bluuebunny/crossref_metadata_2025 huggingface dataset:
Retinal Venular Tortuosity Jointly with Retinal Amyloid Burden Correlates with Verbal Memory Loss: A Pilot Study
Authors: Dumitrascu Oana M. , Rosenberry Ryan , Sherman Dale S. , Khansari Maziyar M. , Sheyn Julia , Torbati Tania , Sherzai Ayesha , Sherzai Dean , Johnson Kenneth O. , Czeszynski Alan D. , Verdooner Steven , Black Keith L. , Frautschy Sally , Lyden Patrick D. , Shi Yonggang , Cheng Susan , Koronyo Yosef , Koronyo-Hamaoui Maya
Published: October 2021

Introduction: Retinal imaging is a non-invasive tool to study both retinal vasculature and neurodegeneration. In this exploratory retinal curcumin-fluorescence imaging (RFI) study, we sought to determine whether retinal vascular features combined with retinal amyloid burden correlate with the neurocognitive status. Methods: We used quantitative RFI in a cohort of patients with cognitive impairment to automatically compute retinal amyloid burden. Retinal blood vessels were segmented, and the vessel tortuosity index (VTI), inflection index, and branching angle were quantified. We assessed the correlations between retinal vascular and amyloid parameters, and cognitive domain Z-scores using linear regression models. Results: Thirty-four subjects were enrolled and twenty-nine (55% female, mean age 64 ± 6 years) were included in the combined retinal amyloid and vascular analysis. Eleven subjects had normal cognition and 18 had impaired cognition. Retinal VTI was discriminated among cognitive scores. The combined proximal mid-periphery amyloid count and venous VTI index exhibited significant differences between cognitively impaired and cognitively normal subjects (0.49 ± 1.1 vs. 0.91 ± 1.4, p = 0.006), and correlated with both the Wechsler Memory Scale-IV and SF-36 mental component score Z-scores (p < 0.05). Conclusion: This pilot study showed that retinal venular VTI combined with the proximal mid-periphery amyloid count could predict verbal memory loss. Future research is needed to finesse the clinical application of this retinal imaging-based technology.
Accumulation of Paneth Cells in Early Colorectal Adenomas Is Associated with Beta-Catenin Signaling and Poor Patient Prognosis
Authors: López-Arribillaga Erika , Yan Bing , Lobo-Jarne Teresa , Guillén Yolanda , Menéndez Silvia , Andreu Montserrat , Bigas Anna , Iglesias Mar , Espinosa Lluís
Published: October 2021

Background: Previous studies in mice indicated that Paneth cells and c-Kit-positive goblet cells represent the stem cell niche of the small intestine and colon, respectively, partly by supporting Wnt and Notch activation. Whether these cell populations play a similar role in human intestinal cancer remains unexplored. Methods: We performed histopathological evaluation and immunohistochemical analysis of early colorectal adenomas and carcinoma adenoma from patients at the Hospital del Mar in Barcelona. We then determined the possible correlation between the different parameters analyzed and with patient outcomes. Results: Paneth cells accumulate in a subset of human colorectal adenomas directly associated with Notch and Wnt/β-catenin activation. Adenoma areas containing Paneth cells display increased vessel density in the lamina propria and higher levels of the stem cell marker EphB2. In an in-house cohort of 200 colorectal adenoma samples, we also observed a significant correlation between the presence of Paneth cells and Wnt activation. Kaplan–Meier analysis indicated that early adenoma patients carrying Paneth cell-positive tumors display reduced disease-free survival compared with patients with Paneth cell-free lesions. Conclusions: Our results indicate that Paneth cells contribute to the initial steps of cancer progression by providing the stem cell niche to adenoma cells, which could be therapeutically exploited.
First Episode Psychosis and Schizophrenia Are Systemic Neuro-Immune Disorders Triggered by a Biotic Stimulus in Individuals with Reduced Immune Regulation and Neuroprotection
Authors: Maes Michael , Plaimas Kitiporn , Suratanee Apichat , Noto Cristiano , Kanchanatawan Buranee
Published: October 2021

