UAV-based slope failure detection using deep-learning convolutional neural networks

Slope failures occur when parts of a slope collapse abruptly under the influence of gravity, often triggered by a rainfall event or earthquake. The resulting slope failures often cause problems in mountainous or hilly regions, and the detection of slope failure is therefore an important topic for research. Most of the methods currently used for mapping and modelling slope failures rely on classification algorithms or feature extraction, but the spatial complexity of slope failures, the uncertainties inherent in expert knowledge, and problems in transferability, all combine to inhibit slope failure detection. In an attempt to overcome some of these problems we have analyzed the potential of deep learning convolutional neural networks (CNNs) for slope failure detection, in an area along a road section in the northern Himalayas, India. We used optical data from unmanned aerial vehicles (UAVs) over two separate study areas. Different CNN designs were used to produce eight different slope failure distribution maps, which were then compared with manually extracted slope failure polygons using different accuracy assessment metrics such as the precision, F-score, and mean intersection-over-union (mIOU). A slope failure inventory data set was produced for each of the study areas using a frequency-area distribution (FAD). The CNN approach that was found to perform best (precision accuracy assessment of almost 90% precision, F-score 85%, mIOU 74%) was one that used a window size of 64 × 64 pixels for the sample patches, and included slope data as an additional input layer. The additional information from the slope data helped to discriminate between slope failure areas and roads, which had similar spectral characteristics in the optical imagery. We concluded that the effectiveness of CNNs for slope failure detection was strongly dependent on their design (i.e., the window size selected for the sample patch, the data used, and the training strategies), but that CNNs are currently only designed by trial and error. While CNNs can be powerful tools, such trial and error strategies make it difficult to explain why a particular pooling or layer numbering works better than any other

Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success

Micropropagation and conservation of selected endangered anticancer medicinal plants from the Western Ghats of India

Globally, cancer is a constant battle which severely affects the human population. The major limitations of the anticancer drugs are the deleterious side effects on the quality of life. Plants play a vital role in curing many diseases with minimal or no side effects. Phytocompounds derived from various medicinal plants serve as the best source of drugs to treat cancer. The global demand for phytomedicines is mostly reached by the medicinal herbs from the tropical nations of the world even though many plant species are threatened with extinction. India is one of the mega diverse countries of the world due to its ecological habitats, latitudinal variation, and diverse climatic range. Western Ghats of India is one of the most important depositories of endemic herbs. It is found along the stretch of south western part of India and constitutes rain forest with more than 4000 diverse medicinal plant species. In recent times, many of these therapeutically valued herbs have become endangered and are being included under the red-listed plant category in this region. Due to a sharp rise in the demand for plant-based products, this rich collection is diminishing at an alarming rate that eventually triggered dangerous to biodiversity. Thus, conservation of the endangered medicinal plants has become a matter of importance. The conservation by using only in situ approaches may not be sufficient enough to safeguard such a huge bio-resource of endangered medicinal plants. Hence, the use of biotechnological methods would be vital to complement the ex vitro protection programs and help to reestablish endangered plant species. In this backdrop, the key tools of biotechnology that could assist plant conservation were developed in terms of in vitro regeneration, seed banking, DNA storage, pollen storage, germplasm storage, gene bank (field gene banking), tissue bank, and cryopreservation. In this chapter, an attempt has been made to critically review major endangered medicinal plants that possess anticancer compounds and their conservation aspects by integrating various biotechnological tool

Downregulation of uPAR and Cathepsin B Induces Apoptosis via Regulation of Bcl-2 and Bax and Inhibition of the PI3K/Akt Pathway in Gliomas

