bluuebunny/crossref_metadata_2025 huggingface dataset:
Phase 2B randomized controlled trial of NP001 in amyotrophic lateral sclerosis: Pre‐specified and post hoc analyses
Authors: Miller Robert G. , Zhang Rongzhen , Bracci Paige M. , Azhir Ari , Barohn Richard , Bedlack Richard , Benatar Michael , Berry James D. , Cudkowicz Merit , Kasarskis Edward J. , Mitsumoto Hiroshi , Manousakis Georgios , Walk David , Oskarsson Bjorn , Shefner Jeremy , McGrath Michael S.
Published: January 2022

Introduction/Aims

ALS is a heterogeneous disease that may be complicated or in part driven by inflammation. NP001, a regulator of macrophage activation, was associated with slowing disease progression in those with higher levels of the plasma inflammatory marker C‐reactive protein (CRP) in phase 2A studies in ALS. Here, we evaluate the effects of NP001 in a phase 2B trial, and perform a post hoc analysis with combined data from the preceding phase 2A trial.

Methods

The phase 2B trial enrolled 138 participants within 3 y of symptom onset and with plasma hs‐CRP values >1.13 mg/L. They were randomized 1:1 to receive either placebo or NP001 for 6 mo. Change from baseline ALSFRS‐R scores was the primary efficacy endpoint. Secondary endpoints included vital capacity (VC) change from baseline and percentage of participants showing no decline of ALSFRS‐R score over 6 mo (non‐progressor).

Results

The phase 2B study did not show significant differences between placebo and active treatment with respect to change in ALSFRS‐R scores, or VC. The drug was safe and well tolerated. A post hoc analysis identified a 40‐ to 65‐y‐old subset in which NP001‐treated patients demonstrated slower declines in ALSFRS‐R score by 36% and VC loss by 51% compared with placebo. A greater number of non‐progressors were NP001‐treated compared with placebo (p = .004).

Discussion

Although the phase 2B trial failed to meet its primary endpoints, post hoc analyses identified a subgroup whose decline in ALSFRS‐R and VC scores were significantly slower than placebo. Further studies will be required to validate these findings.
Finite element analysis of the singularly perturbed parabolic reaction‐diffusion problems with retarded argument
Authors: Khari Kartikay , Kumar Vivek
Published: January 2022

In this article, a parameter uniform Galerkin finite element method for solving singularly perturbed parabolic reaction diffusion problems with retarded argument is proposed. The solution of this class of problems exhibits parabolic boundary layers. The domain is discretized with a piecewise uniform mesh (Shishkin mesh) for spatial variable to capture the exponential behavior of the solution in the boundary layer region and backward‐Euler method on a equidistant mesh in the time direction. The method is proved to be unconditional stable and parameter uniform. The method is shown to be accurate of order in maximum norm using Green's function approach. The convergence of the proposed method does not depend on the singular perturbation parameter.
Conductive polymer sorbent for extraction and determination of resveratrol and polydatin in Polygonum cuspidatum root samples
Authors: Xue Xuemei , Deng Fei , Ge Zhenkai , Meng Jingwen , Liu Xiya , Zhao Yongheng , Hu Yanggen , Ling Xu
Published: January 2022

Introduction

The quantitative analysis of trace resveratrol and polydatin in plant tissues is suitable for elucidation of the compounds' mechanisms of action.

Objectives

The main objective of this work was to develop a feasible and effective sample pretreatment method to measure the concentrations of resveratrol and polydatin in complex samples.

Methodology

A polymer sorbent, poly(2‐mercaptobenzimidazole), was electrochemically prepared and utilized for selective extraction, while resveratrol and polydatin were used as target analytes. The sorbent was characterized by cyclic voltammetry, scanning electron microscopy and Fourier transform infrared spectroscopy. After extraction and elution, the analytes were analyzed by a Thermo U3000 HPLC system. Several affecting parameters, including the volume of elution solution, sample pH value, sample flow rate and sample volume, were evaluated and optimized.

Results

The proposed method showed good linearity with low limits of detection (from 0.5 to 0.8 ng·mL−1) and ideal accuracy with spiked recoveries from 81.30% to 99.16%. A good enrichment factor (more than 200‐fold) together with good sensitivity was obtained with this method. Analysis of resveratrol and polydatin in Polygonum cuspidatum samples by this method is efficient.