There is evidence that schizophrenia is characterized by activation of the immune-inflammatory response (IRS) and compensatory immune-regulatory systems (CIRS) and lowered neuroprotection. Studies performed on antipsychotic-naïve first episode psychosis (AN-FEP) and schizophrenia (FES) patients are important as they may disclose the pathogenesis of FES. However, the protein–protein interaction (PPI) network of FEP/FES is not established. The aim of the current study was to delineate a) the characteristics of the PPI network of AN-FEP and its transition to FES; and b) the biological functions, pathways, and molecular patterns, which are over-represented in FEP/FES. Toward this end, we used PPI network, enrichment, and annotation analyses. FEP and FEP/FES are strongly associated with a response to a bacterium, alterations in Toll-Like Receptor-4 and nuclear factor-κB signaling, and the Janus kinases/signal transducer and activator of the transcription proteins pathway. Specific molecular complexes of the peripheral immune response are associated with microglial activation, neuroinflammation, and gliogenesis. FEP/FES is accompanied by lowered protection against inflammation, in part attributable to dysfunctional miRNA maturation, deficits in neurotrophin and Wnt/catenin signaling, and adherens junction organization. Multiple interactions between reduced brain derived neurotrophic factor, E-cadherin, and β-catenin and disrupted schizophrenia-1 (DISC1) expression increase the vulnerability to the neurotoxic effects of immune molecules, including cytokines and complement factors. In summary: FEP and FES are systemic neuro-immune disorders that are probably triggered by a bacterial stimulus which induces neuro-immune toxicity cascades that are overexpressed in people with reduced anti-inflammatory and miRNA protections, cell–cell junction organization, and neurotrophin and Wnt/catenin signaling.
Effect of Oxidized LDL on Platelet Shape, Spreading, and Migration Investigated with Deep Learning Platelet Morphometry
Authors: Seifert Jan , von Eysmondt Hendrik , Chatterjee Madhumita , Gawaz Meinrad , Schäffer Tilman E.
Published: October 2021

Platelets are functionally versatile blood cells involved in thrombosis, hemostasis, atherosclerosis, and immune response. Platelet interaction with the immediate microenvironment in blood, vasculature, and tissues alters platelet morphology. The quantification of platelet morphodynamics by geometrical parameters (morphometry) can provide important insights into how platelets sense and respond to stimulatory cues in their vicinity. However, the extraction of platelet shapes from phase contrast microscopy images by conventional image processing is difficult. Here, we used a convolutional neural network (CNN) to develop a deep-learning-based approach for the unbiased extraction of information on platelet morphodynamics by phase contrast microscopy. We then investigated the effect of normal and oxidized low-density lipoproteins (LDL, oxLDL) on platelet morphodynamics, spreading, and haptotactic migration. Exposure of platelets to oxLDL led to a decreased spreading area and rate on fibrinogen, accompanied by increased formation of filopodia and impaired formation of lamellipodia. Haptotactic platelet migration was affected by both LDL and oxLDL in terms of decreased migration velocity and reduced directional persistence. Our results demonstrate the use of deep learning in investigating platelet morphodynamics and reveal differential effects of LDL and oxLDL on platelet morphology and platelet–matrix interaction.
Editorial: Organic Fluorescent Materials as Chemical Sensors
Authors: Bao Yinyin
Published: October 2021

The last two decades have witnessed a significant development of fluorescent chemosensors with high sensitivity and selectivity, fast response and in situ detection [...]
How Smart Is It to Go to Bed with the Phone? The Impact of Short-Wavelength Light and Affective States on Sleep and Circadian Rhythms
Authors: Schmid Sarah R. , Höhn Christopher , Bothe Kathrin , Plamberger Christina P. , Angerer Monika , Pletzer Belinda , Hoedlmoser Kerstin
Published: October 2021

