bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of CYP303A1 and its potential application based on ZIF‐8 nanoparticle‐wrapped dsRNA in Nilaparvata lugens (Stål)
Authors: Du Zuyi , Zhang Guijian , Yu Chang , Qin Yao , He Shun , Li Jianhong , Guo Le , Wan Hu
Published: October 2024

BACKGROUND

RNA interference (RNAi) technology has been put forward as a promising method for pest control and resistance management. Mining highly efficient lethal genes and constructing stable double‐stranded RNA (dsRNA) delivery systems are of great significance to improve the application potential of RNAi technology.

RESULTS

In this study, we characterized a molting‐related gene, NlCYP303A1, in Nilaparvata lugens that was highly expressed in the cuticle and at the end stages of each instar in nymphs. Silencing the expression of NlCYP303A1 in N. lugens resulted in a deformed phenotype and a significant increase in mortality. Furthermore, interfering with NlCYP303A1 changed the relative expression of key genes in the chitin synthesis and degradation pathway. Finally, we used the nanocarrier zeolitic imidazolate framework‐8 (ZIF‐8) to load dsNlCYP303A1, forming a complex denoted as dsNlCYP303A1@ZIF‐8. The results of both feeding and rice‐seedling dip experiments indicated that the expression of NlCYP303A1 was dramatically and persistently suppressed by the dsNlCYP303A1@ZIF‐8 treatment, compared with treatment with dsNlCYP303A1, suggesting that ZIF‐8 can enhance the interference efficiency as well as the stability of dsNlCYP303A1.

CONCLUSIONS

Our results demonstrate that the lethal gene NlCYP303A1 can be employed as an excellent target for RNAi technology by loading onto a nano‐delivery system, and provide new insights into the creation of innovative pest control approaches. © 2024 Society of Chemical Industry.
Combining satellite and field data reveals Congo's forest types structure, functioning and composition
Authors: Picard Juliette , Nungi‐Pambu Dembi Maïalicah M. , Barbier Nicolas , Cornu Guillaume , Couteron Pierre , Forni Eric , Gibbon Gwili , Lim Felix , Ploton Pierre , Pouteau Robin , Tresson Paul , van Loon Tom , Viennois Gaëlle , Réjou‐Méchain Maxime
Published: October 2024

Tropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental drivers of the main forest types, their forest structure, their floristic and functional compositions and their faunistic composition. To build the map, we used Sentinel‐2 satellite images and recent deep learning architectures. We tested the effect of topographically determined water availability on vegetation type distribution by linking the map with a water drainage depth proxy (HAND, height above the nearest drainage index). We also described vegetation type structure and composition (floristic, functional and associated fauna) by linking the map with data from large inventories and derived from satellite images. We found that water drainage depth is a major driver of forest type distribution and that the different forest types are characterized by different structure, composition and functions, bringing new insights about their origins and successional dynamics. We discuss not only the crucial role of soil–water depth, but also the importance of consistently reproducing such maps through time to develop an accurate monitoring of tropical forest types and functions, and we provide insights on peculiar forest types (Marantaceae forests and monodominant Gilbertiodendron forests) on which future studies should focus more. Under the current context of global change, expected to trigger major forest structural and compositional changes in the tropics, an appropriate monitoring strategy of the spatio‐temporal dynamics of forest types and their associated floristic and faunistic composition would considerably help anticipate detrimental shifts.
Deep Neural Network‐Based Accelerated Failure Time Models Using Rank Loss
Authors: Kim Gwangsu , Park Jeongho , Kang Sangwook
Published: October 2024

An accelerated failure time (AFT) model assumes a log‐linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of model has been considered a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the non‐linearity of predictors when modeling the mean. Deep neural networks (DNNs) have received much attention over the past few decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing non‐linearity. Here, we propose applying a DNN to fit AFT models using Gehan‐type loss combined with a sub‐sampling technique. Finite sample properties of the proposed DNN and rank‐based AFT model (DeepR‐AFT) were investigated via an extensive simulation study. The DeepR‐AFT model showed superior performance over its parametric and semiparametric counterparts when the predictor was nonlinear. For linear predictors, DeepR‐AFT performed better when the dimensions of the covariates were large. The superior performance of the proposed DeepR‐AFT was demonstrated using three real datasets.
Testing Similarity of Parametric Competing Risks Models for Identifying Potentially Similar Pathways in Healthcare
Authors: Möllenhoff Kathrin , Binder Nadine , Dette Holger
Published: October 2024

The identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transition intensities may be specified by a variety of parametric distributions, thus in particular being possibly time‐dependent. We assess the similarity between two such models by examining the transitions between different health states. This research introduces a method to measure the maximum differences in transition intensities over time, leading to the development of a test procedure for assessing similarity. We propose a parametric bootstrap approach for this purpose and provide a proof to confirm the validity of this procedure. The performance of our proposed method is evaluated through a simulation study, considering a range of sample sizes, differing amounts of censoring, and various thresholds for similarity. Finally, we demonstrate the practical application of our approach with a case study from urological clinical routine practice, which inspired this research.
Modulating Aggregation Behavior by Ternary Strategies for Efficient and Stable Thick‐Film Organic Solar Cells
Authors: Xu Jie , Wang Lei , Jeong Seonghun , Xia Shuangshuang , Zhao Xiaohong , Yang Changduk , Hu Yu , Yuan Zhongyi
Published: October 2024

Functional third components targeted to improve a specific property of organic solar cells is an effective strategy. However, introducing a third component to simultaneously improve efficiency and stability and achieve good performance in thick‐film devices has rarely been reported. Herein, low diffusion third components IDCN and ID2CN are reported to achieve a power conversion efficiency (PCE) of 18.08% and a high short‐circuit current (J SC) of 27.82 mA cm−2, one of the highest values based on PM6:Y6. They increase light harvesting in the range of 400–500 nm while enhancing energy transfer via Förster resonance energy transfer (FRET). A tightly ordered molecular arrangement is achieved by modulating the preaggregation and film formation kinetics of Y6, which enhance exciton dissociation and charge transport. Moreover, the low‐diffusion third component can effectively restrict the diffusion of Y6 to improve the morphology stability, and the T90 lifetime is increased from 689 to 1545 h. In 300 nm thick‐film devices, PM6:ID2CN:Y6 achieves a PCE of 15.01%, much higher than PM6:Y6's 12.83%, demonstrating the great potential of ID2CN in thick‐film devices.
11.88% Efficient Flexible Ag‐Free CZTSSe Solar Cell: Spontaneously Tailoring the Alkali Metal Level
Authors: Xu Han , Meng Rutao , Xu Xuejun , Liu Yue , Sun Yali , Zhang Yi
Published: October 2024

Alkali metal is the requirement for highly efficient Cu2ZnSn(S, Se)4 (CZTSSe) solar cells, thus it is crucial to additionally incorporate alkali metal into the absorber layer for flexible solar cells. However, the efficiency of flexible CZTSSe devices reported to date, based on the conventional alkali incorporation strategies, still lags behind those made on rigid substrates. One of the main issues is the inability to control the alkali content and distribution in the absorber layer. Here, a facile alkaline incorporation approach is proposed, effectively regulating the content and distribution of alkali metals in the film. Such a method can spontaneously tailor the alkali metal content to a proper level, thus leading to the suppression of non‐radiative recombination and a better carrier transport through the enhanced film quality and the optimized band binding structure. Finally, a champion flexible CZTSSe solar cell with an efficiency of 11.88% is achieved, the highest reported efficiency for a CZTSSe solar cell without noble Ag doping. This study affords an innovative spontaneous alkali‐doping design for the preparation of high‐performance flexible CZTSSe solar cells and provides a deeper insight into the extent of alkali metal doping.
Room‐Temperature Single‐Phase Synthesis of Semiconducting Metal‐Covalent Organic Frameworks With Microenvironment‐Tuned Photocatalytic Efficiency
Authors: Wu Dongchuang , Zhang Qiongshan , Yin Shiyu , Song Congying , Gu Ning , Wang Dong , Cai Tao , Zhang Bin
Published: October 2024

In order to improve the solubility of metallated monomers and product crystallinity, metal–covalent organic frameworks (MCOFs) are commonly prepared via high‐temperature sol‐vothermal synthesis. However, it hampers the direct extraction of crystallization evolution information. Exploring facile room‐temperature strategies for both synthesizing MCOFs and exploiting the crystallinity mechanism is extremely desired. Herein, by a novel single‐phase synthetic strategy, three MCOFs with different microstructure is rapidly prepared based on the Schiff base reaction between planarity‐tunable C3v monomers and metallated monomers at room temperature. Based on detailed time‐dependent experiments and theoretical calculations, it is found that there is a planarity‐tuned and competitive growth relationship between disordered structures and crystal nucleus for the first time. The high planarity of monomers boosts the formation of crystal nucleus and rapid growth, suppressing the forming of amorphous structures. In addition, the microenvironment effect on selective photocatalytic coupling of benzylamine (BA) is investigated. The strong donor‐acceptor (D‐A) MCOF exhibits efficient photocatalytic activity with a high conversion rate of 99% and high selectivity of 99% in 5 h under the 520 nm light irradiation. This work opens a new pathway to scalable and efficient synthesis of highly crystalline MCOFs.
Effect of toughening agent and flame‐retardant additives on the recycled polypropylene/oil palm empty fruit bunch laminated composites
Authors: Abd Wahab Nur Ain Adila , Jaafar Mariatti , Che Hassan Nor Hasima , Sanz Raúl García
Published: October 2024

