bluuebunny/crossref_metadata_2025 huggingface dataset:
An overview: Big data analysis by deep learning and image processing
Authors: Ahmed Yousef Ameen Esmail , Yue Biao , Gu Zongliang , Yang Jingyu
Published: February 2023

Big Data Analysis and Deep Learning are two fields of data science that are getting a proportion of interest nowadays. The relevance of big data has increased as a result of the massive amounts of domain-specific data that many public and private firms have gathered. This data may be useful for research on topics like national security, fraud, and medical informatics and detection. Huge volumes of data are analyzed by businesses like Google and Microsoft for business analysis and decision-making that affects both present and future technology. By using deep learning algorithms, high-level information is extracted. Multifaceted intellections are articulated as data illustrations using a tiered erudition approach. On the basis of comparably simpler abstractions created at the level before, sophisticated abstractions are learnt at a given level. The field of image processing is still in its infancy. Agriculture, textiles, transportation, and other fields have all made use of image alteration, image coding, compression, picture segmentation, and other technologies. Traditional image processing techniques, on the other hand, are unable to handle the huge quantity of image mockups available today. As a result, to raise the level and efficiency of image processing, a novel methodical area has emerged that focuses on exploring big data-based image processing technology and developing an image processing archetypal. The image processing prototypical based on big data offers recompenses including strong repeatability, high accuracy, extensive applicability, good flexibility, and a high potential for information reduction, according to existing big data research results. In this review, potential of Deep Learning and images processing to address some of the most challenging problems in Big Data Analytics, including the extraction of multifaceted designs from gigantic data sets, semantic indexing, data tagging, rapid facts repossession, and simplification of discriminative tasks will be explored. The integration and interaction of the three main topic is image processing, deep learning, and big data will be explained in this review. All three of these areas hold great promise for a variety of industries. The research issues highlighted in the integration and interplay of these broad domains are examined, as are some potential study avenues.
A bibliometric analysis on financial engineering studies
Authors: Jena Jyoti Ranjan , Panigrahi Rashmi Ranjan , Shrivastava Avinash K.
Published: February 2023

Due to the availability of innovative financial products and services, financial engineering has expanded to include more practices and engineering techniques (FinTech, InsurTech, and other technologies). Financial engineering (FE) has succeeded through the stock market, financial instruments, and service developments. This study combines qualitative and quantitative approaches with bibliometric analysis to address a vacuum in financial literature. Co-citation, co-occurrence, and bibliographic coupling analysis techniques are used to make inferences about the structure of FE research in finance from January 2007 to December 2022. The study used 347 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through “R” to justify study objectives. The relevance of authors, journals, and organizations in a contemporary topic study is recognized using bibliometrics analysis. The study topic analysis revealed that modern portfolio theory acknowledges the importance of FE’s revolutionary products, such as FinTech and InsurTech, as recent advances in risk management techniques. Future research could take a multi-dimensional approach based on the current theme. New research themes emerged from the study, i.e., Financial Engineering and emerging financial markets and risk management, investment and financial crisis, FinTech, and InsurTech innovation, and Option pricing.
DFPC-SLAM: A Dynamic Feature Point Constraints-Based SLAM Using Stereo Vision for Dynamic Environment
Authors: Zeng Bo , Song Chengqun , Jun Cheng , Kang Yuhang
Published: February 2023

Visual SLAM methods usually presuppose that the scene is static, so the SLAM algorithm for mobile robots in dynamic scenes often results in a significant decrease in accuracy due to the influence of dynamic objects. In this paper, feature points are divided into dynamic and static from semantic information and multi-view geometry information, and then static region feature points are added to the pose-optimization, and static scene maps are established for dynamic scenes. Finally, experiments are conducted in dynamic scenes using the KITTI dataset, and the results show that the proposed algorithm has higher accuracy in highly dynamic scenes compared to the visual SLAM baseline.
High-resolution core data and machine learning schemes applied to rock facies classification
Authors: Germay Christophe , Lhomme Tanguy , Perneder Luc
Published: February 2023