Previously, we presented our preliminary results (N = 14) investigating the effects of short-wavelength light from a smartphone during the evening on sleep and circadian rhythms (Höhn et al., 2021). Here, we now demonstrate our full sample (N = 33 men), where polysomnography and body temperature were recorded during three experimental nights and subjects read for 90 min on a smartphone with or without a filter or from a book. Cortisol, melatonin and affectivity were assessed before and after sleep. These results confirm our earlier findings, indicating reduced slow-wave-sleep and -activity in the first night quarter after reading on the smartphone without a filter. The same was true for the cortisol-awakening-response. Although subjective sleepiness was not affected, the evening melatonin increase was attenuated in both smartphone conditions. Accordingly, the distal-proximal skin temperature gradient increased less after short-wavelength light exposure than after reading a book. Interestingly, we could unravel within this full dataset that higher positive affectivity in the evening predicted better subjective but not objective sleep quality. Our results show disruptive consequences of short-wavelength light for sleep and circadian rhythmicity with a partially attenuating effect of blue-light filters. Furthermore, affective states influence subjective sleep quality and should be considered, whenever investigating sleep and circadian rhythms.
Is Morphometry an Indicator of the Number of Sexy Syllables in the Song of Yellow-Fronted Canary (Serinus mozambicus)?
Authors: Akueson Adoté Hervé Gildas , Alaye Ayédèguè Eustache , Akossou Arcadius Yves Justin
Published: October 2021

Birds use song for several goals as defending territory, alerting a partner to the presence of a predator, or wooing a female for breeding. This study aims to identify the morphometric parameters which determine the variability of the number of syllables in the “sexy” songs of yellow-fronted canary. For this purpose, sixteen morphometric parameters were measured among 170 yellow-fronted canaries captured, in four agroecological zones in North Benin. The results showed that the morphometry of yellow-fronted canary varies according to its habitat and the type of song. The categorization of the sample studied allowed to retain two groups which differ in the importance of body characters and the number of sexy syllables issued when singing. Yellow-fronted canaries from the Sudanese climate zone not only have superior morphometric characters compared to other zones, but also have a song composed of several different sexy syllables. A binary logistic regression model with selection of variables revealed that the index of beak shape, abdomen length, and tail length are the determining characteristics of the number of sexy syllables. These results are needed for future breeding and conservation studies of the species.
Ultrasound-Guided Pleural Effusion Drainage: Effect on Oxygenation, Respiratory Mechanics, and Liberation from Mechanical Ventilation in Surgical Intensive Care Unit Patients
Authors: Fang Hsin-Yueh , Chang Ko-Wei , Chao Yin-Kai
Published: October 2021

The question as to whether an aggressive management of post-operative pleural effusion may improve clinical outcomes after major surgery remains unanswered. The aim of this study was to investigate the effect of ultrasound-guided pleural effusion drainage on oxygenation, respiratory mechanics, and liberation from mechanical ventilation in surgical intensive care unit patients. Oxygenation and respiratory mechanics were measured before and after drainage. Over an 18-month period, a total of 62 patients were analyzed. The mean drainage volume during the first 24 h was 864 ± 493 mL, and there were no procedural complications. Both the mean PaO2/FiO2 ratio and lung compliance improved after drainage. Additionally, 41.9% (n = 26) of patients were ventilator-free within 72 h after drainage. Multivariable logistic regression analysis revealed that non-cardiovascular or thoracic surgery (odds ratio [OR] = 4.968, p = 0.046), a longer time interval from operation to the onset of pleural effusion (OR = 1.165, p = 0.005), and a higher peak airway pressure (OR = 1.303, p = 0.009) were independent adverse predictors for being free from mechanical ventilation within 72 h after drainage. Specifically, patients with a time from surgery to the onset of pleural effusion ≤6 days—but not those with an interval >6 days—showed a significant post-procedural improvement in terms of PaO2/FiO2 ratio, PaCO2, peak airway pressure, and dynamic lung compliance. In summary, ultrasound-guided pleural effusion drainage resulted in significant clinical benefits in mechanically ventilated ICU patients after major surgery—especially in those with early-onset effusion who received thoracic surgery.
Drones, Deep Learning, and Endangered Plants: A Method for Population-Level Census Using Image Analysis
Authors: Rominger Kody R. , Meyer Susan E.
Published: October 2021