Conclusion

The method developed in this work exhibits several significant merits, including easy operation and high extraction efficiency, indicating that electrochemically prepared polymer sorbent is useful for sample pretreatment and analysis of traditional Chinese medicine samples.
Effect of an active biodegradable package made from bean flour and açaí seed extract on the quality of olive oil
Authors: Nogueira Daiane , Marasca Natasha Spindola , Latorres Juliana Machado , Costa Jorge Alberto Vieira , Martins Vilásia Guimarães
Published: January 2022

The COVID‐19 pandemic has aggravated existing problems associated with the high consumption of plastic products and their incorrect disposal, resulting in an increase in social, economic, and environmental problems. Thus, new combinations of biodegradable materials for food packaging have become the target of frequent research. The present study aimed to produce and evaluate active biodegradable films based on red bean flour and açaí seed extract and also to evaluate the quality of extra virgin olive oil stored in packaging produced for a period of 16 days at 60°C. Films of red bean flour (Phaseolus vulgaris) with 0.5% and 10% açaí seed extract were developed. The addition of the extract to the films increased their tensile strength by 100%, reduced their solubility and transparency, and increased their relative crystallinity. Furthermore, the antioxidant activity presented by the films increased in line with the concentration of extract added. The extra virgin olive oil was packaged in materials produced from bean flour films with 10% extract and maintained its quality parameters within the limits established by legislation. These results suggest the discovery of a biodegradable and antioxidant packaging that can be used to pack olive oil and other fatty food products.
Plasma‐deposited AgOx‐doped TiOx coatings enable rapid antibacterial activity based on ROS generation
Authors: Hegemann Dirk , Hanselmann Barbara , Zuber Flavia , Pan Fei , Gaiser Sandra , Rupper Patrick , Maniura‐Weber Katharina , Ruffieux Kurt , Ren Qun
Published: January 2022

To enable a rapid‐acting antibacterial mechanism without the release of biocidal substances, TiO2 catalysts have been considered based on the generation of reactive oxygen species (ROS). Doping with dissimilar metals generates electron‐hole pairs with narrow band gaps promoting the production of ROS. Here, plasma technology is investigated to deposit Ag nano islets on defective TiOx films, stabilized by plasma postoxidation suppressing Ag ion release. Importantly, ROS generation is maintained upon storage in the dark yet with diminishing efficacy; however, it can be restored by exposure to visible light. The rapid‐acting antibacterial properties are found to strongly correlate with ROS generation, which can even be maintained by functionalization with hydrophobic plasma polymer films. The cytocompatible coatings offer promising applications for implants and other medical devices.
Distributed optimization for a class of uncertain nonlinear multi‐agent systems with arbitrary relative degree subject to exogenous disturbances
Authors: Liu Puzhao , Li Ranran
Published: January 2022

This paper studies the distributed optimization problem on the framework of a class of arbitrary relative degree uncertain nonlinear multi‐agent systems with the presence of exogenous disturbances. Based on the internal model principle and the pseudo gradient approach, a novel continuous‐time distributed optimization control protocol is proposed to make all the agents reach optimal consensus by utilizing local cost functions information and neighbors' states information. The main advantage of our algorithm is that it can run on systems with more complex dynamics, while the existing algorithms apply only to integral‐type systems or unity relative degree nonlinear systems. Moreover, the convergence of the algorithm is proved by Lyapunov function and graph theory analysis. Finally, the distributed protocol is physically implemented by circuits and tested on a group of Chua's circuit systems to show the effectiveness of the theoretical results.
Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress
Authors: Yu Peixin , Zhou Tao , Luo Hui , Liu Xia , Shi Peijun , Zhao Xiang , Xiao Zhiqiang , Zhang Yajie , Zhou Peifang
Published: January 2022

Spatiotemporal patterns of water stress caused by global warming has significantly affected gross primary productivity (GPP). However, its impact is hard to capture as the water stress of different timescales simultaneously influence GPP through the effects of time lag and legacy. As a result, synthetically considering the roles of water stress in the current year and previous years in GPP is very important for accurate modeling of GPP. In this study, we introduced the water stress indicators of standardized precipitation evapotranspiration index (SPEI) at different timescales, GPP observations from global flux network and the remote sensing‐based indexes to models of convolutional neural network (CNN). We built the optimal CNN model which effectively simulated the spatial patterns of GPP and detected their trends in interannual variation. The findings were as follows: (1) The CNN model with introduction of multi‐timescale SPEIs that reflect both current and previous years' water conditions improved the accuracy of modeled GPP. This result indicated that although GPP is most sensitive to current water conditions, it is also clearly affected by previous years' water conditions. (2) The optimal CNN model that considered previous years' water conditions not only aptly simulated the spatial distribution of GPP, but also showed advantages in simulating the interannual fluctuations of GPP and its response to drought. Ignoring the previous water conditions will underestimate the interannual fluctuations of GPP and the impact of drought on GPP.
An efficient penalized estimation approach for semiparametric linear transformation models with interval‐censored data
Authors: Lu Minggen , Liu Yan , Li Chin‐Shang , Sun Jianguo
Published: January 2022

