Universidade Paulista

IMPORTANCE: Cancer is the second leading cause of death worldwide. Current estimates on the burden of cancer are needed for cancer control planning. OBJECTIVE: To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 32 cancers in 195 countries and territories from 1990 to 2015. EVIDENCE REVIEW: Cancer mortality was estimated using vital registration system data, cancer registry incidence data (transformed to mortality estimates using separately estimated mortality to incidence [MI] ratios), and verbal autopsy data. Cancer incidence was calculated by dividing mortality estimates through the modeled MI ratios. To calculate cancer prevalence, MI ratios were used to model survival. To calculate YLDs, prevalence estimates were multiplied by disability weights. The YLLs were estimated by multiplying age-specific cancer deaths by the reference life expectancy. DALYs were estimated as the sum of YLDs and YLLs. A sociodemographic index (SDI) was created for each location based on income per capita, educational attainment, and fertility. Countries were categorized by SDI quintiles to summarize results. FINDINGS: In 2015, there were 17.5 million cancer cases worldwide and 8.7 million deaths. Between 2005 and 2015, cancer cases increased by 33%, with population aging contributing 16%, population growth 13%, and changes in age-specific rates contributing 4%. For men, the most common cancer globally was prostate cancer (1.6 million cases). Tracheal, bronchus, and lung cancer was the leading cause of cancer deaths and DALYs in men (1.2 million deaths and 25.9 million DALYs). For women, the most common cancer was breast cancer (2.4 million cases). Breast cancer was also the leading cause of cancer deaths and DALYs for women (523 000 deaths and 15.1 million DALYs). Overall, cancer caused 208.3 million DALYs worldwide in 2015 for both sexes combined. Between 2005 and 2015, age-standardized incidence rates for all cancers combined increased in 174 of 195 countries or territories. Age-standardized death rates (ASDRs) for all cancers combined decreased within that timeframe in 140 of 195 countries or territories. Countries with an increase in the ASDR due to all cancers were largely located on the African continent. Of all cancers, deaths between 2005 and 2015 decreased significantly for Hodgkin lymphoma (-6.1% [95% uncertainty interval (UI), -10.6% to -1.3%]). The number of deaths also decreased for esophageal cancer, stomach cancer, and chronic myeloid leukemia, although these results were not statistically significant. CONCLUSION AND RELEVANCE: As part of the epidemiological transition, cancer incidence is expected to increase in the future, further straining limited health care resources. Appropriate allocation of resources for cancer prevention, early diagnosis, and curative and palliative care requires detailed knowledge of the local burden of cancer. The GBD 2015 study results demonstrate that progress is possible in the war against cancer. However, the major findings also highlight an unmet need for cancer prevention efforts, including tobacco control, vaccination, and the promotion of physical activity and a healthy diet.

Atmospheric general circulation models used for climate simulation and weather forecasting require the fluxes of radiation, heat, water vapor, and momentum across the land-atmosphere interface to be specified. These fluxes are calculated by submodels called land surface parameterizations. Over the last 20 years, these parameterizations have evolved from simple, unrealistic schemes into credible representations of the global soil-vegetation-atmosphere transfer system as advances in plant physiological and hydrological research, advances in satellite data interpretation, and the results of large-scale field experiments have been exploited. Some modern schemes incorporate biogeochemical and ecological knowledge and, when coupled with advanced climate and ocean models, will be capable of modeling the biological and physical responses of the Earth system to global change, for example, increasing atmospheric carbon dioxide.

We present cosmological results from a combined analysis of galaxy clustering and weak gravitational lensing, using 1321 deg 2 of griz imaging data from the first year of the Dark Energy Survey (DES Y1). We combine three two-point functions: (i) the cosmic shear correlation function of 26 million source galaxies in four redshift bins, (ii) the galaxy angular autocorrelation function of 650,000 luminous red galaxies in five redshift bins, and (iii) the galaxy-shear cross-correlation of luminous red galaxy positions and source galaxy shears. To demonstrate the robustness of these results, we use independent pairs of galaxy shape, photometric-redshift estimation and validation, and likelihood analysis pipelines. To prevent confirmation bias, the bulk of the analysis was carried out while "blind" to the true results; we describe an extensive suite of systematics checks performed and passed during this blinded phase. The data are modeled in flat CDM and wCDM cosmologies, marginalizing over 20 nuisance parameters, varying 6 (for CDM) or 7 (for wCDM) cosmological parameters including the neutrino mass density and including the 457 457 element analytic covariance matrix. We find consistent cosmological results from these three two-point functions and from their combination obtain S 8 8 m =0.3 0.5 0.773 0.026 -0.020 and m 0.267 0.030 -0.017 for CDM; for wCDM, we find S 8 0.782 0.036 -0.024 , m 0.284 0.033 -0.030 , and w -0.82 0.21 -0.20 at 68% C.L. The precision of these DES Y1 constraints rivals that from the Planck cosmic microwave background measurements, allowing a comparison of structure in the very

The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications. The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream.

Purpose This paper aims to identify, analyse and organise the literature about blockchains in supply chain management (SCM) context (blockchain–SCM integration) and proposes an agenda for future research. This study aims to shed light on what the main current blockchain applications in SCM are, what the main disruptions and challenges are in SCM because of blockchain adoption and what the future of blockchains holds in SCM. Design/methodology/approach This study followed the systematic review approach to analyse and synthesise the extant literature on blockchain–SCM integration. The review analysed 27 papers between 2008 and 2018 in peer-reviewed journals. Findings Blockchain–SCM integration is still in its infancy. Scholars and practitioners are not fully aware of the potential of blockchain technology to disrupt traditional business models. However, the electric power industry seems to have a relatively mature understanding of blockchain–SCM integration, demonstrated by the use of smart contracts. Additionally, the disintermediation provided by blockchain applications has the potential to disrupt traditional industries (e.g. health care, transportation and retail). Research limitations/implications The limitations of this study are represented mainly by the scarcity of studies on blockchain–SCM integration in leading journals and databases. Practical implications This study highlights examples of blockchain–SCM integration, emphasising the need to rethink business models to incorporate blockchain technology. Originality/value This study is the first attempt to synthesise existing publications about the blockchain–SCM integration, shedding light on the disruption caused by, and the necessity of, the SCM reconfigurations.

A growing body of literature on the 2019 novel coronavirus (SARS-CoV-2) is becoming available, but a synthesis of available data has not been conducted. We performed a scoping review of currently available clinical, epidemiological, laboratory, and chest imaging data related to the SARS-CoV-2 infection. We searched MEDLINE, Cochrane CENTRAL, EMBASE, Scopus and LILACS from 01 January 2019 to 24 February 2020. Study selection, data extraction and risk of bias assessment were performed by two independent reviewers. Qualitative synthesis and meta-analysis were conducted using the clinical and laboratory data, and random-effects models were applied to estimate pooled results. A total of 61 studies were included (59,254 patients). The most common disease-related symptoms were fever (82%, 95% confidence interval (CI) 56%–99%; n = 4410), cough (61%, 95% CI 39%–81%; n = 3985), muscle aches and/or fatigue (36%, 95% CI 18%–55%; n = 3778), dyspnea (26%, 95% CI 12%–41%; n = 3700), headache in 12% (95% CI 4%–23%, n = 3598 patients), sore throat in 10% (95% CI 5%–17%, n = 1387) and gastrointestinal symptoms in 9% (95% CI 3%–17%, n = 1744). Laboratory findings were described in a lower number of patients and revealed lymphopenia (0.93 × 109/L, 95% CI 0.83–1.03 × 109/L, n = 464) and abnormal C-reactive protein (33.72 mg/dL, 95% CI 21.54–45.91 mg/dL; n = 1637). Radiological findings varied, but mostly described ground-glass opacities and consolidation. Data on treatment options were limited. All-cause mortality was 0.3% (95% CI 0.0%–1.0%; n = 53,631). Epidemiological studies showed that mortality was higher in males and elderly patients. The majority of reported clinical symptoms and laboratory findings related to SARS-CoV-2 infection are non-specific. Clinical suspicion, accompanied by a relevant epidemiological history, should be followed by early imaging and virological assay.

