Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional and Sensory Evaluation of Yoghurt Incorporated with Unripe False Horn Plantain (Musa paradisiaca var. “apentu”)

Unripe plantain (Musa paradisiaca L.) is rich in nutrients including minerals, vitamin C, and carbohydrates particularly resistant starches with prebiotic properties. However, the fruit is challenged with limited utilisation, and this contributes to its high postharvest losses along the production and supply chain. Information is lacking on incorporating plantain (UPF) in functional dairy food product development. In a completely randomized design, the study evaluated the effect of unripe false horn plantain (var. “apentu”) flour (UPF) incorporation (w/v), at 0% (control), 2%, 4%, and 6%, on the composition and sensory quality of yoghurt. The results showed that higher UPF percent incorporation resulted in yoghurts having lower moisture and higher total solid values as well as enhanced nutritional values, in terms of protein, zinc, potassium, calcium, and vitamin C (P < 0.05). Mean pH and total titratable acidity values of the yoghurt products were in the ranges of 3.40-3.65 and 1.00-130%, respectively. Conversely, an increase in UPF incorporation generally reduced consumer likeness scores for yoghurt sensory characteristics including appearance, texture, flavour, taste, aftertaste, and overall acceptability. The control AZ product received the highest ratings in all sensory attributes evaluated. Compared with the control AZ, the BX (2% w/w UPF) yoghurt showed better nutritional quality as well as had comparable ratings for the sensory attributes, particularly in terms of appearance, texture, and flavour. Thus, the formulation containing 2% UPF has the best potential for the production of value-added functional yoghurt, which will be acceptable. However, for high acceptability, further research is needed to improve the impact of UPF incorporation on the overall sensory quality of yoghurt. The study suggests that UPF can serve as a potential supplement for improving the value of yoghurt, and this also contributes to reducing postharvest losses of plantain as a key food security resource. Also, the study findings contribute baseline information to guide future research on functional dairy product development with unripe plantain.
Dynamics of Serologic Change to Gluten in Celiac Disease Patients

Serologic measures of tissue transglutaminase (tTG) immunoglobulin A (IgA) and deamidated gliadin peptide (DGP) IgA and immunoglobulin G (IgG) are hallmark tests utilized when diagnosing individuals for celiac disease (CeD) and for monitoring adherence to a gluten-free diet (GFD), currently the only available treatment for CeD. We address two issues in this study: (i) the relapse to seropositivity for CeD patients who resume a gluten containing diet and (ii) the correlation between two different tTG-IgA assays near the upper limit of normal (ULN) designated thresholds. Regarding the first issue, often a suspected CeD individual is put back on a gluten diet to return to their serologic levels. However, we show it requires a substantial amount of gluten for serology to return to a positive level. For example, in one study of 22 patients treated with placebo and taking 84 g of gluten over 6 weeks, only two converted from seronegative to seropositive for tTG-IgA. Regarding the second topic, we compare the relationship for different serologic assays, namely tTG-IgA AB (recombinant, ULN = 4 units/mL) vs. tTG-IgA (non-recombinant, ULN = 20 units). There is a strong correlation between both measurements as evidenced by a Pearson coefficient of R = 0.8584; however, we observed that the cross-correlation in terms of sensitivity and specificity improved substantially by using an ULN value of three instead of four for the tTG-IgA AB (recombinant) assay. This result suggests that assay thresholds used for initial diagnosis in patients who have not yet started a GFD may need to be adjusted for monitoring and in the setting of a diagnostic gluten challenge.
Diversity of Fecal Indicator Enterococci among Different Hosts: Importance to Water Contamination Source Tracking

Enterococcus spp. are common bacteria present in the intestinal tracts of animals and are used as fecal indicators in aquatic environments. On the other hand, enterococci are also known as opportunistic pathogens. Elucidating their composition in the intestinal tracts of domestic animals can assist in estimating the sources of fecal contamination in aquatic environments. However, information on the species and composition of enterococci in animal hosts (except humans) is still lacking. In this study, enterococci were isolated from the feces of cattle, pigs, birds, and humans using selective media. Enterococcal species were identified using mass spectrometry technology, and each host was characterized by diversity and cluster analysis. The most dominant species were E. hirae in cattle, E. faecium in birds, and E. faecalis in pigs and humans. Cattle had the highest alpha diversity, with high interindividual and livestock farm diversity. The dominant enterococcal species in pigs and humans were identical, and cluster analysis showed that the majority of the two hosts’ species clustered together.
Downscaling an intense precipitation event in complex terrain: the importance of high grid resolution

