bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Properties of Lyophilized Extracts of Olive Fruit, Pomegranate and Orange Peel Extracts against Foodborne Pathogenic and Spoilage Bacteria and Fungi In Vitro and in Food Matrices
Authors: Mitsagga Chrysanthi , Petrotos Konstantinos , Giavasis Ioannis
Published: November 2021

Several novel antimicrobials with different concentrations of olive, pomegranate, and orange fruit pulp extracts were produced from agricultural byproducts and, after lyophilization, their antimicrobial activity and potential synergistic effects were evaluated in vitro and in food samples against foodborne pathogenic and spoilage bacteria and fungi. The Minimum Inhibitory of the tested bacteria was 7.5% or 10%, while fungi were inhibited at a concentration of 10% or above. The optical density of bacterial and yeast cultures was reduced to a different extent with all tested antimicrobial powders, compared to a control without antimicrobials, and mycelium growth of fungi was also restricted with extracts containing at least 90% olive extract. In food samples with inoculated pathogens and spoilage bacteria and fungi, the 100% olive extract was most inhibitory against E. coli, S. typhimurium, and L. monocytogenes in fresh burger and cheese spread samples (by 0.6 to 1.8 log cfu/g), except that S. typhimurium was better inhibited by a 90% olive and 10% pomegranate extract in burgers. The latter extract was also the most effective in controlling the growth of inoculated fungi (Aspergillus niger, Penicillium italicum, Rhodotorula mucilaginosa) in both yogurt and tomato juice samples, where it reduced fungal growth by 1–2.2 log cfu/g at the end of storage period. The results demonstrate that these novel encapsulated extracts could serve as natural antimicrobials of wide spectrum, in order to replace synthetic preservatives in foods and cosmetics.
Influence of the Physico-Chemical Properties of Model Compounds on the Mean Sizes and Retention Rate of Gliadin Nanoparticles
Authors: Voci Silvia , Fresta Massimo , Cosco Donato
Published: November 2021

Vegetal proteins have emerged as appealing starting materials for the development of various drug delivery systems, and their use for obtaining polymeric nanoparticles has been profitably exploited in multidisciplinary fields. Wheat gliadin, the water-insoluble storage protein of gluten, is characterized by a great amount of hydrophobic amino acid residues and notable mucoadhesive features. This biopolymer can be easily manipulated to form colloidal carriers, films and fibers by means of bio-acceptable solvents and easy preparation procedures. In this investigation, four model compounds characterized by different octanol/water partition coefficient (logP) values were encapsulated in gliadin nanoparticles, with the aim of investigating the influence of their physico-chemical properties on the cargo features and technological characteristics of the protein nanocarriers. The results demonstrate that the chemical structure, solubility and molecular weight of the compounds used are able to dramatically modulate the mean sizes and the entrapment efficiency of gliadin nanoparticles. This demonstrates the importance of a preformulation investigation when a molecule needs to be encapsulated in this type of polymeric carrier.
Spectrum of Neuroimaging Findings in Post-COVID-19 Vaccination: A Case Series and Review of Literature
Authors: Sriwastava Shitiz , Shrestha Ashish K. , Khalid Syed Hassan , Colantonio Mark A. , Nwafor Divine , Srivastava Samiksha
Published: November 2021

Background and Purpose: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in Wuhan, China in December 2019. Symptoms range from mild flu-like symptoms to more severe presentations, including pneumonia, acute respiratory distress syndrome (ARDS), and even death. In response to the COVID-19 pandemic, the Emergency Use Authorization (EUA) approved the use of several vaccines. Because vaccines have been fast-tracked for emergency use, the short and long-term safety profile has been an area of concern. The aim of this paper is to extensively review published literature regarding post-COVID-19 vaccination neurological complications and characterize neuroimaging findings from three case presentations for early diagnosis and treatment. Methods: The analysis includes data from PubMed and Google Scholar. Articles included were retrieved from database inception beginning December 2020 with no language restrictions. Terms used include “SARS-CoV-2”, “post Covid vaccination”, “neurological complications”, “Guillain-barre Syndrome”, “Transverse-myelitis”, “Cerebral Venous Sinus thrombosis”, and “Cerebral hemorrhage”. Results: The literature review yielded several neurological complications post vaccination, including cerebral sinus venous thrombosis, transverse myelitis, Guillain-Barré Syndrome and optic neuritis, to name a few. Patient case presentation findings were consistent with documented results in published literature. Conclusions: We present a case series with a thorough literature review documenting adverse neurological affects following COVID-19 vaccination. Our case presentations and literature review highlight the importance of neuroimaging when diagnosing post-COVID-19 vaccination adverse effects. MRI imaging study is an important tool to be considered in patients presenting with post-COVID-19 vaccination-related unexplained neurological symptoms for accurate diagnosis.
Urinary Concentrations of (+)-Catechin and (-)-Epicatechin as Biomarkers of Dietary Intake of Flavan-3-ols in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study
Authors: Almanza-Aguilera Enrique , Ceballos-Sánchez Daniela , Achaintre David , Rothwell Joseph A , Laouali Nasser , Severi Gianluca , Katzke Verena , Johnson Theron , Schulze Matthias B , Palli Domenico , Gargano Giuliana , de Magistris Maria Santucci , Tumino Rosario , Sacerdote Carlotta , Scalbert Augustin , Zamora-Ros Raul
Published: November 2021

This study examines the correlation of acute and habitual dietary intake of flavan-3-ol monomers, proanthocyanidins, theaflavins, and their main food sources with the urinary concentrations of (+)-catechin and (-)-epicatechin in the European Prospective Investigation into Cancer and Nutrition study (EPIC). Participants (N = 419, men and women) provided 24-h urine samples and completed a 24-h dietary recall (24-HDR) on the same day. Acute and habitual dietary data were collected using a standardized 24-HDR software and a validated dietary questionnaire, respectively. Intake of flavan-3-ols was estimated using the Phenol-Explorer database. Concentrations of (+)-catechin and (-)-epicatechin in 24-h urine were analyzed using tandem mass spectrometry after enzymatic deconjugation. Simple and partial Spearman’s correlations showed that urinary concentrations of (+)-catechin, (-)-epicatechin and their sum were more strongly correlated with acute than with habitual intake of individual and total monomers (acute rpartial = 0.13–0.54, p < 0.05; and habitual rpartial = 0.14–0.28, p < 0.01), proanthocyanidins (acute rpartial = 0.24–0.49, p < 0.001; and habitual rpartial = 0.10–0.15, p < 0.05), theaflavins (acute rpartial = 0.22–0.31, p < 0.001; and habitual rpartial = 0.20–0.26, p < 0.01), and total flavan-3-ols (acute rpartial = 0.40–0.48, p < 0.001; and habitual rpartial = 0.23–0.33, p < 0.001). Similarly, urinary concentrations of flavan-3-ols were weakly correlated with both acute (rpartial = 0.12–0.30, p < 0.05) and habitual intake (rpartial = 0.10–0.27, p < 0.05) of apple and pear, stone fruits, berries, chocolate and chocolate products, cakes and pastries, tea, herbal tea, wine, red wine, and beer and cider. Moreover, all comparable correlations were stronger for urinary (-)-epicatechin than for (+)-catechin. In conclusion, our data support the use of urinary concentrations of (+)-catechin and (-)-epicatechin, especially as short-term nutritional biomarkers of dietary catechin, epicatechin and total flavan-3-ol monomers.
Association between Dietary Fiber Intake and Incidence of Depression and Anxiety in Patients with Essential Hypertension
Authors: Liu Yuanyuan , Ju Yang , Cui Lingling , Liu Ting , Hou Yunying , Wu Qing , Ojo Omorogieva , Du Xiaojiao , Wang Xiaohua
Published: November 2021

(1) Background: Our previous study found that the dietary fiber supplement in patients with hypertension increased SCFA-producers, Bififidobacterium and Spirillum in the gut microbiota, which may be associated with improvement of depression and anxiety through the gut–brain axis. However, only a few studies have explored the association between dietary fiber intake (DFI) and the incidence of depression and anxiety in hypertensive patients. (2) Methods: A cross-sectional survey was conducted in one comprehensive hospital and one community clinic aimed at understanding the status of DFI and the association between DFI and incidences of depression and anxiety in hypertensive patients. Levels of DFI were obtained through a two-24 h diet recall. According to the levels of DFI from low to high, the participants were divided into Q1, Q2, Q3 and Q4 groups. The Reported Outcomes Measurement Information System short form v1.0-Depression 8b and Anxiety 8a were used to assess patients’ levels of depression and anxiety. (3) Results: A total of 459 hypertensive patients were recruited and the daily DFI was 10.4 g. The incidences of hypertension combined with depression and anxiety were 19.6% and 18.5%, respectively. Regression analysis showed statistically significant associations between DFI and depression (B = −0.346, p = 0.001) and anxiety score (B = −0.565, p < 0.001). In logistic regression, after the covariates were adjusted, DFI was associated with the incidence of depression in Q3 (OR 2.641, 95% CI 1.050–6.640) and with that of anxiety in Q1 (OR 2.757, 95% CI 1.035–7.346), compared with Q4. (4) Conclusions: A higher consumption of DF was a protective factor for depression and anxiety in hypertensive patients.
Comparing Assessment Tools as Candidates for Personalized Nutritional Evaluation of Senior Citizens in a Nursing Home
Authors: Sousa-Catita Diogo , Bernardo Maria Alexandra , Santos Carla Adriana , Silva Maria Leonor , Mascarenhas Paulo , Godinho Catarina , Fonseca Jorge
Published: November 2021

Nutrition is an important health issue for seniors. In nursing homes, simple, inexpensive, fast, and validated tools to assess nutritional risk/status are indispensable. A multisurvey cross-sectional study with a convenient sample was created, comparing five nutritional screening/assessment tools and the time required for each, in order to identify the most useful instrument for a nursing home setting. Nutrition risk/status was evaluated using the following tools: Subjective Global Assessment (SGA), Mini Nutritional Assessment Short Form (MNA-SF), Malnutrition Universal Screening Tool (MUST), Nutritional Risk Screening 2002 (NRS 2002), and calf girth (CG). The time spent completing each tool was recorded. Eighty-three subjects were included. MNA-SF and CG were the screening tools that ranked highest with regards to malnutrition identification. CG failed to identify nutritional risk/malnutrition in seniors with lower limb edema. CG was the fastest tool while SGA was the slowest. This was the first study comparing non-invasive nutritional tools with time expended as a consideration in the implementation. CG is responsive, fast, and reliable in elders without edema. MNA-SF was more efficient at detecting malnutrition cases in the elderly population. Both MNA-SF and CG are considered the most suitable for the nursing home setting.
Association between Diet Quality Indices and Incidence of Type 2 Diabetes in the Melbourne Collaborative Cohort Study
Authors: Hodge Allison M. , Karim Md Nazmul , Hébert James R. , Shivappa Nitin , de Courten Barbora
Published: November 2021

Type 2 diabetes mellitus is a common condition whose incidence is increasing worldwide, and for which obesity and diet are important risk factors. The aim of this study was to assess the association of three diet quality scores with diabetes risk and how much of the association was mediated through body size. The Melbourne Collaborative Cohort Study recruited 41,513 men and women aged 40–69 years during 1990–1994. At baseline, data were collected on lifestyle and diet, anthropometric measures were performed. Incident diabetes was assessed by self-report at follow-up surveys in 1994–1998 and 2003–2007. The associations between the dietary inflammatory index (DII®), Mediterranean Diet Score (MDS) and the Alternative Healthy Eating Index—2010 and incident diabetes were assessed using Poisson regression, adjusting for age, sex, physical activity, smoking, alcohol consumption, socio-economic status (area based) and family history of diabetes. Data from 39,185 participants were included in the analysis and 1989 cases of diabetes were identified. Both DII and AHEI-2010 were associated with diabetes incidence, but MDS was not. In the top quintile of DII (most pro-inflammatory) vs. the least inflammatory quintile IRR was 1.49 95% CI (1.30, 1.72), p trend across quintiles <0.001. For AHEI-2010 the IRR was 0.67 (0.58, 0.78), p trend <0.001 for the healthiest vs. the least healthy quintile. Mediation analysis indicated that body size (body mass index/waist to hip ratio) mediated 35–48% of the association with incident diabetes for the AHEI and DII. Healthier diets may reduce risk of diabetes both by reducing weight gain and other mechanisms such as reducing inflammation.
Reproducibility and Validity of a Food Frequency Questionnaire (FFQ) Developed for Middle-Aged and Older Adults in Semarang, Indonesia
Authors: Syauqy Ahmad , Afifah Diana Nur , Purwanti Rachma , Nissa Choirun , Fitranti Deny Yudi , Chao Jane C.-J.
Published: November 2021

We assessed the reproducibility and validity of a food frequency questionnaire (FFQ) among middle-aged and older adults in Semarang, Indonesia. A total of 259 subjects aged 40–80 years completed two FFQs (nine-month apart) and nine 24 h dietary recalls (24HDRs, as a reference method). The reproducibility of the FFQ was analyzed using correlation coefficient, intra-class correlation coefficient (ICC), weighted kappa statistics and misclassification analysis. The validity was estimated by comparing the data acquired from FFQ1 and 24HDRs. The crude Pearson’s correlation coefficients and ICC for total energy and nutrients between FFQ1 and FFQ2 ranged from 0.50 to 0.81 and 0.44 to 0.78, respectively. Energy adjustment decreased the correlation coefficients for most nutrients. The crude, energy-adjusted and de-attenuated correlation coefficients for FFQ1 and 24HDRs ranged from 0.41 to 0.70, 0.31 to 0.89 and 0.54 to 0.82, respectively. The agreement rates for the same or adjacent quartile classifications were 81.1–94.6% for two FFQs and 80.7–89.6% for FFQ1 and 24HDRs. The weighted kappa values were 0.21 to 0.42 for two FFQs and 0.20 to 0.34 for FFQ1 and 24HDRs. A positive mean difference was found in the Bland–Altman analyses for energy and macronutrients. The FFQ could be acceptable for nutritional epidemiology study among Indonesians.
Association between Dietary Pattern and Periodontitis—A Cross-Sectional Study
Authors: Altun Ersin , Walther Carolin , Borof Katrin , Petersen Elina , Lieske Berit , Kasapoudis Dimitros , Jalilvand Navid , Beikler Thomas , Jagemann Bettina , Zyriax Birgit-Christiane , Aarabi Ghazal
Published: November 2021

The aim of the study was to investigate the relationship between specific known dietary patterns and the prevalence of periodontal disease in a northern population-based cohort study. We evaluated data from 6209 participants of the Hamburg City Health Study (HCHS). The HCHS is a prospective cohort study and is registered at ClinicalTrial.gov (NCT03934957). Dietary intake was assessed with the food frequency questionnaire (FFQ2). Periodontal examination included probing depth, gingival recession, plaque index, and bleeding on probing. Descriptive analyses were stratified by periodontitis severity. Ordinal logistic regression models were used to determine the association. Ordinal regression analyses revealed a significant association between higher adherence to the DASH diet/Mediterranean diet and lower odds to be affected by periodontal diseases in an unadjusted model (OR: 0.92; 95% CI: 0.87, 0.97; p < 0.001/OR: 0.93; 95% CI: 0.91, 0.96; p < 0.001) and an adjusted model (age, sex, diabetes) (OR: 0.94; 95% CI: 0.89, 1.00; p < 0.0365/OR: 0.97; 95% CI: 0.94, 1.00; p < 0.0359). The current cross-sectional study identified a significant association between higher adherence to the DASH and Mediterranean diets and lower odds to be affected by periodontal diseases (irrespective of disease severity). Future randomized controlled trials are needed to evaluate to which extent macro- and micronutrition can affect periodontitis initiation/progression.
Improved Quality of Life, Fitness, Mental Health and Cardiovascular Risk Factors with a Publicly Funded Bariatric Lifestyle Intervention for Adults with Severe Obesity: A Prospective Cohort Study
Authors: Brazil John Francis , Gibson Irene , Dunne Denise , Hynes Lisa B. , Harris Aisling , Bakir Mustafa , Keegan Dylan , McGuire Brian , Hynes Mary , Collins Chris , Foy Siobhan , Seery Suzanne , Bassett Paul , Davenport Colin , Jones Jennifer , Finucane Francis M.
Published: November 2021

Background: Lifestyle modification is the cornerstone of management for patients with severe and complicated obesity, but the effects of structured lifestyle programmes on quality of life, anxiety and depression scores and cardiovascular risk factors are not well-described. We sought to describe changes in self-reported quality of life and mental health-related outcomes as well as cardiovascular risk factors in patients completing a 10-week multidisciplinary lifestyle-modification programme. Methods: We conducted a prospective cohort study of all patients referred from our bariatric service who completed the programme between 2013 and 2019. In addition to weight, body mass index (BMI), blood pressure, HbA1c, lipid profile and functional capacity, we quantified health-related quality of life using the Dartmouth COOP Questionnaire and the European Quality of Life Questionnaire Visual Analogue Scale (EQVAS) and mental health using the Hospital Anxiety and Depression Scale (HADS). Results: Of 1122 patients who started the programme, 877 (78.2%) completed it and were included in per protocol analyses. Mean age was 47.3 ± 11.9 years, 66.9% were female, 34.8% were in full- or part-time employment and 69.4% were entitled to state-provided medical care. BMI decreased from 47.0 ± 7.8 to 46.2 ± 7.8 kg m−2 and weight decreased from 131.6 ± 25.5 to 129.5 ± 25.4 kg (both p < 0.001). There were significant reductions in anxiety and depression scores and improvements in all Dartmouth COOP domains. The EQVAS score increased from 52 ± 22 to 63 ± 19 (p < 0.001). Small but statistically significant reductions in LDL cholesterol, systolic blood pressure and HBA1c were also observed. Conclusions: Adults with severe and complicated obesity completing a specialised bariatric lifestyle-modification programme showed significant improvements in self-reported mental health and quality of life, in addition to reductions in cardiovascular risk factors.
Synthetic Microbiomes on the Rise—Application in Deciphering the Role of Microbes in Host Health and Disease
Authors: Bolsega Silvia , Bleich André , Basic Marijana
Published: November 2021

