Higher-order Quasi-Monte Carlo Training of Deep Neural Networks

We present a novel algorithmic approach and an error analysis leveraging Quasi-Monte Carlo points for training deep neural network (DNN) surrogates of Data-to-Observable (DtO) maps in engineering design. Our analysis reveals higher-order consistent, deterministic choices of training points in the input data space for deep and shallow Neural Networks with holomorphic activation functions such as tanh. These novel training points are proved to facilitate higher-order decay (in terms of the number of training samples) of the underlying generalization error, with consistency error bounds that are free from the curse of dimensionality in the input data space, provided that DNN weights in hidden layers satisfy certain summability conditions. We present numerical experiments for DtO maps from elliptic and parabolic PDEs with uncertain inputs that confirm the theoretical analysis

Perfect vortex modes for nondestructive characterization of mode dependent loss in ring core fibers

Ring core fibers (RCF) enable high-performance modal multiplexing with low crosstalk and can support orbital angular momentum (OAM) modes. RCFs are challenging to characterize due to the lack of commercial multiplexers, especially for high OAM orders. For fibers supporting large numbers of modes, typical cutback techniques for characterization are extremely wasteful of fiber, especially as one cutback is required for each mode. We show the differential modal loss across modes 3 to 10 was significantly underestimated using an OTDR when exciting modes individually or when exciting all modes indiscriminately. We exploit perfect vortex beams to achieve reliable and nondestructive characterization of mode-dependent loss (MDL) for OAM modes. Perfect vortex beams allow us to maximize the coupling efficiency at each mode launch, increasing the accuracy of MDL estimate. We fabricated fiber with a refractive index difference between the ring core and the cladding of a 5.1×10⁻². For this fiber, mode orders 3 to 10 are the most suitable for data transmission and were the focus of our work (the fiber support up to OAM order 13). Such a high index difference can lead to MDL. We demonstrate that the modal loss spans from 2.14 to 4.38 dB/km for orders 3 to 10

Information content of ozone retrieval algorithms

The algorithms are characterized that were used for production processing by the major suppliers of ozone data to show quantitatively: how the retrieved profile is related to the actual profile (This characterizes the altitude range and vertical resolution of the data); the nature of systematic errors in the retrieved profiles, including their vertical structure and relation to uncertain instrumental parameters; how trends in the real ozone are reflected in trends in the retrieved ozone profile; and how trends in other quantities (both instrumental and atmospheric) might appear as trends in the ozone profile. No serious deficiencies were found in the algorithms used in generating the major available ozone data sets. As the measurements are all indirect in someway, and the retrieved profiles have different characteristics, data from different instruments are not directly comparable

CD4+ T cells that enter the draining lymph nodes after antigen injection participate in the primary response and become central–memory cells

We explored the relationship between the time of naive CD4+ T cell exposure to antigen in the primary immune response and the quality of the memory cells produced. Naive CD4+ T cells that migrated into the skin-draining lymph nodes after subcutaneous antigen injection accounted for about half of the antigen-specific population present at the peak of clonal expansion. These late-arriving T cells divided less and more retained the central–memory marker CD62L than the T cells that resided in the draining lymph nodes at the time of antigen injection. The fewer cell divisions were related to competition with resident T cells that expanded earlier in the response and a reduction in the number of dendritic cells displaying peptide–major histocompatibility complex (MHC) II complexes at later times after antigen injection. The progeny of late-arriving T cells possessed the phenotype of central–memory cells, and proliferated more extensively during the secondary response than the progeny of the resident T cells. The results suggest that late arrival into lymph nodes and exposure to antigen-presenting cells displaying lower numbers of peptide–MHC II complexes in the presence of competing T cells ensures that some antigen-specific CD4+ T cells divide less in the primary response and become central–memory cells

Priority maps for pollinator habitat enhancement schemes in semi-natural grasslands