A census of endangered plant populations is critical to determining their size, spatial distribution, and geographical extent. Traditional, on-the-ground methods for collecting census data are labor-intensive, time-consuming, and expensive. Use of drone imagery coupled with application of rapidly advancing deep learning technology could greatly reduce the effort and cost of collecting and analyzing population-level data across relatively large areas. We used a customization of the YOLOv5 object detection model to identify and count individual dwarf bear poppy (Arctomecon humilis) plants in drone imagery obtained at 40 m altitude. We compared human-based and model-based detection at 40 m on n = 11 test plots for two areas that differed in image quality. The model out-performed human visual poppy detection for precision and recall, and was 1100× faster at inference/evaluation on the test plots. Model inference precision was 0.83, and recall was 0.74, while human evaluation resulted in precision of 0.67, and recall of 0.71. Both model and human performance were better in the area with higher-quality imagery, suggesting that image quality is a primary factor limiting model performance. Evaluation of drone-based census imagery from the 255 ha Webb Hill population with our customized YOLOv5 model was completed in <3 h and provided a reasonable estimate of population size (7414 poppies) with minimal investment of on-the-ground resources.
Impact of Residual Stress on a Polysilicon Channel in Scaled 3D NAND Flash Memory
Authors: Lee Juyoung , Yoon Dong-Gwan , Sim Jae-Min , Song Yun-Heub
Published: October 2021

The effects of residual stress on a tungsten gate in a polysilicon channel in scaled 3D NAND flash memories were investigated using a technology computer-aided design simulation. The NAND strings, with respect to the distance from the tungsten slit, were also analyzed. The scaling of the spacer thickness and hole diameter induced compressive stress on the polysilicon channel. Moreover, the residual stress of polysilicon channel in the string near the tungsten slit had greater compressive stress than the string farther away. The increase in compressive stress in the polysilicon channel degraded the Bit-Line current (Ion) due to stress-induced electron mobility deterioration. Moreover, a threshold voltage shift (∆Vth) occurred in the negative direction due to conduction band lowering.
Internet of Drones Intrusion Detection Using Deep Learning
Authors: Ramadan Rabie A. , Emara Abdel-Hamid , Al-Sarem Mohammed , Elhamahmy Mohamed
Published: October 2021

Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.
Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers
Authors: Ghanooni Pooria , Habibi Hamed , Yazdani Amirmehdi , Wang Hai , MahmoudZadeh Somaiyeh , Mahmoudi Amin
Published: October 2021

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.
Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
Authors: Zhang Xiaoguo , Zhang Zihan , Wang Qing , Yang Yuan
Published: October 2021

Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on χ2 test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based χ2 test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.
Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
Authors: Zhou Jianlong , Huang Weidong , Chen Fang
Published: October 2021

Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach, to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs, respectively. These lines are generated effectively using a recursive function. The dependence of ML models with a dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations.
Sustainability of Shallow Geothermal Energy for Building Air-Conditioning
Authors: Aquino Andrea , Scrucca Flavio , Bonamente Emanuele
Published: October 2021

Geothermal heat pumps have a widespread diffusion as they are able to deliver relatively higher energy output than other systems for building air-conditioning. The exploitation of low-enthalpy geothermal energy, however, presents crucial sustainability issues. This review investigates the primary forms of the environmental impact of geothermal heat pumps and the strategies for their mitigation. As life-cycle analyses shows that the highest impacts arise from installation and operation stages, most optimization studies focus on system thermodynamics, aiming at maximizing the energy performance via the optimization in the design of the different components interacting with the ground and serviced building. There are environmental studies of great relevance that investigate how the climate and ground properties affect the system sustainability and map the most suitable location for geothermal exploitation. Based on this review, ground-source heat pumps are a promising technology for the decarbonization of the building sector. However, a sustainable design of such systems is more complex than conventional air-conditioning systems, and it needs a holistic and multi-disciplinary approach to include the broad environmental boundaries to fully understand the environmental consequences of their operation.
Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
Authors: Lee Wen-Shing , Lin Wen-Hsin , Cheng Chin-Chi , Lin Chien-Yu
Published: October 2021

Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is composed of two evolutions: particle evolution and team evolution. The partial load ratio (PLR) of each operating chiller and the on-off state of each chiller are the particle evolution parameters and team evolution parameters, respectively. To evaluate the performance of the proposed method, this paper adopts three case studies so the results generated from the proposed algorithm TPSO, the original particle swarm optimization (PSO) and other recently published algorithms can be compared. In these three case studies, the optimal results generated by using TPSO algorithm are the same as those by other compared algorithms. In case 1 under 5717 RT and 5334 RT cooling load, the results generated using the TPSO are lower than those by the original PSO in the amounts of 63.35 and 79.33 kW, respectively. The results indicated that the TPSO algorithm not only enabled the optimal solution in minimizing energy consumption, but also demonstrated the best stability when compared to other algorithms. In conclusion, the presented TPSO algorithm is an efficient and promising new algorithm for solving the OCL problem.
Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
Authors: Cha Ji-Won , Joo Sung-Kwan
Published: October 2021