Periodontitis is a highly prevalent disease. As it progresses, it causes serious morbidity in the form of periodontal abscesses and tooth loss and, in the latter stages, pain. It is also now known that periodontitis is strongly associated with several nonoral diseases. Thus, patients with periodontitis are at greater risk for the development and/or exacerbation of diabetes, chronic obstructive pulmonary disease, and cardiovascular diseases, among other conditions. Although it is without question that specific groups of oral bacteria which populate dental plaque play a causative role in the development of periodontitis, it is now thought that once this disease has been triggered, other factors play an equal, and possibly more important, role in its progression, particularly in severe cases or in cases that prove difficult to treat. In this regard, we allude to the host response, specifically the notion that the host, once infected with oral periodontal pathogenic bacteria, will mount a defense response mediated largely through the innate immune system. The most abundant cell type of the innate immune system - polymorphonuclear neutrophils - can, when protecting the host from microbial invasion, mount a response that includes upregulation of proinflammatory cytokines, matrix metalloproteinases, and reactive oxygen species, all of which then contribute to the tissue damage and loss of teeth commonly associated with periodontitis. Of the mechanisms referred to here, we suggest that upregulation of reactive oxygen species might play one of the most important roles in the establishment and progression of periodontitis (as well as in other diseases of inflammation) through the development of oxidative stress. In this overview, we discuss both innate and epigenetic factors (eg, diabetes, smoking) that lead to the development of oxidative stress. This oxidative stress then provides an environment conducive to the destructive processes observed in periodontitis. Therefore, we shall describe some of the fundamental characteristics of oxidative stress and its effects on the periodontium, discuss the diseases and other factors that cause oxidative stress, and, finally, review potentially novel therapeutic approaches for the management (and possibly even the reversal) of periodontitis, which rely on the use of therapies, such as resveratrol and other antioxidants, that provide increased antioxidant activity in the host.

The new evolution of the production and industrial process called Industry 4.0, and its related technologies such as the Internet of Things, big data analytics, and cyber–physical systems, among others, still have an unknown potential impact on sustainability and the environment. In this paper, we conduct a literature-based analysis to discuss the sustainability impact and challenges of Industry 4.0 from four different scenarios: deployment, operation and technologies, integration and compliance with the sustainable development goals, and long-run scenarios. From these scenarios, our analysis resulted in positive or negative impacts related to the basic production inputs and outputs flows: raw material, energy and information consumption and product and waste disposal. As the main results, we identified both positive and negative expected impacts, with some predominance of positives that can be considered positive secondary effects derived from Industry 4.0 activities. However, only through integrating Industry 4.0 with the sustainable development goals in an eco-innovation platform, can it really ensure environmental performance. It is expected that this work can contribute to helping stakeholders, practitioners and governments to advance solutions to deal with the outcomes emerging through the massive adoption of those technologies, as well as supporting the expected positive impacts through policies and financial initiatives.

Measurements of two-and four-particle angular correlations for charged particles emitted in pPb collisions are presented over a wide range in pseudorapidity and full azimuth. The data, corresponding to an integrated luminosity of approximately 31 nb -1 , were collected during the 2013 LHC pPb run at a nucleon-nucleon center-of-mass energy of 5.02 TeV by the CMS experiment. The results are compared to 2.76 TeV semi-peripheral PbPb collision data, collected during the 2011 PbPb run, covering a similar range of particle multiplicities. The observed correlations are characterized by the near-side (| | 0) associated pair yields and the azimuthal anisotropy Fourier harmonics (v n ). The second-order (v 2 ) and third-order (v 3 ) anisotropy harmonics are extracted using the two-particle azimuthal correlation technique. A four-particle correlation method is also applied to obtain the value of v 2 and further explore the multi-particle nature of the correlations. Both associated pair yields and anisotropy harmonics are studied as a function of particle multiplicity and transverse momentum. The associated pair yields, the four-particle v 2 , and the v 3 become apparent at about the same multiplicity. A remarkable similarity in the v 3 signal as a function of multiplicity is observed between the pPb and PbPb systems. Predictions based on the color glass condensate and hydrodynamic models are compared to the experimental results.

The data of four networks that can be used in carrying out comparative studies with methods for transmission network expansion planning are given. These networks are of various types and different levels of complexity. The main mathematical formulations used in transmission expansion studies—transportation models, hybrid models, DC power flow models, and disjunctive models are also summarised and compared. The main algorithm families are reviewed—both analytical, combinatorial and heuristic approaches. Optimal solutions are not yet known for some of the four networks when more accurate models (e.g. the DC model) are used to represent the power flow equations—the state of the art with regard to this is also summarised. This should serve as a challenge to authors searching for new, more efficient methods.

Experience economy is the last segment in the evolution of the market, and it is characterized by the fact that consumers do not acquire goods, products or services, but experiences that they integrate in their biography, and consequently in their identity. Customer Experience, possibly the latest revolution in business thinking along with the digital transformation, seeks the design and management of truly customer-centric experiences. This revolution is spreading across different sectors, among which the health sector should necessarily be considered. This talk covers the fundamental ideas within the concept of customer experience, as well as it provides information and suggestions about how to design and deliver an optimal patient experience.

