Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Proteomic analysis of the human vitreous humor in Retinal Detachment

Over the past few years, there has been an intensification in onset of ocular diseases among worldwide population. The incidence of eye pathologies upsurges with advancing age, leading to a decline of normal eye function and even to blindness. Ocular diseases are frequently triggered by chronic disorders, such as diabetes or hypertension, or by alterations in retinal positioning, which occurs in retinal detachment (RD). Recent studies indicates that human vitreous humor (VH) suffers proteome alterations according to the actual physiological and pathological state of the retina. However, there are few published articles regarding RD proteome. Hence, this study is focused in the proteomic analysis of VH samples in RD and posterior identification of the present proteins in these samples, as well as to understand in what way they are connected and how they act. In this way, we can get access to the VH proteome in RD patients, which is our main goal. To achieve these goals, several strategies were combined, including high abundant proteins depletion, protein fractionation and analysis by mass spectrometry (MS), in order to maximize the number of identified proteins. The final optimized strategy combined protein fractionation using liquid chromatography (LC), SDS-PAGE, and protein identification by MALDI-TOF/TOF. Using this methodology, we found 236 proteins with a 95% confidence, using ProteinPilot, where 46 proteins share common biological associations, with a minimum score of 0.900 according to STRING10. The majority of these 46 proteins are involved in regulation processes and share binding functions. Simultaneously, the same VH group sample was analyzed by LC and MALDI-TOF/TOF, in order to understand the importance of the implementation of SDS-PAGE in the process. Thus, we verified that with LC-MALDI only 110 proteins were identified. In conclusion, the developed strategy allowed to find proteins which were not identified using other proteomic strategies.Nos últimos anos, tem-se verificado um grande incremento de doenças oculares na população mundial. A incidência destas patologias surge com a progressão da idade, levando a um declínio da função ocular normal e, em alguns casos, podendo levar à cegueira. As patologias oculares são frequentemente desencadeadas por doenças crónicas, tais como diabetes ou hipertensão, ou por alterações no posicionamento da retina, o que ocorre no descolamento de retina (DR). Estudos recentes indicam que o humor vítreo (HV) humano sofre alterações proteómicas, de acordo com o estado fisiológico e patológico da retina. No entanto, existem poucos estudos publicados sobre o proteoma do HV no DR. Assim, este estudo centra-se essencialmente na análise proteómica de amostras de HV em DR, e posterior identificação das proteínas presentes nestas amostras, de que forma estão interligadas e como atuam. Desta forma, pode ter-se acesso ao proteoma do HV em doentes com descolamento de retina, que é o principal objetivo deste trabalho. Para atingir estes objetivos, diversas estratégias foram combinadas, incluindo a depleção de proteínas abundantes, fracionamento e análise de proteínas por espectrometria de massa (MS), a fim de maximizar o número de proteínas identificadas. A estratégia final otimizada combinou o fracionamento de proteínas por cromatografia líquida (LC) e SDS-PAGE e a identificação das proteínas por MALDI-TOF/TOF. Usando esta metodologia, foram identificadas 236 proteínas com uma confiança de 95%, usando o ProteinPilot, onde 46 proteínas partilham associações biológicas comuns, com um score mínimo de 0.900 de acordo com o STRING10. A maioria dessas 46 proteínas estão envolvidas em processos de regulação e têm funções de ligação. Em simultâneo, analisou-se o mesmo grupo de amostras através de LC e MALDI-TOF/TOF, de forma a compreender a importância da implementação de SDS-PAGE no processo. Verificou-se que através de LC-MALDI, apenas 110 proteínas foram identificadas. Em conclusão, a estratégia desenvolvida (LC-SDS-MALDI) permitiu encontrar proteínas que não tinham sido anteriormente identificadas usando outras estratégias proteómicas

Release of insulin from PLGA-alginate dressing stimulates regenerative healing of burn wounds in rats