We consider efficient estimation of flexible transformation models with interval‐censored data. To reduce the dimension of semiparametric models, the unknown monotone function is approximated via a monotoneB‐spline. A penalization technique is used to provide computationally efficient estimation of all parameters. To accomplish model fitting and inference, an easy to implement nested iterative expectation‐maximization (EM) algorithm is developed for estimation, and a simple variance‐covariance estimation approach is proposed which makes large‐sample inference for the regression parameters possible. Theoretically, we show that the estimator of the unknown monotone increasing function achieves the optimal rate of convergence, and the estimators of the regression parameters are asymptotically normal and efficient under the appropriate selection of the order of the smoothing parameter and the knots of the spline space. The proposed penalized procedure is assessed through extensive numerical experiments and implemented inRpackagePenIC. The proposed methodology is further illustrated via a signal tandmobiel study.
Five‐factor model personality traits and 24‐h urinary cortisol in the Baltimore Longitudinal Study of Aging
Authors: Sutin Angelina R. , Moffat Scott D. , Resnick Susan M. , Ferrucci Luigi , Aschwanden Damaris , Sesker Amanda A. , Luchetti Martina , Terracciano Antonio
Published: January 2022

Stress is implicated in models of personality and health as a mechanism that explains why traits like conscientiousness and neuroticism are associated with long‐term health outcomes. Evidence for an association between personality and cortisol, a biological marker of stress, however, has been inconsistent. This study examined the association between Five‐Factor Model personality traits and 24‐h urinary cortisol (operationalised as a ratio of urinary free cortisol to creatinine) measured up to 12 times over intervals as long as 30 years in the Baltimore Longitudinal Study of Aging (Mage = 61.21, SD = 15.46; 49% female). There was a modest association between conscientiousness and lower mean‐level cortisol that was attenuated only slightly in the fully‐adjusted model. Neuroticism and the other traits were unrelated to cortisol levels, and none of the traits was related to cortisol change over time. The null association for neuroticism suggests that its relation with long‐term health may be primarily through pathways other than cortisol. The modest association between conscientiousness and 24‐h urinary cortisol replicates a previous finding with a longer‐term measure of cortisol measured from hair, which calls for more research on the robustness and replicability of this finding. Cortisol may be one pathway through which conscientiousness is associated with health outcomes.
Manipulating the Electronic Structure of Graphite Intercalation Compounds for Boosting the Bifunctional Oxygen Catalytic Performance
Authors: Wei Licheng , Zhang Yufei , Yang Yang , Ye Minghui , Li Cheng Chao
Published: January 2022

Developing highly efficient bifunctional catalysts for the oxygen reduction and oxygen evolution reaction (ORR/OER) can open possibilities for future zinc air batteries (ZABs). Herein, cost‐effective and highly conductive few‐layer ferric and nickel chloride co‐intercalated graphite intercalation compounds (FeCl3‐NiCl2‐GIC) are designed as bifunctional oxygen catalysts for ZAB. The optimized few‐layer FeCl3‐NiCl2‐GIC catalyst exhibits a small overpotential of 276 mV at 10 mA cm–2 for the OER and achieves a high onset potential of 0.89 V for the ORR. The theoretical analysis demonstrates the electron‐rich state on the carbon layers of FeCl3‐NiCl2‐GIC during the catalytic process favors the kinetics of electron transfer and lowers the absorption energy barriers for intermediates. Impressively, the ZAB assembled with few‐layer FeCl3‐NiCl2‐GIC catalyst displays a 160 h cycling stability and a high energy efficiency of 72.6%. This work also suggests the possibility of utilizing layer electronic structure regulation on graphite intercalation compounds as effective bifunctional catalysts for ZABs.
Bioresponsive nanogels for protein delivery
Authors: Xu Xiao , Shen Shiyang , Mo Ran
Published: January 2022

Protein drugs have attracted more attentions due to their high specificity and efficacy. However, the poor stability, plasma degradation, inferior cell membrane permeability, and immunogenicity severely limit the in vivo application of protein drugs. Nanogels are the nanosized crosslinked gels with high water‐loading capacities and large cavities for protein loading, which are able to increase the stability and decrease the immunogenicity for protein delivery. The bioresponsive nanogels possess the capability of programmatically releasing the protein drugs in an on‐demand manner at the target sites with distinct biosignals, which show considerable potential to increase the therapeutic efficacies and decrease the adverse effects of the protein drugs. In this review, we outline the recent advance in the bioresponsive nanogels for delivery of protein drugs, and survey the design of new materials and formulations that can respond typical biosignals, such as temperature, pH, reductive potential, and enzyme expression. The prospects and challenges are also discussed.
Progress and challenges in functional nanomaterial‐based suspension array technology for multiplexed biodetection
Authors: Wu Weijie , Liu Xinyi , Li Wanwan
Published: January 2022

Suspension array technology (SAT) has aroused substantial interest due to its strong multiplexed detection capability, quick binding kinetics, high throughput, and high detection sensitivity. Although rapid developments in material science have led to tremendous progress with regards to SAT, several challenges still exist that limit the performance of SAT, including limited barcode numbers, unpredictable barcode signal and suboptimal detection sensitivity. Therefore, major efforts are still needed to further improve performance to fulfill the higher requirements of precision diagnosis. In this review, we focus on summarizing recent advances regarding encoded microspheres and labels for functional nanomaterial‐based suspension array technology, then analyze the current challenges and propose corresponding solutions. We anticipate that this review will be helpful for the future design of functional nanomaterial‐based SAT platforms with superior performance.
Potential of on‐chip analysis and engineering techniques for extracellular vesicle bioproduction for therapeutics
Authors: Piffoux Max , Silva Amanda K. A. , Gazeau Florence , Salmon Hugo
Published: January 2022