A large number of newly published and unpublished hectare plots in Amazonia and the Guiana Shield area allow an analysis of family composition and testing of hypotheses concerning alpha-diversity in the south American rain forest. Using data from 94 plots the family-level floristic patterns in wet tropical South America are described. To test diversity patterns, 268 plots are used in this large area. Contrary to a widely held belief, western Amazonian plots are not necessarily the most diverse. Several central Amazonian plots have equal or even higher tree diversity. Annual rainfall is not a good estimator for tree diversity in the Amazonia area and Guiana shield. Plots in the Guiana Shield area (and eastern Amazonia) usually have lower diversity than those in central or western Amazonia. It is argued that this is not because of low rainfall or low nutrient status of the soil but because of the small area of the relatively isolated rain forest area in eastern Amazonia and the Guiana Shield. The low diversity on nutrient-poor white sand soils in the Amazon basin is not necessarily due to their low nutrient status but is, at least partly, caused by their small extent and fragmented nature.

Artificial Intelligence (AI) offers a promising solution for building and promoting more resilient supply chains. However, the literature is highly dispersed regarding the application of AI in supply-chain management. The literature to date lacks a decision-making framework for identifying and applying powerful AI techniques to build supply-chain resilience (SCRes), curbing advances in research and practice on this interesting interface. In this paper, we propose an integrated Multi-criteria decision-making (MCDM) technique powered by AI-based algorithms such as Fuzzy systems, Wavelet Neural Networks (WNN) and Evaluation based on Distance from Average Solution (EDAS) to identify patterns in AI techniques for developing different SCRes strategies. The analysis was informed by data collected from 479 manufacturing companies to determine the most significant AI applications used for SCRes. The findings show that fuzzy logic programming, machine learning big data, and agent-based systems are the most promising techniques used to promote SCRes strategies. The study findings support decision-makers by providing an integrated decision-making framework to guide practitioners in AI deployment for building SCRes.

Previous studies have shown a high prevalence of toxoplasmosis and the frequent occurrence of ocular disease in Brazil. To identify the genotypes of parasite strains associated with ocular disease, we compared 25 clinical and animal isolates of Toxoplasma gondii from Brazil to previously characterized clonal lineages from North America and Europe. Multilocus nested polymerase chain reaction analysis was combined with direct sequencing of a polymorphic intron to classify strains by phylogenetic methods. The genotypes of T. gondii strains isolated from Brazil were highly divergent when compared to the previously described clonal lineages. Several new predominant genotypes were identified from different regions of Brazil, including 2 small outbreaks attributable to foodborne or waterborne infection. These findings show that the genetic makeup of T. gondii is more complex than previously recognized and suggest that unique or divergent genotypes may contribute to different clinical outcomes of toxoplasmosis in different localities.

The adoption of technologies by the operations and supply chain management (OSCM) field is leading to extraordinary disruptions. And with the rapid emergence of cutting-edge and more disruptive technologies, the OSCM is striving to take advantage of such innovations, but they are bringing in their wake a number of challenges. One of those disruptive technologies is blockchain, which is increasingly accepted in virtually all industries. This study aims to investigate the blockchain technology (BCT) adoption behaviour and possible barriers in the Brazilian OSCM context. We developed a model drawing on the unified theory of acceptance and use of technology (UTAUT) model, the supply chain literature, and the emerging literature on BCT. We empirically validated the proposed model with Brazilian operations and supply chain professionals by using the partial least squares structural equation modelling (PLS-SEM). Our findings revealed that facilitating conditions, trust, social influence, and effort expectancy are the most critical constructs that directly affect BCT adoption. Unexpectedly, performance expectancy appeared not decisive in terms of predicting BCT adoption. This study contributes to advancing and stimulating the theory about BCT adoption behaviour in supply chains, as well as important managerial implications, which may be more critical for emerging economies.

