Pathogenic Yersinia and Listeria monocytogenes in organic pork production

The goal of this study is to determine the prevalence of pathogenic Yersinia and Listeria monocytogenes in organic pork production and assess risks in different steps of the pork production chain

URWARE Wastewater Treatment Plant Models

In this document, a large number of mathematical models describing various physical, biological and chemical processes in a wastewater treatment plant are presented. In most cases, the models are fairly simple as they are inteded for steady-state simulations based on yearly averages of the influent wastewater charteristics. The combination of models forms a software tool namned URWARE (URban WAter REsearch model), which is a tool for substance flow analysis and energy calculations in wastewater treatment systems. All models are based on the MATLAB/Simulink platform. The different models can be combined in any fashion thereby creating various system structures that can be analysed and compared. The models keep track of numerous substances related to wastewater, such as organic substances, nutrients and to some extent heavy metals. Within all models the release of environmentally hazardous substances to water, soil and air are calculated. The results can be used as one (of many) information pathways for strategic decision making related to urban water systems. The individual models are described one by one and their behaviours are exemplified by simulation results. In many cases, comparisons with other available software models are given to validate (or at least make plausible) the results produced by the fairly simple URWARE models. Simulation results based on a combination of models into a complete wastewater treatment plant are also presented. However, details about the underlying theory and hypotheses on which the models are based cannot always be sufficiently described due to space limitations. Moreover, users should always be critical to any results predicted by models and not take them for granted simply because they have been calculated by a computer model. Knowledge about the real processes and comparisons with real data are imperative to evaluate whether the predicted results are reasonable or not. For the interested reader the complete source code to all models is included in an extensive appendix. A modelling task of this magnitude is never completed. The proposed models can always be further developed, improved and refined. Consequently, the models described in this document only represent the first version of the URWARE software. Many issues remain to be solved and further improvements to be done. However, it is the hope of the authors that the presented models are mature enough to be used, evaluated, modified and improved by other users outside the URWARE task group

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis

AbstractBackgroundOtitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere.MethodsA method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method.FindingsAn accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope.InterpretationThe high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations

Accumulation of Phenolic Acids during Storage over Differently Handled Fresh Carrots

Carrots contain a significant content of phenolic compounds, mainly phenolic acids. Technological processing of carrots inflicts wounding stress and induces accumulation of these compounds, especially caffeic acid derivatives, in the periderm tissue. In this study, the effect of minimal processing (polishing, washing, peeling, and grating) on the retention of soluble phenolic acids in carrots was monitored during cold storage. Storage for up to 4 weeks and 24 h was used for whole and grated carrot samples, respectively. Total phenolic acid levels found in differently processed carrots varied greatly at the beginning of the storage period and on dry weight basis they ranged from 228 ± 67.9 mg/kg (grated carrot) to 996 ± 177 mg/kg (machine washed). In each case, processing followed by storage induced phenolic acid accumulation in the carrots. At the end of the experiment (4 weeks at +8 °C), untreated and machine-washed carrots contained ca. 4-fold more phenolic acids than at day 0. Similarly, polished carrots contained 9-fold and peeled carrots 31-fold more phenolic acids than at day 0. The phenolic acid content in grated carrot doubled after 24 h storage at +4 °C. Individual phenolic acids were characterized by high resolution mass spectrometry. MS data strongly suggest the presence of daucic acid conjugates of phenolic acids in carrot. Storage time did not have statistically similar effect on all compounds and generally in a way that dicaffeoyldaucic acid had the highest increase. This research provides important information for primary production, packaging, catering, the fresh-cut industry and consumers regarding the selection of healthier minimally processed carrots.202

Evaluation of the Retinopathy of Prematurity Activity Scale (ROP-ActS) in a randomised controlled trial aiming for prevention of severe ROP : A substudy of the Mega Donna Mega trial

Objective The current grading of retinopathy of prematurity (ROP) does not sufficiently discriminate disease severity for evaluation of trial interventions. The published ROP Activity Scales (original: ROP-ActS and modified: mROP-ActS), describing increasing severity of ROP, versus the categorical variables severe ROP, stage, zone and plus disease were evaluated as discriminators of the effect of an ROP preventive treatment. Methods and analysis The Mega Donna Mega trial investigated ROP in infants born <28-week gestational age (GA), randomised to arachidonic acid (AA) and docosahexaenoic acid (DHA) supplementation or no supplementation. Of 207 infants, 86% with finalised ROP screening were included in this substudy. ROP-ActS versus standard variables were evaluated using Fisher’s non-parametric permutation test, multivariable logistic and linear regression and marginal fractional response models. Results The AA:DHA group (n=84) and the control group (n=93) were well balanced. The maximum ROP-ActS measurement was numerically but not significantly lower in the AA:DHA group (mean: 4.0 (95% CI 2.9 to 5.0)) versus the control group (mean: 5.3 (95% CI 4.1 to 6.4)), p=0.11. In infants with any ROP, the corresponding scale measurements were 6.8 (95% CI 5.4 to 8.2) and 8.7 (95% CI 7.5 to 10.0), p=0.039. Longitudinal profiles of the scale were visually distinguished for the categories of sex and GA for the intervention versus control. Conclusions The preventive effect of AA:DHA supplementation versus no supplementation was better discriminated by the trial’s primary outcome, severe ROP, than by ROP-ActS. The sensitivity and the linear qualities of ROP-ActS require further validations on large data sets and perhaps modifications. Trial registration number NCT03201588