This paper presents core analysis practices developed to increase the value of cores in reservoir characterization workflows. Core analysis traditionally implies extensive rock testing programmes that require large numbers of plug samples. Numerous stakeholders compete for intact core material and often do not base sample selection criteria on objective and reliable information. As a consequence, samples dedicated to core analysis programmes consume a significant fraction of the material available, yet they are selected on the basis of very little
a priori
information and therefore do not necessarily capture the complexity of the targeted formations. In an attempt to change this paradigm, we promote a more integrated core analysis solution combining transdisciplinary, high-resolution, non-destructive measurements on whole cores, for an early yet objective description of cores and the rapid estimation of formation properties. The first section describes multidisciplinary and non-destructive tests designed to increase the value of information from cores while keeping a minimal footprint. Core samples are scanned along a fit-for-purpose surface, with a collection of sensors interfaced to the same table-top equipment. Technologies including ultra-high-resolution pictures, elemental composition and the direct measurements of geomechanical properties provide continuous and high-resolution profiles with a unique depth reference. Panoramic pictures are processed to extract textural and colour features and grain size distributions. Grain size distributions are backed up by analysis of topographical maps created with a laser scan. The results are combined under one unique format, thereby easing interdisciplinary work from the verification of standard tests (routine core analysis, rock mechanical test) to the construction of robust predictive rock models. The paper describes machine learning schemes applied to core datasets. Unsupervised schemes are designed to identify rock facies, while supervised schemes are used to classify tested rocks into predefined rock facies with known petrophysical properties. Case studies highlight the benefits of this approach of core analysis in conjunction with artificial intelligence for the automated identification and classification of rock facies. This novel approach to core analysis leverages a detailed and comprehensive knowledge of the distributions of core properties, available under one unique format for all disciplines, which eases interdisciplinary work and significantly improves existing core analysis standards. It also provides a sound basis to train artificial intelligence rock reservoir property predictors linking well-log data to core-based lithofacies signatures.
Enabling secure subsurface storage in future energy systems: an introduction
Authors: Miocic Johannes M. , Heinemann Niklas , Alcalde Juan , Edlmann Katriona , Schultz Richard A.
Published: February 2023

Geological structures in the subsurface have been used for the storage of energy and waste products for over a century. Depleted oil and gas fields, saline aquifers or engineered caverns in salt or crystalline rocks are used worldwide to store energy fluids intended to provide demand buffers and sustained energy supply. The transition of our energy system into a clean, renewable-based system will most likely require an expansion of these subsurface storage activities, to host a wide variety of energy products (e.g. natural gas, hydrogen, heat or waste energy products, like CO
2
) to balance the inherent intermittence of the renewable energy supply. Ensuring the safety and effectiveness of these subsurface storage operations is therefore crucial to achieve the sought-after renewable energy transition while ensuring energy security.
Designing a Dual-level Facial Expression Evaluation System for Performers Using Geometric Features and Petri Nets
Authors: Kale Manjeeta R. , Rege Priti P. , Joshi Radhika D.
Published: February 2023

The existing methods of Facial Expression Recognition (FER) primarily analyze six basic expressions, namely, surprise, happiness, anger, sadness, fear, and disgust. The Indian performing arts use three more well-defined expressions—peaceful, proud, and erotic. This study proposes an intelligent dual-level expression evaluation system that classifies performance-specific expressions into nine classes, assigns intensity level to the expression, and suggests modifications to the user for precise exhibition of an expression. At decision level-1 of a dual-level system, an 11-state model is designed to classify the nine expressions. The model is verified using the Colored Petri Net that helps analyze the rules used for the classification. Decision level-1 is also implemented using input feature database and SVM classifier, which yields 95.77% accuracy. Further, at decision level-2, SVM is used to assign an intensity level to the correctly classified images. In case of incorrectly exhibited expressions, feedback is provided to the user about the incorrect facial component state. The application-specific image dataset is used for the present study. The qualitative comparison with the other FER approaches is also carried out. With the increasing popularity of Indian classical dance in Western and Asian countries, the dual-level system enables learners of performing arts to practice, evaluate, and improvise their expression skills.
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Authors: Nauta Meike , Trienes Jan , Pathak Shreyasi , Nguyen Elisa , Peters Michelle , Schmitt Yasmin , Schlötterer Jörg , van Keulen Maurice , Seifert Christin
Published: February 2023

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the past 7 years at major AI and ML conferences that introduce an XAI method. We find that one in three papers evaluate exclusively with anecdotal evidence, and one in five papers evaluate with users. This survey also contributes to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. Our systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark, and compare new and existing XAI methods. The Co-12 categorization scheme and our identified evaluation methods open up opportunities to include quantitative metrics as optimization criteria during model training to optimize for accuracy and interpretability simultaneously.
Modern Code Reviews—Survey of Literature and Practice
Authors: Badampudi Deepika , Unterkalmsteiner Michael , Britto Ricardo
Published: February 2023

Background:
Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist, it is u
a
nknown whether the research community has targeted themes that practitioners consider important.

