Not-from-concentrate pilot plant ‘Wonderful’ cultivar pomegranate juice changes: Volatiles

Pilot plant ultrafiltration was used to mimic the dominant U.S. commercial pomegranate juice extraction method (hydraulic pressing whole fruit), to deliver a not-from-concentrate (NFC) juice that was high-temperature short-time pasteurized and stored at 4 and 25 °C. Recovered were 46 compounds, of which 38 were routinely isolated and subjected to analysis of variance to assess these NFC juices. Herein, 18 of the 21 consensus pomegranate compounds were recovered. Ultrafiltration resulted in significant decreases for many compounds. Conversely, pasteurization resulted in compound increases. Highly significant decreases in 12 consensus compounds were observed during storage. Principal component analysis demonstrated clearly which compounds were tightly associated, and how storage samples behaved very similarly, independent of temperature. Based on these data and previous work we reported, this solid-phase microextraction (SPME) method delivered a robust ‘Wonderful’ volatile profile in NFC juices that is likely superior qualitatively and perhaps quantitatively to typical commercial offerings

Meta-analysis of the effect of an essential oil–containing mouthrinse on gingivitis and plaque

AbstractBackgroundStandard recommendations for oral hygiene practices have focused on mechanical methods (toothbrushing and interdental cleaning). Published evidence indicates antimicrobial mouthrinses provide oral health benefits beyond mechanical methods alone. The purpose of this meta-analysis was to evaluate the combined effectiveness of mechanical methods with essential oil–containing mouthrinses (MMEO) versus mechanical methods (MM) alone in achieving site-specific, healthy gingival tissue and reducing plaque and gingivitis.Types of Studies ReviewedAll industry-sponsored clinical trials investigating the antigingivitis and antiplaque effects of essential oil (EO)–containing mouthrinses conducted from 1980 to 2012 were reviewed; 29 of 32 studies met the inclusion criteria of 6 months or longer duration, randomized, observer-masked, placebo-controlled, and with individual-level site-specific data. By-study treatment effects were estimated through generalized linear models for binary data and analysis of covariance for continuous data, and then combined using standard meta-analysis techniques; heterogeneity was also assessed.ResultsSummary odds ratios for a healthy gingival site and for a plaque-free site were, respectively, 5.0 (95% confidence interval [CI], 3.3-7.5) and 7.8 (95% CI, 5.4-11.2) for MMEO participants versus MM participants at 6 months. The summary percentage reductions in whole-mouth mean gingivitis and plaque at 6 months were 16.0 (95% CI, 11.3-20.7) and 27.7 (95% CI, 22.4-32.9), respectively. Responder analyses using aggregate individual-level data showed 44.8% of MMEO participants and 14.4% of MM participants achieved at least 50% healthy sites in their mouths at 6 months. Similarly, 36.9% of MMEO participants and 5.5% of MM participants achieved at least 50% plaque-free sites in their mouths at 6 months.Conclusions and Practical ImplicationsThis is the first meta-analysis to demonstrate the clinically significant, site-specific benefit of adjunctive EO treatment in people within a 6-month period (that is, between dental visits)

Predicting the Impact of Climate Change on Threatened Species in UK Waters

Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis) and angelshark (Squatina squatina)

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Matrix assisted laser desorption ionization-time-of-flight mass spectrometry identification of mycobacterium bovis in bovinae

