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Abstract Background Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. Methods Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1–16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection–Fourier transform infrared (ATR–FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1–9 days (young, non-infectious) and 10–16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. Results The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. Conclusions This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations. Graphical Abstract

Robotic arms are extensively employed across various disciplines, within both structured and unstructured workspaces, as well as across numerous applications. These components serve to address a multitude of challenges, thus enabling their integration into collaborative tasks with humans. In order to address these emerging challenges, there is a growing demand for more efficient path-planning algorithms. Hence, this study introduces a technique based on homotopy continuation methods, offering a distinctive and innovative approach to tackling these issues. The methodology proposed in this study enables the modeling of robotic arms with multiple degrees of freedom. It is proficient in operating within narrow corridors and achieves a collision-free path, provided the workspace allows for it. Due to its deterministic nature, the solution path is consistently reproducible. Furthermore, the computation times and RAM memory consumption achieved for hyper-redundant robotic arms fall within the range of seconds and kilobytes (KB). For anthropomorphic or classical robotic arms, computation times are on the order of milliseconds, as evidenced by the case studies presented in this paper. The method was validated using a Thermo Electron robotic arm, specifically the CRS CataLyst-5 model. This validation demonstrated its potential for application in the field of industrial robotics.

An open circuit fault diagnosis scheme for the power switches of the output inverters in a cascaded H bridge multilevel converter is proposed in this research work, which is designed to be used as part of a large-scale photovoltaic generation system with a rating of 1 MW and 13.2 kV. A two stage classifier is the main core of the technique, in which two different approaches are combined, 1) a signal processing algorithm and 2) a machine learning method, having an artificial neural network classifier as its main model. The general fault diagnosis scheme is developed in a per-phase basis of the converter, using only three neural network classifiers for the three phase configuration. The method is suitable to diagnose open circuit faults under different irradiance conditions and parametric variations, and is specified to keep the same level of complexity even if the general structure of the topology is changed. The total sensor count is not incremented since the proposed algorithm uses the individual dc bus voltages and the output currents already required to execute the control system. The performance of the fault diagnosis scheme is tested at a simulation level using Matlab - Simulink / Simscape, considering a wide range of irradiance conditions, even under an irregular distribution of this parameter, and also assuming single open circuit faults in the power switches of one phase.

In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this filter utilizing exclusively edge-preservation-oriented metrics such as the gradient conduction mean square error (GCMSE), the edge-based structural similarity (EBSSIM), and the multi-scale structural similarity (MS-SSIM); we conducted a statistical assessment of the performance of the filter based on those metrics by using the Berkeley segmentation dataset benchmark as a test corpus. Experimental data reveal that our approach achieves higher performance compared to conventional edge filters for all the metrics considered in this study. This is supported by the statistical analysis we carried out; specifically, the FCCF demonstrates a consistent enhancement in edge detection. We also conducted additional experiments for determining the main filter parameters, which we found to be optimal for a broad spectrum of images. The results underscore the FCCF’s potential to make significant contributions to the advancement of image processing techniques since many practical applications such as medical imaging, image enhancement, and computer vision rely heavily on edge detection filters.

Rural areas in various locations are facing electricity shortages and are compelled to heavily rely on non-renewable and ecologically harmful fossil fuels as their primary source of energy. To address this issue, microgrids were proposed as a solution to provide energy to these areas. An IEEE working group, the SESDC Working Group, was established to investigate the feasibility of implementing isolated microgrids as solutions in these communities. However, it has been identified that a proper software tool for microgrid planning is needed to accurately analyze the optimal microgrid configuration. Thus, a user-friendly and secure web-based system for microgrid planning was proposed. This tool was developed using the Django web development framework and Python programming language. It was then validated with users to ensure that the data entered is calculated correctly and produced the expected results.

