A Brief Review of Machine Learning Algorithms in Forest Fires Science

Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power

The induction and function of the anti-inflammatory fate of TH17 cells

TH17 cells exemplify environmental immune adaptation: they can acquire both a pathogenic and an anti-inflammatory fate. However, it is not known whether the anti-inflammatory fate is merely a vestigial trait, or whether it serves to preserve the integrity of the host tissues. Here we show that the capacity of TH17 cells to acquire an anti-inflammatory fate is necessary to sustain immunological tolerance, yet it impairs immune protection against S. aureus. Additionally, we find that TGF-β signalling via Smad3/Smad4 is sufficient for the expression of the anti-inflammatory cytokine, IL-10, in TH17 cells. Our data thus indicate a key function of TH17 cell plasticity in maintaining immune homeostasis, and dissect the molecular mechanisms explaining the functional flexibility of TH17 cells with regard to environmental changes.Fil: Xu, Hao. University of Yale. School of Medicine; Estados UnidosFil: Agalioti, Theodora. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Zhao, Jun. University of Yale. School of Medicine; Estados UnidosFil: Steglich, Babett. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Wahib, Ramez. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Amezcua Vesely, Maria Carolina. University of Yale. School of Medicine; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Bielecki, Piotr. University of Yale. School of Medicine; Estados UnidosFil: Bailis, Will. University of Yale. School of Medicine; Estados UnidosFil: Jackson, Ruaidhri. University of Yale. School of Medicine; Estados UnidosFil: Perez, Daniel. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Izbicki, Jakob. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Licona-Limón, Paula. University of Yale. School of Medicine; Estados UnidosFil: Kaartinen, Vesa. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Geginat, Jens. University Medical Center Hamburg-Eppendorf; AlemaniaFil: Esplugues, Enric. University of Yale. School of Medicine; Estados UnidosFil: Tolosa, Eva. University of Yale. School of Medicine; Estados UnidosFil: Huber, Samuel. University of Yale. School of Medicine; Estados UnidosFil: Flavell, Richard A.. University of Yale. School of Medicine; Estados UnidosFil: Gagliani, Nicola. University Medical Center Hamburg-Eppendorf; Alemani

IL-22BP controls the progression of liver metastasis in colorectal cancer

BackgroundThe immune system plays a pivotal role in cancer progression. Interleukin 22 binding protein (IL-22BP), a natural antagonist of the cytokine interleukin 22 (IL-22) has been shown to control the progression of colorectal cancer (CRC). However, the role of IL-22BP in the process of metastasis formation remains unknown.MethodsWe used two different murine in vivo metastasis models using the MC38 and LLC cancer cell lines and studied lung and liver metastasis formation after intracaecal or intrasplenic injection of cancer cells. Furthermore, IL22BP expression was measured in a clinical cohort of CRC patients and correlated with metastatic tumor stages.ResultsOur data indicate that low levels of IL-22BP are associated with advanced (metastatic) tumor stages in colorectal cancer. Using two different murine in vivo models we show that IL-22BP indeed controls the progression of liver but not lung metastasis in mice.ConclusionsWe here demonstrate a crucial role of IL-22BP in controlling metastasis progression. Thus, IL-22 might represent a future therapeutic target against the progression of metastatic CRC

TGF-β signaling in Th17 cells promotes IL-22 production and colitis-associated colon cancer

IL-22 has dual functions during tumorigenesis. Short term IL-22 production protects against genotoxic stress, whereas uncontrolled IL-22 activity promotes tumor growth; therefore, tight regulation of IL-22 is essential. TGF-β1 promotes the differentiation of Th17 cells, which are known to be a major source of IL-22, but the effect of TGF-β signaling on the production of IL-22 in CD4+ T cells is controversial. Here we show an increased presence of IL-17+IL-22+ cells and TGF-β1 in colorectal cancer compared to normal adjacent tissue, whereas the frequency of IL-22 single producing cells is not changed. Accordingly, TGF-β signaling in CD4+ T cells (specifically Th17 cells) promotes the emergence of IL-22-producing Th17 cells and thereby tumorigenesis in mice. IL-22 single producing T cells, however, are not dependent on TGF-β signaling. We show that TGF-β, via AhR induction, and PI3K signaling promotes IL-22 production in Th17 cells.Fil: Perez, Laura Garcia. Department Of Medicine, University Medical Center Hamb; AlemaniaFil: Kempski, Jan. Universitat Hamburg; AlemaniaFil: McGee, Heather M.. Universitat Hamburg; AlemaniaFil: Pelzcar, Penelope. Universitat Hamburg; AlemaniaFil: Agalioti, Theodora. Universitat Hamburg; AlemaniaFil: Giannou, Anastasios. Universitat Hamburg; AlemaniaFil: Konczalla, Leonie. Universitat Hamburg; AlemaniaFil: Brockmann, Leonie. Universitat Hamburg; AlemaniaFil: Wahib, Ramez. Universitat Hamburg; AlemaniaFil: Xu, Hao. University of Yale. School of Medicine; Estados UnidosFil: Amezcua Vesely, Maria Carolina. University of Yale. School of Medicine; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Soukou, Shiwa. Universitat Hamburg; AlemaniaFil: Steglich, Babett. Universitat Hamburg; AlemaniaFil: Bedke, Tanja. Universitat Hamburg; AlemaniaFil: Manthey, Carolin. Universitat Hamburg; AlemaniaFil: Seiz, Oliver. Universitat Hamburg; AlemaniaFil: Diercks, Björn-Philipp. Universitat Hamburg; AlemaniaFil: Gnafakis, Stylianos. Universitat Hamburg; AlemaniaFil: Guse, Andreas H.. Universitat Hamburg; AlemaniaFil: Perez, Daniel. Universitat Hamburg; AlemaniaFil: Izbicki, Jakob R.. Universitat Hamburg; AlemaniaFil: Gagliani, Nicola. Universitat Hamburg; AlemaniaFil: Flavell, Richard A.. University of Yale. School of Medicine; Estados UnidosFil: Huber, Samuel. Universitat Hamburg; Alemani