Increased behind-the-meter (BTM) solar generation causes additional errors in short-term load forecasting. To ensure power grid reliability, it is necessary to consider the influence of the behind-the-meter distributed resources. This study proposes a method to estimate the size of behind-the-meter assets by region to enhance load forecasting accuracy. This paper proposes a semi-supervised approach to BTM capacity estimation, including PV and battery energy storage systems (BESSs), to improve net load forecast using a probabilistic approach. A co-optimization is proposed to simultaneously optimize the hidden BTM capacity estimation and the expected improvement to the net load forecast. Finally, this paper presents a net load forecasting method that incorporates the results of BTM capacity estimation. To describe the efficiency of the proposed method, a study was conducted using actual utility data. The numerical results show that the proposed method improves the load forecasting accuracy by revealing the gross load pattern and reducing the influence of the BTM patterns.
Assessing Black Locust Biomass Accumulation in Restoration Plantations
Authors: Spyroglou Gavriil , Fotelli Mariangela , Nanos Nikos , Radoglou Kalliopi
Published: October 2021

Forests (either natural or planted) play a key role in climate change mitigation due to their huge carbon-storing potential. In the 1980s, the Hellenic Public Power Corporation (HPPC) started the rehabilitation of lignite post-mining areas in Northwest Greece by planting mainly black locust (Robinia pseudoacacia L.). Today, these plantations occupy about 2570 ha, but the accumulation of Above Ground Biomass (AGB) and deadwood has not been assessed to date. Therefore, we aimed at estimating these biomass pools by calibrating an allometric model for AGB, performing an inventory for both pools and predicting the spatial distribution of AGB. 214 sample plots of 100 m2 each were set up through systematic sampling in a grid dimension of 500 × 500 m and tree dbh and height were recorded. AGB was estimated using an exponential allometric model and performing inventory measurements and was on average 57.6 t ha−1. Kriging analysis reliably estimated mean AGB, but produced errors in the prediction of high and low biomass values, related to the high fragmentation and heterogeneity of the studied area. Mean estimated AGB was low compared with European biomass yield tables for black locust. Similarly, standing deadwood was low (6–10%) and decay degrees were mostly 1 and 2, indicating recent deadwood formation. The overall low biomass accumulation in the studied black locust restoration plantations may be partially attributed to their young age (5–30 years old), but is comparable to that reported in black locust restoration plantation in extremely degraded sites. Thus, black locust successfully adapted to the studied depositions of former mines and its accumulated biomass has the potential to improve the carbon footprint of the region. However, the invasiveness of the species should be considered for future management planning of these restoration plantations.
Security Challenges for Light Emitting Systems
Authors: Hamada Louiza , Lorenz Pascal , Gilg Marc
Published: October 2021

Although visible light communication (VLC) channels are more secure than radio frequency channels, the broadcast nature of VLC links renders them open to eavesdropping. As a result, VLC networks must provide security in order to safeguard the user’s data from eavesdroppers. In the literature, keyless security techniques have been developed to offer security for VLC. Even though these techniques provide strong security against eavesdroppers, they are difficult to deploy. Key generation algorithms are critical for securing wireless connections. Nonetheless, in many situations, the typical key generation methods may be quite complicated and costly. They consume scarce resources, such as bandwidth. In this paper, we propose a novel key extraction procedure that uses error-correcting coding and one time pad (OTP) to improve the security of VLC networks and the validity of data. This system will not have any interference problems with other devices. We also explain error correction while sending a message across a network, and suggest a change to the Berlekamp–Massey (BM) algorithm for error identification and assessment. Because each OOK signal frame is encrypted by a different key, the proposed protocol provides high physical layer security; it allows for key extraction based on the messages sent, so an intruder can never break the encryption system, even if the latter knows the protocol with which we encrypted the message; our protocol also enables for error transmission rate correction and bit mismatch rates with on-the-fly key fetch. The results presented in this paper were performed using MATLAB.
Effect of Osmotic Pretreatment Combined with Vacuum Impregnation or High Pressure on the Water Diffusion Coefficients of Convection Drying: Case Study on Apples
Authors: Janowicz Monika , Ciurzyńska Agnieszka , Lenart Andrzej
Published: October 2021