A well-known problem of visual object tracking is the difficulty of accurately estimating the object trajectory under conditions of environmental disturbances in the bounding box (BB) of a video camera. In this paper, we consider BB variations as Gaussian-Markov colored measurement noise (CMN). In order to perform accurate tracking in the presence of CMN, we use measurement differencing and develop a robust general unbiased finite impulse response (GUFIR) filter and use the general Kalman filter (GKF) as a benchmark. The GUFIR and GKF algorithms are tested by the “Car4” benchmark. It is shown that, in terms of the tracking precision and under the heavy disturbance with the $0.65 \leqslant \Psi \leqslant 0.95$ coloredness factor, the best tracking performance is achieved using the robust GUFIR filter. When $\Psi < 0.6$ , the GUFIR and GKF algorithms perform near equally. In the extreme point of $\Psi = 1.0$ , where the Gauss-Markov CMN loses the stationarity, both algorithms provide zero precision and become inefficient. In general, it is concluded that the GUFIR filter, which ignores any zero mean disturbance and initial values, is much more suitable for applications in visual object tracking than Kalman-like algorithms relying on complete object information.

The aberrant aggregation of α-synuclein (αS), a disordered protein primarily expressed in neuronal cells, is strongly associated with the underlying mechanisms of Parkinson’s disease. It is now established that αS has a weak affinity for metal ions and that these interactions alter its conformational properties by generally promoting self-assembly into amyloids. Here, we characterised the nature of the conformational changes associated with metal binding by αS using nuclear magnetic resonance (NMR) to measure the exchange of the backbone amide protons at a residue specific resolution. We complemented these experiments with 15N relaxation and chemical shift perturbations to obtain a comprehensive map of the interaction between αS and divalent (Ca2+, Cu2+, Mn2+, and Zn2+) and monovalent (Cu+) metal ions. The data identified specific effects that the individual cations exert on the conformational properties of αS. In particular, binding to calcium and zinc generated a reduction of the protection factors in the C-terminal region of the protein, whereas both Cu(II) and Cu(I) did not alter the amide proton exchange along the αS sequence. Changes in the R2/R1 ratios from 15N relaxation experiments were, however, detected as a result of the interaction between αS and Cu+ or Zn2+, indicating that binding to these metals induces conformational perturbations in distinctive regions of the protein. Collectively our data suggest that multiple mechanisms of enhanced αS aggregation are associated with the binding of the analysed metals.

Inspired by the ongoing disruption to businesses across the world, this research focuses on how the COVID-19 pandemic has affected the contribution of eCommerce to small and medium-sized enterprises (SMEs). Our study seeks to establish an eCommerce-driven response to this natural disruption, by asking the questions; How do eCommerce platforms impact SMEs? How does eCommerce affect an SME’s three major business functions during a global disruption? We employ a qualitative case study method, using interviews as our primary data source, along with secondary data from industry and company records. We discuss these case studies through the framework of the actor network theory (ANT), identifying eCommerce and other platforms that SMEs use as actors in their network. We interviewed eight SMEs involved in the physical sale and distribution of consumer goods, each of which had been operating for at least two years and had a maximum of 70 employees. On average, we found that 44% of the SMEs in this study benefitted from using eCommerce in key business areas, with 46% improving their operations, 47% improving sales and marketing, and 39% improving finance. We also found that SME adoption of eCommerce during the pandemic grew in response to these benefits. Of the eight companies we studied, four had begun developing full eCommerce operations and three more planned to develop them as the global situation further normalizes.

The objective of this work is to select a Machine Learning Technique (MLT) to develop a fault diagnosis scheme for the power switching devices of the High Frequency link (HF link) in a grid-tied Photovoltaic (PV) system, without increasing the total number of sensors, and being capable to operate online. Artificial Neural Network (ANN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Naive Bayes (NB) algorithms are considered to solve the problem of fault classification. These four MLTs are compared using the specificity and sensitivity indexes. The inputs of the models are obtained from the mean value of the signals given by the Discrete Wavelet Transform (DWT) of the dc link voltage and the power extracted from the PV panels. Support vector machine algorithm is chosen as the most suitable classifier to diagnose single and simultaneous open circuit faults with lower computational effort. Simulation and real-time hardware-based experimental tests demonstrate that the MLTs are suitable and reliable to diagnose open circuit faults in a wide range of irradiance levels, ranging from 200 W/m2 to 1000 W/m2, even under 6 % and 12 % measurement errors, without increasing the overall system cost.