The increasing awareness of the environmental problems caused by the use of plastics has led to a demand for sustainable materials. The concept of turning waste into wealth has been considered in the study where the waste materials are value‐added into new composite materials that are recyclable, durable, and fire‐resistant. This effort aims to meet global environmental goals and the demand for high‐performance and sustainable building materials. The purpose of this current study is to develop laminated composites based on the combination of recycled plastic and oil palm empty fruit bunch (OPEFB). The addition of various loading of ammonium polyphosphate (APP) and 6 wt% of ethylene vinyl acetate (EVA) in the formulation was considered to enhance the performance of the composite laminates. The rPP and additives were compounded using a two‐roll mill. Then the rPP sheet and OPEFB were laminated using compression molding techniques. Results showed that rPP with addition of 30 wt% APP and 6 wt% EVA exhibited the highest thermal stability and the lowest melt flow index (MFI) value compared to other samples. Composites with 20 wt% APP and 6 wt% EVA showed 56% increase in flexural properties and 33% increase in impact strength compared to that of rPP/OPEFB composite sample without EVA. Addition of 30 wt% in recycled PP and OPEFB demonstrated self‐extinguished properties when exposed to fire compared to other samples. These findings indicated that rPP/OPEFB laminated composites with the addition of APP and EVA have a potential to be used in applications that require good strength and fire performance. The use of rPP/OPEFB composites with addition of APP and EVA has a potential to reduce virgin plastic consumption and agriculture material waste and establishing an environmentally friendly material for construction sector.

Highlights

Additives included in recycled PP enhance its thermal stability and reduce MFI.

Recycled PP with OPEFB composite self‐extinguished at 30 wt% APP.

The highest flexural strength is shown by composite laminates with 20 wt% APP and 6 wt% EVA.

rPP/OPEFB laminates are ideal for building materials.
Sterile pyogranuloma/granuloma syndrome in two dogs presenting with extensive ulceration and systemic illness
Authors: Ludwig Courtney B. , Vogelnest Linda J.
Published: October 2024

This report describes two cases of canine sterile pyogranuloma/granuloma syndrome presenting with fulminant extensive ulcerations, pyrexia and systemic malaise; one case with concurrent unilateral uveitis. This presentation contrasts to typically reported cases, and due to the severity of signs, urgent distinction from aggressive infectious causes was required. Both cases required extended immunosuppressive treatments for resolution.
Reforestation effects on low flows: Review of public perceptions and scientific evidence
Authors: van Meerveld Ilja , Seibert Jan
Published: October 2024

This review article focuses on the complex relationships between forests and water, particularly the effects of forests on streamflow during meteorological droughts. The impact of forests on water resources is a long‐standing research topic, but there are also many common beliefs that are not based on scientific evidence or only selective evidence. We critically examine the origin of some of the common public misconceptions and review the wealth of studies on how forests impact precipitation, soil water dynamics, evapotranspiration, and streamflow. Generally, reforestation increases evapotranspiration and decreases groundwater recharge and streamflow. However, some of the evaporated water will return as precipitation, potentially offsetting some of the increased evapotranspiration losses. Where reforestation leads to more extensive infiltration and recharge due to the effects of forests on the soil's hydraulic properties, it might increase streamflow during dry periods. Although these individual processes have been studied, predicting the impacts of forests on streamflow remains challenging as the effects are site‐specific and depend on many factors, such as the climate, the forest‐ and soil‐characteristics before and after reforestation, and the hydrogeological setting. However, a more accurate and nuanced understanding of the role of forests on hydrology and a better ability to predict where and when the net effects of reforestation are positive or negative is crucial for sustainable forest and water management.

This article is categorized under:

Science of Water > Science of Water

Science of Water > Water and Environmental Change

Science of Water > Hydrological Processes

Engineering Water > Sustainable Engineering of Water
Wess-Zumino-Witten terms of Sp QCD by bordism theory
Authors: Saito Shota
Published: October 2024

Abstract

We investigate the four-dimensional Wess-Zumino-Witten (WZW) terms within the framework of Sp quantum chromodynamics (QCD) using invertible field theory through bordism theory. We present a novel approach aimed at circumventing both perturbative and non-perturbative gauge anomalies on spacetime manifolds endowed with spin structures. We study both ungauged and gauged WZW terms including the problems of the topological consistency of gauged WZW terms.
Periodically modulated solitary waves of the CH–KP-I equation
Authors: Nilsson Dag , Svensson Seth Douglas , Wang Yuexun
Published: October 2024

We consider the CH–KP-I equation. For this equation, we prove the existence of steady solutions, which are solitary in one horizontal direction and periodic in the other. We show that such waves bifurcate from the line solitary wave solutions, i.e. solitary wave solutions to the Camassa–Holm equation, in a dimension-breaking bifurcation. This is achieved through reformulating the problem as a dynamical system for a perturbation of the line solitary wave solutions, where the periodic direction takes the role of time, then applying the Lyapunov–Iooss theorem.
Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis
Authors: Sibandze Phila , Kalumba Ahmed Mukalazi , H. Aljaddani Amal , Zhou Leocadia , Afuye Gbenga Abayomi
Published: October 2024

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.
Development of Asymmetric Convection in a Tropical Cyclone under Environmental Uniform Flow and Vertical Wind Shear
Authors: Li Yubin , Jiang Yuncong , Chan Johnny C. L. , Cheung Kevin K. W.
Published: October 2024

Idealized numerical simulations have been carried out to reveal the complexity in the development of asymmetric convection in a tropical cyclone (TC) under the influence of an environment with either uniform flow, vertical wind shear (VWS), or both. Results show that rainwater is enhanced to the right of the motion in the outer rainband, but such enhancement occurs in the upshear-left area of the inner-core region. Additionally, due to the asymmetries introduced by environmental flow, wavenumber-1 temperature and height anomalies develop at a radius of ~1000 km in the upper levels. A sub-vortex aside from the TC center encompassing the wavenumber-1 warm center appears, and asymmetric horizontal winds emerge, which, in turn, changes the storm-scale (within 400 km) VWS. Deep convection in the inner core closely follows the changing storm-scale VWS when its magnitude is larger than 2 m s–1 and is located downshear of the storm-scale VWS in all the experiments with environmental flow. In the outer rainbands, the maximum boundary layer convergence is mainly controlled by the direction of motion and is located in the rear-right quadrant. These results extend upon the findings of previous studies in three aspects: (1) The discovery of the roughly linear combination effect from the uniform flow and large-scale VWS; (2) The development of upper-level asymmetric winds on a 1000-km scale through the interaction between the TC vortex and environmental flow, resulting in changes in the storm-scale VWS pattern within the TC area; (3) The revelation that TC asymmetric convection closely aligns with the direction-varying storm-scale VWS instead of the initially designated VWS.
Resolving social dilemmas with minimal reward transfer
Authors: Willis Richard , Du Yali , Leibo Joel Z. , Luck Michael
Published: October 2024

Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In response, we formalise social dilemmas and introduce a novel metric, the general self-interest level, to quantify the disparity between individual and group rationality in such scenarios. This metric represents the maximum proportion of their individual rewards that agents can retain while ensuring that a social welfare optimum becomes a dominant strategy. Our approach diverges from traditional concepts of altruism, instead focusing on strategic reward redistribution. By transferring rewards among agents in a manner that aligns individual and group incentives, rational agents will maximise collective welfare while pursuing their own interests. We provide an algorithm to compute efficient transfer structures for an arbitrary number of agents, and introduce novel multi-player social dilemma games to illustrate the effectiveness of our method. This work provides both a descriptive tool for analysing social dilemmas and a prescriptive solution for resolving them via efficient reward transfer contracts. Applications include mechanism design, where we can assess the impact on collaborative behaviour of modifications to models of environments.
HIV Denial in the COVID Era
Authors: Smith Tara C.
Published: October 2024

Though scientific consensus regarding HIV causation of AIDS was reached decades ago, denial of this conclusion remains. The popularity of such denial has waxed and waned over the years, ebbing as evidence supporting HIV causation mounted, building again as the internet facilitated connection between denial groups and the general public, and waning following media attention to the death of a prominent denier and her child and data showing the cost of human life in South Africa. Decades removed from these phenomena, HIV denial is experiencing another resurgence, coupled to mounting distrust of public health, pharmaceutical companies, and mainstream medicine. This paper examines the history and current state of HIV denial in the context of the COVID pandemic and its consequences. An understanding of the effect of this phenomenon and evidence-based ways to counter it are lacking. Community-based interventions and motivational interviewing may serve to contain such misinformation in high-risk communities.
Deep models for multi-view 3D object recognition: a review
Authors: Alzahrani Mona , Usman Muhammad , Jarraya Salma Kammoun , Anwar Saeed , Helmy Tarek
Published: October 2024

