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These processes did not select a specific ecological strategy as inferred by a trait approach coupled to our taxonomic approach. Instead the front favored higher richness within widespread strategies, resulting in functional redundancy. We detail how fine-scale ocean physics affect phytoplankton diversity and suppose that this interplay is a major control on regional ecosystems.Harnessing the immune-system to eradicate cancer is becoming a reality in recent years. Engineered immune cells, such as chimeric antigen receptor (CAR) T cells, are facing the danger of an overt life-threatening immune response due to the ON-target OFF-tumor cytotoxicity and Cytokine Release Syndrome. We therefore developed synthetic promoters for regulation of gene expression under the control of inflammation and Hypoxia-induced signals that are associated with the tumor microenvironment (TME). We termed this methodology as chimeric-antigen-receptor-tumor-induced-vector (CARTIV). For proof of concept, we studied synthetic promoters based on promoter-responsive elements (PREs) of IFNγ, TNFα and hypoxia; triple PRE-based CARTIV promoter manifested a synergistic activity in cell-lines and potent activation in human primary T-cells. CARTIV platform can improve safety of CAR T-cells or other engineered immune-cells, providing TME-focused activity and opening a therapeutic window for many tumor-associated antigens that are also expressed by non-tumor healthy tissues.The aim of this study was to demonstrate the usefulness of SwiftScan with a low-energy high-resolution and sensitivity (LEHRS) collimator for bone scintigraphy using a novel bone phantom simulating the human body. SwiftScan planar image of lateral view was acquired in clinical condition; thereafter, each planar image of different blend ratio (0-80%) of Crality 2D processing were created. SwiftScan planar images with reduced acquisition time by 25-75% were created by Poisson’s resampling processing. SwiftScan single photon emission computed tomography (SPECT) was acquired with step-and-shoot and continuous mode, and SPECT images were reconstructed using a three-dimensional ordered subset expectation maximization incorporating attenuation, scatter and spatial resolution corrections. SwiftScan planar image showed a high contrast to noise ratio (CNR) and low percent of the coefficient of variance (%CV) compared with conventional planar image. The CNR of the tumor parts in SwiftScan SPECT was higher than that of the conventional SPECT image of step and shoot acquisition, while the %CV showed the lowest value in all systems. In conclusion, SwiftScan planar and SPECT images were able to reduce the image noise compared with planar and SPECT image with a low-energy high-resolution collimator, so that SwiftScan planar and SPECT images could be obtained a high CNR. Furthermore, the SwiftScan planar image was able to reduce the acquisition time by 25% when the blend ratio of Clarity 2D processing set to more than 40%.Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. Tebipenem Pivoxil chemical structure As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-worlicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.We have developed a process for fabricating patient specific Magnetic Resonance Imaging (MRI) Radio-frequency (RF) receive coil arrays using additive manufacturing. Our process involves spray deposition of silver nanoparticle inks and dielectric materials onto 3D printed substrates to form high-quality resonant circuits. In this paper, we describe the material selection and characterization, process optimization, and design and testing of a prototype 4-channel neck array for carotid imaging. We show that sprayed polystyrene can form a low loss dielectric layer in a parallel plate capacitor. We also demonstrate that by using sprayed silver nanoparticle ink as conductive traces, our devices are still dominated by sample noise, rather than material losses. These results are critical for maintaining high Signal-to-Noise-Ratio (SNR) in clinical settings. Finally, our prototype patient specific coil array exhibits higher SNR (5 × in the periphery, 1.4 × in the center) than a commercially available array designed to fit the majority of subjects when tested on our custom neck phantom. 3D printed substrates ensure an optimum fit to complex body parts, improve diagnostic image quality, and enable reproducible placement on subjects.Photorefractive materials exhibit an interesting plasticity under the influence of an optical field. By extending the finite-difference time-domain method to include the photorefractive effect, we explore how this property can be exploited in the context of neuromorphic computing for telecom applications. By first priming the photorefractive material with a random bit stream, the material reorganizes itself to better recognize simple patterns in the stream. We demonstrate this by simulating a typical reservoir computing setup, which gets a significant performance boost on performing the XOR on two consecutive bits in the stream after this initial priming step.