The large amount of text that is generated daily on the web through comments on social networks, blog posts and open-ended question surveys, among others, demonstrates that text data is used frequently, and therefore; its processing becomes a challenge for researchers. The topic modeling is one of the emerging techniques in text mining; it is based on the discovery of latent data and the search for relationships among text documents. In this paper, the objective of the research is to evaluate a generic methodology based on topic modeling and text network modeling, that allows researchers to gather valuable information from surveys that use open-ended questions. To achieve this, this methodology has been evaluated through the use of a case study in which the responses to a teacher self-assessment survey in an Ecuadorian university have been studied. The main contribution of the article is the inclusion of clustering algorithms in order to complement the results obtained when executing topic modeling. The proposed methodology is based on four phases: (a) Construction of a text database, (b) Text mining and topic modeling, (c) Topic network modeling and (d) The relevance of the identified topics. In previous works, it has been observed that the human interpretative contribution plays an important role in the process, especially in phases (a) and (d). For this reason, the visualization interfaces, such as graphs and dendograms, are of critical importance for researchers in order allow topic to efficiently analyze the results of the topic modeling. As a result of this case study, a compendium of the main strategies that teachers carry out in their classes with the aim of improving student retention is presented. In addition, the proposed methodology can be extended to the analysis of the unstructured textual information found in blogs, social networks, forums, etc.

In the recent years, software applications for medical assistance, including the tele-rehabilitation, have known a high and continuous presence in the medical field. Guaranteeing the correct use of these applications induces to incorporate a study of usability in the life cycle of any interactive system. This paper synthesizes the results obtained from a study of effectiveness, efficiency, and subjective user satisfaction conducted with ePHoRt. ePHoRt is a web-based platform for home motor rehabilitation; 39 participants tested the platform. We report the empirical results based on the subjective usability perception and self-reported feedback based on the IBM Computer System Usability Questionnaire. The results suggest that ePHoRt is useful, effective, efficient, easy to use, and its interfaces are acceptable. Overall, the participants are satisfied to use the platform. The main findings of this paper show that the user guidance is a critical aspect to ensure a good usability of the tele-rehabilitation platform.

The conversion of otherwise soluble proteins into insoluble amyloid aggregates is associated with a range of neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases, as well as non-neuropathic conditions such as type II diabetes and systemic amyloidoses. It is increasingly evident that the most pernicious species among those forming during protein aggregation are small prefibrillar oligomers. In this review, we describe the recent progress in the characterization of the cellular and molecular interactions by toxic misfolded protein oligomers. A fundamental interaction by these aggregates involves biological membranes, resulting in two major model mechanisms at the onset of the cellular toxicity. These include the membrane disruption model, resulting in calcium imbalance, mitochondrial dysfunction and intracellular reactive oxygen species, and the direct interaction with membrane proteins, leading to the alteration of their native function. A key challenge remains in the characterization of transient interactions involving heterogeneous protein aggregates. Solving this task is crucial in the quest of identifying suitable therapeutic approaches to suppress the cellular toxicity in protein misfolding diseases.

The 78 kDa glucose-regulated protein (GRP78), a member of the 70 kDa heat-shock family of molecular chaperones (HSP70), is essential for the regulation of the unfolded protein response (UPR) resulting from cellular endoplasmic reticulum (ER) stress. During ER stress, GRP78 evades retention mechanisms and is translocated to the cell surface (csGRP78) where it functions as an autoantigen. Autoantibodies to GRP78 appear in prostate, ovarian, gastric, malignant melanoma, and colorectal cancers. They are also found in autoimmune pathologies such as rheumatoid arthritis (RA), neuromyelitis optica (NMO), anti-myelin oligodendrocyte glycoprotein antibody-associated disorder (AMOGAD), Lambert-Eaton myasthenic syndrome (LEMS), multiple sclerosis (MS), neuropsychiatric systemic lupus erythematosus (NPSLE) and type 1 diabetes (T1D). In NMO, MS, and NPSLE these autoantibodies disrupt and move across the blood-brain barrier (BBB), facilitating their entry and that of other pathogenic antibodies to the brain. Although csGRP78 is common in both cancer and autoimmune diseases, there are major differences in the specificity of its autoantibodies. Here, we discuss how ER mechanisms modulate csGRP78 antigenicity and the production of autoantibodies, permitting this chaperone to function as a dual compartmentalized receptor with independent signaling pathways that promote either pro-proliferative or apoptotic signaling, depending on whether the autoantibodies bind csGRP78 N- or C-terminal regions.