Objectives:
The objectives are to provide an overview of MCR research, analyze the practitioners’ opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues.

Method:
We conducted a systematic mapping study to survey state of the art until and including 2021, employed the Q-Methodology to analyze the practitioners’ perception of the relevance of MCR research, and analyzed the primary studies’ research impact.

Results:
We analyzed 244 primary studies, resulting in five themes. As a result of the 1,300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor– and support systems–related research.

Conclusion:
These results indicate a misalignment between the state of the art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda that can potentially increase the impact of MCR research.
TEVL: Trilinear Encoder for Video-language Representation Learning
Authors: Man Xin , Shao Jie , Chen Feiyu , Zhang Mingxing , Shen Heng Tao
Published: February 2023

Pre-training model on large-scale unlabeled web videos followed by task-specific fine-tuning is a canonical approach to learning video and language representations. However, the accompanying Automatic Speech Recognition (ASR) transcripts in these videos are directly transcribed from audio, which may be inconsistent with visual information and would impair the language modeling ability of the model. Meanwhile, previous V-L models fuse visual and language modality features using single- or dual-stream architectures, which are not suitable for the current situation. Besides, traditional V-L research focuses mainly on the interaction between vision and language modalities and leaves the modeling of relationships within modalities untouched. To address these issues and maintain a small manual labor cost, we add automatically extracted dense captions as a supplementary text and propose a new trilinear video-language interaction framework TEVL (Trilinear Encoder for Video-Language representation learning). TEVL contains three unimodal encoders, a TRIlinear encOder (TRIO) block, and a temporal Transformer. TRIO is specially designed to support effective text-vision-text interaction, which encourages inter-modal cooperation while maintaining intra-modal dependencies. We pre-train TEVL on the HowTo100M and TV datasets with four task objectives. Experimental results demonstrate that TEVL can learn powerful video-text representation and achieve competitive performance on three downstream tasks, including multimodal video captioning, video Question Answering (QA), as well as video and language inference. Implementation code is available at
https://github.com/Gufrannn/TEVL
.
Evolving Social Media Background Representation with Frequency Weights and Co-Occurrence Graphs
Authors: Zhang Yihong , Fang Xiu Susie , Hara Takahiro
Published: February 2023

Social media as a background information source has been utilized in many practical computational tasks, such as stock price prediction, epidemic tracking, and product recommendation. However, proper representation of an evolving social media background is still in an early research stage. In this article, we propose a representation method that considers temporal novelties as well as the fine details of word inter-dependencies. Our method is based on the tf-idf and graph embedding techniques. The proposed method has superiority over other representation methods because it takes the advantage of both the temporal aspect of tf-idf and the semantic aspect of graph embeddings. We compare our method with a variety of baselines in two practical application scenarios using real-world data. In tweet popularity prediction, our representation achieves 5.7% less error and 12.8% higher correlation compared to the best baseline. In e-commerce product recommendation, our representation achieves 17% higher hit-rate and 20% higher NDCG compared to the best baseline.
A Rapid Synthesis of LDHs Nanosheets/Electrochemical Reduction of Graphene Oxide Nanocomposites to Simultaneously Detect Ascorbic Acid, Dopamine, and Uric Acid
Authors: Zhang Jiazheng , Ji Xiang , Ye Ningchen , Zhang Haitao , Sun Hao , Xu Chenzhe , Liu Lin , Ma Juanjuan , Tong Zhiwei
Published: February 2023

In this study, we reported a facile and rapid synthesis of NiAl LDH/ERGO and reduced by electrochemical method. Compared with previous studies, this experiment greatly decreased the use of reductive solvents and was environmentally friendly. The nanocomposite material was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) & transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectrometer (FT-IR). A series of electrochemical methods were applied to test the electrochemical performance of modified electrodes. When the modified electrode was used to detect AA, DA and UA, the three substances can be clearly separated and the limits of detection for AA, DA and UA can be 0.85 μM in the range 12.48–232 μM, 0.38 μM in the range 1.06–13.26 μM and 0.42 μM in the range 1.25–23.20 μM (S/N = 3), respectively. The modified electrode could provide a reproducible and stable platform to detect AA, DA, and UA simultaneously.
Mobile application-assisted graded exercise practical: a remote teaching strategy to promote motivation and experiential learning in exercise physiology
Authors: Wong Amanda Huee-Ping , Wong Lik-Wei , Low Ivan Cherh Chiet
Published: February 2023