Burn wound healing involves a complex set of overlapping processes in an environment conducive to ischemia, inflammation, and infection costing $7.5 billion/year in the US alone, in addition to the morbidity and mortality that occur when the burns are extensive. We previously showed that insulin, when topically applied to skin excision wounds, accelerates re-epithelialization, and stimulates angiogenesis. More recently, we developed an alginate sponge dressing (ASD) containing insulin encapsulated in PLGA microparticles that provides a sustained release of bioactive insulin for >20days in a moist and protective environment. We hypothesized that insulin-containing ASD accelerates burn healing and stimulates a more regenerative, less scarring, healing. Using a heat-induced burn injury in rats, we show that burns treated with dressings containing 0.04mg insulin/cm2, every three days for 9 days, have faster closure, faster rate of disintegration of dead tissue, and decreased oxidative stress.In addition, in insulin-treated wounds the pattern of neutrophil inflammatory response suggests faster clearing of the burn dead tissue. We also observe faster resolution of the pro-inflammatory macrophages. We also found that insulin stimulates collagen deposition and maturation with the fibers organized more like a basket weave (normal skin) than aligned and crosslinked (scar tissue). In summary , application of ASD-containing insulin-loaded PLGA particles on burns every three days stimulates faster and more regenerative healing. These results suggest insulin as a potential therapeutic agent in burn healing and, because of its long history of safe use in humans, insulin could become one of the treatments of choice when repair and regeneration are critical for proper tissue function.This work was supported by the National Natural Science Fund of China [grant numbers 81170761 and 81270909 (to Y.L.)]; the Natural Sciences and Engineering Research Council of Canada [grant numbers 204794-2011 (to M.H.) and private donor (to M.M.-G.)]

Connecting global priorities: biodiversity and human health: a state of knowledge review.

Healthy communities rely on well-functioning ecosystems. They provide clean air, fresh water, medicines and food security. They also limit disease and stabilize the climate. But biodiversity loss is happening at unprecedented rates, impacting human health worldwide.The report, Connecting Global Priorities: Biodiversity and Human Health, focuses on the complex and multi-faceted connections between biodiversity and human health, and how the loss of biodiversity and corresponding ecosystem services may negatively influence health. One of the first integrative reviews of its kind, the report brings together knowledge from several scientific disciplines, including public health, conservation, agriculture, epidemiology and development. The book is a joint publication of the Convention on Biological Diversity and World Health Organization. Danny Hunter, Senior Scientist, Bioversity International is one of the Lead Coordinating Authors of the book and co-lead author on two chapters:Chapter 5: Agricultural biodiversity and food security Chapter 6: Biodiversity and nutritio

Distinction of Neurons, Glia and Endothelial Cells in the Cerebral Cortex: An Algorithm Based on Cytological Features

The estimation of the number or density of neurons and types of glial cells and their relative proportions in different brain areas are at the core of rigorous quantitative neuroanatomical studies. Unfortunately, the lack of detailed, updated, systematic, and well-illustrated descriptions of the cytology of neurons and glial cell types, especially in the primate brain, makes such studies especially demanding, often limiting their scope and broad use. Here, following extensive analysis of histological materials and the review of current and classical literature, we compile a list of precise morphological criteria that can facilitate and standardize identification of cells in stained sections examined under the microscope. We describe systematically and in detail the cytological features of neurons and glial cell types in the cerebral cortex of the macaque monkey and the human using semithin and thick sections stained for Nissl. We used this classical staining technique because it labels all cells in the brain in distinct ways. In addition, we corroborate key distinguishing characteristics of different cell types in sections immunolabeled for specific markers counterstained for Nissl and in ultrathin sections processed for electron microscopy. Finally, we summarize the core features that distinguish each cell type in easy-to-use tables and sketches, and structure these key features in an algorithm that can be used to systematically distinguish cellular types in the cerebral cortex. Moreover, we report high inter-observer algorithm reliability, which is a crucial test for obtaining consistent and reproducible cell counts in unbiased stereological studies. This protocol establishes a consistent framework that can be used to reliably identify and quantify cells in the cerebral cortex of primates as well as other mammalian species in health and disease