The intestinal microbiota conveys significant benefits to host physiology. Although multiple chronic disorders have been associated with alterations in the intestinal microbiota composition and function, it is still unclear whether these changes are a cause or a consequence. Hence, to translate microbiome research into clinical application, it is necessary to provide a proof of causality of host–microbiota interactions. This is hampered by the complexity of the gut microbiome and many confounding factors. The application of gnotobiotic animal models associated with synthetic communities allows us to address the cause–effect relationship between the host and intestinal microbiota by reducing the microbiome complexity on a manageable level. In recent years, diverse bacterial communities were assembled to analyze the role of microorganisms in infectious, inflammatory, and metabolic diseases. In this review, we outline their application and features. Furthermore, we discuss the differences between human-derived and model-specific communities. Lastly, we highlight the necessity of generating novel synthetic communities to unravel the microbial role associated with specific health outcomes and disease phenotypes. This understanding is essential for the development of novel non-invasive targeted therapeutic strategies to control and modulate intestinal microbiota in health and disease.
Prevalence and Determinants of Vitamin D Deficiency in 9595 Mongolian Schoolchildren: A Cross-Sectional Study
Authors: Bater Jorick , Bromage Sabri , Jambal Tuyatsetseg , Tsendjav Enkhjargal , Lkhagvasuren Enkhsaikhan , Jutmann Yanjmaa , Martineau Adrian R. , Ganmaa Davaasambuu
Published: November 2021

Population-based data relating to vitamin D status of children in Northeast Asia are lacking. We conducted a cross-sectional study to determine the prevalence and determinants of vitamin D deficiency in 9595 schoolchildren aged 6–13 years in Ulaanbaatar (UB), the capital city of Mongolia. Risk factors for vitamin D deficiency were collected by questionnaire, and serum 25-hydroxyvitamin D (25[OH]D) concentrations were measured using an enzyme-linked fluorescent assay, standardized and categorized as deficient (25[OH]D <10 ng/mL) or not. Odds ratios for associations between independent variables and risk of vitamin D deficiency were calculated using multivariate analysis with adjustment for potential confounders. The prevalence of vitamins D deficiency was 40.6% (95% CI 39.7% to 41.6%). It was independently associated with female gender (adjusted odds ratio [aOR] for girls vs. boys 1.23, 95% CI 1.11–1.35), month of sampling (aORs for December–February vs. June–November 5.28 [4.53–6.15], March–May vs. June–November 14.85 [12.46–17.74]), lower levels of parental education (P for trend <0.001), lower frequency of egg consumption (P for trend <0.001), active tuberculosis (aOR 1.40 [1.03–1.94]), household smoking (aOR 1.13 [1.02 to1.25]), and shorter time outdoors (P for trend <0.001). We report a very high prevalence of vitamin D deficiency among Mongolian schoolchildren, which requires addressing as a public health priority.
Metabolomics Insights into Inflammatory Bowel Disease: A Comprehensive Review
Authors: Aldars-García Laila , Gisbert Javier P. , Chaparro María
Published: November 2021