In this paper, we explored the application of the fractional calculus to digital watermarking. In order to reach this goal, we chose a set of watermarking systems, and then, we altered the watermark detection step of each system. The novelty of our proposal is that we deviated from the trend of proposing new embedding domains computed by using fractional calculus principles. Instead, we proposed to reformulate the detection equation set. Then, this paper contributes to a new equation set for detecting watermarks based on fractional calculus principles. In addition, we provided the proper order for the derivative; this equation set replaces the previous one, given as a result that the detection rate increased up to 88% for the best case compared to the original equation set. The main modification was to replace the system's equations to another set that we derived by using the Riemann-Liouville fractional operator. To verify that if this approach is effective, we performed tests on grayscale images of size 512 × 512; the test covered several attacks including noise addition, JPEG compression, and scaling. The results showed that the modified systems detected more watermarks than their counterparts. The proposed scheme presented better results for 15 out of 21 different tests and the other 6 ended in a draw. It is important to remark that the behavior holds for other embedding domains, for example, the discrete cosine transform coefficients and the singular values decomposition values, which suggests the regularity of the approach.

After the Cold War, India was forced to examine its non-alignment policy. Apparently, a new foreign policy was shaped. It was more ambitious in defining the scope of India’s global interests yet, complying with global governance. But despite this appearance, when official discourse is analyzed and contrasted with evidence, it is possible to note not only the tensions and instabilities in the narrative, but also the continuities of non-alignment. This article aims to investigate the practice of multilateralism in Indian foreign policy considering the cases of SAARC, and BRICS. The article traces the consistencies in how foreign policy has been conceived: restricting multilateralism as well as global governance, and securing India’s national interest and sovereignty.

Después de la Guerra Fría, India sometió su política de no alineación a un proceso de autocrítica y de redefinición. Aparentemente, se articuló una nueva política exterior: asertiva, pero concomitante con la gobernanza global. No obstante, al examinar el discurso oficial y contrastarlo con la evidencia empírica es posible notar no solo las tensiones y las inestabilidades de la narrativa, sino también la continuidad normativa de la no alineación. El objetivo de este artículo es analizar la práctica del multilateralismo en la política exterior india a partir de las experiencias de la SAARC y los BRICS. El argumento central es que la política exterior india caracteriza al multilateralismo y la gobernanza global en tanto limitante del interés y la soberanía nacionales.

The objective of this scoping review is to characterize the current scenario of mobile applications considering accessibility issues for people with cognitive, motor, and sensory disabilities. Nowadays, mobile devices have grown exponentially, giving way to new ways of relating, managing, and working. In this context, mobile devices seek to democratize access to knowledge on different topics; however, the application of accessibility guidelines is neglected. The reviewers extracted the most relevant articles published between 2000 and 2020 from the ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and Web of Science databases. In this scoping review, the PRISMA-ScR checklist was used to extract scientific articles; Cohen’s kappa coefficient = 0.4117 was applied, which implies moderate concordance of reviewers; 22 primary studies were extracted from a total of 211. The results obtained in this research suggest applying WCAG 2.1 in mobile applications to achieve an adequate level of accessibility. Future work suggests designing review tools that include machine learning based on artificial intelligence algorithms.

A significant resonance shift is found in both reflection and transmission coefficients from both parallel and perpendicular excitations in a helical structure. The responses from the perpendicular excitation show better sensitivity than those from the parallel excitation at 480 MHz-560 MHz for 0.2 circumference difference. Additional electromagnetic properties of a helical structure, such as chirality, optical rotatory dispersion and circular dichroism can also further combine to enhance the sensing performance.

El tema de la participación de la escuela de Brentano en la polémica en torno al psicologismo no se deja reducir a una simple y única pregunta, ni se puede responder con una simple y única respuesta, pues presenta una marcada diversidad tanto desde el punto de vista sistemático como histórico, componiéndose de diferentes polémicas que tienen un cierto grado de independencia.