The clinical interest around extracellular vesicles (EVs) started during the year 2000s, now leading to new clinical diagnostic and therapeutic strategies. Due to their outstanding properties as biogenic drug delivery systems and as alternatives to cell therapy, the need to produce EVs with sufficiently high yield and quality for clinical use expedited the development of analytical techniques and dedicated bioproduction methods. Though preclinical studies revealed the potential of EVs to become next generation subcellular therapies that could lead to major breakthroughs in current therapeutics possibilities, they remain complex objects on both a physicochemical and biological level. Here, we review the capacity of microfluidic technologies to match EV‐based therapeutics need for clinical translation via standardized and intensified bioproduction methods. Indeed, some of the current routine tools used in bioproduction are already achieved on chips such as micromixers or particle sorting and analysis using field flow fraction or nanoparticle tracking analyzer. Also, microfluidics communities have developed a wide set of new techniques to isolate and quantify EVs, but the few that are adopted in a bioproduction workflow are well‐established since the 1990s. We first review the different EV generation and loading methods embedded on chip. We focus on EVs preparation methods, from purification to in‐line separative techniques. We finally describe the on‐chip analytical tools to analyze physicochemistry and phenotypes of EVs.
Microfluidic systems toward blood hemostasis monitoring and thrombosis diagnosis: From design principles to micro/nano fabrication technologies
Authors: Xu Yi , Yu Guang , Nie Ruqiong , Wu Zhigang
Published: January 2022

Perfusable endothelialized models of microfluidic systems that recapitulate unique biological and biophysical microvasculature conditions are improved with micro/nano engineering advances for monitoring of blood hemostasis and thrombosis treatment. Although bio‐sensors and monitoring devices significantly advance in measuring platelet aggregation and thrombosis kinetics, currently platelet aggregation tests still do not meet the arising clinical requirements. Trying to seek new solutions for such a demanding from clinics, the present review provides an overview of design principles of microfluidic systems and micro/nano fabrication strategies in studying the platelet adhesion and aggregation. We critically sketch the characteristics of microfluidic systems to elucidate the role of platelets in the complex process of thrombus formation. The importance, benefits, and challenges of introduced principles and methods are discussed. The potential from various of basic research to clinical applications is also briefly discussed to help guide designing more versatile point‐of‐care devices for hemostasis monitoring and thrombosis diagnosis and treatment.
Modelling to Evaluate Climate Resilience of Crop Rotations Under Climate Change
Authors: Kersebaum Kurt Christian
Published: January 2022

Diversification of crop rotations is considered as an option to increase the resilience of European crop production under climate change. Although crop rotation design and management has been identified as an important measure to adapt to and mitigate climate change, most studies on climate change impact or adaptation so far use single-year simulations and/or single crop assessments. Crop response to various management options within a growing season is generally taken into account by most crop models. However, if simulations neglect processes and fluxes between growing seasons and potential carry-over effects related to agronomic management, the long-term sustainability of adaptation and mitigation strategies cannot be properly evaluated. Therefore, the integrated assessment of impacts, adaptation and mitigation options under current and future climatic conditions requires a continuous long-term analysis of crop sequences to take into account carry-over effects as in real conditions. The present paper provides information on crop rotation aspects, which should be considered in modelling, presents the current state of modelling for climate impact assessment, address points of uncertainty and missing aspects in modelling and draws an outlook on potential future developments with special emphasis on crop rotations. In conclusion, crop models require suitable experimental data to parameterize additional crops, which were so far not sufficiently investigated to cope with multiple opportunities in crop rotations.
Water Resource System Modelling for Climate Adaptation
Authors: Borgomeo Edoardo
Published: January 2022

Methods and models for water resource system simulation, risk analysis, and decision analysis provide powerful tools for dealing with the challenge of climate change in the water sector. These models enable learning about the complex behaviour of river basins, testing of alternative adaptation decisions, exploration of uncertainties, and navigation of trade‐offs. This paper briefly describes recent advances in decision analysis and simulation modelling for climate adaptation in the water sector. These advances are now relatively mature and are increasingly being applied by practitioners.
Cross-Sectoral Challenges for Adaptation Modelling
Authors: Aaheim Asbjørn , Orlov Anton , Sillmann Jana
Published: January 2022

Socioeconomic studies on adaptation based on bottom-up approaches have been focusing mainly on local impacts of weather-related variations, thereby neglecting potential remote impacts. There is little knowledge about challenges that relate to the global and long-term character of climate change. By contrast, impact assessment studies using top-down approaches, such as multi-region, multi-sector computable general equilibrium (CGE) models, provide a consistent framework to capture potential remote impacts, which occur through cross-sectoral and cross-regional interactions. Here we present main findings of our economic impact assessments of climate change and adaption modelling. Furthermore, we discuss the challenges for incorporating adaptation measures and policies into macroeconomic models.
Operationalizing Climate Proofing in Decision/Policy Making
Authors: Giupponi Carlo
Published: January 2022

