importance to data and different data support environments have a great impact on data security; The prediction results of BP neural network show that the data security level of Electric Power Construction Institute is basically safe, while that of Longhui Company is average. The prediction value of DSG-DSMM data security evaluation model is basically consistent with the reality. Its research model, research methods and research results are helpful to ensure the data security of enterprises and institutions to a certain extent.
Research on network host risk assessment method based on environmental risk level attack graph

In order to guarantee the information security of campus network, the risk assessment and calculation method of the attack graph model is improved by combining the university network security scenarios, integrating the node environment risk factors, proposing an attack probability calculation method based on CVSS atomic attack difficulty improvement, and generating the attack graph. By simulating the real server network environment, the Bayesian attack graph is constructed for the actual network to obtain the corresponding attack paths and attack probabilities. The attack graph generation model and attack probability calculation method proposed in this paper can improve the efficiency of university network security risk research and evaluation, and have good network security protection effect.
A Personalized Recommendation Fusing Tag Feature and Temporal Context

The rapid growth of users and items provides enormous potential for users to find their interested information. This has attracted a lot of attentions how to use both tag feature and temporal context to improve recommendation accuracy. In this paper, we calculate users’ similarity by using user-tag bipartite, so as to construct user-tag feature vector. Then, we take the temporal context into consideration to dynamically discover neighbors which have higher effect weights. Finally, we fuse the neighbor sets to collaborative filtering algorithm based on the neighborhood. We evaluate the proposed algorithm using a real-world data set and compare the performance with classical baseline methods, showing the improvements in terms of different evaluation.
Application of Cloud Computing in Data Information System Management

The application effect of cloud computing in information management has gradually become prominent, and it has achieved good development with its high efficiency and advantages in information sharing. It has been widely used in medical treatment, education and other fields. The application of cloud computing technology can make the information management system have more intelligent functions, and improve the utilization rate of resources and the efficiency of information processing. The author analyzes the characteristics of cloud computing technology, expounds the development status of cloud computing technology, and analyzes the framework construction and application of cloud computing in information management system in detail.
THE EFFECT OF METACOGNIFY MODEL ON EXPOSITION TEXT LEARNING IN CLASS VIII ISLAMIC HIGH SCHOOL ATHIRAH MAKASSAR

This study aims to determine the effect of metacognitive learning models in exposition text learning in class VIII SMP Islam Athirah Bukit Baruga Makassar with reference to student learning outcomes and student activities. The research method used is pre-experimental research involving one group that is given treatment. The population of this study were all eighth grade students of SMP Islam Athirah Bukit Baruga Makassar in the odd semester of the 2021/2022 academic year. The sample was chosen randomly so that a sample of 78 students was obtained. Data collection was carried out using learning outcomes tests (pretest and posttest) and student activity observation sheets. The data analysis technique used is descriptive and inferential statistical analysis. The results of descriptive and inferential statistical analysis showed: (1) The average score (pretest) was 67.92 in the low category while (posttest) was 91.10 in the very high category, (2) The average percentage of student activity reached 92%, which means Student activity is categorized as very influential (3) the t-test value shows ttable = 1.708 and tcount = 34.18 p = 0.000 which means significant, (4) The gain score = 23.18 means that there is an increase in value. It can be concluded that the metacognitive learning model has an effect on student learning outcomes in text-based Indonesian language learning with the subject matter of exposition texts in class VIII of SMP Islam Athirah Bukit Baruga Makassar.
Sorting method of radar signal batch processing

Radar signal sorting is a key technology in electronic warfare. The quality of signal sorting directly determines the outcome of electronic warfare. The accuracy and real-time performance of signal sorting are two important indicators to evaluate the quality of signal sorting. With the increasingly complex electronic countermeasure environment, there are many radar radiation sources in the battlefield, and the signal aliasing is serious. At the same time, there may be pulse loss and interference pulse in the electromagnetic environment. In such a complex electromagnetic environment, in order to improve the accuracy and real-time of radar signal sorting and master the battlefield initiative, this paper proposes a method of radar signal batch processing sorting based on sliding window. Firstly, the radar signal sorting transient library and the sorted radar signal library are set up, and the transient library is used to store radar signals in the sliding window. Move the sliding window continuously from the starting point. When the number of signals in the transient library is higher than the specified threshold, sort the radar signals in the transient library, and then store the sorted radar signals in the sorted radar signal library and delete them from the transient library. When the sliding window moves to the end point, the signals in the sorted radar signal library are merged. Simulation results show that compared with traditional methods, the proposed method has higher sorting accuracy and better real-time performance when there are more radar pulses and pulse loss.
Research of the Possibilities of Interactive Simulators in Intercultural Communication for the Formation of Students' Algorithmic Thinking