In this study, Matrix Assisted Laser Desorption Ionization-Time-of-Flight (MALDI-TOF) mass spectrometry was used to identify Mycobacterium bovis from cattle and buffalo tissue isolates from the North and South regions of Brazil, grown in solid medium and previously identified by Polymerase Chain Reaction (PCR) based on Region of Difference 4 (RD4), sequencing and spoligotyping. For this purpose, the protein extraction protocol and the mass spectra reference database were optimized for the identification of 80 clinical isolates of mycobacteria. As a result of this optimization, it was possible to identify and differentiate M. bovis from other members of the Mycobacterium tuberculosis complex with 100% specificity, 90.91% sensitivity and 91.25% reliability. MALDI-TOF MS methodology described herein provides successful identification of M. bovis within bovine/bubaline clinical samples, demonstrating its usefulness for bovine tuberculosis diagnosis in the future.Instituto de BiotecnologíaFil: Bacanelli, Gisele. Federal University of Mato Grosso do Sul. Biotechnology and Biodiversity of the Central Western Region Postgraduate Program; BrasilFil: Olarte, Larissa C. Federal University of Mato Grosso do Sul. Biochemistry and Molecular Biology Multicentric Postgraduate Program; BrasilFil: Silva, Marcio Roberto. Empresa Brasileira de Pesquisa Agropecuária (Embrapa). Gado de Leite; BrasilFil: Rodrigues, Rudielle A. Federal University of Mato Grosso do Sul. Faculty of Veterinary Medicine. Veterinary Sciences Postgraduate Program; BrasilFil: Carneiro, Paulo A. M. Michigan State University. Center for Comparative Epidemiology; Estados UnidosFil: Kannene, John B. Michigan State University. Center for Comparative Epidemiology; Estados UnidosFil: Pasquatti, Taynara N. Dom Bosco Catholic University; BrasilFil: Takatani, Haruo. Agricultural Defense Agency of Amazonas; BrasilFil: Zumarraga, Martin Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología; ArgentinaFil: Etges, Rodrigo N. Secretary of Agriculture, Livestock and Irrigation; BrasilFil: Araujo, Flabio Ribeiro de. Empresa Brasileira de Pesquisa Agropecuária (Embrapa). Gado de Corte; BrasilFil: Verbisck, Newton V. Empresa Brasileira de Pesquisa Agropecuária (Embrapa). Gado de Corte; Brasi

A new ghost cell/level set method for moving boundary problems:application to tumor growth

In this paper, we present a ghost cell/level set method for the evolution of interfaces whose normal velocity depend upon the solutions of linear and nonlinear quasi-steady reaction-diffusion equations with curvature-dependent boundary conditions. Our technique includes a ghost cell method that accurately discretizes normal derivative jump boundary conditions without smearing jumps in the tangential derivative; a new iterative method for solving linear and nonlinear quasi-steady reaction-diffusion equations; an adaptive discretization to compute the curvature and normal vectors; and a new discrete approximation to the Heaviside function. We present numerical examples that demonstrate better than 1.5-order convergence for problems where traditional ghost cell methods either fail to converge or attain at best sub-linear accuracy. We apply our techniques to a model of tumor growth in complex, heterogeneous tissues that consists of a nonlinear nutrient equation and a pressure equation with geometry-dependent jump boundary conditions. We simulate the growth of glioblastoma (an aggressive brain tumor) into a large, 1 cm square of brain tissue that includes heterogeneous nutrient delivery and varied biomechanical characteristics (white matter, gray matter, cerebrospinal fluid, and bone), and we observe growth morphologies that are highly dependent upon the variations of the tissue characteristics—an effect observed in real tumor growth

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

Guillain-Barré syndrome: a century of progress

In 1916, Guillain, Barré and Strohl reported on two cases of acute flaccid paralysis with high cerebrospinal fluid protein levels and normal cell counts — novel findings that identified the disease we now know as Guillain–Barré syndrome (GBS). 100 years on, we have made great progress with the clinical and pathological characterization of GBS. Early clinicopathological and animal studies indicated that GBS was an immune-mediated demyelinating disorder, and that severe GBS could result in secondary axonal injury; the current treatments of plasma exchange and intravenous immunoglobulin, which were developed in the 1980s, are based on this premise. Subsequent work has, however, shown that primary axonal injury can be the underlying disease. The association of Campylobacter jejuni strains has led to confirmation that anti-ganglioside antibodies are pathogenic and that axonal GBS involves an antibody and complement-mediated disruption of nodes of Ranvier, neuromuscular junctions and other neuronal and glial membranes. Now, ongoing clinical trials of the complement inhibitor eculizumab are the first targeted immunotherapy in GBS