This review paper focuses on the progress of deep learning-based methods for multi-view 3D object recognition. It covers the state-of-the-art techniques in this field, specifically those that utilize 3D multi-view data as input representation. The paper provides a comprehensive analysis of the pipeline for deep learning-based multi-view 3D object recognition, including the various techniques employed at each stage. It also presents the latest developments in CNN-based and transformer-based models for multi-view 3D object recognition. The review discusses existing models in detail, including the datasets, camera configurations, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance. Additionally, it examines various computer vision applications that use multi-view classification. Finally, it highlights future directions, factors impacting recognition performance, and trends for the development of multi-view 3D object recognition method.
Speech based detection of Alzheimer’s disease: a survey of AI techniques, datasets and challenges
Authors: Ding Kewen , Chetty Madhu , Noori Hoshyar Azadeh , Bhattacharya Tanusri , Klein Britt
Published: October 2024

Alzheimer’s disease (AD) is a growing global concern, exacerbated by an aging population and the high costs associated with traditional detection methods. Recent research has identified speech data as valuable clinical information for AD detection, given its association with the progressive degeneration of brain cells and subsequent impacts on memory, cognition, and language abilities. The ongoing demographic shift toward an aging global population underscores the critical need for affordable and easily available methods for early AD detection and intervention. To address this major challenge, substantial research has recently focused on investigating speech data, aiming to develop efficient and affordable diagnostic tools that align with the demands of our aging society. This paper presents an in-depth review of studies from 2018–2023 utilizing speech for AD detection. Following the PRISMA protocol and a two-stage selection process, we identified 85 publications for analysis. In contrast to previous literature reviews, this paper places a strong emphasis on conducting a rigorous comparative analysis of various Artificial Intelligence (AI) based techniques, categorizing them meticulously based on underlying algorithms. We perform an exhaustive evaluation of research papers leveraging common benchmark datasets, specifically ADReSS and ADReSSo, to assess their performance. In contrast to previous literature reviews, this work makes a significant contribution by overcoming the limitations posed by the absence of standardized tasks and commonly accepted benchmark datasets for comparing different studies. The analysis reveals the dominance of deep learning models, particularly those leveraging pre-trained models like BERT, in AD detection. The integration of acoustic and linguistic features often achieves accuracies above 85%. Despite these advancements, challenges persist in data scarcity, standardization, privacy, and model interpretability. Future directions include improving multilingual recognition, exploring emerging multimodal approaches, and enhancing ASR systems for AD patients. By identifying these key challenges and suggesting future research directions, our review serves as a valuable resource for advancing AD detection techniques and their practical implementation.
Digital deception: generative artificial intelligence in social engineering and phishing
Authors: Schmitt Marc , Flechais Ivan
Published: October 2024

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.
Projecting the impact of COVID-19 on education and intergenerational mobility in Sub-Saharan Africa
Authors: Neidhöfer Guido , Lustig Nora , Larroulet Patricio
Published: October 2024

Using microsimulations, we project the effect of instructional losses caused by COVID-19 on secondary school completion rates and intergenerational mobility of education in eight countries in Sub-Saharan Africa. On average, secondary school completion rates could decrease by 12 percentage points overall and by 16 points for children with low-educated parents. Interestingly, in most countries the gender gap diminishes because, for men, the projected decrease in secondary school completion is higher. A small additional impact on girls’ education due to the potential rise in teenage pregnancy is observed in some countries. Intergenerational mobility of education in the eight Sub-Saharan countries in our sample is expected to decrease, on average, by 10%.
Targeting RAGE-signaling pathways in the repair of rotator-cuff injury
Authors: Rai Vikrant , Deepu Vinitha , Agrawal Devendra K.
Published: October 2024

Rotator cuff injury (RCI) is a common musculoskeletal problem that can have a significant impact on the quality of life and functional abilities of those affected. Novel therapies, including proteomics-based, stem cells, platelet-rich plasma, and exosomes, are being developed to promote rotator-cuff healing. The receptor for advanced glycation end-products (RAGE) is a multifunctional receptor that is expressed on several cell types and is implicated in several physiologic and pathological processes, such as tissue repair, inflammation, and degeneration. Because of its capacity to bind with a variety of ligands and initiate signaling pathways that lead to inflammatory responses in RCI, RAGE plays a crucial role in inflammation. In this critical review article, we discussed the role of RAGE-mediated persistent inflammation in RCI followed by novel factors including PKCs, TIRAP, DIAPH1, and factors related to muscle injury with their therapeutic potential in RCI. These factors involve various aspects of muscle injury and signaling and the possibility of targeting these factors to improve the clinical outcomes in RCI still needs further investigation.
Open access publications drive few visits from Google Search results to institutional repositories
Authors: Orduña-Malea Enrique , Font-Julián Cristina I. , Serrano-Cobos Jorge
Published: October 2024