Remote practical classes, supported by physiological monitoring smartphone applications, were assessed for their utility in facilitating learning and raising student motivation in health sciences education in this study. A comparison of remote practicals with traditional in-class practicals revealed that a remote practical is an effective method for reinforcing physiology learning objectives with the added advantage of increased student motivation. The added value of remote practicals may be attributed to more experiential learning opportunities and increased engagement levels.
Assessing the calculation of conductive and acinar ventilatory heterogeneity indices Scond and Sacin from multiple-breath washout data
Authors: Prisk G. Kim , Rutting Sandra , Bozier Jack , Thamrin Cindy , Robinson Paul D. , Thompson Bruce R.
Published: February 2023

Sensor errors that elevated values of N2 concentration in commercial multiple-breath washout (MBW) devices resulted in errors in calculated parameters including Scond and Sacin. We examined the mechanism of the change in values produced by these errors and explored the appropriateness of the calculation of Scond and Sacin. This work serves to emphasize that the current algorithm in use is appropriate for the calculation of Scond and Sacin.
ICU Admission, Invasive Mechanical Ventilation, and Mortality among Children and Adolescents Hospitalized for COVID-19 in a Private Healthcare System
Authors: Horta Maria da Gloria Cruvinel , Ribeiro Geraldo Jose Coelho , Campos Nelson Otavio Beltrao , de Oliveira Douglas Ribeiro , de Almeida Carvalho Lelia Maria , de Castro Zocrato Karina , dos Reis Daniel Pitchon , Fernandes Mariana Ribeiro , Camelo Ricardo Mesquita , Biscione Fernando Martin , Kelles Silvana Marcia Bruschi
Published: February 2023

Aim. The COVID-19 pandemic devastated healthcare around the world. Data about the COVID-19 outcomes among young people are still scarce. We aim to identify factors associated with the composite outcome among children and adolescents hospitalized due to COVID-19. Methods. We performed a search in the database of a large Brazilian private healthcare system. Insured people aged 21 years or younger who were hospitalized due to COVID-19 from Feb/28th/2020 to Nov/1st/2021 were included. The primary endpoint was the composite outcome consisting of ICU admission, need for invasive mechanical ventilation, or death. Results. We evaluated 199 patients who had an index hospitalization due to COVID-19. The median monthly rate of index hospitalization was 2.7 (interquartile range [IQR], 1.6-3.9) per 100,000 clients aged 21 years or less. The median age of the patients was 4.5 years (IQR, 1.4-14.1). At the index hospitalization, the composite outcome rate was 26.6%. The composite outcome was associated with all the previous coexisting morbidities evaluated. The median follow-up was 249.0 days (IQR, 152.0-438.5). There were 27 readmissions (16 patients) within 30 days after the discharge. Conclusions. In conclusion, hospitalized children and adolescents had a composite outcome rate of 26.6% at the index hospitalization. Having previous chronic morbidity was associated with the composite.
Γ

α
,
β

-BE-Algebras
Authors: Bandaru Ravikumar , Kang Jeong Gi , Alali Amal S. , Jun Young Bae
Published: February 2023

The concept of

Γ

α
,
β

-BE-algebra is introduced as a generalization of a

Γ

α

-BE-algebra and a

Γ

β

-BE-algebra and its properties are studied. We introduced the concepts of

α
,
β

-subalgebra and

α
,
β

-filter of a

Γ

α
,
β

-BE-algebra and discussed the relation between these two concepts. We provided conditions for an

α
,
β

-subalgebra to be an

α
,
β

-filter. We provided equivalent conditions for the formation of an

α
,
β

-filter from a nonempty subset of a

Γ

α
,
β

-BE-algebra. We introduced the concept of

α
,
β

-atomic

Γ

α
,
β

-BE-algebra and studied its properties. We introduced the concept of atomic

α
,
β

-filter a

Γ

α
,
β

-BE-algebra and gave the necessary and sufficient condition for an

α
,
β

-filter to be atomic.
Car Emotion Labeling Based on Color‐SSL Semi‐Supervised Learning Algorithm by Color Augmentation
Authors: Guo Zhuen , Lin Li , Liu Baoqi , Zhang Li , Chang Kaixin , Li Lingyun , Jiang Zuoya , Wu Jinmeng
Published: February 2023

