Research papers

Squeeze-and-Excitation Networks

Jie Hu, Li Shen, Gang Sun

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet.

Artificial intelligence in radiology

Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz, Hugo J.W.L. Aerts

Overview of Game and Content Design for a Mobile Game that will Prepare Students in Calculus and Physics Prerequisites to the Engineering Curriculum

Katherine Smith, John Shull, Patrick S. Heaney, Yuzhong Shen, Anthony Dean, Jennifer Michaeli

As part of a research project which assists veterans as they exit the military, complete engineering degrees, and enter the workforce as engineering professionals, a range of serious games for Science, Technology, Engineering, and Mathematics (STEM) education is under development. The current focus of this development is CAPTIVATE, a serious game to assist student veterans in mastering the calculus and physics skills that are necessary prerequisites to the main engineering curriculum. Building on the development and lessons learned from MAVEN, a game developed previously to help student veterans master precalculus skills, the design and initial implementation for CAPTIVATE involves careful consideration regarding game and instructional design. Many of the positive aspects from the design of MAVEN will be implemented in CAPTIVATE. First, the overall framework developed for MAVEN will be reused in CAPTIVATE. This modular framework involves both a model and process that combine game, instructional, and software design in a way that supports adaptability throughout the design and development cycle. Additionally by embedding concepts in game play similar to well-known board games such as Battleship, computer games such as Minesweeper, and console or mobile games such as Guitar Hero, students will use their calculus and physics skills to complete tasks in a familiar environment. In addition, the game itself will consist of a series of sub-games each focusing on a topic that students traditionally struggle to understand. Furthermore, students will be offered access to learning resources and assessed regularly as they progress through the game. CAPTIVATE will also overcome some shortcomings from the previous development. While MAVEN was developed for desktop deployment, CAPTIVATE is targeted for deployment on a variety of mobile device including Apple and Android phones and tablets to engage students in interactive games that support their endeavor to build a solid foundation in mathematics and science topics. Additionally by creating games that are short and easily accessible, students will be able to engage with the material at a time and place convenient for them. The development of CAPTIVATE supports student veterans as they transition from the military to engineering degree programs and helps to accelerate them through their STEM prerequisite courses.

SWISS-MODEL: homology modelling of protein structures and complexes

Andrew Waterhouse, Martino Bertoni, Stefan Bienert, Gabriel Studer, Gerardo Tauriello, Rafal Gumienny, Florian Heer, Tjaart …

Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms

Sudhir Kumar, Glen Stecher, Michael Li, Christina Knyaz, Koichiro Tamura

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

Estimating the global incidence of traumatic brain injury

Michael C. Dewan, Abbas Rattani, Saksham Gupta, Ronnie E. Baticulon, Ya‐Ching Hung, Maria Punchak, Amit Agrawal, Amos O. Ade…

OBJECTIVE: Traumatic brain injury (TBI)-the "silent epidemic"-contributes to worldwide death and disability more than any other traumatic insult. Yet, TBI incidence and distribution across regions and socioeconomic divides remain unknown. In an effort to promote advocacy, understanding, and targeted intervention, the authors sought to quantify the case burden of TBI across World Health Organization (WHO) regions and World Bank (WB) income groups. METHODS: Open-source epidemiological data on road traffic injuries (RTIs) were used to model the incidence of TBI using literature-derived ratios. First, a systematic review on the proportion of RTIs resulting in TBI was conducted, and a meta-analysis of study-derived proportions was performed. Next, a separate systematic review identified primary source studies describing mechanisms of injury contributing to TBI, and an additional meta-analysis yielded a proportion of TBI that is secondary to the mechanism of RTI. Then, the incidence of RTI as published by the Global Burden of Disease Study 2015 was applied to these two ratios to generate the incidence and estimated case volume of TBI for each WHO region and WB income group. RESULTS: Relevant articles and registries were identified via systematic review; study quality was higher in the high-income countries (HICs) than in the low- and middle-income countries (LMICs). Sixty-nine million (95% CI 64-74 million) individuals worldwide are estimated to sustain a TBI each year. The proportion of TBIs resulting from road traffic collisions was greatest in Africa and Southeast Asia (both 56%) and lowest in North America (25%). The incidence of RTI was similar in Southeast Asia (1.5% of the population per year) and Europe (1.2%). The overall incidence of TBI per 100,000 people was greatest in North America (1299 cases, 95% CI 650-1947) and Europe (1012 cases, 95% CI 911-1113) and least in Africa (801 cases, 95% CI 732-871) and the Eastern Mediterranean (897 cases, 95% CI 771-1023). The LMICs experience nearly 3 times more cases of TBI proportionally than HICs. CONCLUSIONS: Sixty-nine million (95% CI 64-74 million) individuals are estimated to suffer TBI from all causes each year, with the Southeast Asian and Western Pacific regions experiencing the greatest overall burden of disease. Head injury following road traffic collision is more common in LMICs, and the proportion of TBIs secondary to road traffic collision is likewise greatest in these countries. Meanwhile, the estimated incidence of TBI is highest in regions with higher-quality data, specifically in North America and Europe.

Plasma Physics via Computer Simulation

C.K. Birdsall, A. B. Langdon

Divided into three main parts, the book guides the reader to an understanding of the basic concepts in this fascinating field of research. Part 1 introduces you to the fundamental concepts of simulation. It examines one-dimensional electrostatic codes and electromagnetic codes, and describes the numerical methods and analysis. Part 2 explores the mathematics and physics behind the algorithms used in Part 1. In Part 3, the authors address some of the more complicated simulations in two and three dimensions. The book introduces projects to encourage practical work Readers can download plasma modeling and simulation software — the ES1 program — with implementations for PCs and Unix systems along with the original FORTRAN source code. Now available in paperback, Plasma Physics via Computer Simulation is an ideal complement to plasma physics courses and for self-study.

Opportunities and obstacles for deep learning in biology and medicine

Travers Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin, T. Brian, Gregory P. Way, Enrico Ferrero, P…

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Protein Phase Separation: A New Phase in Cell Biology

Steven Boeynaems, Simon Alberti, Nicolas L. Fawzi, Tanja Mittag, Magdalini Polymenidou, Frédéric Rousseau, Joost Schymkowitz…

Examining stress perceptions and coping strategies among Saudi nursing students: A systematic review

Leodoro J. Labrague, Denise M. McEnroe–Petitte, Janet Alexis A. De los Santos, Olaide B. Edet

International Journal of Computer Science Issues

The International Journal of Computer Science Issues (IJCSI) is a refereed journal for scientific papers dealing with any area of computer science research. The purpose of establishing the scientific journal is the assistance in development of science, fast operative publication and storage of materials and results of scientific researches and representation of the scientific conception of the society.

Preliminary evaluation of some aspects of the ecology (growth pattern, condition factor and reproductive biology) of African pike, Hepsetus odoe (Bloch 1794), in Lake Eleiyele, Ibadan, Nigeria

Oluniyi Solomon Ogunola, Olawale Ahmed Onada, A.E. Falaye

A preliminary investigation of length-weight relationship, condition factor and reproductive ecology of African pike fish, Hepsetus odoe , was conducted between June to August 2010 in Lake Eleiyele. A total of 90 samples of the fish species were caught by fishermen using gill nets. The results obtained showed that fish species had isometric growth pattern (contrary to negative allometry expected) with the growth exponent, b value of 2.64, that is not statistically different from 3 and with high statistical power of 0.866 when tested. The mean condition factor, K , was found to be 2.6, indicating that the fish species were in good condition in the study area. For reproductive biology, mean fecundity and gonadosomatic index were found to be 1349 eggs and 3.53% respectively. This study was necessary to fill the knowledge gap and provide information for management and conservation of the investigated species in the lake system. This study recommended that further research needs to be conducted because the sampling was carried out in the rainy season period and within a quarter of a year.

Randomization, Bootstrap and Monte Carlo Methods in Biology

Bryan F. J. Manly

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications. This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals. Similar to randomization, the book shows how bootstrapping, or resampling, can be used for confidence intervals and tests of significance. It also explores how to use Monte Carlo methods to test hypotheses and construct confidence intervals.New to the Third EditionUpdated information on regression and time series analysis, multivariate methods, survival and growth data as well as software for computational statisticsReferences that reflect recent developments in methodology and computing techniquesAdditional references on new applications of computer-intensive methods in biologyProviding comprehensive coverage of computer-intensive applications while also offering data sets online, Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition supplies a solid foundation for the ever-expanding field of statistics and quantitative analysis in biology.

Instant messaging and nursing students' clinical learning experience

Christoph Pimmer, Florian Brühlmann, Titilayo Dorothy Odetola, Isaac Oluwafemi Dipeolu, Urs Gröhbiel, Ademola J. Ajuwon

Handbook of Mathematical Functions

David R. Lide

The Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables was the culmination of a quarter century of National Bureau of Standards (NBS) work on core mathematical tools. Evaluating commonly occurring mathematical functions has been a fundamental need as long as mathematics has been applied to the solution of practical problems. In 1938, NBS initiated its Mathematical Tables Project to satisfy the increasing demand for extensive and accurate tables of functions. The Handbook project occurred during the period when general-purpose electronic computing machinery was first coming into use in government research laboratories. A number of difficult mathematical problems that emerged in the course of developing the Handbook engaged researchers in the NBS Applied Mathematics Division for a number of years after its publication. Two of these are especially noteworthy, the first having to do with stability of computations and the second with precision.

Artificial Intelligence in Service

Ming‐Hui Huang, Roland T. Rust

Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.

Comparison of WRF local and nonlocal boundary layer Physics in Greater Kuala Lumpur, Malaysia

Maggie Chel Gee Ooi, Andy Chan, S Kumarenthiran, Kenobi Isima Morris, Muhammad Yaasiin Oozeer, M. A. Islam, Siti Aekbal Sall…

Abstract The urban boundary layer (UBL) is the internal advection layer of atmosphere above urban region which determines the exchanges of momentum, water and other atmospheric constituents between the urban land surface and the free troposphere. This paper tested the performance of three planetary boundary layer (PBL) physics schemes of Weather Research and Forecast (WRF) software to ensure the appropriate representation of vertical structure of UBL in Greater Kuala Lumpur (GKL). Comparison was conducted on the performance of respective PBL schemes to generate vertical and near-surface weather profile and rainfall. Mellor-Yamada- Janjíc (MYJ) local PBL scheme coupled with Eta MM5 surface layer scheme was found to predict the near-surface temperature and wind profile and mixing height better than the nonlocal schemes during the intermonsoonal period with least influences of the synoptic background weather.

Shedding light on the cell biology of extracellular vesicles

Guillaume van Niel, Gisela D’Angelo, Graça Raposo

Artificial intelligence and statistics

Bin Yu, Karl Kumbier

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors’ collaborative research.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Amina Adadi, Mohammed Berrada

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.

The biology and management of non-small cell lung cancer

Roy S. Herbst, Daniel Morgensztern, Chris Boshoff

Non-Hermitian physics and PT symmetry

Ramy El‐Ganainy, Konstantinos G. Makris, Mercedeh Khajavikhan, Ziad H. Musslimani, Stefan Rotter, Demetrios N. Christodoulid…

The Biological Activities of Vitamin D and Its Receptor in Relation to Calcium and Bone Homeostasis, Cancer, Immune and Cardiovascular Systems, Skin Biology, and Oral Health

R. A. G. Khammissa, Jeanine Fourie, Mojakgomo Hendrick Motswaledi, Raoul Ballyram, J Lemmer, L. Feller

Vitamin D plays an important role in calcium homeostasis and bone metabolism, with the capacity to modulate innate and adaptive immune function, cardiovascular function, and proliferation and differentiation of both normal and malignant keratinocytes. 1,25(OH) 2 D, the biologically active form of vitamin D, exerts most of its functions through the almost universally distributed nuclear vitamin D receptor (VDR). Upon stimulation by 1,25(OH) 2 D, VDR forms a heterodimer with the retinoid X receptor (RXR). In turn, VDR/RXR binds to DNA sequences termed vitamin D response elements in target genes, regulating gene transcription. In order to exert its biological effects, VDR signalling interacts with other intracellular signalling pathways. In some cases 1,25(OH) 2 D exerts its biological effects without regulating either gene expression or protein synthesis. Although the regulatory role of vitamin D in many biological processes is well documented, there is not enough evidence to support the therapeutic use of vitamin D supplementation in the prevention or treatment of infectious, immunoinflammatory, or hyperproliferative disorders. In this review we highlight the effects of 1,25(OH) 2 D on bone and calcium homeostasis, on cancer, and refer to its effects on the cardiovascular and immune systems.

On the exact and numerical solutions to a nonlinear model arising in mathematical biology

Asıf Yokuş, Tukur Abdulkadir Sulaıman, Hacı Mehmet Başkonuş, Sibel Paşalı Atmaca

This study acquires the exact and numerical approximations of a reaction-convection-diffusion equation arising in mathematical bi- ology namely; Murry equation through its analytical solutions obtained by using a mathematical approach; the modified exp(-Ψ( η ))-expansion function method. We successfully obtained the kink-type and singular soliton solutions with the hyperbolic function structure to this equa- tion. We performed the numerical simulations (3D and 2D) of the obtained analytical solutions under suitable values of parameters. We obtained the approximate numerical and exact solutions to this equa- tion by utilizing the finite forward difference scheme by taking one of the obtained analytical solutions into consideration. We investigate the stability of the finite forward difference method with the equation through the Fourier-Von Neumann analysis. We present the L 2 and L ∞ error norms of the approximations. The numerical and exact approx- imations are compared and the comparison is supported by a graphic plot. All the computations and the graphics plots in this study are car- ried out with help of the Matlab and Wolfram Mathematica softwares. Finally, we submit a comprehensive conclusion to this study.