Saunderspehrson7481

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study.Fires determine vegetation patterns, impact human societies, and are a part of complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Although humans have historically used and managed fires, the current role of anthropogenic drivers of fires remains less quantified. Whereas patterns in fire-climate interactions are consistent across the globe, fire-human-vegetation relationships vary strongly by region. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability as compared to many state-of-the-art fire models, with global spatial correlation of 0.92, monthly temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.60, between predicted and observed burned area. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of burned area to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. 2,3-Butanedione-2-monoxime Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.Analysis of several pulse shape properties generated by a Geiger Mueller (GM) detector and its dependence on applied voltage was performed. The two-source method was utilized to measure deadtime while simultaneously capturing pulse shape parameters on an oscilloscope. A wide range of operating voltages (600-1200 V) beyond the recommended operating voltage of 900 V was investigated using three radioactive sources (204Tl, 137Cs, 22Na). This study investigates the relationship between operating voltage, pulse shape properties, and deadtime of the detector. Based on the data, it is found that deadtime decreases with increasing voltage from 600 to 650 V. At these low voltages (600-650 V), the collection time was long, allowing sufficient time for some recombination to take place. Increasing the voltage in this range decreased the collection time, and hence deadtime decreased. It is also observed that rise and fall time were at their highest at these applied voltages. Increasing the voltage further would result in gas multiplication, where deadtime and pulse width are observed to be increasing. After reaching the maximum point of deadtime (~ 250 µs at ~ 700 V), deadtime started to exponentially decrease until a plateau was reached. In this region, it is observed that detector deadtime and operating voltage show a strong correlation with positive pulse width, rise and fall time, cycle mean, and area. link2 Therefore, this study confirms a correlation between detector deadtime, operating voltage, and pulse shape properties. The results will validate our hypothesis that deadtime phenomena at different operating voltages are phenomenologically different.18Ni-300 maraging steel manufactured by selective laser melting was plasma nitrided to improve its wear and corrosion resistance. The effects of a prior solution treatment, aging and the combination of both on the microstructure and the properties after nitriding were investigated. The results were compared with conventionally produced 18Ni-300 counterparts subjected to the same heat- and thermo-chemical treatments. The plasma nitriding was performed under the same conditions (temperature of 520 °C and time of 6 h) as the aging in order to investigate whether the nitriding and the aging could be carried out simultaneously in a single step. The aim of this work was to provide a better understanding of the morphology and chemical composition of the nitrided layer in the additive-manufactured maraging steel as a function of the prior heat treatments and to compare the wear and corrosion resistance with those of conventional maraging steel. The results show that nitriding without any prior aging leads to cracks in the compound layer, while nitriding of the prior-heat-treated additive-manufactured maraging steel leads to benefits from the thermochemical treatment in terms of wear and corrosion resistance. Some explanations for the origins of the cracks and pores in the nitride layers are provided.The initiation of apoptosis is a core mechanism in cellular biology by which organisms control the removal of damaged or unnecessary cells. The irreversible activation of caspases is essential for apoptosis, and mathematical models have demonstrated that the process is tightly regulated by positive feedback and a bistable switch. BAX and SMAC are often dysregulated in diseases such as cancer or neurodegeneration and are two key regulators that interact with the caspase system generating the apoptotic switch. Here we present a mathematical model of how BAX and SMAC control the apoptotic switch. Formulated as a system of ordinary differential equations, the model summarises experimental and computational evidence from the literature and incorporates the biochemical mechanisms of how BAX and SMAC interact with the components of the caspase system. Using simulations and bifurcation analysis, we find that both BAX and SMAC regulate the time-delay and activation threshold of the apoptotic switch. Interestingly, the model predicted that BAX (not SMAC) controls the amplitude of the apoptotic switch. Cell culture experiments using siRNA mediated BAX and SMAC knockdowns validated this model prediction. link3 We further validated the model using data of the NCI-60 cell line panel using BAX protein expression as a cell-line specific parameter and show that model simulations correlated with the cellular response to DNA damaging drugs and established a defined threshold for caspase activation that could distinguish between sensitive and resistant melanoma cells. In summary, we present an experimentally validated dynamic model that summarises our current knowledge of how BAX and SMAC regulate the bistable properties of irreversible caspase activation during apoptosis.Diverse taxa have undergone phenological shifts in response to anthropogenic climate change. While such shifts generally follow predicted patterns, they are not uniform, and interspecific variation may have important ecological consequences. We evaluated relationships among species' phenological shifts (mean flight date, duration of flight period), ecological traits (larval trophic specialization, larval diet composition, voltinism), and population trends in a butterfly community in Pennsylvania, USA, where the summer growing season has become warmer, wetter, and longer. Data were collected over 7-19 years from 18 species or species groups, including the extremely rare eastern regal fritillary Speyeria idalia idalia. Both the direction and magnitude of phenological change over time was linked to species traits. Polyphagous species advanced and prolonged the duration of their flight period while oligophagous species delayed and shortened theirs. Herb feeders advanced their flight periods while woody feeders delayed theirs.