Glioma is the most commonly diagnosed primary brain tumor and is characterized by invasive and infiltrative behavior. uPAR and cathepsin B are known to be overexpressed in high-grade gliomas and are strongly correlated with invasive cancer phenotypes.In the present study, we observed that simultaneous downregulation of uPAR and cathepsin B induces upregulation of some pro-apoptotic genes and suppression of anti-apoptotic genes in human glioma cells. uPAR and cathepsin B (pCU)-downregulated cells exhibited decreases in the Bcl-2/Bax ratio and initiated the collapse of mitochondrial membrane potential. We also observed that the broad caspase inhibitor, Z-Asp-2, 6-dichlorobenzoylmethylketone rescued pCU-induced apoptosis in U251 cells but not in 5310 cells. Immunoblot analysis of caspase-9 immunoprecipitates for Apaf-1 showed that uPAR and cathepsin B knockdown activated apoptosome complex formation in U251 cells. Downregulation of uPAR and cathepsin B also retarded nuclear translocation and interfered with DNA binding activity of CREB in both U251 and 5310 cells. Further western blotting analysis demonstrated that downregulation of uPAR and cathepsin B significantly decreased expression of the signaling molecules p-PDGFR-β, p-PI3K and p-Akt. An increase in the number of TUNEL-positive cells, increased Bax expression, and decreased Bcl-2 expression in nude mice brain tumor sections and brain tissue lysates confirm our in vitro results.In conclusion, RNAi-mediated downregulation of uPAR and cathepsin B initiates caspase-dependent mitochondrial apoptosis in U251 cells and caspase-independent mitochondrial apoptosis in 5310 cells. Thus, targeting uPAR and cathepsin B-mediated signaling using siRNA may serve as a novel therapeutic strategy for the treatment of gliomas

Cord Blood Stem Cell-Mediated Induction of Apoptosis in Glioma Downregulates X-Linked Inhibitor of Apoptosis Protein (XIAP)

XIAP (X-linked inhibitor of apoptosis protein) is one of the most important members of the apoptosis inhibitor family. XIAP is upregulated in various malignancies, including human glioblastoma. It promotes invasion, metastasis, growth and survival of malignant cells. We hypothesized that downregulation of XIAP by human umbilical cord blood mesenchymal stem cells (hUCBSC) in glioma cells would cause them to undergo apoptotic death.We observed the effect of hUCBSC on two malignant glioma cell lines (SNB19 and U251) and two glioma xenograft cell lines (4910 and 5310). In co-cultures of glioma cells with hUCBSC, proliferation of glioma cells was significantly inhibited. This is associated with increased cytotoxicity of glioma cells, which led to glioma cell death. Stem cells induced apoptosis in glioma cells, which was evaluated by TUNEL assay, FACS analyses and immunoblotting. The induction of apoptosis is associated with inhibition of XIAP in co-cultures of hUCBSC. Similar results were obtained by the treatment of glioma cells with shRNA to downregulate XIAP (siXIAP). Downregulation of XIAP resulted in activation of caspase-3 and caspase-9 to trigger apoptosis in glioma cells. Apoptosis is characterized by the loss of mitochondrial membrane potential and upregulation of mitochondrial apoptotic proteins Bax and Bad. Cell death of glioma cells was marked by downregulation of Akt and phospho-Akt molecules. We observed similar results under in vivo conditions in U251- and 5310-injected nude mice brains, which were treated with hUCBSC. Under in vivo conditions, Smac/DIABLO was found to be colocalized in the nucleus, showing that hUCBSC induced apoptosis is mediated by inhibition of XIAP and activation of Smac/DIABLO.Our results indicate that downregulation of XIAP by hUCBSC treatment induces apoptosis, which led to the death of the glioma cells and xenograft cells. This study demonstrates the therapeutic potential of XIAP and hUCBSC to treat malignant gliomas

SPARC Overexpression Inhibits Cell Proliferation in Neuroblastoma and Is Partly Mediated by Tumor Suppressor Protein PTEN and AKT

