Federal Institute of São Paulo

The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies. Over the past decades, rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats. Yet, in spite of the progress of molecular methods, knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging. In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels. Combining the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge, we reannotated 10,210 and 151 fungal and Stramenopila genera, respectively. This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera, designed for rapid functional assignments of environmental studies. In order to assign the trait states to fungal species hypotheses, the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences. On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1% dissimilarity threshold.

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

There is broad consensus that the diversity of functional traits within species assemblages drives several ecological processes. It is also widely recognized that rare species are the first to become extinct following human-induced disturbances. Surprisingly, however, the functional importance of rare species is still poorly understood, particularly in tropical species-rich assemblages where the majority of species are rare, and the rate of species extinction can be high. Here, we investigated the consequences of local and regional extinctions on the functional structure of species assemblages. We used three extensive datasets (stream fish from the Brazilian Amazon, rainforest trees from French Guiana, and birds from the Australian Wet Tropics) and built an integrative measure of species rarity versus commonness, combining local abundance, geographical range, and habitat breadth. Using different scenarios of species loss, we found a disproportionate impact of rare species extinction for the three groups, with significant reductions in levels of functional richness, specialization, and originality of assemblages, which may severely undermine the integrity of ecological processes. The whole breadth of functional abilities within species assemblages, which is disproportionately supported by rare species, is certainly critical in maintaining ecosystems particularly under the ongoing rapid environmental transitions.

Abstract The Basidiomycota constitutes a major phylum of the kingdom Fungi and is second in species numbers to the Ascomycota. The present work provides an overview of all validly published, currently used basidiomycete genera to date in a single document. An outline of all genera of Basidiomycota is provided, which includes 1928 currently used genera names, with 1263 synonyms, which are distributed in 241 families, 68 orders, 18 classes and four subphyla. We provide brief notes for each accepted genus including information on classification, number of accepted species, type species, life mode, habitat, distribution, and sequence information. Furthermore, three phylogenetic analyses with combined LSU, SSU, 5.8s, rpb1, rpb2, and ef1 datasets for the subphyla Agaricomycotina, Pucciniomycotina and Ustilaginomycotina are conducted, respectively. Divergence time estimates are provided to the family level with 632 species from 62 orders, 168 families and 605 genera. Our study indicates that the divergence times of the subphyla in Basidiomycota are 406–430 Mya, classes are 211–383 Mya, and orders are 99–323 Mya, which are largely consistent with previous studies. In this study, all phylogenetically supported families were dated, with the families of Agaricomycotina diverging from 27–178 Mya, Pucciniomycotina from 85–222 Mya, and Ustilaginomycotina from 79–177 Mya. Divergence times as additional criterion in ranking provide additional evidence to resolve taxonomic problems in the Basidiomycota taxonomic system, and also provide a better understanding of their phylogeny and evolution.

BACKGROUND: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. RESULTS: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. CONCLUSIONS: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Static lung volume (LV) measurements have a number of clinical and research applications; however, no previous studies have provided reference values for such tests using a healthy sample of the adult Brazilian population. With this as our main purpose, we prospectively evaluated 100 non-smoking subjects (50 males and 50 females), 20 to 80 years old, randomly selected from more than 8,000 individuals. Gender-specific linear prediction equations were developed by multiple regression analysis with total lung capacity (TLC), functional residual capacity (FRC), residual volume (RV), RV/TLC ratio and inspiratory capacity (IC) as dependent variables, and with age, height, weight, lean body mass and indexes of physical fitness as independent ones. Simpler demographic and anthropometric variables were as useful as more complex measurements in predicting LV values, independent of gender and age (R2 values ranging from 0.49 to 0.78, P < 0.001). Interestingly, prediction equations from North American and European studies overestimated the LV at low volumes and underestimated them at high volumes (P < 0.05). Our results, therefore, provide a more appropriate frame of reference to evaluate the normalcy of static lung volume values in Brazilian males and females aged 20 to 80 years.

Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.

Although green innovation is seen by companies as a way to meet regulatory pressures and customers' needs, there is still no consensus about its impact on performance. This paper examines the role of green innovation intensity on financial performance based on data from 356 multinationals firms using a fixed effect panel regression. We found three main results. First, there is no significant association of green innovation's intensity with firm financial performance in the immediate year. Second, the association is positive, lasts during the subsequent years and becomes expressively higher after 2 years. Third, the degree of internationalization does not moderate this relationship. These findings provide empirical evidence that the return of green innovations is conditional to time but not to how much a multinational firm is internationalized.

Currently, there is an increasing number of patients that are treated in-home, mainly in countries such as Japan, USA and Europe. As well as this, the number of elderly people has increased significantly in the last 15 years and these people are often treated in-home and at times enter into a critical situation that may require help (e.g. when facing an accident, or becoming depressed). Advances in ubiquitous computing and the Internet of Things (IoT) have provided efficient and cheap equipments that include wireless communication and cameras, such as smartphones or embedded devices like Raspberry Pi. Embedded computing enables the deployment of Health Smart Homes (HSH) that can enhance in-home medical treatment. The use of camera and image processing on IoT is still an application that has not been fully explored in the literature, especially in the context of HSH. Although use of images has been widely exploited to address issues such as safety and surveillance in the house, they have been little employed to assist patients and/or elderly people as part of the home-care systems. In our view, these images can help nurses or caregivers to assist patients in need of timely help, and the implementation of this application can be extremely easy and cheap when aided by IoT technologies. This article discusses the use of patient images and emotional detection to assist patients and elderly people within an in-home healthcare context. We also discuss the existing literature and show that most of the studies in this area do not make use of images for the purpose of monitoring patients. In addition, there are few studies that take into account the patient’s emotional state, which is crucial for them to be able to recover from a disease. Finally, we outline our prototype which runs on multiple computing platforms and show results that demonstrate the feasibility of our approach.

Wild mushrooms are a vital source of income and nutrition for many poor communities and of value to recreational foragers. Literature relating to the edibility of mushroom species continues to expand, driven by an increasing demand for wild mushrooms, a wider interest in foraging, and the study of traditional foods. Although numerous case reports have been published on edible mushrooms, doubt and confusion persist regarding which species are safe and suitable to consume. Case reports often differ, and the evidence supporting the stated properties of mushrooms can be incomplete or ambiguous. The need for greater clarity on edible species is further underlined by increases in mushroom-related poisonings. We propose a system for categorizing mushroom species and assigning a final edibility status. Using this system, we reviewed 2,786 mushroom species from 99 countries, accessing 9,783 case reports, from over 1,100 sources. We identified 2,189 edible species, of which 2,006 can be consumed safely, and a further 183 species which required some form of pretreatment prior to safe consumption or were associated with allergic reactions by some. We identified 471 species of uncertain edibility because of missing or incomplete evidence of consumption, and 76 unconfirmed species because of unresolved, differing opinions on edibility and toxicity. This is the most comprehensive list of edible mushrooms available to date, demonstrating the huge number of mushrooms species consumed. Our review highlights the need for further information on uncertain and clash species, and the need to present evidence in a clear, unambiguous, and consistent manner.

Vat photopolymerization is an additive manufacturing process that produces high-performance ceramic parts. A critical step in the process is the preparation of a suspension that meets the requirements of high ceramic loading and proper rheological behavior, since an increase in solid loading might compromise the suspension rheology, resulting in non-uniform layer recoating. This review examines the rheological behavior of ceramic suspensions for vat photopolymerization, discussing the influence of the suspension formulation (solid loading, ceramic particle size and size distribution, monomers, diluents, and dispersants) on rheological aspects such as viscosity, shear-thinning/thickening behavior, critical shear rate, yield stress, and thixotropy. It provides a summary of the best formulations, which achieved low viscosity (<3 Pa.s) and high solid loading (>40 vol%), and reports the main trends and challenges of ceramic vat photopolymerization, suggesting general guidelines for the preparation of highly loaded photocurable ceramic suspensions with low viscosity.

Juvenile hormone (JH) plays a crucial role in insect reproduction, but its molecular mode of action only became clear within the last decade. We here review recent findings revealing the intricate crosstalk between JH and ecdysone signaling with nutrient sensing pathways in Drosophila melanogaster, Aedes aegypti, Tribolium castaneum and Locusta migratoria. The finding for a critical role of ecdysis triggering hormone (ETH) in both molting and ooogenesis now also highlights the importance of an integrated view of development and reproduction. Furthermore, insights from non-model insects, especially so social Hymenoptera and termites, where JH function gradually becomes decoupled from reproduction and plays a role in division of labor, emphasize the need to consider life cycle and life history strategies when studying insect reproductive physiology.