Given the importance of Google Search in generating visits to institutional repositories (IR), a lack of visibility in search engine results pages can hinder the possibility of their publications being found, read, downloaded, and, eventually, cited. To address this, institutions need to evaluate the visibility of their repositories to determine what actions might be implemented to enhance them. However, measuring the search engine optimization (SEO) visibility of IRs requires a highly accurate, technically feasible method. This study constitutes the first attempt to design such a method, specifically applied here to measuring the IR visibility of Spain’s national university system in Google Search based on a set of SEO-based metrics derived from the Ubersuggest SEO tool. A comprehensive dataset spanning three months and comprising 217,589 bibliographic records and 316,899 organic keywords is used as a baseline. Our findings show that many records deposited in these repositories are not ranked among the top positions in Google Search results, and that the most visible records are mainly academic works (theses and dissertations) written in Spanish in the Humanities and Social Sciences. However, most visits are generated by a small number of records. All in all, our results call into question the role played by IRs in attracting readers via Google Search to the institutions’ scientific heritage and serve to underscore the prevailing emphasis within IRs on preservation as opposed to online dissemination. Potential improvements might be achieved using enhanced metadata schemes and normalized description practices, as well as by adopting other actionable insights that can strengthen the online visibility of IRs. This study increases understanding of the role played by web indicators in assessing the web-based impact of research outputs deposited in IRs, and should be of particular interest for a range of stakeholders, including open access and open science advocates, research agencies, library practitioners, repository developers, and website administrators.
Double-loop importance sampling for McKean–Vlasov stochastic differential equation
Authors: Ben Rached Nadhir , Haji-Ali Abdul-Lateef , Subbiah Pillai Shyam Mohan , Tempone Raúl
Published: October 2024

This paper investigates Monte Carlo (MC) methods to estimate probabilities of rare events associated with the solution to the d-dimensional McKean–Vlasov stochastic differential equation (MV-SDE). MV-SDEs are usually approximated using a stochastic interacting P-particle system, which is a set of P coupled d-dimensional stochastic differential equations (SDEs). Importance sampling (IS) is a common technique for reducing high relative variance of MC estimators of rare-event probabilities. We first derive a zero-variance IS change of measure for the quantity of interest by using stochastic optimal control theory. However, when this change of measure is applied to stochastic particle systems, it yields a $$P \times d$$

P
×
d

-dimensional partial differential control equation (PDE), which is computationally expensive to solve. To address this issue, we use the decoupling approach introduced in (dos Reis et al. 2023), generating a d-dimensional control PDE for a zero-variance estimator of the decoupled SDE. Based on this approach, we develop a computationally efficient double loop MC (DLMC) estimator. We conduct a comprehensive numerical error and work analysis of the DLMC estimator. As a result, we show optimal complexity of $$\mathcal {O}\left( \textrm{TOL}_{\textrm{r}}^{-4}\right) $$

O

TOL

r

-
4

with a significantly reduced constant to achieve a prescribed relative error tolerance $$\textrm{TOL}_{\textrm{r}}$$

TOL
r

. Subsequently, we propose an adaptive DLMC method combined with IS to numerically estimate rare-event probabilities, substantially reducing relative variance and computational runtimes required to achieve a given $$\textrm{TOL}_{\textrm{r}}$$

TOL
r

compared with standard MC estimators in the absence of IS. Numerical experiments are performed on the Kuramoto model from statistical physics.
Impact of vegetation coverage and configuration on urban temperatures: a comparative study of 31 provincial capital cities in China
Authors: Wang Chengcong , Ren Zhibin , Zhang Peng , Guo Yujie , Hong Shengyang , Hong Wenhai , Wang Xinyu , Geng Ruoxuan , Meng Fanyue
Published: October 2024