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles; then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

Nowadays, serious games have become a beneficial resource in the learning process; they are part of our culture and promote social inclusion. Designing accessible serious games is a complete challenge, even more for non-experts. Most existing serious games do not meet accessibility standards because of a lack of methods that include standards and help create more accessible serious games. For this reason, our research presents a heuristic method with three modifications to Giorgio Brajnik’s barrier walkthrough method and based on the Web Content Accessibility Guidelines 2.1 (WCAG 2.1). We defined 28 barriers for the users with low vision and the related impact and persistence variables by defining severity ranges to evaluate accessibility. This method allows measuring the accessibility of web-based serious games; the method proposed in this article can be a good help for non-experts. As a case study, this heuristic method was applied to 40 web-based serious games. The evaluators concluded that serious games should apply WCAG 2.1 to achieve an adequate and inclusive accessibility level. However, this study has limitations; the heuristic method depends on the evaluators’ experience. This work can contribute to studies related to accessibility heuristics in serious games; it can also help construct a software tool that applies WCAG 2.1 and helps experts and non-experts evaluate accessibility in serious games.

Objective: To determine the impact of using the CLINITEK AUWi System to screen out negative urine samples intended for culture on patient length of stay and culture results at a community teaching hospital. Design and methods: We used a retrospective cohort design to compare length of stay for patients admitted before and after implementation of the CLINITEK AUWi System to screen urine samples prior to culture. Before implementation, all urine samples were sent to an external laboratory for culture. After implementation, urine samples were screened first, and culture was only performed for those samples above the 325 bacteria/µl cutoff. We assessed patient length of stay before and after implementation. Results: Our study included 306 samples (168 pre, 138 post). In the post-implementation group, 60.9% of cultures were screened negative and not sent for culture, resulting in fewer negative culture results (74.4% vs 40.7%, p

Nowadays, one of the learning resources in the educational area are serious games, also called training games; they are games designed with a different purpose than fun, whose main objective is to reinforce the new concepts more creatively. However, not all existing serious games are accessible in a way that allows access to a more significant number of users. Therefore, this research proposes to apply a combined method to evaluate accessibility in serious games, considering the Web Content Accessibility Guidelines (WCAG) 2.1. As a case study, we evaluated the accessibility of 82 serious games developed by Physical Education Technology Interactive Simulations at the University of Colorado. We propose to replicate this combined method for users with various types of disabilities, considering the various accessibility barriers. As future work, we suggest generating an accessibility heuristic evaluation focused on serious games, based on the accessibility issues identified. Finally, we believe it is essential to strengthen accessibility policies in each country, as well as implement best practices that generate innovation by incorporating diversity in building and designing more inclusive serious games.

Aim of study: This study aims to identify and characterize the spatial and temporal evolution of the types of vegetation that are most affected by forest fires in Europe. The characterization of the fuels is an important issue of the fire regime in each specific ecosystem while, on the other hand, fire is an important disturbance for global vegetation dynamics.Area of study: Southern European countries: Portugal, Spain, France, Italy and Greece.Material and Methods: Corine Land Cover maps for 2000 and 2006 (CLC2000, CLC2006) and burned area (BA) perimeters, from 2000 to 2013 in Europe are combined to access the spatial and temporal evolution of the types of vegetation that are most affected by wild fires using descriptive statistics and Geographical Information System (GIS) techniques.Main results: The spatial and temporal distribution of BA perimeters, vegetation and burnt vegetation by wild fires was performed and different statistics were obtained for Mediterranean and entire Europe, confirming the usefulness of the proposed land cover system. A fire proneness index is proposed to assess the fire selectivity of land cover classes. The index allowed to quantify and to compare the propensity of vegetation classes and countries to fire.Research highlights: The usefulness and efficiency of the land cover classification scheme and fire proneness index. The differences between northern Europe and southern Europe and among the Mediterranean region in what concerns to vegetation cover, fire incidence, area burnt in land cover classes and fire proneness between classes for the different countries.Keywords: Fire proneness; Mixed forests; Land cover/land use; Fire regime; Europe; GIS; Corine land cover.