The requirements for the results of mastering the main educational program by students determine that a digital school graduate must have a culture of thinking, the ability to generalize, analyze, perceive information (including in a foreign language), set a goal and choose ways to achieve it.The development of relevant skills is actively taking place in the lessons of computer science, mathematics, physics, chemistry.Intercultural communication in the study of a foreign language and literature provides certain didactic opportunities for the formation of abilities to perform mental operations, choose the best way to achieve a goal, etc.To form the appropriate skills that determine the essence of algorithmic thinking, the authors propose to include the interactive simulators in the foreign language communicative activity of students.The methodology is based on the analysis of the capabilities of interactive simulators for various purposes: in online games (Lingo Play, Kid Mama), in quests (Learnis), in puzzles, crossword puzzles, and quizzes.The eTreniki online constructor is used (Kokla, Krypton, Morfanki, Kartofan services) to create a game simulator in the classroom.Research results.The students of the experimental group study services for creating interactive simulators and use them in intercultural communication.In conclusion, the possibilities of interactive simulators for the development of algorithmic thinking of schoolchildren in intercultural communication are formulated: activating cognition, determination of the structure of actions to achieve the goal, evaluation and analysis of the result, etc.
Research on Multi-objective Optimization of Economic Performance of Solar Thermal Power System

Aiming at the problems of high cost and low efficiency of solar thermal power generation system, CMOPSO classical optimization algorithm is used to analyze the multi-objective optimization of its economic performance. Firstly, a multi-objective optimization model of total generating capacity, normalized KWH cost and total investment of the power station is established under the constraint condition of load deficit rate. Secondly, two optimization algorithms CMOPSO are used to optimize and analyze the non-inferior solution sets of the solar thermal power station with the capacity of 10MW and 50MW respectively. Finally, taking some parameters of the solar thermal power demonstration project under construction in western China, the results can be seen to be consistent with the actual and have a certain engineering reference value.
Morphological and Distribution Pattern of Calcifications on Full Field Digital Mammography versus Digital Breast Tomosynthesis and Comparison of Diagnostic Abilities of the Two Modalities: A Retrospective Study

Introduction: Microcalcification (MC) is an effective and sometimes the only indicator of breast cancer. Early detection and characterisation of malignant MC can facilitate early diagnosis and timely treatment of breast cancer. However, due to the small size and low contrast as compared to the background parenchyma, it is difficult and time-consuming for radiologists to accurately evaluate MC. Aim: To compare the diagnostic abilities of Full Field Digital Mammography (FFDM) and Digital Breast Tomosynthesis (DBT) in the detection and characterisation of breast calcifications. Materials and Methods: This retrospective descriptive study was conducted in the year 2022 at the breast imaging unit of Dr. Ram Manohar Lohia Institute of Medical Sciences, Lucknow, Uttar Pradesh, India and data of patients who had undergone FFDM and DBT between March 2019- September 2020 was collected. Mammograms of 702 women with 1217 breasts were evaluated and MC was detected in 622 breasts. Based on the morphology and distribution pattern, Breast Imaging Reporting and Data System (BI-RADS) assessment Category was assigned from 2 to 5. Cases with BI-RADS 2 and 3 were followed-up by repeat Mammograms at 6-month to 1-year intervals. Cases with BI-RADS 4 and 5 were biopsied under stereotactic or ultrasound guidance. Histopathology findings and the stability of the calcifications on sequential mammograms were considered the gold standard for final BI-RADS categorisation. The Chi-square test was applied for the comparison of FFDM and DBT. Results: Typically benign morphology MC was noted in 508 (81.67%) of breasts on FFDM and 505 (80.67%) on DBT. Suspicious morphology MC was noted in 114 (18.33%) of breasts on FFDM and 121 (19.33%) on DBT. Vascular calcification was the most common benign MC seen in 233 cases (37.45%) on FFDM and 244 cases (38.9%) on DBT. Similarly, fine pleomorphic was the most common suspicious morphology MC on FFDM and DBT both seen in 47 cases (7.5%) and 44 cases (7.02%), respectively. The most common distribution pattern was diffuse seen in 582 cases (93.56%) on FFDM and 583 cases (93.13%) on DBT, respectively. No significant difference was observed (p=0.283) in the rate of detection or characterisation of MC by FFDM or DBT. The sensitivity of both modalities were almost similar (97.7% and 97.9%) without any significant difference (p=0.278). Similarly, there was no difference in the specificity (94.9% and 94.4%, respectively, p=0.289). Conclusion: The performance of FFDM and DBT for the detection and characterisation of MC is not significantly different.