bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractSpatiotemporal 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.
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

AbstractStress 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

AbstractDeveloping 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

AbstractProtein 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

AbstractSuspension 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

AbstractThe 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

AbstractPerfusable 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

AbstractDiversification 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

AbstractMethods 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

AbstractSocioeconomic 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

AbstractThe 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

AbstractThere 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

AbstractThis 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

AbstractSeveral 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

AbstractRecently, 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

AbstractImage 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

AbstractFace 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.