The purpose of this work is to present an operational approach to include consideration of global change drivers (climatic, economic, social, etc.) in support to the design of local policies or investment plans. In both cases decision/policy makers typically have sets of plausible solutions and decisions to be taken in terms of choices among sets of plausible solutions with the best knowledge about the future dynamics of endogenous and exogenous system variables. The ambition is to identify the preferable solution(s) (in terms of technical performances, acceptance by stakeholders, cost–benefit ratio, etc.) in a medium term perspective, (e.g., 10–40 years), with current knowledge about the problem and under the effect of important sources of uncertainty (both aleatory and epistemic). Common to most decision contexts in a medium term perspective typical of both investment decisions and adaptation policies is the prevalence of economic signals in the shorter term and of climatic signals in the longer term. Models play a fundamental role in both cases, but they rarely cover the whole set of variables needed for decision making and the outcomes usually require integration of qualitative expert knowledge or simply subjective judgements. Multi-criteria analysis coupled with uncertainty analysis can contribute with methodologically sound and operational solutions. This paper elaborates on a series of recent cases with the ambition to extract common elements for a general methodological framework.
Reducing and Managing Uncertainty of Adaptation Recommendations to Increase user's Uptake
Authors: Ruiz-Ramos Margarita , Rodríguez Alfredo
Published: January 2022

Abstract

There are many challenges that adaptation science faces for an effective application of the results and recommendations found. Among the most important are the estimation, management and interpretation of uncertainty. In this article, we present our approach to managing uncertainty in agricultural projections using a combination of techniques to identify uncertainties, exclude unviable outcomes, consider possible futures probabilistically, and select the most robust projections for adaptation. Through an example of the adaptation of winter wheat in Spain, we show how this approach is effective in increasing the probability of avoiding maladaptation and improving the applicability and assimilation of scientific results by users.
DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame
Authors: Hernandez-Ortega Javier , Tolosana Ruben , Fierrez Julian , Morales Aythami
Published: January 2022

This chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the recent fake detector named DeepFakesON-Phys that is based on a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. DeepFakesON-Phys has been experimentally evaluated using the latest public databases in the field: Celeb-DF v2 and DFDC. The results achieved for DeepFake detection based on a single frame are over 98% AUC (Area Under the Curve) on both databases, proving the success of fake detectors based on physiological measurement to detect the latest DeepFake videos. In this chapter, we also propose and study heuristical and statistical approaches for performing continuous DeepFake detection by combining scores from consecutive frames with low latency and high accuracy (100% on the Celeb-DF v2 evaluation dataset). We show that combining scores extracted from short-time video sequences can improve the discrimination power of DeepFakesON-Phys.
Capsule-Forensics Networks for Deepfake Detection
Authors: Nguyen Huy H. , Yamagishi Junichi , Echizen Isao
Published: January 2022

Several sophisticated convolutional neural network (CNN) architectures have been devised that have achieved impressive results in various domains. One downside of this success is the advent of attacks using deepfakes, a family of tools that enable anyone to use a personal computer to easily create fake videos of someone from a short video found online. Several detectors have been introduced to deal with such attacks. To achieve state-of-the-art performance, CNN-based detectors have usually been upgraded by increasing their depth and/or their width, adding more internal connections, or fusing several features or predicted probabilities from multiple CNNs. As a result, CNN-based detectors have become bigger, consume more memory and computation power, and require more training data. Moreover, there is concern about their generalizability to deal with unseen manipulation methods. In this chapter, we argue that our forensic-oriented capsule network overcomes these limitations and is more suitable than conventional CNNs to detect deepfakes. The superiority of our “Capsule-Forensics” network is due to the use of a pretrained feature extractor, statistical pooling layers, and a dynamic routing algorithm. This design enables the Capsule-Forensics network to outperform a CNN with a similar design and to be from 5 to 11 times smaller than a CNN with similar performance.
Future Trends in Digital Face Manipulation and Detection
Authors: Tolosana Ruben , Rathgeb Christian , Vera-Rodriguez Ruben , Busch Christoph , Verdoliva Luisa , Lyu Siwei , Nguyen Huy H. , Yamagishi Junichi , Echizen Isao , Rot Peter , Grm Klemen , Štruc Vitomir , Dantcheva Antitza , Akhtar Zahid , Romero-Tapiador Sergio , Fierrez Julian , Morales Aythami , Ortega-Garcia Javier , Kindt Els , Jasserand Catherine , Kalvet Tarmo , Tiits Marek
Published: January 2022