Animal poisons and venoms are comprised of different classes of molecules displaying wide-ranging pharmacological activities. This review aims to provide an in-depth view of toxin-based compounds from terrestrial and marine organisms used as diagnostic tools, experimental molecules to validate postulated therapeutic targets, drug libraries, prototypes for the design of drugs, cosmeceuticals, and therapeutic agents. However, making these molecules applicable requires extensive preclinical trials, with some applications also demanding clinical trials, in order to validate their molecular target, mechanism of action, effective dose, potential adverse effects, as well as other fundamental parameters. Here we go through the pitfalls for a toxin-based potential therapeutic drug to become eligible for clinical trials and marketing. The manuscript also presents an overview of the current picture for several molecules from different animal venoms and poisons (such as those from amphibians, cone snails, hymenopterans, scorpions, sea anemones, snakes, spiders, tetraodontiformes, bats, and shrews) that have been used in clinical trials. Advances and perspectives on the therapeutic potential of molecules from other underexploited animals, such as caterpillars and ticks, are also reported. The challenges faced during the lengthy and costly preclinical and clinical studies and how to overcome these hindrances are also discussed for that drug candidates going to the bedside. It covers most of the drugs developed using toxins, the molecules that have failed and those that are currently in clinical trials. The article presents a detailed overview of toxins that have been used as therapeutic agents, including their discovery, formulation, dosage, indications, main adverse effects, and pregnancy and breastfeeding prescription warnings. Toxins in diagnosis, as well as cosmeceuticals and atypical therapies (bee venom and leech therapies) are also reported. The level of cumulative and detailed information provided in this review may help pharmacists, physicians, biotechnologists, pharmacologists, and scientists interested in toxinology, drug discovery, and development of toxin-based products.

Interest in the role of extracellular vesicles in various diseases including cancer has been increasing. Extracellular vesicles include microvesicles, exosomes, apoptotic bodies, and argosomes, and are classified by size, content, synthesis, and function. Currently, the best characterized are exosomes and microvesicles. Exosomes are small vesicles (40-100 nm) involved in intercellular communication regardless of the distance between them. They are found in various biological fluids such as plasma, serum, and breast milk, and are formed from multivesicular bodies through the inward budding of the endosome membrane. Microvesicles are 100-1000 nm vesicles released from the cell by the outward budding of the plasma membrane. The therapeutic potential of extracellular vesicles is very broad, with applications including a route of drug delivery and as biomarkers for diagnosis. Extracellular vesicles extracted from stem cells may be used for treatment of many diseases including kidney diseases. This review highlights mechanisms of synthesis and function, and the potential uses of well-characterized extracellular vesicles, mainly exosomes, with a special focus on renal functions and diseases.

Purpose The Industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional business models and hastening the need for a redesign and digitisation of activities. In this context, the literature concerning the digital supply chain (DSC) and its capabilities are in the early stages. To bridge this gap, the purpose of this paper is to propose a framework for digital supply chain capabilities (DSCCs). Design/methodology/approach This paper uses a narrative literature approach, based on the main Industry 4.0 elements, supply chain and the emerging literature concerning DSC disruptions, to build an integrative framework to shed light on DSCCs. Findings The study identifies seven basic capabilities that shape the DSCC framework and six main enabler technologies, derived from 13 propositions. Research limitations/implications The proposed framework can bring valuable insights for future research development, although it has not been tested yet. Practical implications Managers, practitioners and all involved in the digitalisation phenomenon can utilise the framework as a starting point for other business digitalisation projects. Originality/value This study contributes to advancing the DSC literature, providing a well-articulated discussion and a framework regarding the capabilities, as well as 13 propositions that can generate valuable insights for other studies.

The emergence of Industry 4.0 has brought in its wake an important number of challenges and opportunities for organisations across the globe. To cope with such a fast-changing environment, organisations have been steadily implementing different types of technologies, and at different stages. One of the most disruptive and promising technology is blockchain, and its potential to transform various aspects of organisations’ business and operations, including the supply chain relationships, is tremendous. In line with the global research trend in this domain, this paper proposes a multi-stage model of adoption (intention, adoption, and routinisation stages), for a better understanding of blockchain diffusion across supply chains. We drew on the diffusion of innovations theory, the resource-based view, dynamic capability, the technology adoption model, and the institutional theory to propose a multi-stage model. We validated the model using PLS-SEM, which was applied on data collected in India and the U.S. Our results showed that, from one country to another, there are essential differences in the variables that determine blockchain innovation and in the stage of diffusion. Additionally, our proposed model provided a good explanation at all stages of blockchain diffusion. This study offers significant and valuable contributions in terms of theory and management.

A search for invisible decays of a Higgs boson is performed using proton-proton collision data collected with the CMS detector at the LHC in 2016 at a center-of-mass energy s = 13 TeV, corresponding to an integrated luminosity of 35.9 fb -1 . The search targets the production of a Higgs boson via vector boson fusion. The data are found to be in agreement with the background contributions from standard model processes. An observed (expected) upper limit of 0.33 (0.25), at 95% confidence level, is placed on the branching fraction of the Higgs boson decay to invisible particles, assuming standard model production rates and a Higgs boson mass of 125.09 GeV. Results from a combination of this analysis and other direct searches for invisible decays of the Higgs boson, performed using data collected at