Secreted protein acidic and rich in cysteine (SPARC) is also known as BM-40 or Osteonectin, a multi-functional protein modulating cell–cell and cell–matrix interactions. In cancer, SPARC is not only linked with a highly aggressive phenotype, but it also acts as a tumor suppressor. In the present study, we sought to characterize the function of SPARC and its role in sensitizing neuroblastoma cells to radio-therapy. SPARC overexpression in neuroblastoma cells inhibited cell proliferation in vitro. Additionally, SPARC overexpression significantly suppressed the activity of AKT and this suppression was accompanied by an increase in the tumor suppressor protein PTEN both in vitro and in vivo. Restoration of neuroblastoma cell radio-sensitivity was achieved by overexpression of SPARC in neuroblastoma cells in vitro and in vivo. To confirm the role of the AKT in proliferation inhibited by SPARC overexpression, we transfected neuroblastoma cells with a plasmid vector carrying myr-AKT. Myr-AKT overexpression reversed SPARC-mediated PTEN and increased proliferation of neuroblastoma cells in vitro. PTEN overexpression in parallel with SPARC siRNA resulted in decreased AKT phosphorylation and proliferation in vitro. Taken together, these results establish SPARC as an effector of AKT-PTEN-mediated inhibition of proliferation in neuroblastoma in vitro and in vivo

Co-Depletion of Cathepsin B and uPAR Induces G0/G1 Arrest in Glioma via FOXO3a Mediated p27Kip1 Upregulation

Cathepsin B and urokinase plasminogen activator receptor (uPAR) are both known to be overexpressed in gliomas. Our previous work and that of others strongly suggest a relationship between the infiltrative phenotype of glioma and the expression of cathepsin B and uPAR. Though their role in migration and adhesion are well studied the effect of these molecules on cell cycle progression has not been thoroughly examined.Cathepsin B and uPAR single and bicistronic siRNA plasmids were used to downregulate these molecules in SNB19 and U251 glioma cells. FACS analysis and BrdU incorporation assay demonstrated G0/G1 arrest and decreased proliferation with the treatments, respectively. Immunoblot and immunocyto analysis demonstrated increased expression of p27(Kip1) and its nuclear localization with the knockdown of cathepsin B and uPAR. These effects could be mediated by alphaVbeta3/PI3K/AKT/FOXO pathway as observed by the decreased alphaVbeta3 expression, PI3K and AKT phosphorylation accompanied by elevated FOXO3a levels. These results were further confirmed with the increased expression of p27(Kip1) and FOXO3a when treated with Ly294002 (10 microM) and increased luciferase expression with the siRNA and Ly294002 treatments when the FOXO binding promoter region of p27(Kip1) was used. Our treatment also reduced the expression of cyclin D1, cyclin D2, p-Rb and cyclin E while the expression of Cdk2 was unaffected. Of note, the Cdk2-cyclin E complex formation was reduced significantly.Our study indicates that cathepsin B and uPAR knockdown induces G0/G1 arrest by modulating the PI3K/AKT signaling pathway and further increases expression of p27(Kip1) accompanied by the binding of FOXO3a to its promoter. Taken together, our findings provide molecular mechanism for the G0/G1 arrest induced by the downregulation of cathepsin B and uPAR in SNB19 and U251 glioma cells

Discovery of four recessive developmental disorders using probabilistic genotype and phenotype matching among 4,125 families.

Discovery of most autosomal recessive disease-associated genes has involved analysis of large, often consanguineous multiplex families or small cohorts of unrelated individuals with a well-defined clinical condition. Discovery of new dominant causes of rare, genetically heterogeneous developmental disorders has been revolutionized by exome analysis of large cohorts of phenotypically diverse parent-offspring trios. Here we analyzed 4,125 families with diverse, rare and genetically heterogeneous developmental disorders and identified four new autosomal recessive disorders. These four disorders were identified by integrating Mendelian filtering (selecting probands with rare, biallelic and putatively damaging variants in the same gene) with statistical assessments of (i) the likelihood of sampling the observed genotypes from the general population and (ii) the phenotypic similarity of patients with recessive variants in the same candidate gene. This new paradigm promises to catalyze the discovery of novel recessive disorders, especially those with less consistent or nonspecific clinical presentations and those caused predominantly by compound heterozygous genotypes