Recently, digital face manipulation and its detection have sparked large interest in industry and academia around the world. Numerous approaches have been proposed in the literature to create realistic face manipulations, such as DeepFakes and face morphs. To the human eye manipulated images and videos can be almost indistinguishable from real content. Although impressive progress has been reported in the automatic detection of such face manipulations, this research field is often considered to be a cat and mouse game. This chapter briefly discusses the state of the art of digital face manipulation and detection. Issues and challenges that need to be tackled by the research community are summarized, along with future trends in the field.
Multimedia Forensics Before the Deep Learning Era
Authors: Cozzolino Davide , Verdoliva Luisa
Published: January 2022

Image manipulation is as old as photography itself, and powerful media editing tools have been around for a long time. Using such conventional signal processing methods, it is possible to modify images and videos obtaining very realistic results. This chapter is devoted to describe the most effective strategies to detect the widespread manipulations that rely on traditional approaches and do not require a deep learning strategy. In particular, we will focus on manipulations like adding, replicating, or removing objects and present the major lines of research in multimedia forensics before the deep learning era and the rise of deepfakes. The most popular approaches look for artifacts related to the in-camera processing chain (camera-based clues) or the out-camera processing history (editing-based clues). We will focus on methods that rely on the extraction of a camera fingerprint and need some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review the most interesting features of machine learning- based methods and finally present the major challenges in this area.
Morph Creation and Vulnerability of Face Recognition Systems to Morphing
Authors: Ferrara Matteo , Franco Annalisa
Published: January 2022

Face recognition in controlled environments is nowadays considered rather reliable, and very good accuracy levels can be achieved by state-of-the-art systems in controlled scenarios. However, even under these desirable conditions, digital image alterations can severely affect the recognition performance. In particular, several studies show that automatic face recognition systems are very sensitive to the so-called face morphing attack, where face images of two individuals are mixed to produce a new face image containing facial features of both subjects. Face morphing represents nowadays a big security threat particularly in the context of electronic identity documentsbecause it can be successfully exploited for criminal intents, for instance to fool Automated Border Control (ABC) systems thus overcoming security controls at the borders. This chapter will describe the face morphing process, in an overview ranging from the traditional techniques based on geometry warping and texture blending to the most recent and innovative approaches based on deep neural networks. Moreover, the sensitivity of state-of-the-art face recognition algorithms to the face morphing attack will be assessed using morphed images of different quality generated using various morphing methods to identify possible factors influencing the probability of success of the attack.
Adversarial Attacks on Face Recognition Systems
Authors: Xu Ying , Raja Kiran , Ramachandra Raghavendra , Busch Christoph
Published: January 2022

Face recognition has been widely used for identity verification both in supervised and unsupervised access control applications. The advancement in deep neural networks has opened up the possibility of scaling it to multiple applications. Despite the improvement in performance, deep network-based Face Recognition Systems (FRS) are not well prepared against adversarial attacks at the deployment level. The output performance of such FRS can be drastically impacted simply by changing the trained parameters, for instance, by changing the number of layers, subnetworks, loss and activation functions. This chapter will first demonstrate the impact on biometric performance using a publicly available face dataset. Further to this, this chapter will also present some strategies to defend against such attacks by incorporating defense mechanisms at the training level to mitigate the performance degradation. With the empirical evaluation of the deep FRS with and without a defense mechanism, we demonstrate the impact on biometric performance for the completeness of the chapter.
Future Aircraft and the Future of Aircraft Noise
Authors: Knobloch Karsten , Manoha Eric , Atinault Olivier , Barrier Raphaël , Polacsek Cyril , Lorteau Mathieu , Casalino Damiano , Ragni Daniele , Romani Gianluca , Centracchio Francesco , Rossetti Monica , Cioffi Ilaria , Iemma Umberto , Cipolla Vittorio , Frediani Aldo , Jaron Robert , Enghardt Lars
Published: January 2022

In order to cope with increasing air traffic and the requirement to decrease the overall footprint of the aviation sector—making it more sustainably and acceptable for the whole society—drastic technology improvements are required beside all other measures. This includes also the development of novel aircraft configurations and associated technologies which are anticipated to bring significant improvements for fuel burn, gaseous and noise emissions compared to the current state and the current evolutionary development. Several research projects all over the world have been investigating specific technologies to address these goals individually, or novel—sometimes also called “disruptive” —aircraft concepts as a whole. The chapter provides a small glimpse on these activities—mainly from a point of view of recent European funded research activities like Horizon2020 projects ARTEM, PARSIFAL, and SENECA being by no-way complete or exhaustive. The focus of this collection is on noise implications of exemplary novel concepts as this is one of the most complicated and least addressed topics in the assessment of aircraft configurations in an early design stage. Beside the boundary layer ingestion concept, the design process for a blended wing body aircraft is described, a box-wing concept is presented and an outlook on emerging supersonic air transport is given.
Introduction
Authors: Schweitzer Reinhard
Published: January 2022