Diversified query expansion

La diversification des résultats de recherche (DRR) vise à sélectionner divers documents à partir des résultats de recherche afin de couvrir autant d’intentions que possible. Dans les approches existantes, on suppose que les résultats initiaux sont suffisamment diversifiés et couvrent bien les aspects de la requête. Or, on observe souvent que les résultats initiaux n’arrivent pas à couvrir certains aspects. Dans cette thèse, nous proposons une nouvelle approche de DRR qui consiste à diversifier l’expansion de requête (DER) afin d’avoir une meilleure couverture des aspects. Les termes d’expansion sont sélectionnés à partir d’une ou de plusieurs ressource(s) suivant le principe de pertinence marginale maximale. Dans notre première contribution, nous proposons une méthode pour DER au niveau des termes où la similarité entre les termes est mesurée superficiellement à l’aide des ressources. Quand plusieurs ressources sont utilisées pour DER, elles ont été uniformément combinées dans la littérature, ce qui permet d’ignorer la contribution individuelle de chaque ressource par rapport à la requête. Dans la seconde contribution de cette thèse, nous proposons une nouvelle méthode de pondération de ressources selon la requête. Notre méthode utilise un ensemble de caractéristiques qui sont intégrées à un modèle de régression linéaire, et génère à partir de chaque ressource un nombre de termes d’expansion proportionnellement au poids de cette ressource. Les méthodes proposées pour DER se concentrent sur l’élimination de la redondance entre les termes d’expansion sans se soucier si les termes sélectionnés couvrent effectivement les différents aspects de la requête. Pour pallier à cet inconvénient, nous introduisons dans la troisième contribution de cette thèse une nouvelle méthode pour DER au niveau des aspects. Notre méthode est entraînée de façon supervisée selon le principe que les termes reliés doivent correspondre au même aspect. Cette méthode permet de sélectionner des termes d’expansion à un niveau sémantique latent afin de couvrir autant que possible différents aspects de la requête. De plus, cette méthode autorise l’intégration de plusieurs ressources afin de suggérer des termes d’expansion, et supporte l’intégration de plusieurs contraintes telles que la contrainte de dispersion. Nous évaluons nos méthodes à l’aide des données de ClueWeb09B et de trois collections de requêtes de TRECWeb track et montrons l’utilité de nos approches par rapport aux méthodes existantes.Search Result Diversification (SRD) aims to select diverse documents from the search results in order to cover as many search intents as possible. For the existing approaches, a prerequisite is that the initial retrieval results contain diverse documents and ensure a good coverage of the query aspects. In this thesis, we investigate a new approach to SRD by diversifying the query, namely diversified query expansion (DQE). Expansion terms are selected either from a single resource or from multiple resources following the Maximal Marginal Relevance principle. In the first contribution, we propose a new term-level DQE method in which word similarity is determined at the surface (term) level based on the resources. When different resources are used for the purpose of DQE, they are combined in a uniform way, thus totally ignoring the contribution differences among resources. In practice the usefulness of a resource greatly changes depending on the query. In the second contribution, we propose a new method of query level resource weighting for DQE. Our method is based on a set of features which are integrated into a linear regression model and generates for a resource a number of expansion candidates that is proportional to the weight of that resource. Existing DQE methods focus on removing the redundancy among selected expansion terms and no attention has been paid on how well the selected expansion terms can indeed cover the query aspects. Consequently, it is not clear how we can cope with the semantic relations between terms. To overcome this drawback, our third contribution in this thesis aims to introduce a novel method for aspect-level DQE which relies on an explicit modeling of query aspects based on embedding. Our method (called latent semantic aspect embedding) is trained in a supervised manner according to the principle that related terms should correspond to the same aspects. This method allows us to select expansion terms at a latent semantic level in order to cover as much as possible the aspects of a given query. In addition, this method also incorporates several different external resources to suggest potential expansion terms, and supports several constraints, such as the sparsity constraint. We evaluate our methods using ClueWeb09B dataset and three query sets from TRECWeb tracks, and show the usefulness of our proposed approaches compared to the state-of-the-art approaches

Development of a Novel Humanized Single Chain Antibody-Streptococcal Superantigen-Derived Immunotherapy Targeting the 5T4 Oncofetal Antigen

Superantigens (SAgs) are microbial toxins that cross-link T cell receptors with major histocompatibility complex (MHC) class II (MHC II) molecules leading to the activation of large numbers of T cells. Herein, the development and preclinical testing of a novel tumour-targeted SAg (TTS) therapeutic built using the streptococcal pyrogenic exotoxin C (SpeC) SAg and targeting cancer cells expressing the 5T4 tumour-associated antigen (TAA) was described. To inhibit potentially harmful widespread immune cell activation, a SpeC mutation within the high-affinity MHC II binding interface was generated (SpeCD203A) that demonstrated a pronounced reduction in mitogenic activity, yet this mutant could still induce immune cell-mediated cancer cell death in vitro. To target 5T4+cancer cells, a humanized single-chain variable fragment (scFv) antibody to recognize 5T4 (scFv5T4) was engineered. Specific targeting of scFv5T4 was verified. SpeCD203A used to scFv5T4 maintained the ability to activate and induce immune cell-mediated cytotoxicity of colon cancer cells. Using a xenograft model of established human colon cancer, it was demonstrated that the SpeC-based TTS was able to control the growth and spread of large tumours in vivo. This required both TAA targeting by scFv5T4 and functional SAg activity. These studies lay the foundation for the development of streptococcal SAgs as \u27next-generation\u27 TTSs for cancer immunotherapy

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival