Dietary contribution of wild edible plants to women's diets in the buffer zone around the Lama forest, Benin - an underutilized potential

Rural populations in developing countries face food insecurity and malnutrition despite being surrounded by extraordinary biodiversity. The international community increasingly recognizes the role of agro-biodiversity and Wild Edible Plants (WEPs) in their contributions to managing risk and building resilience and sustainable food systems. Studies on real contributions of WEPs to peoples’ diets, however, are uncommon. This study assessed the contribution of WEPs to diets of women living in the buffer zone of the Lama forest in southern Benin. During the long dry season, a cross-sectional survey was carried out on 120 women, covering their knowledge and attitudes towards WEPs and two non-consecutive 24-h recalls of their WEP consumption. Contribution of WEPs to total dietary intake was low due to infrequent use and small portion sizes. The highest nutrient contributions of WEPs measured were for copper (13.9 %) and iron (4.6 %) but the majority of the women had intake values below the Estimated Average Requirements (EARs) for these elements - copper 65 % and iron 91 % Women’s dietary diversity was significantly higher among WEP consumers than non-consumers, mainly due to higher consumption of dark green leafy vegetables. WEPs were less consumed as a replacement for other foods but rather as a complement to the diet. The study population generally appreciated WEPs, while some constraints were reported regarding preparation, conservation and commercialization. Before widely promoting WEP consumption in order to exploit their dietary potential, additional investigations are needed into their nutrient composition, cultural and market value, their sustainable harvest levels and possible integration into innovative farming systems

Graduate String Quartet: Kathryn Gray, Violin; Joung-Sook Noh, Violin; Ruth C. Boedecker, Viola; Kyung Jin Lee, Cello; November 28, 1972

Hayden AuditoriumTuesday EveningNovember 28, 19728:15 p.m

Validating quantitative PCR assays for cfDNA detection without DNA extraction in exercising SLE patients

Circulating cell-free DNA (cfDNA) has been investigated as a screening tool for many diseases. To avoid expensive and time-consuming DNA isolation, direct quantification PCR assays can be established. However, rigorous validation is required to provide reliable data in the clinical and non-clinical context. Considering the International Organization for Standardization, as well as bioanalytical method validation guidelines, we provide a comprehensive procedure to validate assays for cfDNA quantification from blood plasma without DNA isolation. A 90 and 222 bp assay was validated to study the kinetics of cfDNA after exercise in patients with systemic lupus erythematosus (SLE). The assays showed ultra-low limit of quantification (LOQ) with 0.47 and 0.69 ng/ml, repeatability ≤ 11.6% (95% CI 8.1–20.3), and intermediate precision ≤ 12.1% (95% CI 9.2–17.7). Incurred sample reanalysis confirmed the precision of the procedure. The additional consideration of pre-analytical factors shows that centrifugation speed and temperature do not change cfDNA concentrations. In SLE patients cfDNA increases ~ twofold after a walking exercise, normalizing after 60 min of rest. The established assays allow reliable and cost-efficient quantification of cfDNA in minute amounts of plasma in the clinical setting. Additionally, the assay can be used as a tool to determine the impact of pre-analytical factors and validate cfDNA quantity and quality of isolated samples

Immunoadsorption and plasma exchange : efficient treatment options for neurological autoimmune diseases

Background Therapeutic plasma exchange (TPE) and immunoadsorption (IA) are first or second line treatment options in patients with neurological autoimmune diseases, including multiple sclerosis, neuromyelitis optica spectrum disorders (NMSOD), chronic inflammatory demyelinating polyneuropathy, acute inflammatory demyelinating polyradiculoneuropathy (Guillain-Barré syndrome), and autoimmune encephalitis. Methods In this prospective randomized controlled monocentric study, we assessed safety and efficacy of therapy with IA or TPE in patients with neurological autoimmune diseases. Treatment response was assessed using various neurological scores as well by measuring immunoglobulin and cytokine concentrations. Clinical outcome was evaluated by application of specific scores for the underlying diseases. Results A total of 32 patients were analyzed. Among these, 19 patients were treated with TPE and 13 patients with IA. IA and TPE therapy showed a comparable significant treatment response. In patients with MS and NMOSD, mean EDSS before and after treatment showed a significant reduction after treatment with IA. We observed a significant reduction of the pro-inflammatory cytokines IL-12, lL-17, IL-6, INF-γ, and tumor necrosis factor alpha during IA treatment, whereas this reduction was not seen in patients treated with TPE. Conclusions In summary, both IA and TPE were effective and safe procedures for treating neurological autoimmune diseases. However, there was a trend towards longer therapy response in patients treated with IA compared to TPE, possibly related to a reduction in plasma levels of pro-inflammatory cytokines seen only in the IA-treated group

Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.Comment: Preprint of the paper accepted at IWANN 201

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Assessment of a novel smartglass-based point-of-care fusion approach for mixed reality-assisted targeted prostate biopsy: A pilot proof-of-concept study

PurposeWhile several biopsy techniques and platforms for magnetic resonance imaging (MRI)-guided targeted biopsy of the prostate have been established, none of them has proven definite superiority. Augmented and virtual reality (mixed reality) smartglasses have emerged as an innovative technology to support image-guidance and optimize accuracy during medical interventions. We aimed to investigate the benefits of smartglasses for MRI-guided mixed reality-assisted cognitive targeted biopsy of the prostate.MethodsFor prospectively collected patients with suspect prostate PIRADS lesions, multiparametric MRI was uploaded to a smartglass (Microsoft® Hololens I), and smartglass-assisted targeted biopsy (SMART TB) of the prostate was executed by generation of a cognitive fusion technology at the point-of-care. Detection rates of prostate cancer (PCA) were compared between SMART TB and 12-core systematic biopsy. Assessment of SMART-TB was executed by the two performing surgeons based on 10 domains on a 10-point scale ranging from bad (1) to excellent (10).ResultsSMART TB and systematic biopsy of the prostate were performed for 10 patients with a total of 17 suspect PIRADS lesions (PIRADS 3, n = 6; PIRADS 4, n = 6; PIRADS 5, n = 5). PCA detection rate per core was significant (p < 0.05) higher for SMART TB (47%) than for systematic biopsy (19%). Likelihood for PCA according to each core of a PIRADS lesion (17%, PIRADS 3; 58%, PIRADS 4; 67%, PIRADS 5) demonstrated convenient accuracy. Feasibility scores for SMART TB were high for practicality (10), multitasking (10), execution speed (9), comfort (8), improvement of surgery (8) and image quality (8), medium for physical stress (6) and device handling (6) and low for device weight (5) and battery autonomy (4).ConclusionSMART TB has the potential to increase accuracy for PCA detection and might enhance cognitive MRI-guided targeted prostate biopsy in the future

Incident type 2 diabetes attributable to suboptimal diet in 184 countries

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.814.4 million) incident T2D cases, representing 70.3% (68.871.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.027.1%)), excess refined rice and wheat intake (24.6% (22.327.2%)) and excess processed meat intake (20.3% (18.323.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.487.7%)) and Latin America and the Caribbean (81.8% (80.183.4%)); and lowest proportional burdens were in South Asia (55.4% (52.160.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally. (c) 2023, The Author(s)

Children's and adolescents' rising animal-source food intakes in 1990-2018 were impacted by age, region, parental education and urbanicity

Animal-source foods (ASF) provide nutrition for children and adolescents physical and cognitive development. Here, we use data from the Global Dietary Database and Bayesian hierarchical models to quantify global, regional and national ASF intakes between 1990 and 2018 by age group across 185 countries, representing 93% of the worlds child population. Mean ASF intake was 1.9 servings per day, representing 16% of children consuming at least three daily servings. Intake was similar between boys and girls, but higher among urban children with educated parents. Consumption varied by age from 0.6 at <1 year to 2.5 servings per day at 1519 years. Between 1990 and 2018, mean ASF intake increased by 0.5 servings per week, with increases in all regions except sub-Saharan Africa. In 2018, total ASF consumption was highest in Russia, Brazil, Mexico and Turkey, and lowest in Uganda, India, Kenya and Bangladesh. These findings can inform policy to address malnutrition through targeted ASF consumption programmes. (c) 2023, The Author(s)