In the era of emotional consumption, it has become a hot topic that commodities meet consumers’ emotional needs. As a necessity of life, the car also needs to meet the needs of consumers. To achieve that consumers can purchase cars according to their emotional needs, we need to label cars with emotional words. The car’s appearance is the crucial medium of emotional information transmission, especially the car’s color is an essential emotional factor. As the first impression of products, color affects people’s emotional attitude. Therefore, introducing color features into the training process of sample marking is an excellent idea for intelligent labeling of a large number of product emotions. This paper proposes a semi‐supervised learning method, Color‐SSL, based on color data augmentation to realize the label of car emotion. Color‐SSL takes FlexMatch as the framework of a semi‐supervised learning model and augments data by extracting subject color. Compared with the baseline method, the accuracy of this method improved by 3.2%, 8.3%, 8.6%, and 1.4% with 10, 50, 100, and 200 training samples and 1000 test samples. The results show that Color‐SSL obtains the best emotion‐label result (94%). In addition, this study publishes pictures of emotional car datasets with high resolution, orthogonal perspective, and uniform background.
A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator
Authors: Liu Yiting , Sui Lianjie , Li Peijuan , Zhang Lei , Wu Qingzheng , Du Junfeng , Liu Yawen , Yu Hanqi
Published: February 2023

The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real‐time localization and map‐matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative calculation method with high computational complexity and a large amount of computation. Furthermore, due to the dependence of the fixed separation threshold, the algorithms for extracting the linear features of laser radar data are typically undersegment and oversegment. As a result, this paper proposes a radar linear feature fitting algorithm that combines adaptive clustering and corner detection operators. First, bilateral filtering is used to reduce noise and remove invalid data points. Second, the LiDAR data points are classified using adaptive threshold clustering of distance and density. The corner detection operator is applied to the classified data points to determine all possible corners then. Finally, the least square method is used to linearly fit each class and the identified corners within each class. The simulation and experimental results demonstrate that this method avoids the influence of noise points and a fixed segmentation threshold on corner point extraction effectively. The standard variance of length is 9.41 × 10−5m2 for corner feature extraction and localization in the dataset Cartographer ROS 2D Laser SLAM at Deutsches Museum. When compared to PDBS (point distance based methods) and IEPF (iterative end point fit), only about half the time is used, the accuracy of partition processing is improved by 11.6%, and the accuracy of corner detection is improved by 20.1%. The proposed algorithm can extract the corner features of data frames and linear positioning through experimental verification accurately. The features of the laser scan data that fit are more realistic. It has higher calculation efficiency and position accuracy. It ensures real‐time mobile robot map construction and is appropriate for autonomous robot map algorithms developed in embedded systems.
Preseismic Perturbations and their Inhomogeneity as Computed from Ground‐ and Space‐Based Investigation during the 2016 Fukushima Earthquake
Authors: Biswas Sagardweep , Kundu Subrata , Sasmal Sudipta , Politis Dimitrios Z. , Potirakis Stelios M. , Hayakawa Masashi
Published: February 2023

We present the atmospheric anomalies instigated through seismogenic sources by a multichannel observation using ground‐ and satellite‐based systems. This study emphasizes the seismic event which happened on the east coast of Japan, near the Fukushima Prefecture on November 21, 2016 (in UTC), with a magnitude of 6.9 and a depth of 11.4 km. We mainly focus on the atmospheric and ionospheric irregularities via acoustic and electromagnetic channels originating from earthquakes in the process of the lithosphere, atmosphere, and ionospheric coupling (LAIC) mechanism. In the acoustic channel, we study the seismogenic atmospheric gravity wave (AGW) which perturbs the local lower atmosphere. The observation of nighttime fluctuations in the very low frequency (VLF) signals and total electron content (TEC) is used to investigate the atmospheric perturbation through the electromagnetic channel. For the ground‐based observations, a VLF signal network consisting of 5 receivers in Japan is used to study by recording the VLF amplitude transmitted from the Japanese transmitter JJI (22.2 kHz). VLF nighttime fluctuation is used to check the unusualities due to the earthquake. Preseismic wavelike structures having periods of AGW are observed in the nighttime signal. Direct investigation of such AGWs is done by computing the potential energy related to AGW from the sounding of the atmosphere using broadband emission radiometry (SABER) temperature profiles mounted on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite. Ionospheric TEC inspection is done by using a ground‐based global navigation satellite system (GNSS) receiver from the International GNSS Survey (IGS) station MIZU in Japan and observing anomalies in diurnal TEC around 6 and 10 days prior to the earthquake. We also obtain the wavelike structure of AGW from the small‐scale fluctuation of TEC using wavelet analysis. All the parameters are found to be preseismic for this earthquake; the acoustics channel gives more consistent outcomes than the electromagnetic channel.
LncRNA LINC01207 Could Positively Regulate the Development of Colorectal Cancer
Authors: Hu Gaowu , Chen Wenquan , Peng Wei , Cao Yongqing
Published: February 2023