NK cell phenotype and baseline nutrient receptor expression: Samples were compared using Wilcoxon matched-pairs signed rank tests and multiplicity was controlled for by FDR testing.

<p>Bars indicate the median, significance was defined as p≤0.05 (*). A. viSNE representation of peripheral blood- (PBMC, top row), liver- (middle row) and spleen (bottom row) derived NK cells and their expression of CD56, CD16, CXCR6, CD57 and CD127. B. Expression (Median fluorescence intensity, MdFI) of Glut1 on CD56^brightCD16^- (purple) and CD56^dimCD16⁺ (teal) tissue-resident (TR), tissue-derived (TD) and peripheral blood (PB) NK cells from paired liver-blood (left diagram, n = 12) and spleen-blood (right diagram, n = 11) samples. C. Expression (MdFI) of CD98 on CD56^brightCD16^- (purple) and CD56^dimCD16⁺ (teal) tissue-resident (TR), tissue-derived (TD) and peripheral blood (PB) NK cells from paired liver-blood (left diagram, n = 12) and spleen-blood (right diagram, n = 11) samples. D. Expression (MdFI) of CD71 on CD56^brightCD16^- (purple) and CD56^dimCD16⁺ (teal) tissue-resident (TR), tissue-derived (TD) and peripheral blood (PB) NK cells from paired liver-blood (left diagram, n = 12) and spleen-blood (right diagram, n = 11) samples.</p

Effects of cytokine stimulation on CD98 expression: Samples were compared using Wilcoxon matched-pairs signed rank tests and multiplicity was controlled for by FDR testing.

<p>Bars indicate the median, significance was defined as p≤0.05 (*). A. Representative histograms of CD98 expression on unstimulated and stimulated CD56^bright CXCR6⁺ (I, grey: unstimulated; purple: stimulated), CD56^dim CXCR6⁺ (II, grey: unstimulated; teal: stimulated), CD56^bright CXCR6^- (III, grey: unstimulated; purple: stimulated) and CD56^dim CXCR6^- (IV, grey: unstimulated; teal: stimulated) NK cells from blood (left), liver (middle) and spleen (right) samples. B. Expression (MdFI) of CD98 on unstimulated (grey) and CD56^brightCD16^- (purple) and CD56^dimCD16⁺ (teal) tissue-resident (TR), tissue-derived (TD) and peripheral blood (PB) NK cells from paired liver-blood (left diagram, n = 12) and spleen-blood (right diagram, n = 11) samples.</p

Relevant clinical data of the spleen cohort.

<p>Relevant clinical data of the spleen cohort.</p

Relevant clinical data of the liver cohort.

<p>Relevant clinical data of the liver cohort.</p

Expression of CXCR6 in NK cells in tissues.

<p>Expression of CXCR6 in NK cells in tissues.</p

Effects of cytokine stimulation on CD71 expression: Samples were compared using Wilcoxon matched-pairs signed rank tests and multiplicity was controlled for by FDR testing.

<p>Bars indicate the median, significance was defined as p≤0.05 (*). A. Representative histograms of CD71 expression on unstimulated and stimulated CD56^bright CXCR6⁺ (I, grey: unstimulated; purple: stimulated), CD56^dim CXCR6⁺ (II, grey: unstimulated; teal: stimulated), CD56^bright CXCR6^- (III, grey: unstimulated; purple: stimulated) and CD56^dim CXCR6^- (IV, grey: unstimulated; teal: stimulated) NK cells from blood (left), liver (middle) and spleen (right) samples. B. Expression (MdFI) of CD71 on unstimulated (grey) and CD56^brightCD16^- (purple) and CD56^dimCD16⁺ (teal) tissue-resident (TR), tissue-derived (TD) and peripheral blood (PB) NK cells from paired liver-blood (left diagram, n = 12) and spleen-blood (right diagram, n = 11) samples.</p