Urban vegetation plays a crucial role in regulating temperatures and heat waves in urban areas. However, the influence of vegetation coverage and its configuration on surface temperatures in different climate zones at a national scale is unclear. To address this, we utilized high-resolution data to detect spatial patterns for 31 provincial capital cities in China. We integrated day and night surface temperatures to determine the influence of vegetative coverage and configuration on urban temperatures across different climate zones and city sizes. Our study revealed that a subtropical monsoon climate and medium-sized cities had the highest vegetative coverage and shape complexity. The best connectivity and agglomeration of vegetation were found in a temperate monsoon climate and large cities. In contrast, small cities, especially those under a temperate continental climate, had low vegetation coverage, high fragmentation, and weak agglomeration and connectivity. In addition, vegetative coverage had a negative impact on daytime surface temperatures, especially in large cities in a subtropical monsoon climate. However, an increase in vegetation coverage could result in warming at night in small cities in temperate continental climates. Although urban vegetation configuration also contributed to moderating surface temperatures, especially at night, they did not surpass the influence of vegetation coverage. The effect on nighttime temperatures of the configuration of vegetation increased by 3–6% relative to that of daytime temperatures, especially in large cities in a temperate monsoon climate. The contribution vegetation coverage and configuration interaction to cooling efficiency decreased at night, especially in medium-sized cities in a temperate continental climate by 3–5%. In addition, this study identified several moderating effects of natural and social factors on the relationship between urban vegetation coverage and surface temperatures. High duration of sunshine, low humidity and high wind speed significantly enhanced the negative impact of vegetation coverage on surface temperatures. In addition, the moderating effect of vegetation coverage was more pronounced in low population density cities and high gross domestic product. This study enhances understanding of the ecological functions of urban vegetation and provides a valuable scientific basis and strategic recommendations for optimizing urban vegetation and improving urban environmental quality.
Assessing Military Mental Health during the Pandemic: A Five Country Collaboration
Authors: Lee Jennifer E. C. , Bennett Clare , Bennett Neanne , Bouak Fethi , Goldenberg Irina , Harrison Kate , Edge Heather McCuaig , Bell Amy Millikan , Quartana Phillip J. , Simms Maj Amos , Adler Amy B.
Published: October 2024

Purpose of Review

Members of a technical panel representing Australia, Canada, New Zealand, the UK, and the US collaborated to develop surveys designed to provide military leaders with information to guide decisions early in the COVID-19 pandemic. The goal of this paper is to provide an overview of this collaboration and a review of findings from the resulting body of work.

Recent Findings

While surveys pointed to relatively favorable mental health and perceptions of leadership among military personnel early in the pandemic, these observations did not reflect the experiences of personnel deployed in COVID-19 response operations, nor were these observations reflective of later stages of the pandemic.

Establishing and leveraging networks that enable the rapid development of employee surveys and sharing of results can serve as a pathway for empowering military leaders in times of crisis. Organizational support and leadership decisions are especially critical for maintaining well-being among personnel during crises.
Distinguishing 2-knots admitting circle actions by fundamental groups
Authors: Fukuda Mizuki , Ishikawa Masaharu
Published: October 2024

A 2-sphere embedded in the 4-sphere invariant under a circle action is called a branched twist spin. A branched twist spin is constructed from a 1-knot in the 3-sphere and a pair of coprime integers uniquely. In this paper, we study, for each pair of coprime integers, if two different 1-knots yield the same branched twist spin, and prove that such a pair of 1-knots does not exist in most cases. Fundamental groups of 3-orbifolds of cyclic type are obtained as quotient groups of the fundamental groups of the complements of branched twist spins. We use these groups for distinguishing branched twist spins.
Effects of traditional Chinese medicine on outcomes and costs of dementia care: results from a retrospective real-world study
Authors: Weng Yi-Xiang , Yang Chien-Chung , Hsu Wen-Chuin , Kuo Raymond N.
Published: October 2024

Objectives

This study aims to assess the impact of Traditional Chinese Medicine (TCM) on dementia patients, utilizing real-world data. Specifically, it seeks to evaluate how TCM influences clinical outcomes by examining changes in the Clinical Dementia Rating (CDR) and Mini-Mental State Examination (MMSE) scores, as well as its effect on medical expenses over a two-year period. Data from a multi-center research database spanning from 2004 to 2021 will be used to achieve these objectives, addressing the current gap in empirical data concerning intuitive outcomes and cognitive function assessments.

Methods

Propensity score matching was adopted to improve comparability among the intervention and control groups. Due to repeated dependent variable measurements, the generalized estimating equation was used to control for socio-demographic characteristics, regional characteristics, and Western medicine treatments for dementia.

Results

After propensity score matching, a total of 441 research subjects were included: 90 in the TCM intervention group and 351 in the non-TCM intervention group. The results of multivariate regression analysis showed that compared with the non-TCM intervention group, the MMSE scores in the TCM intervention group increased by 0.608 points each year. The annual change in CDR scores in the TCM intervention group was 0.702 times that of the non-TCM utilization group. After TCM intervention, annual outpatient expenses increased by US$492.2, hospitalization expenses increased by US$324.3, and total medical expenses increased by US$815.9, compared with the non-intervention group.