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3) and low (

Deborah Wolbrette, Mario Gonzalez, Soraya Samii, Javier Banchs, Erica Penny-Peterson, Gerald NaccarelliPenn State Heart and Vascular Institute, Penn State Milton S Hershey Medical Center, Hershey, Pennsylvania, USAAbstract: Dronedarone, a new Class III antiarrhythmic agent, has now been approved by the US Food and Drug Administration for use in patients with atrial fibrillation or atrial flutter. Approval came in March 2009 due to the positive results of the ATHENA trial showing significant reductions in all-cause mortality and cardiovascular hospitalization with dronedarone use. A post hoc analysis of the ATHENA data also suggested a decrease in stroke risk with this agent. However, due to safety concerns in the heart failure population in the earlier ANDROMEDA trial, dronedarone is not recommended for patients with an ejection fraction 35% and no recent decompensated heart failure, dronedarone should be considered earlier than amiodarone in the treatment algorithm.Keywords: dronedarone, amiodarone, atrial fibrillation, atrial flutter

BackgroundCOVID-19 vaccination rates among U.S. young adults, particularly in communities of color, remain lower than other age groups. We conducted a qualitative, community-based participatory study to explore beliefs and attitudes about COVID-19 vaccines among young adults in Black/African American, Latinx, and Asian American or Pacific Islander (AAPI) communities in the San Francisco Bay Area.MethodsWe conducted six focus groups between June and August 2021. Participants were recruited by partnering with community-based organizations in the San Francisco Bay Area. Focus groups included Black/African American (N=13), Latinx (N=20), and AAPI (N=12) participants between 18 and 30 years of age. Emerging themes were identified using a modified Grounded Theory approach.ResultsProminent themes among all three racial-ethnic groups included mistrust in medical and government institutions, strong conviction about self-agency in health decision-making, and exposure to a thicket of contradictory information and misinformation in social media. Social benefit and a sense of familial and societal responsibility were often mentioned as reasons to get vaccinated. Young adult mistrust had a generational flavor fueled by anger about increasing inequity, the profit-orientation of pharmaceutical companies and health institutions, society's failure to rectify injustice, and pessimism about life prospects.ConclusionFactors influencing vaccine readiness among Black/African American, Latinx, and AAPI young adults have a distinct generational and life-course texture. Outreach efforts should appeal to young adults' interest in family and social responsibility and the social benefits of vaccination, while being cognizant of the friction mandates pose for young adults' sense of self-agency. Efforts will be most effective coming from trusted messengers with a proven commitment to communities of color and health equity.

Stroke is the most common complication of atrial fibrillation (AF). Guidelines recommend anticoagulant treatment in patients with CHA2DS2VASc scores of >2. Registry data suggests that almost half of patients who should be on therapeutic anticoagulation for stroke prevention in AF (SPAF) are not. Warfarin and more recently developed agents, the “novel anticoagulants” (NOACs) reduce the risk of embolic strokes. In addition, the NOACs also reduce intracranial hemorrhage (ICH) by over 50% compared to warfarin. Anticoagulation and bridging strategies involving cardioversion, catheter ablation, and invasive/surgical procedures are reviewed. The development of reversal agents for NOACs and the introduction of left atrial appendage occluding devices will evolve the use of newer strategies for preventing stroke in high risk AF patients.

Although sustainable forest management (SFM) has been widely adopted as a policy and management goal, high rates of forest loss and degradation are still occurring in many areas. Human activities such as logging, livestock husbandry, crop cultivation, infrastructural development, and use of fire are causing widespread loss of biodiversity, restricting progress toward SFM. In such situations, there is an urgent need for tools that can provide an integrated assessment of human impacts on forest biodiversity and that can support decision making related to forest use. This paper summarizes the experience gained by an international collaborative research effort spanning more than a decade, focusing on the tropical montane forests of Mexico and the temperate rain forests of southern South America, both of which are global conservation priorities. The lessons learned from this research are identified, specifically in relation to developing an integrated modeling framework for achieving SFM. Experience has highlighted a number of challenges that need to be overcome in such areas, including the lack of information regarding ecological processes and species characteristics and a lack of forest inventory data, which hinders model parameterization. Quantitative models are poorly developed for some ecological phenomena, such as edge effects and genetic diversity, limiting model integration. Establishment of participatory approaches to forest management is difficult, as a supportive institutional and policy environment is often lacking. However, experience to date suggests that the modeling toolkit approach suggested by Sturvetant et al. (2008) could be of value in such areas. Suggestions are made regarding desirable elements of such a toolkit to support participatory-research approaches in domains characterized by high uncertainty, including Bayesian Belief Networks, spatial multi-criteria analysis, and scenario planning.