Inflammatory bowel disease (IBD) is a chronic, complex relapsing disorder characterised by immune dysregulation, gut microbiota alteration, and disturbed intestinal permeability. The diagnosis and the management of IBD are challenging due to the recurrent nature and complex evolution of the disease. Furthermore, the molecular mechanism underlying the aetiology and pathogenesis of IBD is still poorly understood. There is an unmet need for novel, reliable, and noninvasive tools for diagnosing and monitoring IBD. In addition, metabolomic profiles may provide a priori determination of optimal therapeutics and reveal novel targets for therapies. This review tries to gather scientific evidence to summarise the emerging contribution of metabolomics to elucidate the mechanisms underlying IBD and changes associated with disease phenotype and therapies, as well as to identify biomarkers with metabolic imbalance in those patients. Metabolite changes during health and disease could provide insights into the disease pathogenesis and the discovery of novel indicators for the diagnosis and prognosis assessment of IBD. Metabolomic studies in IBD have shown changes in tricarboxylic acid cycle intermediates, amino-acid and fatty-acid metabolism, and oxidative pathways. Metabolomics has made progress towards identifying metabolic alterations that may provide clinically useful biomarkers and a deeper understanding of the disease. However, at present, there is insufficient evidence evaluating the predictive accuracy of these molecular signatures and their diagnostic ability, which is necessary before metabolomic data can be translated into clinical practice.
Iodine and Selenium Biofortification of Chervil Plants Treated with Silicon Nanoparticles
Authors: Golubkina Nadezhda , Moldovan Anastasia , Fedotov Mikhail , Kekina Helene , Kharchenko Viktor , Folmanis Gundar , Alpatov Andrey , Caruso Gianluca
Published: November 2021

Production of functional food with high levels of selenium (Se) and iodine (I) obtained via plant biofortification shows significant difficulties due to the complex interaction between the two elements. Taking into account the known beneficial effect of silicon (Si) on plant growth and development, single and joint foliar biofortification of chervil plants with potassium iodide (150 mg L−1) and sodium selenate (10 mg L−1) was carried out in a pot experiment with and without Si nanoparticles foliar supplementation. Compared to control plants, nano-Si (14 mg L−1) increased shoot biomass in all treatments: by 4.8 times with Si; by 2.8 times with I + Si; by 5.6 times with Se + Si; by 4.0 times with I + Se + Si. The correspondent increases in root biomass were 4.5, 8.7, 13.3 and 10.0 times, respectively. The growth stimulation effect of Se, I and I + Se treatments resulted in a 2.7, 3.5 and 3.6 times increase for chervil shoots and 1.6, 3.1 and 8.6 times for roots, respectively. Nano-Si improved I biofortification levels by twice, while I and Se enhanced the plant content of each other. All treatments decreased nitrate levels, compared to control, and increased the photopigment accumulation. Improvement of total antioxidant activity and phenolic content was recorded only under the joint application of Se + I + Si. Foliar nano-Si treatment affected other element content in plants: decreased Na+ accumulation in single and joint supplementation with Se and I, restored Fe, Mn and Cr amount compared to the decreased levels recorded in separately Se and I fortified plants and promoted Al accumulation both with or without Se and I biofortification. The results of this research suggest high prospects of foliar nano-Si supply for enhancing both growth and joint I/Se biofortification of chervil.
Automated Bale Mapping Using Machine Learning and Photogrammetry
Authors: Yamada William , Zhao Wei , Digman Matthew
Published: November 2021

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m.
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform
Authors: Aghababaei Masoumeh , Ebrahimi Ataollah , Naghipour Ali Asghar , Asadi Esmaeil , Verrelst Jochem
Published: November 2021

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.
A Dual-Frequency Cloud Radar for Observations of Precipitation and Cloud in Tibet: Description and Preliminary Measurements
Authors: Huo Juan , Bi Yongheng , Liu Bo , Han Congzheng , Duan Minzheng
Published: November 2021

A new dual-frequency Doppler polarimetric cloud radar (DDCR), working at 35-GHz (Ka-band radar, wavelength: 8.6 mm) and 94-GHz (W-band radar, wavelength: 3.2 mm) frequencies, has been in operation at Yangbajing Observatory on the Tibetan Plateau (China) for more than a year at the time of writing. Calculations and field observations show that the DDCR has a high detection sensitivity of −39.2 dBZ at 10 km and −33 dBZ at 10 km for the 94-GHz radar and 35-GHz radar, respectively. The radar reflectivity measured by the two radars illustrates different characteristics for different types of cloud: for precipitation, the attenuation caused by liquid cloud droplets is obviously more serious for the 94-GHz radar than the 35-GHz radar (the difference reaches 40 dB in some cases), and the 94-GHz radar lost signals due to serious attenuation by heavy rainfall; while for clouds dominated by ice crystals where the attenuation significantly weakens, the 94-GHz radar shows better detection ability than the 35-GHz radar. Observations in the Tibetan region show that the 35-GHz radar is prone to missing cloud near the edge, such as the cloud-top portion, resulting in underestimation of the cloud-top height (CTH). Statistical analysis based on one year of observations shows that the mean CTH measured by the 94-GHz radar in the Tibetan region is approximately 600 m higher than that measured by the 35-GHz radar. The analysis in this paper shows that the DDCR, with its dual-frequency design, provides more valuable information than simpler configurations, and will therefore play an important role in improving our understanding of clouds and precipitation in the Tibetan region.
Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar
Authors: Thurai Merhala , Bringi Viswanathan , Wolff David , Marks David A. , Gatlin Patrick N. , Wingo Matthew T.
Published: November 2021

