Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning

In this paper, we introduce a system based on transfer learning for detecting segment misalignment in multimirror satellites, such as future CubeSat designs and the James Webb Space Telescope (JWST), using image-based methods. When a mirror segment becomes misaligned due to various environmental factors, such as space debris, the images can become distorted with a shifted copy of itself called a "ghost image". To detect whether segments are misaligned, we use pre-trained, large-scale image models trained on the Fast Fourier Transform (FFT) of patches of satellite images in grayscale. Multi-mirror designs can use any arbitrary number of mirrors. For our purposes, the tests were performed on simulated CubeSats with 4, 6, and 8 segments. For system design, we took this into account when we want to know when a satellite has a misaligned segment and how many segments are misaligned. The intensity of the ghost image is directly proportional to the number of segments misaligned. Models trained for intensity classification attempted to classify N-1 segments. Across eight classes, binary models were able to achieve a classification accuracy of 98.75%, and models for intensity classification were able to achieve an accuracy of 98.05%

Sulf1 has ligand-dependent effects on canonical and non-canonical Wnt signalling

Wnt signalling plays essential roles during embryonic development and is known to be mis-regulated in human disease. There are many molecular mechanisms that ensure tight regulation of Wnt activity. One such regulator is the heparan-sulfate-specific 6-O-endosulfatase Sulf1. Sulf1 acts extracellularly to modify the structure of heparan sulfate chains to affect the bio-availability of Wnt ligands. Sulf1 could, therefore, influence the formation of Wnt signalling complexes to modulate the activation of both canonical and non-canonical pathways. In this study, we use well-established assays in Xenopus to investigate the ability of Sulf1 to modify canonical and non-canonical Wnt signalling. In addition, we model the ability of Sulf1 to influence morphogen gradients using fluorescently tagged Wnt ligands in ectodermal explants. We show that Sulf1 overexpression has ligand-specific effects on Wnt signalling: it affects membrane accumulation and extracellular levels of tagged Wnt8a and Wnt11b ligands differently, and inhibits the activity of canonical Wnt8a but enhances the activity of non-canonical Wnt11b

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

Fishes from the upper Yuruá river, Amazon basin, Peru

We report results of an ichthyological survey of the upper Rio Yuruá in southeastern Peru. Collections were made at low water (July-August, 2008) near the headwaters of the Brazilian Rio Juruá. This is the first of four expeditions to the Fitzcarrald Arch — an upland associated with the Miocene-Pliocene rise of the Peruvian Andes — with the goal of comparing the ichthyofauna across the headwaters of the largest tributary basins in the western Amazon (Ucayali, Juruá, Purús and Madeira). We recorded a total of 117 species in 28 families and 10 orders, with all species accompanied by tissue samples preserved in 100% ethanol for subsequent DNA analysis, and high-resolution digital images of voucher specimens with live color to facilitate accurate identification. From interviews with local fishers and comparisons with other ichthyological surveys of the region we estimate the actual diversity of fishes in the upper Juruá to exceed 200 species

Short- and Long-Term Effectiveness of a Subject’s Specific Novel Brain and Vestibular Rehabilitation Treatment Modality in Combat Veterans Suffering from PTSD

Introduction: Treatment for post-traumatic stress disorder (PTSD) in combat veterans that have a long-term positive clinical effect has the potential to modify the treatment of PTSD. This outcome may result in changed and saved lives of our service personnel and their families. In a previous before–after-intervention study, we demonstrated high statistical and substantively significant short-term changes in the Clinician Administered DSM-IV PTSD Scale (CAPS) scores after a 2-week trial of a subject’s particular novel brain and vestibular rehabilitation (VR) program. The long-term maintenance of PTSD severity reduction was the subject of this study. Material and methods We studied the short- and long-term effectiveness of a subject’s particular novel brain and VR treatment of PTSD in subjects who had suffered combat-related traumatic brain injuries in terms of PTSD symptom reduction. The trial was registered as ClinicalTrials.gov Identifier: NCT02003352. We analyzed the difference in the CAPS scores pre- and post-treatment (1 week and 3 months) using our subjects as their matched controls. Results: The generalized least squares (GLS) technique demonstrated that with our 26 subjects in the 3 timed groups the R2 within groups was 0.000, R2 between groups was 0.000, and overall the R2 was 0.000. The GLS regression was strongly statistically significant z = 21.29, p < 0.001, 95% CI [58.7, 70.63]. The linear predictive margins over time demonstrated strong statistical and substantive significance of decreasing PTSD severity scores for all timed CAPS tests. Discussion Our investigation has the promise of the development of superior outcomes of treatments in this area that will benefit a global society. The length of the treatment intervention involved (2 weeks) is less that other currently available treatments and has profound implications for cost, duration of disability, and outcomes in the treatment of PTSD in combat veterans

Identification of Neural Outgrowth Genes using Genome-Wide RNAi

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system

Spectrum Awareness: Deep Learning and Isolation Forest Approaches for Open-set Identification of Signals

Over the next decade, 5G networks will become more and more prevalent in everyday life. This will provide solutions to current limitations by allowing access to bands previously unavailable to civilian communication networks. However, this also provides new challenges primarily for the military operations. Radar bands have traditionally operated primarily in the sub-6 GHz region. In the past, these bands were off limits to civilian communications. However, that changed when they were opened up in the 2010's. With these bands now being forced to co-exist with commercial users, military operators need systems to identify the signals within a spectrum environment. In this thesis, we extend current research in the area of signal identification by using previous work in the area to construct a deep learning-based classifier that is able to classify a signal as either as a communication waveform (Single-Carrier (SC), Single-Carrier Frequency Division Multiple Access (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM), Amplitude Modulation (AM), Frequency Modulation (FM)) or a radar waveform (Linear Frequency Modulation (LFM) or Phase-coded). However, the downside to this method is that the classifier is based on the assumption that all possible signals within the spectrum environment are within the training dataset. To account for this, we have proposed a novel classifier design for detection of unknown signals outside of the training dataset. This two-classifier system forms an open-set recognition (OSR) system that is used to provide more situational awareness for operators.M.S.Over the next decade, next-generation communications will become prevalent in everyday life providing solutions to limitation previously experienced by older networks. However, this also brings about new challenges. Bands in the electromagnetic spectrum that were reserved for military use are now being opened up to commercial users. This means that military and civilian networks now have a challenge of co-existence that must be addressed. One way to address this is being aware of what signals are operating in the bands such as either communication signals, radar signals, or both. In this thesis, we will developed a system that can do that task of identifying a signal as one of five communication waveforms or two radar waveforms by using machine learning techniques. We also develop a new technique for identifying unknown signals that might be operating within these bands to further help military and civilian operators monitor the spectrum

Culture of penaeid shrimp in brackfish water ponds receiving thermal effluents

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Bibliography: leaves 79-84.Not availabl

Culture of penaeid shrimp in brackfish water ponds receiving thermal effluents

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Bibliography: leaves 79-84.Not availabl