Context: Systematic Review (SR) is a methodology used to find and aggregate relevant existing evidence about a specific research topic of interest. It can be very time-consuming depending on the number of gathered studies that need to be analyzed by researchers. One of the relevant tools found in the literature and preliminarily evaluated by researchers of SRs is StArt, which supports the whole SR process. It has been downloaded by users from more than twenty countries. Objective: To present new features available in StArt to support SR activities. Method: Based on users' feedback and the literature, new features were implemented and are available in the tool, like the SCAS strategy, snowballing techniques, the frequency of keywords and a word cloud for search string refining, collaboration among reviewers, and the StArt online community. Results: The new features, according to users' positive feedback, make the tool more robust to support the conduct of SRs. Conclusion: StArt is a tool that has been continuously developed such that new features are often available to improve the support for the SR process. The StArt online community can improve the interaction among users, facilitating the identification of improvements and new useful features.

Small and medium enterprises (SMEs) are responsible for 90% of all business and 50% of employment globally, mostly female jobs. Therefore, measuring SMEs' performance under the digital transformation (DT) through methods that encompass sustainability represents an essential tool for reducing poverty and gender inequality (United Nations Sustainable Development Goals). We aimed to describe and analyze the state-of-art performance evaluations of digital transformation in SMEs, mainly focusing on performance measurement. Also, we aimed to determine whether the tools encompass the three pillars of sustainability (environmental, social, and economic). Through a systematic literature review (SLR), a search on Web of Science (WoS) and Scopus resulted in the acceptance of 74 peer-reviewed papers published until December 2021. Additionally, a bibliometrics investigation was executed. Although there was no time restriction, the oldest paper was published in 2016, indicating that DT is a new research topic with increasing interest. Italy, China, and Finland are the countries that have the most published on the theme. Based on the results, a conceptual framework is proposed. Also, two future research directions are presented and discussed, one for theoretical and another for practical research. Among the theoretical development, it is essential to work on a widely accepted SME definition. Among the practical research, nine directions are identified-e.g., applying big data, sectorial and regional prioritization, cross-temporal investigations etc. Researchers can follow the presented avenues and roads to guide their researchers toward the most relevant topics with the most urgent necessity of investigation.

This review article presents an overview of instruments available on the market for measurement of particulate matter. The main instruments and methods of measuring concentration (gravimetric, optical, and microbalance) and size distribution Scanning Mobility Particle Sizer (SMPS), Electrical Low Pressure Impactor (ELPI), and others were described and compared. The aim of this work was to help researchers choose the most suitable equipment to measure particulate matter. When choosing a measuring instrument, a researcher must clearly define the purpose of the study and determine whether it meets the main specifications of the equipment. ELPI and SMPS are the suitable devices for measuring fine particles; the ELPI works in real time. In health-related studies, a Diffusion Charger is the instrument that best characterizes the surface of ultrafine particles. Several methods and different particle measuring instruments should be used to confirm the values obtained during sampling.

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

Abstract Supported by the dynamic capabilities literature, this study examines the linkages between environmental factors, organizational factors and green innovation performance in light of the moderating effect of green absorptive capacity. Based on a sample of 78 firms in the electric power industry in Brazil, a mediation‐moderation model is proposed and tested using partial least squares structural equation modelling, which allows the analysis of both direct and indirect relationships. The results yielded general support for the study hypotheses, suggesting that (i) environmental factors have both a direct and indirect positive effect on green innovation performance, (ii) organizational factors fully mediate the effect of environmental factors on green innovation performance, and (iii) green absorptive capacity has a positive moderating impact on the mediation model. These findings provide empirical evidence of a better understanding of the antecedents of green innovation performance by highlighting the role of green absorptive capacity.

ResumoEsse artigo apresenta uma revisão dos tipos vegetacionais do estado do Ceará a partir de sua diversidade paisagística, centrando-se, notadamente, nas condições climáticas e unidades geomorfológicas. Com base em levantamentos de campo, literatura especializada e mapas das unidades fitoecológicas e geomorfológicas, pretendemos expor, de forma didática, a caracterização, distribuição e principais ameaças antrópicas concernentes a cada vegetação. Por fim, utilizamos métodos de análise multivariada para comparar a similaridade de espécies entre os diferentes levantamentos florísticos disponíveis para o estado e discutimos os padrões biogeográficos observados.