The paper presents water diffusion coefficients as providing a significant contribution to the creation of a comprehensive database and knowledge of weight variation during the drying process of raw plant materials that is used for modelling the technological process and designing innovative products. Dehydration is one of the most widely used methods for improving the stability and durability of fruit and vegetables because it reduces water activity and microbial activity, and minimises the physical and chemical changes during storage. The considerable impact of pressure on heat exchange and weight during the convection drying process of osmotically pretreated apples is demonstrated. The course of the drying curves and the drying rate is determined by the use of pressures of 0.02 and 500 MPa. Varied pressure applied during osmotic impregnation significantly influences the value of the diffusion coefficient: the average determined for the entire course of the drying curve and the average determined in the intervals of the reduced water content. The lowest values of the average water diffusion coefficient are obtained for apples preboiled under overpressure conditions and, at the same time, the determined diffusion coefficients in the water content are characterised on the drying curve by a clearly decreasing course until the reduced water content reaches approximately 0.2.
High Pressure Processing vs. Thermal Pasteurization of Whole Concord Grape Puree: Effect on Nutritional Value, Quality Parameters and Refrigerated Shelf Life
Authors: Li Yuanyuan , Padilla-Zakour Olga I.
Published: October 2021

High-pressure processing (HPP) is utilized for food preservation as it can ensure product safety at low temperatures, meeting consumers’ demand for fresh-like and minimally processed products. The purpose of this study was to determine the effects of HPP (600 MPa, 3 min, 5 °C) and pasteurization by heat treatment (HT, 63 °C, 3 min) on the production of a novel whole Concord grape puree product (with skin and seeds, no waste), and the shelf-life of the puree under refrigerated storage (4 °C). Microbial load, physicochemical properties, phenolic content and antioxidant activity, composition and sensorial attributes of puree samples were evaluated. HPP- and HT-treated purees were microbiologically stable for at least 4 months under refrigeration, with less microbial growth and longer shelf life for HPP samples. HPP and HT samples had similar levels of phenolic contents and antioxidant activities throughout the 4-month refrigerated storage period, even though HPP retained >75% PPO and POD enzyme activities while those of HT were less than 25%. Inclusion of seeds in the puree product significantly increased the fiber, protein, total fatty acid, and linoleic acid contents. Sensory results showed that HPP-treated puree retained more fresh-like grape attributes, had better consistency, and showed significantly higher ratings in consumer overall liking, product ranking, and purchase intent than the HT puree (p < 0.05).
Tartary Buckwheat: A New Plant-Based Ingredient to Enrich Corn-Based Gluten-Free Formulations
Authors: Appiani Marta , Rabitti Noemi Sofia , Proserpio Cristina , Pagliarini Ella , Laureati Monica
Published: October 2021

Tartary buckwheat is a pseudocereal receiving increasing attention as a minor crop interesting for agrobiodiversity conservation and sustainability. It is rich in bioactive substances which, however, may lead to sensory properties undesirable to the consumer, such as bitterness and astringency. The aim was to evaluate consumers’ perception and overall liking of food products enriched with tartary or common buckwheat. A total of 120 consumers (56% women) aged 20–60 years (mean age ± SD: 38.8 ± 13.0 years) evaluated six samples of a corn-based gluten-free formulation enriched by increasing concentrations (20%, 30%, 40%) of either common (CB) or tartary buckwheat (TB) flour for overall liking and appropriateness of sensory properties. Results showed significant differences (p < 0.0001) in liking among samples. Considering all subjects, liking decreased with the increase of tartary buckwheat additions, although TB20 and TB30 samples were well accepted and comparable to all CB samples. TB40 was the least liked product. Two clusters of consumers showing opposite behaviours according to liking were found. One cluster (30%) showed an increased liking with the increasing amount of tartary buckwheat. These results show that by keeping the concentration of tartary buckwheat up to 30%, it is possible to develop new products accepted by consumers.
Dissipation Dynamics, Terminal Residues and Dietary Risk Assessment of Two Isomers of Dimethacarb in Rice by HPLC-MS/MS
Authors: Tang Shouying , Meng Xiurou , Wang Yongkang , Shi Xueqin , Feng Tianyou , Hu Deyu , Zhang Yuping
Published: October 2021