What happens in institutions like schools or hospitals when local service provision overlaps with the control of national borders? Such overlap is unavoidable if unlawful residents are to be excluded from mainstream public services. With this explicit aim, governments not only modify the rules and established practices of welfare provision, but also encourage the people who administer and deliver these services to incorporate the logic of immigration control into their everyday work. To identify and better understand the concrete mechanisms that either help or hinder such internalisation of immigration control, this study systematically compares three spheres of service provision – healthcare, education and social assistance – across two distinctive legal-political environments: Barcelona/Spain and London/UK. Looking at official policies as well as their implementation, it primarily draws on a total of almost 90 semi-structured interviews with irregular residents, providers and administrators of local services, and representatives of NGOs and local government. Its innovative analytical framework helps to map and explain the significant variation in how immigration control works within different institutions and how individual actors occupying key positions in these can reproduce, contest, or readjust formal structures of inclusion and exclusion.
Managing Irregularity Through the Provision of Public Education
Authors: Schweitzer Reinhard
Published: January 2022

This chapter applies the conceptual and analytical frameworks to the institutional sphere of education and thereby draws systematic comparisons between the two national and local contexts. First, I closely examine the relevant laws and policies enacted at various administrative levels as well as local implementation practices, by drawing on relevant documents and the original accounts of my interview respondents. This allows me to map the various ethical concerns, practical difficulties and organisational conflicts that either migrant irregularity itself, or the internalisation of its control, creates for individuals and the institutions they work for. At the end of the chapter, and with the help of my analytical framework, I summarise the main differences between the two contexts in terms of how they position the various categories of workers in relation to migrant irregularity and its control.
A homological approach to pseudoisotopy theory. I
Authors: Krannich Manuel
Published: January 2022

We construct a zig–zag from the once delooped space of pseudoisotopies of a closed 2n-disc to the once looped algebraic K-theory space of the integers and show that the maps involved are p-locally $$(2n-4)$$

(
2
n
-
4
)

-connected for $$n\,{>}\,3$$

n

>

3

and large primes p. The proof uses the computation of the stable homology of the moduli space of high-dimensional handlebodies due to Botvinnik–Perlmutter and is independent of the classical approach to pseudoisotopy theory based on Igusa’s stability theorem and work of Waldhausen. Combined with a result of Randal-Williams, one consequence of this identification is a calculation of the rational homotopy groups of $$\mathrm {BDiff}_\partial (D^{2n+1})$$

BDiff
∂

(

D

2
n
+
1

)

in degrees up to $$2n-5$$

2
n
-
5

.
Studies on archaeological olive fruitstones from the Archaic and Punic periods (7th–3rd century bc) of Sardinia, Italy
Authors: Mureddu Maria , Ucchesu Mariano , Sarigu Marco , Del Vais Carla , Piras Fabio , Sanna Ignazio , Bacchetta Gianluigi
Published: January 2022

Recent archaeological investigations in the lagunas di Santa Giusta and Mistras, waterlogged sites in central-west Sardinia, Italy, have enabled the recovery of archaeobotanical remains, exceptional in terms of quantity and preservation, dated to the Archaic and Punic periods in the 7th–3rd century bc. Among the finds was a significant amount of Olea europaea fruitstones (endocarps), which are discussed here. The morphometric features of these fruitstones, extrapolated by image analysis, were analysed statistically and compared to modern wild olive populations and cultivars. Thanks to the image analysis, it was possible to recognize the presence of O. europaea var. sylvestris (wild olive) and O. europaea var. europaea (cultivated olive) from the Archaic and Punic periods and to make suggestions about their use. Moreover, most of the cultivated type fruitstones identified by the statistical analysis can be attributed to a group of modern Sardinian cultivars, providing new data on the origin of cultivation and use of olives in Sardinia.
Understanding and reducing the disaster risk of landslide-induced tsunamis: a short summary of the panel discussion in the World Tsunami Awareness Day Special Event of the Fifth World Landslide Forum
Authors: Sassa Shinji , Grilli Stephan T. , Tappin David R. , Sassa Kyoji , Karnawati Dwikorita , Gusiakov Viacheslav K. , Løvholt Finn
Published: January 2022

A World Tsunami Awareness Day Special Event was held in hybrid mode on 5 November 2021, during the Fifth World Landslide Forum, in Kyoto, Japan. In this context, a panel discussion was organized across America, Europe, and Asia, with the goal to better understand and reduce the disaster risk of landslide-induced tsunamis, consistent with the Kyoto Landslide Commitment 2020. This article presents a short summary of this panel discussion.
Accessing the invisible population of low-risk gamblers, issues with screening, testing and theory: a systematic review
Authors: Davies Nyle Hawk , Roderique-Davies Gareth , Drummond Laura Catherine , Torrance Jamie , Sabolova Klara , Thomas Samantha , John Bev
Published: January 2022

Background

While it is a generally accepted fact that many gambling screening tools are not fit for purpose when employed as part of a public health framework, the evidence supporting this claim is sporadic. The aim of this review is to identify and evaluate the gambling screening tools currently in use and examine their utility as part of a public health approach to harm reduction, providing a holistic snapshot of the field.