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities.
Spatiotemporal Trends of Bora Bora’s Shoreline Classification and Movement Using High-Resolution Imagery from 1955 to 2019
Authors: Gairin Emma , Collin Antoine , James Dorothée , Maueau Tehani , Roncin Yoann , Lefort Lucas , Dolique Franck , Jeanson Matthieu , Lecchini David
Published: November 2021

Coastal urbanisation is a widespread phenomenon throughout the world and is often linked to increased erosion. Small Pacific islands are not spared from this issue, which is of great importance in the context of climate change. The French Polynesian island of Bora Bora was used as a case study to investigate the historical evolution of its coastline classification and position from 1955 to 2019. A time series of very high-resolution aerial imagery was processed to highlight the changes of the island’s coastline. The overall length of natural shores, including beaches, decreased by 46% from 1955 to 2019 while human-made shores such as seawalls increased by 476%, and as of 2019 represented 61% of the coastline. This evolution alters sedimentary processes: the time series of aerial images highlights increased erosion in the vicinity of seawalls and embankments, leading to the incremental need to construct additional walls. In addition, the gradual removal of natural shoreline types modifies landscapes and may negatively impact marine biodiversity. Through documenting coastal changes to Bora Bora over time, this study highlights the impacts of human-made structures on erosional processes and underscores the need for sustainable coastal management plans in French Polynesia.
Impacts of the Microclimate of a Large Urban Park on Its Surrounding Built Environment in the Summertime
Authors: Amani-Beni Majid , Zhang Biao , Xie Gao-Di , Odgaard A. Jacob
Published: November 2021

The cooling effect of green spaces as an ecological solution to mitigate urban climate change is well documented. However, the factors influencing the microclimate in the built environment around forest parks, diurnal variations of their impact and their degree of importance have not been explicitly addressed. We attempted to quantify how much various landscape parameters, including land cover and spatial location, impact the ambient air and surface temperature in the area around Beijing’s Olympic Forest Park. Data were taken along strategically located traverses inside and outside the park. We found: (1) The air temperature during the day was 1.0–3.5 °C lower in the park than in the surrounding area; the surface temperature was 1.7–4.8 °C lower; air humidity in the park increased by 8.7–15.1%; and the human comfort index reduced to 1.8–6.9, all generating a more comfortable thermal environment in the park than in the surrounding area. (2) The distance to the park and the green space ratio of the park’s surrounding area are significant factors for regulating its microclimate. A 1 km increase in distance to the park caused the temperature to increase by 0.83 °C; when the green space ratio increased by 10%, the temperature dropped by 0.16 °C on average. The impact of these two parameters was more obvious in the afternoon than in the middle of the day or in the morning. The green space ratio could be used for designing a more stable thermal environment. (3) Land cover affects surface temperature more than it does air temperature. Our data suggest that an urban plan with an even distribution of green space would provide the greatest thermal comfort.
Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
Authors: Reza Selim , Oliveira Hugo S. , Machado José J. M. , Tavares João Manuel R. S.
Published: November 2021

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.
Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time
Authors: Qing Zengyu , Lu Zongxing , Cai Yingjie , Wang Jing
Published: November 2021

The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.
Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
Authors: Cwalina Krzysztof K. , Rajchowski Piotr , Olejniczak Alicja , Błaszkiewicz Olga , Burczyk Robert
Published: November 2021

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.
Integration and Testing of a High-Torque Servo-Driven Joint and Its Electronic Controller with Application in a Prototype Upper Limb Exoskeleton
Authors: Vélez-Guerrero Manuel Andrés , Callejas-Cuervo Mauro , Mazzoleni Stefano
Published: November 2021