Conclusions

TCM interventions significantly decelerate cognitive decline in dementia patients, evidenced by slower reductions in MMSE scores and mitigated increases in CDR scores. However, these benefits are accompanied by increased medical expenses, particularly for outpatient care. Future healthcare strategies should balance the cognitive benefits of TCM with its economic impact, advocating for its inclusion in dementia care protocols.
Practice and reflections on regional mutual recognition of clinical research ethics review——based on survey results from medical and health institutions in Jiangsu Province
Authors: Liu Xiaolin , Hong Qian , Huang Jingzhi , Zhu Hongsen , Zhao Qing , Bao Jun
Published: October 2024

With the continuous advancement of medical research, the number of multi-center clinical studies has been steadily increasing, bringing about significant challenges to ethical review. Improving the efficiency and quality of ethical review has become a necessity driven by national policies and the development of the industry. Mutual recognition of ethical reviews is one of the most effective ways to enhance the efficiency of ethical review in multi-center clinical trials. The promotion of mutual recognition of ethical reviews is both feasible and practically significant; however, progress has been less than ideal. Through conducting surveys, this study attempts to understand the status of mutual recognition of ethical reviews in Jiangsu Province, identify the reasons hindering the implementation of mutual recognition, and propose possible solutions. The results indicate that while most medical and health institutions are aware of the policies related to mutual recognition of ethical reviews, the majority remain in a wait-and-see attitude. Ensuring policy guarantees and the high quality and homogeneity of ethical review across institutions are considered necessary conditions for achieving mutual recognition of ethical reviews.
Zircon Xenocrysts From Easter Island (Rapa Nui) Reveal Hotspot Activity Since the Middle Jurassic
Authors: Rojas‐Agramonte Yamirka , Pardo Natalia , van Hinsbergen Douwe J. J. , Winter Christian , Marroquín‐Gómez María Paula , Liu Shoujie , Gerdes Axel , Albert Richard , Wu Shitou , García‐Casco Antonio
Published: October 2024

We report the finding of mantle‐derived zircon grains retrieved from red soils, regoliths, and beach sands from Easter Island, that are much older than the island volcanism (0–2.5 Ma) and its underlying lithosphere (Pliocene, 3–4.8 Ma). A large population of 0–165 Ma old zircons have coherent oxygen (δ18O 3.8–5.9‰) and hafnium (εHf(t)+3.5–+12.5) mantle isotopic signatures. These results are consistent with the crystallization of zircon from plume‐related melts. In addition, a chemically diverse population with ages from the Paleozoic to the Archean was found. These older populations are enigmatic but they could represent remnants of ancient subducted sediments. Meanwhile, the ∼0–165 Ma population is interpreted as plume‐derived, suggesting that the hotspot started at least ∼165 Ma ago. A spike of ∼164–160 Ma zircons could represent a Large Igneous Province (LIP) stage upon the first arrival of the plume. We use plate reconstructions to show that such a LIP would have formed on the Phoenix Plate and would have subducted below the Antarctic Peninsula around 100–105 Ma. There, LIP subduction would offer a solution for the enigmatic Palmer Land deformation event, previously proposed to result from a collision with an unknown indenter. The here‐reported “ghost” of a prolonged hotspot activity suggests that fragments of the Easter plume and of the ambient sub‐lithospheric mantle stored and re‐sampled zircon xenocrysts due to convective (re)circulation at the scale of the plume head. Our study demonstrates how zircon geochronology and geochemistry provide novel insights into global‐scale geodynamics, offering new perspectives on the dynamics of mantle plumes and hotspot activity.
¿Libre de la Maleza Estatista? Assessing Neoliberal Promises and Water Markets in Chile
Authors: Reade Malagueño Benji , D'Odorico Paolo
Published: October 2024

Neoliberal approaches to water governance, pioneered in Chile in the 1980s, are reappearing today on the centerstage of the water policy debate. While advocates claim that strong property rights, limits on government authority, and water markets can enhance environmental sustainability, efficiency, neutrality, and equity in the distribution of water rights, limited empirical evidence exists on whether neoliberal policies have delivered on these key promises. In this paper, we combine hydrological analysis with a nationwide data set on government water rights allocations between 1981 and 2021 to determine when and where water has been allocated beyond sustainable limits. We then integrate water market transaction and agricultural data to assess how allocations and scarcity conditions relate to spatial and temporal patterns in irrigation, crop distribution, and water market activity. Our results indicate that 30% of catchments are overallocated, and that continued government allocations of water rights during scarcity exacerbate already‐high inequalities in the distribution of water. We find no evidence that scarcity or water markets induced improvements in numerous efficiency metrics. Overall, our results support growing claims that the neoliberal water model fails to fulfill its key promises, notably to the detriment of nature and marginalized rural communities.
Toward a More Sustainable Future for the Rare Earths Industry
Authors: Karamalidis Athanasios , Eggert Roderick
Published: October 2024

A new book explores how more sustainable methods are being applied to the recovery, processing, and purification of rare earths used in everyday technologies.