Background. LINC01207 expression is associated with colorectal cancer progression. However, the exact role of LINC01207 in colorectal cancer (CRC) is not clear, and further exploration is needed. Methods. Gene expression data of the GSE34053 database were used to explore the differential expressed genes (DEGs) between colon cancer cells and normal cells. The gene expression profiling interactive analysis (GEPIA) was used to determine the differential expression of LINC01207 between CRC and normal tissues and the association between the expression of LINC01207 and survival in patients with CRC. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were performed to obtain the biological processes and pathways associated with DEGs and LINC01207 coexpressed genes in CRC. The qRT-PCR was used to determine the LINC01207 level in CRC cell lines and tissue samples. CCK-8 assay was employed to measure cell viability and Transwell assay to assess cell invasion and migration. Results. In this study, a total of 954 DEGs were identified, including 282 upregulated and 672 downregulated genes. LINC01207 was significantly upregulated in CRC samples with a poor prognosis. LINC01207 was also associated with pathways such as ECM-receptor interaction, O-glycan processing, and TNF signaling pathway in CRC. Knockdown of LINC01207 inhibited the migration, invasion, and proliferation of CRC cells. Conclusion. LINC01207 might act as an oncogene and promote the progression of CRC. Our study suggested that LINC01207 had the potential to be a novel biomarker for CRC detection and a therapeutic target for CRC treatment.
A Brief Review of Systems, Cybernetics, and Complexity
Authors: Tabilo Alvarez Jorge , Ramírez-Correa Patricio
Published: February 2023

We believe that the ongoing global pandemic has highlighted the need for comprehensive approaches to address issues that transcend geographical and cultural boundaries. Therefore, this article aims to provide a general but abstract review to allow readers of a broad spectrum to learn the basic principles of three related concepts: systems, cybernetics, and complexity. Additionally, to better exemplify these concepts, we offer a review of works from the last decade that use systems theory, complexity, and cybernetics for their development. In this context, the result of this review will allow for breaking down the barriers of reductionist silos of knowledge and fostering a multidisciplinary and interdisciplinary dialogue.
The Prophylaxis Effect of Ephedrine on Hemodynamic Variation in Patients Undergoing Percutaneous Nephrolithotomy Surgery with Spinal Anesthesia
Authors: Sane Shahryar , Nasiri Ali Akbar , Bahrami Ayatay , Kamalov Zaynitdin , Hamoud Alshahrani Shadia , Sajid Abdulabbas Hadi , Darvishzadehdaledari Shahram , Golabi Parang , Kazemi Haki Behzad
Published: February 2023

Background. Performing spinal anesthesia with at least hemodynamic variation and complications is always challenging for anesthesiologists. In this study, we investigated the effect of ephedrine and placebo on hemodynamic changes in patients undergoing percutaneous nephrolithotomy with spinal anesthesia. Methods. This randomized, double-blind prospective clinical trial was conducted on 120 patients aged 20‒60 years with ASA (American Society of Anesthesiologists) classes I and II. Patients who were candidates for percutaneous nephrolithotomy with spinal anesthesia were divided into intervention (received 1 cc = 5 mg ephedrine) and control groups (received 1 cc normal saline). All vital parameters, including HR (heart rate) and NIBP (noninvasive blood pressure), were recorded perioperatively T0–T25) and finally at the end of surgery time (Tf). The results were analyzed by SPSS software version 23, and a

P

value ≤0.05 was considered significant. Results. The mean arterial pressure during surgery between T3 and T9 and the mean heart rate in times of T3–T8 in the intervention group were higher than in the control group, and this difference was statistically significant (

P
<
0.05

). The incidence of hypotension, bradycardia, nausea, and vomiting and the amount of prescribed ephedrine, atropine, and ondansetron in the control group were higher than in the intervention group (

P
=
0.001

). Seven patients in the control group and four in the intervention group had shivering, but this difference was not statistically significant (

P
=
0.43

). Conclusion. This study showed the effectiveness of the prescription of 5 mg ephedrine two minutes before changing from the lithotomy position to the supine in maintaining hemodynamic stability, reducing hypotension, bradycardia, nausea, and vomiting, and the amount of prescribed ephedrine, atropine, and ondansetron. Trial Registrations. This trial is registered with IRCT20160430027677N22.
Cancer Epidemiology in Hispanic Populations: An Analysis of Funded Observational Research at the National Cancer Institute
Authors: Elena Joanne W. , Gallicchio Lisa , Pottinger Camille A. , Akif Kaitlin E. , Hanisch Rachel , Kennedy Amy E. , Lai Gabriel Y. , Mahabir Somdat , Martin Damali N. , Srinivasan Shobha , Yu Catherine T. , Lam Tram K.
Published: February 2023

Background:

More than 62 million people self-identified as Hispanic/Latino (H/L) in the 2020 United States census. The U.S. H/L population has higher burden of certain cancers compared with their non-Hispanic White counterparts.

Methods:

Key term search using the NIH Query/View/Report (QVR) system, along with Research, Condition, and Disease Categorization codes identified cancer epidemiology research grants in H/L populations funded by the NCI as a primary or secondary funder from fiscal years 2016 through 2021. Three reviewers identified eligible grants based on specified inclusion/exclusion criteria and a codebook for consistency extracting key characteristics.

Results:

A total of 450 grants were identified through the QVR system using key words related to H/Ls; 41 cancer epidemiology grants remained after applying exclusion criteria. These grants contained specific aims focused on H/Ls (32%) or included H/Ls as part of a racial/ethnic comparison (68%). NCI was the primary funder of the majority of the grants (85%), and most of the research grants focused on cancer etiology (44%) and/or survivorship (49%). Few grants (10%) investigated environmental exposures.

Conclusions:

This article provides an overview of NCI-funded cancer epidemiology research in H/L populations from 2016 to 2021. Future cancer epidemiology research should reflect the changing dynamics of the U.S. demography with diverse, representative populations and well-characterized ethnicity.

Impact:

Research that carefully measures the relevant biological, environmental, behavioral, psychologic, sociocultural, and clinical risk factors will be critical to better understanding the nuanced patterns influencing cancer-related outcomes in the heterogenous H/L population.
The Search for Genomic Biomarkers of Response to Immunotherapy in Ovarian Cancer
Authors: Al-Rawi Duaa H. , Rusk Nicole , Friedman Claire F.
Published: February 2023

Immune checkpoint blockade has been ineffective in ovarian cancer, and there is an ongoing effort to identify biomarkers of therapeutic benefit. Despite promising preclinical data, a substudy of the IMagyn050 trial found that patients with homologous recombination deficient tumors did not have improved progression-free survival with the addition of the PD-L1 inhibitor atezolizumab.

See related article by Landen et al., p. 1698
Probabilistic Mixture Models Improve Calibration of Panel-derived Tumor Mutational Burden in the Context of both Tumor-normal and Tumor-only Sequencing
Authors: Anaya Jordan , Sidhom John-William , Cummings Craig A. , Baras Alexander S. ,   for AACR Project GENIE Consortium
Published: February 2023

Background:

Tumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel–based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability. With TMB cutoffs being developed from panel-based assays, there is a need to understand how to properly estimate exomic TMB values from different panel-based assays.

Design:

Our approach to calibration of panel-derived TMB to exomic TMB proposes the use of probabilistic mixture models that allow for nonlinear relationships along with heteroscedastic error. We examined various inputs including nonsynonymous, synonymous, and hotspot counts along with genetic ancestry. Using The Cancer Genome Atlas cohort, we generated a tumor-only version of the panel-restricted data by reintroducing private germline variants.

Results:

We were able to model more accurately the distribution of both tumor-normal and tumor-only data using the proposed probabilistic mixture models as compared with linear regression. Applying a model trained on tumor-normal data to tumor-only input results in biased TMB predictions. Including synonymous mutations resulted in better regression metrics across both data types, but ultimately a model able to dynamically weight the various input mutation types exhibited optimal performance. Including genetic ancestry improved model performance only in the context of tumor-only data, wherein private germline variants are observed.

Significance:

A probabilistic mixture model better models the nonlinearity and heteroscedasticity of the data as compared with linear regression. Tumor-only panel data are needed to properly calibrate tumor-only panels to exomic TMB. Leveraging the uncertainty of point estimates from these models better informs cohort stratification in terms of TMB.
Factors that Influence Career Choice of Graduating Pharmacy Students
Authors: Gargalicano Charles Angel G. , De Oca Phillip Raymund , Galicia Hernane Jr. S. , Arosa April Aileen Mae , Radadon Shania Tyne A. , Gargalicano Florida G.
Published: February 2023