Methods

A range of index tests measuring aspects of problem gambling were examined, including the South Oaks Gambling Screen (SOGS) and the Problem Gambling Severity Index (PGSI), among others. This review also examined a range of reference standards including the Diagnostic Interview for Gambling Severity (DIGS) and screening tools such as the SOGS.

Results

The present review supports the belief held by many within the gambling research community that there is a need for a paradigm shift in the way gambling harm is conceptualised and measured, to facilitate early identification and harm prevention.

Discussion

This review has identified a number of meaningful deficits regarding the overall quality of the psychometric testing employed when validating gambling screening tools. Primary among these was the lack of a consistent and reliable reference standard within many of the studies. Currently there are very few screening tools discussed in the literature that show good utility in the domain of public health, due to the focus on symptoms rather than risk factors. As such, these tools are generally ill-suited for identifying preclinical or low-risk gamblers.
COVID-19 Vaccination Uptake and Hesitancy in a National Sample of Australian Gay and Bisexual Men
Authors: Holt Martin , MacGibbon James , Bavinton Benjamin , Broady Timothy , Clackett Shawn , Ellard Jeanne , Kolstee Johann , Molyneux Angus , Murphy Dean , Power Cherie , de Wit John
Published: January 2022

Minority groups may face additional barriers to vaccination. In April–June 2021, we assessed the level of COVID-19 vaccination and willingness to be vaccinated in a national, online survey of 1280 gay and bisexual men in Australia. Over a quarter of the sample (28.0%) had been partially or fully vaccinated, and 80.0% of the unvaccinated were willing to be vaccinated. Vaccination was independently associated with older age, being university educated, and HIV status (with HIV-positive participants being more likely and untested participants less likely to be vaccinated). Willingness to be vaccinated was independently associated with living in a capital city and being university educated. Those who had lost income or their job due to COVID-19 were less willing to be vaccinated. Our results suggest encouraging COVID-19 vaccination among those with lower levels of health literacy and supporting those who have experienced financial stress because of the pandemic.
General Performance Score for classification problems
Authors: De Diego Isaac Martín , Redondo Ana R. , Fernández Rubén R. , Navarro Jorge , Moguerza Javier M.
Published: January 2022

Several performance metrics are currently available to evaluate the performance of Machine Learning (ML) models in classification problems. ML models are usually assessed using a single measure because it facilitates the comparison between several models. However, there is no silver bullet since each performance metric emphasizes a different aspect of the classification. Thus, the choice depends on the particular requirements and characteristics of the problem. An additional problem arises in multi-class classification problems, since most of the well-known metrics are only directly applicable to binary classification problems. In this paper, we propose the General Performance Score (GPS), a methodological approach to build performance metrics for binary and multi-class classification problems. The basic idea behind GPS is to combine a set of individual metrics, penalising low values in any of them. Thus, users can combine several performance metrics that are relevant in the particular problem based on their preferences obtaining a conservative combination. Different GPS-based performance metrics are compared with alternatives in classification problems using real and simulated datasets. The metrics built using the proposed method improve the stability and explainability of the usual performance metrics. Finally, the GPS brings benefits in both new research lines and practical usage, where performance metrics tailored for each particular problem are considered.
Constructing a Ferroptosis-related Long Non-coding RNA Signature to Predict the Prognostic of Head and Neck Squamous Cell Carcinoma Patients by Bioinformatic Analysis
Authors: Lu Rui , Li Zhiyong , Yin Shucheng
Published: January 2022

Ferroptosis is a novel discovered iron-dependent mode of regulated cell death (RCD) which characterized non-apoptosis. Researches have shown the effect of ferroptosis in the biological activities of tumors. But there is no relevant study showing the relationship between ferroptosis-related genes and head and neck squamous cell carcinoma (HNSCC). In this study, we paid attention to several ferroptosis-related lncRNAs in head and neck squamous cell carcinoma and its clinical features. HNSCC data were obtained from The Cancer Genome Atlas (TCGA) database. Ferroptosis-related lncRNAs were selected by the coexpression network. Then, we identified prognostic ferroptosis-related lncRNAs in HNSCC patients and constructed a ferroptosis-related lncRNAs signature by Lasso cox regression. Besides, GSEA analysis was performed to explore the functional enrichment of ferroptosis-related lncRNAs. Nine ferroptosis-related lncRNAs (AC004687.1, AL450992.2, AC010894.2, AL451085.2, AC104083.1, LIPE-AS1, AC108010.1, CTBP1-DT, and PTCSC2) were identified to have the independent prognostic value in HNSCC patients and the ferroptosis-related lncRNAs signature was constructed based on these nine genes. According to the risk score of the signature, the high-risk group had shorter overall survival (OS) compared with the low-risk group. Risk score showed to be an independent factor for the patients with HNSCC. Additionally, the nomogram was generated and the nine lncRNAs were mainly enriched in phagocytosis, metabolism, and chemokine signaling pathways. The ferroptosis-related lncRNA signature has effects on the prognostic prediction of patients with HNSCC which may serve as potential therapeutic targets for patients with HNSCC.