Dimethacarb is a carbamate insecticide developed in China that contains 3,5-dimethylphenyl methylcarbamate (XMC) and 3,4-dimethylphenyl methylcarbamate (MPMC) isomers. Dimethacarb has been registered for use in rice in China, but no residue or degradation of dimethacarb in rice has been reported and the maximum residue limits (MRLs) for rice have not been established. A versatile high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed with modified QuEChERS sample preparation to determine two isomers of dimethacarb in rice. The average recovery of XMC and MPMC in brown rice, rice husk, and rice straw ranged from 71.69 to 100.60%, with spike levels of 0.01 to 1 mg/kg and relative standard deviations (RSDs) of 0.21 to 8.41%. Field experiments showed that the half-lives of XMC and MPMC in rice straw were 4.08 to 4.23 days and 3.48 to 3.69 days, respectively. Final residues of XMC and MPMC in rice husk after 21 days of spraying at six sites ranged from 0.23–2.65 mg/kg and 0.06–1.10 mg/kg, and <0.01–0.16 mg/kg and <0.01–0.04 mg/kg in brown rice. The ratio of XMC to MPMC content in the rice husk differed from the original 50% dimethacarb EC, indicating the difference in the degradation rate of XMC and MPMC. The estimated risk quotient (RQ) for both XMC and MPMC was less than 30%. These data for residues from six representative locations could provide a reference for establishing the MRL of dimethacarb in rice.
Strategies for Nitrite Replacement in Fermented Sausages and Effect of High Pressure Processing against Salmonella spp. and Listeria innocua
Authors: Lopez Constanza Maria , Dallolio Giuliano , Bonilauri Paolo , Rebecchi Annalisa
Published: October 2021

The development of nitrite-free meat products is a current industrial concern. Many efforts have been attempted to replace the nitrite effect in cured meats colour formation and pathogens control. Our previous work evidenced that lactic acid and a cold ripening were the best hurdle technologies for nitrite-free fermented sausages from metabolomics. In the first part of this work, we investigated the effect of lactic acid compared with both two alternative additives (glucono-D-lactone and a mix of sodium di-acetate/sodium lactate) and with low-nitrite sausages, all of them following either cold or traditional ripening. For this purpose, microbiological analysis, pH, water activity (aw), and a sensory study were performed. All nitrite-free sausages (cold or traditional ripened) showed quality and safety traits similar to low-nitrite traditionally ripened ones used as control. In addition, sensory study revealed that sausages with lactic acid were the most preferred cold ripened samples, supporting that this is an optimal strategy for the production of nitrite-free sausages. We selected this product for further studies. Indeed, in the second part, we evaluated the impact of ripening, and other hurdle technologies as High Pressure Processing (HPP) and under-vacuum storage against Listeria innocua and Salmonella spp. by a challenge test. Maximal declines were obtained for ripening along with HPP (i.e., 4.74 and 3.83 log CFU/g for L. innocua and Salmonella spp., respectively), suggesting that HPP might guarantee nitrite-free sausages safety. Although the quality of raw materials remains essential, these hurdle strategies largely contributed to nitrite-free sausages safety, offering a promising tool for the meat industry.
Antifungal and Aflatoxin-Reducing Activity of β-Glucan Isolated from Pichia norvegensis Grown on Tofu Wastewater
Authors: Utama Gemilang Lara , Suraloka Mahardhika Puspa Arum , Rialita Tita , Balia Roostita Lobo
Published: October 2021