The adoption of the “nine-star pharmacist” concept by the International Pharmaceutical Federation has transformed the pharmacy practice from a drug and quality control-focused profession to a multifaceted field. However, there is still inequitable pharmacist distribution among career sectors. Also, alarming is the exceeding interest in a specific pharmacy field. Hence, it is crucial to assess pharmacy students' attitudes and preferences toward various pharmaceutical sectors. This descriptive research determined the career preference of 4th- year Pharmacy students of a Private College for Academic year 2021- 2022. It also determined the factors that influence their choice. Data analysis was done using frequency, percentage, mean, Standard Deviation, and Kruskal-Wallis in SPSS. Results revealed that most graduating prefer community pharmacy as their career choice. It also highlighted that experience of the curriculum course, training, influence of family & friends, and knowledge of the career opportunities in various pharmacy fields are factors that influence their career choice. Lastly, career preference alone is not a sufficient indicator to measure influence on choice of pharmacy graduates, and other variables for demographic profiling should be included. Moreover, a support program in the pharmacy curriculum for systematic delivery of information and skills pertinent to effective development of competent professional pharmacists is encouraged.
Family Perfectionism among Russian College Students
Authors: Camp Emily E. , Sheveleva Marina S. , Permyakova Tatiana M. , Wang Kenneth T.
Published: February 2023

Background. Research documenting the consequences of perfectionism on psychopathology and academic achievement across diverse cultures proliferates. This paper situates the multidimensional model of perfectionism and the role of family perfectionism within a Russian context. Objective. The main purposes are to investigate the psychometric properties of the Family Almost Perfect Scale (FAPS) among Russian college students and to explore whether the different types of perfectionistic families found in past studies are replicated in the sample. The impact of both personal and family aspects of perfectionism on psychological and academic outcomes is investigated. Design. The psychometric properties of a Russian family perfectionism measure were examined using 169 students (50 men, 119 women), recruited at a national university in Perm, Russia. Their overall average age was 19.60 (SD = 0.63), ranging from 18 to 23 (Men: M = 19.72, SD = 0.76; Women: M = 19.55, SD = 0.56). Results. Results indicated that the adjusted 15-item Russian Family Almost Perfect Scale (FAPS) yielded adequate factor structure, construct validity, and internal consistency reliability. The distinctively adaptive and maladaptive natures of the Family Standards and Family Discrepancy subscales were supported through correlations with psychological distress measures, as well as the three different types of perfectionistic families that were replicated through cluster analyses. The adaptive, maladaptive, and non-perfectionistic families mirrored the groups found in past studies. In comparing individuals of various family types, those from maladaptive perfectionistic families reported higher levels of depressive mood and anxiety than those from adaptive perfectionistic families. Conclusion. Findings implicate the relevancy of this construct to college students’ psychological well-being. The Russian FAPS could be used in future research to further explore perceived family perfectionism among Russian-speaking populations.
The Resilience of Russian Families in the Face of Modern Challenges: A Pilot Study
Authors: Odintsova Maria A. , Lubovsky Dmitry V. , Ivanova Polina A. , Gusarova Elena S.
Published: February 2023

Background. A challenge is considered a “wake-up call” for family resilience, requiring a proper response (willingness to evaluate, understand an event and its signals, and also give an adequate response). Family resilience is defined as an adequate response to challenges, that is, the ability to cope with them based on the cultural-historical context and family resources, such as clear and open family communication and connectedness, the use of social resources, a broad system of values ​​and senses, the ability to derive meaning from adversity, acceptance, and flexibility. Objective. This article reports on a study which aimed to analyze components of the resilience of Russian families in response to life circumstances that have become challenges for them. Design. The study was conducted from March 20 to May 7, 2022. Participants took an online survey on Yandex-forms; the link to the survey was distributed through social networks on public pages and in private messages. Two hundred seventy-four (274) representatives of Russian families responded, including 234 women and 40 men (14.6%) ranging from age 17 to 65 (cf. 34.1+12.5). After removing the data of 15 participants who did not report a negative event in their families, the final sample consisted of 259 people. Results. Challenges for modern Russian families can be categorized as loss challenges, relationship challenges, global challenges, challenges of illness, and challenges of financial well-being. The challenges of loss stimulate flexibility of response, acceptance, and overcoming suffering through mutual understanding. Global challenges and the challenges of illness awaken family spirituality. The ability to discuss problems together and share decision making becomes a resource to meet the challenge of families’ financial well-being. Confidence in solving problems and a positive outlook become resources to face relationship challenges. The intensity of events is a signal for a family to evoke communication and connectedness, acceptance, and flexibility, as well as family resilience as a whole. Conclusion. There is a connection between the difficulties that respondents regard as challenges and the characteristics of their family resilience; the most adequate response to challenges is to increase family resilience.
Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets
Authors: Geiss Andrew , Hardin Joseph C.
Published: February 2023

Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data.

Significance Statement

Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.