ABSTRACT Floods due to intense rainfall are a major hazard to both people and infrastructure in western Norway. Here steep orography enhances precipitation and the complex terrain channels the runoff into narrow valleys and small rivers. In this study we investigate a major rainfall and flooding event in October 2014. We compare high-resolution numerical simulations with measurements from rain gauges deployed in the impacted region. Our study has two objectives: (i) to understand the dynamical processes that drove the high rainfall and (ii) the importance of high grid resolution to resolve intense rainfall in complex terrain. This is of great interest for numerical weather prediction and hydrological modelling. Our approach is to dynamically downscale the ERA-Interim reanalysis with the Weather Research and Forecasting model (WRF). We find that WRF gives a substantially better representation of precipitation both in terms of absolute values as well as spatial and temporal distributions than a coarse resolution reanalysis. The largest improvement between the WRF simulations is found when we decrease the horizontal model grid spacing from 9 km to 3 km. Only minor additional improvements are obtained when downscaling further to 1 km. We believe that this is mainly related to the orography in the study area and its representation in the model. Realistic representations of gravity waves and the seeder–feeder effect seem to play crucial roles in reproducing the precipitation distribution correctly. An analysis of associated wavelengths shows the importance of the shortest resolvable length scales. On these scales our simulations also show differences in accumulated precipitation of up to 300 mm over four days, further emphasising the need for resolving short wavelengths. Therefore, our results clearly demonstrate the need for high-resolution dynamical downscaling for extreme weather impact studies in regions with complex terrain.
Association between sleep duration and antibody acquisition after mRNA vaccination against SARS-CoV-2

Introduction Sleep enhances the antibody response to vaccination, but the relationship between sleep and mRNA vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not fully understood. Methods In this prospective observational study, we investigated the influence of sleep habits on immune acquisition induced by mRNA vaccines against SARS-CoV-2 in 48 healthy adults (BNT-162b2, n=34; mRNA-1273, n=14; female, n=30, 62.5%; male, n=18, 37.5%; median age, 39.5 years; interquartile range, 33.0–44.0 years) from June 2021 to January 2022. The study measured sleep duration using actigraphy and sleep diaries, which covered the periods of the initial and booster vaccinations. Results Multivariable linear regression analysis showed that actigraphy-measured objective sleep duration 3 and 7 days after the booster vaccination was independently and significantly correlated with higher antibody titers (B=0.003; 95% confidence interval, 0.000–0.005; Beta=0.337; p=0.02), even after controlling for covariates, including age, sex, the type of vaccine, and reactogenicity to the vaccination. Associations between acquired antibody titer and average objective sleep duration before vaccination, and any period of subjective sleep duration measured by sleep diary were negligible. Discussion Longer objective, but not subjective, sleep duration after booster vaccination enhances antibody response. Hence, encouraging citizens to sleep longer after mRNA vaccination, especially after a booster dose, may increase protection against SARS-CoV-2. Study registration This study is registered at the University Hospital Medical Information Network Center (UMIN: https://www.umin.ac.jp) on July 30, 2021, #UMIN000045009.
Understanding the Cultural Dimension on the Web Homepage Preferences and Visual Exploration

In an experimental study conducted with thirty-two French and Japanese users, we investigated the impact of culture (i.e., cultural cognitive style) on users’ preferences and their visual exploration of homepages with two different designs: French-ness and Japan-ness design. Two main results have been obtained: (1) Even if homepages are culturally designed, preferences of the user is not related to this culturecentered design; (2) The visual exploration of homepages is subordinate to the culture of the user, i.e. is not really related to the design.
MTRA-CNN: A Multi-Scale Transfer Learning Framework for Glaucoma Classification in Retinal Fundus Images

In the diagnosis of glaucoma based on deep learning, it is more meaningful for ophthalmologist to make a graded diagnosis using fundus images to reflect the degree of disease. Such multi-classification diagnosis tasks require a larger amount of data to enable the neural network to extract more feature information, while it is very difficult in medical field. To address these issues, we propose a novel Multi-Scale Transfer Learning framework (MTRA-CNN) which can make a graded diagnosis effectively based on graded glaucoma dataset with small volume: (1) Aiming at the problem of small data volume, we innovatively apply multi-stage transfer learning technique in glaucoma, in which the advantage of different types of datasets such as ImageNet and Ocular disease intelligent recognition (ODIR) are taken. (2) We innovatively combine the Residual attention(RA) block, a functional module designed specifically for fundus images, with transfer learning techniques and pre-trained it on the ODIR dataset to further improve the network’s upper-level feature extraction ability of glaucoma fundus images. (3) To achieve better diagnostic performance, we propose a novel multi-scale transfer learning method by combining the two-stage transfer learning with one-stage transfer learning. To validate our framework, we conducted experiments on the private glaucoma 4 classification dataset, and the results show that the accuracy of our method is 86.8%, compared to the method without any transfer learning, the accuracy of our experiments improved by 19.79%.
Molecular Mechanisms Linking Diabetes with Increased Risk of Thrombosis