Conserving semi-natural grasslands, a threatened habitat type in European landscapes, is increasingly recognized as a measure to conserve pollinators. Our aim was to test if (a) prediction maps of solitary bee species richness could be used to rank semi-natural grasslands in terms of their potential for supporting wild bees, and (b) if such predictions extend current assessment criteria that determine which grasslands are eligible for being listed under habitat conservation schemes. We sampled wild bee communities in 52 semi-natural grasslands in southeast Norway. We conducted an across-year validation, using data from 2019 (32 sites) to model bee species richness, and used data from 2020 (20 sites) to validate predictions. We then conducted a leave-one-out cross-validation, iteratively using data from 51 sites to parameterize our model, and validating predictions on the withheld site. Finally, we used data from all 52 sites to update the model and tested if predicted species richness within the 1075 grasslands in our region was reflected in current assessment criteria scores assigned to those grasslands. Models from across-year, and leave-one-out cross-validations, predicted 39%, and 43% of bee species richness in semi-natural grasslands, respectively. Model predictions and current criteria of semi-natural grassland quality were not strongly related (R2 adjusted = 0.01), suggesting that prediction models can add a valuable extra dimension when prioritizing between semi-natural grassland for pollinator habitat conservation. Our findings illustrate how spatial prediction models can provide management authorities with a valuable tool for prioritizing where to direct habitat enhancement schemes in order to improve conservation effectiveness. Pollinators Bees Grassland Semi-natural Management ConservationpublishedVersio

Black soldier fly (Diptera: Stratiomyidae) larval heat generation and management

: Mass production of black soldier fly, Hermetia illucens (L.) (Diptera: Stratiomyidae), larvae results in massive heat generation, which impacts facility management, waste conversion, and larval production. We tested daily substrate temperatures with different population densities (i.e., 0, 500, 1000, 5000, and 10 000 larvae/pan), different population sizes (i.e., 166, 1000, and 10 000 larvae at a fixed feed ratio) and air temperatures (i.e., 20 and 30 °C) on various production parameters. Impacts of shifting larvae from 30 to 20 °C on either day 9 or 11 were also determined. Larval activity increased substrate temperatures significantly (i.e., at least 10 °C above air temperatures). Low air temperature favored growth with the higher population sizes while high temperature favored growth with low population sizes. The greatest average individual larval weights (e.g., 0.126 and 0.124 g) and feed conversion ratios (e.g., 1.92 and 2.08 g/g) were recorded for either 10 000 larvae reared at 20 °C or 100 larvae reared at 30 °C. Shifting temperatures from high (30 °C) to low (20 °C) in between (∼10-d-old larvae) impacted larval production weights (16% increases) and feed conversion ratios (increased 14%). Facilities should consider the impact of larval density, population size, and air temperature during black soldier fly mass production as these factors impact overall larval production

Transcriptome Profiling Reveals Matrisome Alteration as a Key Feature of Ovarian Cancer Progression

BACKGROUND: Ovarian cancer is the most lethal gynecologic malignancy. There is a lack of comprehensive investigation of disease initiation and progression, including gene expression changes during early metastatic colonization. METHODS: RNA-sequencing (RNA-seq) was done with matched primary tumors and fallopian tubes (n = 8 pairs) as well as matched metastatic and primary tumors (n = 11 pairs) from ovarian cancer patients. Since these are end point analyses, it was combined with RNA-seq using high-grade serous ovarian cancer cells seeded on an organotypic three-dimensional (3D) culture model of the omentum, mimicking early metastasis. This comprehensive approach revealed key changes in gene expression occurring in ovarian cancer initiation and metastasis, including early metastatic colonization. RESULTS: 2987 genes were significantly deregulated in primary tumors compared to fallopian tubes, 845 genes were differentially expressed in metastasis compared to primary tumors and 304 genes were common to both. An assessment of patient metastasis and 3D omental culture model of early metastatic colonization revealed 144 common genes that were altered during early colonization and remain deregulated even in the fully developed metastasis. Deregulation of the matrisome was a key process in early and late metastasis. CONCLUSION: These findings will help in understanding the key pathways involved in ovarian cancer progression and eventually targeting those pathways for therapeutic interventions