Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes

Shape estimation of sweetpotato (SP) storage roots is inherently challenging due to their varied size and shape characteristics. Even measuring "simple" metrics, such as length and width, requires significant time investments either directly in-field or afterward using automated graders. In this paper, we present the results of a model that can perform grading and provide yield estimates directly in the field quicker than manual measurements. Detectron2, a library consisting of deep-learning object detection algorithms, was used to implement Mask R-CNN, an instance segmentation model. This model was deployed for in-field grade estimation of SPs and evaluated against an optical sorter. Storage roots from various clones imaged with a cellphone during trials between 2019 and 2020, were used in the model's training and validation to fine-tune a model to detect SPs. Our results showed that the model could distinguish individual SPs in various environmental conditions including variations in lighting and soil characteristics. RMSE for length, width, and weight, from the model compared to a commercial optical sorter, were 0.66 cm, 1.22 cm, and 74.73 g, respectively, while the RMSE of root counts per plot was 5.27 roots, with r^2 = 0.8. This phenotyping strategy has the potential enable rapid yield estimates in the field without the need for sophisticated and costly optical sorters and may be more readily deployed in environments with limited access to these kinds of resources or facilities.Comment: 21 pages, 11 figure

Upper limits on the strength of periodic gravitational waves from PSR J1939+2134

The first science run of the LIGO and GEO gravitational wave detectors presented the opportunity to test methods of searching for gravitational waves from known pulsars. Here we present new direct upper limits on the strength of waves from the pulsar PSR J1939+2134 using two independent analysis methods, one in the frequency domain using frequentist statistics and one in the time domain using Bayesian inference. Both methods show that the strain amplitude at Earth from this pulsar is less than a few times $10^{-22}$.Comment: 7 pages, 1 figure, to appear in the Proceedings of the 5th Edoardo Amaldi Conference on Gravitational Waves, Tirrenia, Pisa, Italy, 6-11 July 200

Improving the sensitivity to gravitational-wave sources by modifying the input-output optics of advanced interferometers

We study frequency dependent (FD) input-output schemes for signal-recycling interferometers, the baseline design of Advanced LIGO and the current configuration of GEO 600. Complementary to a recent proposal by Harms et al. to use FD input squeezing and ordinary homodyne detection, we explore a scheme which uses ordinary squeezed vacuum, but FD readout. Both schemes, which are sub-optimal among all possible input-output schemes, provide a global noise suppression by the power squeeze factor, while being realizable by using detuned Fabry-Perot cavities as input/output filters. At high frequencies, the two schemes are shown to be equivalent, while at low frequencies our scheme gives better performance than that of Harms et al., and is nearly fully optimal. We then study the sensitivity improvement achievable by these schemes in Advanced LIGO era (with 30-m filter cavities and current estimates of filter-mirror losses and thermal noise), for neutron star binary inspirals, and for narrowband GW sources such as low-mass X-ray binaries and known radio pulsars. Optical losses are shown to be a major obstacle for the actual implementation of these techniques in Advanced LIGO. On time scales of third-generation interferometers, like EURO/LIGO-III (~2012), with kilometer-scale filter cavities, a signal-recycling interferometer with the FD readout scheme explored in this paper can have performances comparable to existing proposals. [abridged]Comment: Figs. 9 and 12 corrected; Appendix added for narrowband data analysi

Search for gravitational wave bursts in LIGO's third science run

We report on a search for gravitational wave bursts in data from the three LIGO interferometric detectors during their third science run. The search targets subsecond bursts in the frequency range 100-1100 Hz for which no waveform model is assumed, and has a sensitivity in terms of the root-sum-square (rss) strain amplitude of hrss ~ 10^{-20} / sqrt(Hz). No gravitational wave signals were detected in the 8 days of analyzed data.Comment: 12 pages, 6 figures. Amaldi-6 conference proceedings to be published in Classical and Quantum Gravit

Host Cell Transcriptome Profile during Wild-Type and Attenuated Dengue Virus Infection

10.1371/journal.pntd.0002107PLoS Neglected Tropical Diseases73

The Type 2 Diabetes Knowledge Portal: an Open access Genetic Resource Dedicated to Type 2 Diabetes and Related Traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP\u27s comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results

T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection.

The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory 'help' signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The 'non-exhausted' T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities

NEK5 activity regulates the mesenchymal and migratory phenotype in breast cancer cells

Purpose Breast cancer remains a prominent global disease affecting women worldwide despite the emergence of novel therapeutic regimens. Metastasis is responsible for most cancer-related deaths, and acquisition of a mesenchymal and migratory cancer cell phenotypes contributes to this devastating disease. The utilization of kinase targets in drug discovery have revolutionized the field of cancer research but despite impressive advancements in kinase-targeting drugs, a large portion of the human kinome remains understudied in cancer. NEK5, a member of the Never-in-mitosis kinase family, is an example of such an understudied kinase. Here, we characterized the function of NEK5 in breast cancer. Methods Stably overexpressing NEK5 cell lines (MCF7) and shRNA knockdown cell lines (MDA-MB-231, TU-BcX-4IC) were utilized. Cell morphology changes were evaluated using immunofluorescence and quantification of cytoskeletal components. Cell proliferation was assessed by Ki-67 staining and transwell migration assays tested cell migration capabilities. In vivo experiments with murine models were necessary to demonstrate NEK5 function in breast cancer tumor growth and metastasis. Results NEK5 activation altered breast cancer cell morphology and promoted cell migration independent of effects on cell proliferation. NEK5 overexpression or knockdown does not alter tumor growth kinetics but promotes or suppresses metastatic potential in a cell type-specific manner, respectively. Conclusion While NEK5 activity modulated cytoskeletal changes and cell motility, NEK5 activity affected cell seeding capabilities but not metastatic colonization or proliferation in vivo. Here we characterized NEK5 function in breast cancer systems and we implicate NEK5 in regulating specific steps of metastatic progression

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Detection chain and electronic readout of the QUBIC instrument

The Q and U Bolometric Interferometer for Cosmology (QUBIC) Technical Demonstrator (TD) aiming to shows the feasibility of the combination of interferometry and bolometric detection. The electronic readout system is based on an array of 128 NbSi Transition Edge Sensors cooled at 350mK readout with 128 SQUIDs at 1K controlled and amplified by an Application Specific Integrated Circuit at 40K. This readout design allows a 128:1 Time Domain Multiplexing. We report the design and the performance of the detection chain in this paper. The technological demonstrator unwent a campaign of test in the lab. Evaluation of the QUBIC bolometers and readout electronics includes the measurement of I-V curves, time constant and the Noise Equivalent Power. Currently the mean Noise Equivalent Power is ~ 2 x 10⁻¹⁶ W/√Hz