This review will provide an overview of what is currently known about mechanisms linking poor glycaemic control with increased thrombotic risk. The leading causes of death in people with diabetes are strokes and cardiovascular disease. Significant morbidity is associated with an increased risk of thrombosis, resulting in myocardial infarction, ischaemic stroke, and peripheral vascular disease, along with the sequelae of these events, including loss of functional ability, heart failure, and amputations. While the increased platelet activity, pro-coagulability, and endothelial dysfunction directly impact this risk, the molecular mechanisms linking poor glycaemic control with increased thrombotic risk remain unclear. This review highlights the complex mechanisms underlying thrombosis prevalence in individuals with diabetes and hyperglycaemia. Post-translational modifications, such as O-GlcNAcylation, play a crucial role in controlling protein function in diabetes. However, the role of O-GlcNAcylation remains poorly understood due to its intricate regulation and the potential involvement of multiple variables. Further research is needed to determine the precise impact of O-GlcNAcylation on specific disease processes.
A Bibliometric Analysis of Eco-Fashion

This research aims to look at the development of research on “Eco-Fashion'' throughout the world and research plans that can be carried out based on journals published on this theme. This research uses qualitative methods with a bibliometric analysis approach. The data used is secondary data with the theme "Eco-Fashion" originating from database Scopus with a total of 133 journal articles. The research results show that in the author's bibliometric mapping, the author whose research was most cited on the theme Eco-fashion is Niinimäki. Furthermore, based on bibliometric keyword mapping, six groups can become lines of research with topics related to (1)Circular Economy Adoption, (2) Impact of Knowledge on consumer sustainability, (3) Green Energy Challenges, (4) Sustainable Raw Material Changes, (5) Fashion Product Values, and (6) Friendly Label Effects on Environment.
Masked Spectrogram Prediction for Unsupervised Domain Adaptation in Speech Enhancement

Supervised learning-based speech enhancement methods often work remarkably well in acoustic situations represented in the training corpus but generalize poorly to out-of-domain situations, i.e. situations not seen during training. This stands in the way of further improvement of these methods in realistic scenarios, as collecting paired noisy-clean recordings in the target application domain is typically not feasible. Recording noisy-only in-domain data is, though, much more practical. In this article, we tackle the problem of unsupervised domain adaptation in speech enhancement. Specifically, we propose a way to use in-domain noisy-only data in the training of a neural network to improve upon a model trained solely on out-of-domain paired data. For this, we make use of masked spectrogram prediction, a technique from self-supervised learning that aims to interpolate masked regions of a spectrogram. We hypothesize that masked spectrogram prediction encourages learning of features that represent well both speech and noise components of the noisy signals. These features can then be used to train a more robust speech enhancement system. We evaluate the proposed method on the VoiceBank-DEMAND and LibriFSD50k databases, with WSJ0-CHiME3 serving as the out-of-domain database. We show that the proposed method outperforms both the out-of-domain system and the baseline approaches, i.e. RemixIT and noisy-target training, and also combines well with the previously proposed RemixIT method.
An updated review of epidemiological characteristics, immune escape, and therapeutic advances of SARS-CoV-2 Omicron XBB.1.5 and other mutants

The rapid evolution of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the emergence of new variants with different genetic profiles, with important implications for public health. The continued emergence of new variants with unique genetic features and potential changes in biological properties poses significant challenges to public health strategies, vaccine development, and therapeutic interventions. Omicron variants have attracted particular attention due to their rapid spread and numerous mutations in key viral proteins. This review aims to provide an updated and comprehensive assessment of the epidemiological characteristics, immune escape potential, and therapeutic advances of the SARS-CoV-2 Omicron XBB.1. 5 variant, as well as other variants.
Forecasting Prices of Agricultural Commodities using Machine Learning for Global Food Security: Towards Sustainable Development Goal 2