Shared CSF Biomarker Profile in Idiopathic Normal Pressure Hydrocephalus and Subcortical Small Vessel Disease

Introduction: In this study, we examine similarities and differences between 52 patients with idiopathic normal pressure hydrocephalus (iNPH) and 17 patients with subcortical small vessel disease (SSVD), in comparison to 28 healthy controls (HCs) by a panel of cerebrospinal fluid (CSF) biomarkers. Methods: We analyzed soluble amyloid precursor protein alpha (sAPPα) and beta (sAPPβ), Aβ isoforms −38, −40, and −42, neurofilament light protein (NFL), glial fibrillary acidic protein (GFAP), myelin basic protein (MBP), matrix metalloproteinases (MMP −1, −2, −3, −9, and −10), and tissue inhibitors of metalloproteinase 1 (TIMP1). Radiological signs of white matter damage were scored using the age-related white matter changes (ARWMC) scale. Results: All amyloid fragments were reduced in iNPH and SSVD (p < 0.05), although more in iNPH than in SSVD in comparison to HC. iNPH and SSVD showed comparable elevations of NFL, MBP, and GFAP (p < 0.05). MMPs were similar in all three groups except for MMP-10, which was increased in iNPH and SSVD. Patients with iNPH had larger ventricles and fewer WMCs than patients with SSVD. Conclusion: The results indicate that patients with iNPH and SSVD share common features of subcortical neuronal degeneration, demyelination, and astroglial response. The reduction in all APP-derived proteins characterizing iNPH patients is also present, indicating that SSVD encompasses similar pathophysiological phenomena as iNPH

Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan

One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate NDVI, EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modeling within a water limited environment. Results show a strong correlation between vegetation indices and gross primary production (GPP), demonstrating the significance of vegetation indices for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modeling in similar semi-arid ecosystems is limited

Plasma and cerebrospinal fluid concentrations of neurofilament light protein correlate in patients with idiopathic normal pressure hydrocephalus

BACKGROUND: Neurofilament light chain protein (NFL), a marker of neuronal axonal degeneration, is increased in cerebrospinal fluid (CSF) of patients with idiopathic normal pressure hydrocephalus (iNPH). Assays for analysis of NFL in plasma are now widely available but plasma NFL has not been reported in iNPH patients. Our aim was to examine plasma NFL in iNPH patients and to evaluate the correlation between plasma and CSF levels, and whether NFL levels are associated with clinical symptoms and outcome after shunt surgery. METHODS: Fifty iNPH patients with median age 73 who had their symptoms assessed with the iNPH scale and plasma and CSF NFL sampled pre- and median 9 months post-operatively. CSF plasma was compared with 50 healthy controls (HC) matched for age and gender. Concentrations of NFL were determined in plasma using an in-house Simoa method and in CSF using a commercially available ELISA method. RESULTS: Plasma NFL was elevated in patients with iNPH compared to HC (iNPH: 45 (30-64) pg/mL; HC: 33 (26-50) (median; Q1-Q3), p = 0.029). Plasma and CSF NFL concentrations correlated in iNPH patients both pre- and postoperatively (r = 0.67 and 0.72, p < 0.001). We found only weak correlations between plasma or CSF NFL and clinical symptoms and no associations with outcome. A postoperative NFL increase was seen in CSF but not in plasma. CONCLUSIONS: Plasma NFL is increased in iNPH patients and concentrations correlate with CSF NFL implying that plasma NFL can be used to assess evidence of axonal degeneration in iNPH. This finding opens a window for plasma samples to be used in future studies of other biomarkers in iNPH. NFL is probably not a very useful marker of symptomatology or for prediction of outcome in iNPH

Wireless for Machine Learning

As data generation increasingly takes place on devices without a wired connection, Machine Learning over wireless networks becomes critical. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support Distributed Machine Learning. This is creating the need for new wireless communication methods. In this survey, we give an exhaustive review of the state of the art wireless methods that are specifically designed to support Machine Learning services. Namely, over-the-air computation and radio resource allocation optimized for Machine Learning. In the over-the-air approach, multiple devices communicate simultaneously over the same time slot and frequency band to exploit the superposition property of wireless channels for gradient averaging over-the-air. In radio resource allocation optimized for Machine Learning, Active Learning metrics allow for data evaluation to greatly optimize the assignment of radio resources. This paper gives a comprehensive introduction to these methods, reviews the most important works, and highlights crucial open problems.Comment: Corrected typo in author name. From the incorrect Maitron to the correct Mairto