Mechatronic systems that allow motorized activation in robotic exoskeletons have evolved according to their specific applications and the characteristics of the actuation system, including parameters such as size, mechanical properties, efficiency, and power draw. Additionally, different control strategies and methods could be implemented in various electronic devices to improve the performance and usability of these devices, which is desirable in any application. This paper proposes the integration and testing of a high-torque, servo-driven joint and its electronic controller, exposing its use in a robotic exoskeleton prototype as a case study. Following a brief background review, the development and implementation of the proposal are presented, allowing the control of the servo-driven joint in terms of torque, rotational velocity, and position through a straightforward, closed-loop control architecture. Additionally, the stability and performance of the servo-driven joint were assessed with and without load. In conclusion and based on the obtained results, the servo-driven joint and its control system demonstrate consistent performance under the proposed test protocol (max values: angular velocity 97 °/s, torque 33 Nm, positioning RMSE 1.46°), enabling this approach for use in various applications related to robotic exoskeletons, including human performance enhancement, rehabilitation, or support for daily living activities.
Vayu: An Open-Source Toolbox for Visualization and Analysis of Crowd-Sourced Sensor Data
Authors: Mahajan Sachit
Published: November 2021

Recent advances in sensor technology and the availability of low-cost and low-power sensors have changed the air quality monitoring paradigm. These sensors are being widely used by scientists and citizens for monitoring air quality at finer spatial-temporal resolution. Such practices are opening up opportunities to enhance the traditional monitoring networks, but at the same time, these sensors are producing large data sets that can become overwhelming and challenging when it comes to the scientific tools and skills required to analyze the data. To address this challenge, an open-source, robust, and cross-platform sensor data analysis toolbox called Vayu is developed that allows researchers and citizens to do detailed and reproducible analyses of air quality data. Vayu combines the power of visualization and statistical analysis using a simple and intuitive graphical user interface. Additionally, it offers a comprehensive set of tools for systematic analysis such as data conversion, interpolation, aggregation, and prediction. Even though Vayu was developed with air quality research in mind, it can be used to analyze different kinds of time-series data.
Detection of Low Density Lipoprotein—Comparison of Electrochemical Immuno- and Aptasensor
Authors: Rudewicz-Kowalczyk Daria , Grabowska Iwona
Published: November 2021

An elevated level of low density lipoprotein (LDL) can lead to the cardiovascular system-related diseases, such as atherosclerosis and others. Therefore, fast, simple, and accurate methods for LDL detection are very desirable. In this work, the parameters characterizing the electrochemical immuno-and aptasensor for detection of LDL have been compared for the first time. An immunosensor has been designed, for which the anti-apolipoprotein B-100 antibody was covalently attached to 4-aminothiophenol (4-ATP) on the surface of the gold electrode. In the case of an aptasensor, the gold electrode was modified in a mixture of ssDNA aptamer specific for LDL modified with –SH group and 6-mercaptohexanol. Square-wave voltammetry has been used for detection of LDL in PBS containing redox active marker, [Fe(CN)6]3−/4−. Our results show the linear dependence of [Fe(CN)6]3−/4− redox signal changes on LDL concentration for both biosensors, in the range from 0.01 ng/mL to 1.0 ng/mL. The limit of detection was 0.31 and 0.25 ng/mL, for immuno- and aptasensor, respectively. Whereas slightly better selectivity toward human serum albumin (HSA), high density lipoprotein (HDL), and malondialdehyde modified low density lipoprotein (MDA-LDL) has been observed for aptasensor. Moreover, the other components of human blood serum samples did not influence aptasensor sensitivity.
Three-Dimensional Reconstruction of Light Field Based on Phase Similarity
Authors: Feng Wei , Gao Junhui , Qu Tong , Zhou Shiqi , Zhao Daxing
Published: November 2021

Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.
Fractional-Order Sensing and Control: Embedding the Nonlinear Dynamics of Robot Manipulators into the Multidimensional Scaling Method
Authors: Lopes António M. , Tenreiro Machado José A.
Published: November 2021

This paper studies the use of multidimensional scaling (MDS) to assess the performance of fractional-order variable structure controllers (VSCs). The test bed consisted of a revolute planar robotic manipulator. The fractional derivatives required by the VSC can be obtained either by adopting numerical real-time signal processing or by using adequate sensors exhibiting fractional dynamics. Integer (fractional) VCS and fractional (integer) sliding mode combinations with different design parameters were tested. Two performance indices based in the time and frequency domains were adopted to compare the system states. The MDS generated the loci of objects corresponding to the tested cases, and the patterns were interpreted as signatures of the system behavior. Numerical experiments illustrated the feasibility and effectiveness of the approach for assessing and visualizing VSC systems.