- Global food security is vital for promoting human health, upholding social well-being, and ultimately achieving the United Nations’ Sustainable Development Goal (SDG) 2: Zero Hunger. Conversely, it is influenced by a multitude of factors, with the dynamics of agricultural commodity prices playing a significant role. Recognizing the potential of Machine Learning in agricultural applications, this work delves into exploring the price dynamics of key agricultural commodities across various global producers. Through rigorous experimentation and performance comparison, this study analyses suitable Machine Learning methods and proposes a Hybrid SARIMA-LSTM (HySALS) to forecast global prices of agricultural commodities. The effectiveness of the proposed approach is evaluated using historical price data for five important commodities: Wheat, Millet, Sorghum, Maize, and Rice, both on a global average scale and with specific emphasis on developing nations that are either global leaders in the production of these crops or hold a significant production share within their own borders. The training data encompasses the years 2005 to 2017, while testing is conducted for the period from 2018 to 2022, followed by forecasting global prices for these commodities from 2023 to 2030. The insights derived from these forecasts are aimed to assist the decision-making processes of various stakeholders, from farmers to policymakers, thereby contributing to the efforts towards achieving global food security.
Anti-Toxoplasma gondii Antibodies in European Residents: A Systematic Review and Meta-Analysis of Studies Published between 2000 and 2020

Toxoplasmosis has a major impact on animal and public health. Information regarding the seroprevalence of human Toxoplasma gondii infections from a European perspective has not yet been compiled to date. Thus, the present review summarized available resident data from the period 2000–2020. The overall seroprevalence of anti-T. gondii IgG was 32.1%, with great variability between countries (n = 30). The subgroup analysis identified different pooled prevalence data depending on the geographic area (p < 0.0001), target population (p = 0.0147), and serological diagnosis assays used (p = 0.0059). A high heterogeneity (I2 = 100%, p < 0.001; Q = 3.5e+05, d.f. = 135, p < 0.001) and degree of publication bias (Egger’s test = 6.14, p < 0.001) were observed among the 134 studies considered. The occurrence of anti-T. gondii IgM, which was reported in 64.7% of studies, reached a pooled seroprevalence of 0.6%. In addition, among the eight main risk factors identified, “contact with soil”, “consumption of undercooked beef”, and “intake of unwashed vegetables” were the most significantly associated with infections. The fact that one-third of the European population has been exposed to T. gondii justifies extra efforts to harmonize surveillance systems and develop additional risk-factor analyses based on detailed source attribution assessment.
Queue Management system and its relation with Patient Satisfaction of Outpatient Clinics

Background : Patient satisfaction with nursing care has become increasingly important to healthcare providers in recent years. The patient paying a visit to any health facility is expecting to be served in the very shortest time possible and in an efficient manner. Aim : This study aimed to assess the queue management system and its relation with patient satisfaction of outpatient clinics. Design : A descriptive correlational design was employed in this study. Setting: The study was conducted at outpatient clinics affiliated with Banha University Hospital, at Al-Kalubia Governorate. Subjects: The study sample was selected 317 patients out of the total number of 1800 patients. They were chosen by a simple random sampling technique. Data collection tools: Two tools were used for data collection: A queue management practices structure questionnaire and a patient satisfaction questionnaire. Results: The study result revealed that 50% of patients were satisfied with total queue management system practices in the outpatient clinics but only 27% of them were dissatisfied with total queue management system practices in the outpatient clinics. The result also indicated that the patients rated overall satisfaction at 59.5%. Conclusion : The results demonstrated that there were significant strong positive correlations between the dimensions of queue management in terms of service quality, waiting time, and waiting environment with patient satisfaction. Recommendations : Hospital management and staff should continuously be up-to-date technologically and they also be reminded of the value and mission of serving their patients.
Lighting Search Algorithm With Convolutional Neural Network-Based Image Captioning System for Natural Language Processing

Recently, deep learning models have become more prominent due to their tremendous performance for real-time tasks like face recognition, object detection, natural language processing (NLP), instance segmentation, image classification, gesture recognition, and video classification. Image captioning is one of the critical tasks in NLP and computer vision (CV). It completes conversion from image to text; specifically, the model produces description text automatically based on the input images. In this aspect, this article develops a Lighting Search Algorithm (LSA) with a Hybrid Convolutional Neural Network Image Captioning System (LSAHCNN-ICS) for NLP. This introduced LSAHCNN-ICS system develops an end-to-end model which employs convolutional neural network (CNN) based ShuffleNet as an encoder and HCNN as a decoder. At the encoding part, the ShuffleNet model derives feature descriptors of the image. Besides, in the decoding part, the description of text can be generated using the proposed hybrid convolutional neural network (HCNN) model. To achieve improved captioning results, the LSA is applied as a hyperparameter tuning strategy, representing the innovation of the study. The simulation analysis of the presented LSAHCNN-ICS technique is performed on a benchmark database, and the obtained results demonstrated the enhanced outcomes of the LSAHCNN-ICS algorithm over other recent methods with maximum Consensus-based Image Description Evaluation (CIDEr Code) of 43.60, 59.54, and 135.14 on Flickr8k, Flickr30k, and MSCOCO datasets correspondingly.