Yeast can be isolated from tofu wastewater and the cell wall in the form of β-glucan can act as a natural decontaminant agent. This study aimed to isolate and characterize native yeast from tofu wastewater, which can be extracted to obtain β-glucan and then identify the yeast and its β-glucan activity regarding antifungal ability against Aspergillus flavus and aflatoxin-reducing activity towards aflatoxin B1 (AFB1) and B2 (AFB2). Tofu wastewater native yeast was molecularly identified, and the growth observed based on optical density for 96 h and the pH also measured. β-glucan was extracted from native yeast cell walls with the acid-base method and then the inhibition activity towards A. flavus was tested using the well diffusion method and microscopic observation. AFB1 and AFB2 reduction were identified using HPLC LC-MS/MS. The results showed that the native yeast isolated was Pichia norvegensis with a β-glucan yield of 6.59%. Pichia norvegensis and its β-glucan showed an inhibition zone against Aspergillus flavus of 11.33 ± 4.93 and 7.33 ± 3.51 mm, respectively. Total aflatoxin-reducing activity was also shown by Pichia norvegensis of 26.85 ± 2.87%, and β-glucan of 27.30 ± 1.49%, while AFB1- and AFB2-reducing activity by Pichia norvegensis was 36.97 ± 3.07% and 27.13 ± 1.69%, and β-glucan was 27.13 ± 1.69% and 32.59 ± 4.20%, respectively.
The Expression of RAAS Key Receptors, Agtr2 and Bdkrb1, Is Downregulated at an Early Stage in a Rat Model of Wolfram Syndrome
Authors: Punapart Marite , Seppa Kadri , Jagomäe Toomas , Liiv Mailis , Reimets Riin , Kirillov Silvia , Kaasik Allen , Moons Lieve , De Groef Lies , Terasmaa Anton , Vasar Eero , Plaas Mario
Published: October 2021

Wolfram syndrome (WS) 1 is a rare monogenic neurodegenerative disorder caused by mutations in the gene encoding WFS1. Knowledge of the pathophysiology of WS is incomplete and to date, there is no treatment available. Here, we describe early deviations in the renin-angiotensin-aldosterone system (RAAS) and bradykinin pathway (kallikrein kinin system, KKS) observed in a rat model of WS (Wfs1 KO) and the modulative effect of glucagon-like peptide-1 receptor agonist liraglutide (LIR) and anti-epileptic drug valproate (VPA), which have been proven effective in delaying WS progression in WS animal models. We found that the expression of key receptors of the RAAS and KKS, Agtr2 and Bdkrb1, were drastically downregulated both in vitro and in vivo at an early stage in a rat model of WS. Moreover, in Wfs1, KO serum aldosterone levels were substantially decreased and bradykinin levels increased compared to WT animals. Neither treatment nor their combination affected the gene expression levels seen in the Wfs1 KO animals. However, all the treatments elevated serum aldosterone and decreased bradykinin in the Wfs1 KO rats, as well as increasing angiotensin II levels independent of genotype. Altogether, our results indicate that Wfs1 deficiency might disturb the normal functioning of RAAS and KKS and that LIR and VPA have the ability to modulate these systems.
Gene Expression Profiling of Skeletal Muscles
Authors: Alto Sarah I. , Chang Chih-Ning , Brown Kevin , Kioussi Chrissa , Filtz Theresa M.
Published: October 2021

Next-generation sequencing provides an opportunity for an in-depth biocomputational analysis to identify gene expression patterns between soleus and tibialis anterior, two well-characterized skeletal muscles, and analyze their gene expression profiling. RNA read counts were analyzed for differential gene expression using the R package edgeR. Differentially expressed genes were filtered using a false discovery rate of less than 0.05 c, a fold-change value of more than twenty, and an association with overrepresented pathways based on the Reactome pathway over-representation analysis tool. Most of the differentially expressed genes associated with soleus are coded for components of lipid metabolism and unique contractile elements. Differentially expressed genes associated with tibialis anterior encoded mostly for glucose and glycogen metabolic pathway regulatory enzymes and calcium-sensitive contractile components. These gene expression distinctions partly explain the genetic basis for skeletal muscle specialization, and they may help to explain skeletal muscle susceptibility to disease and drugs and further refine tissue engineering approaches.
Genome-Wide Landscape of North-Eastern European Populations: A View from Lithuania
Authors: Urnikyte Alina , Molyte Alma , Kučinskas Vaidutis
Published: October 2021

There are still several unanswered questions regarding about ancient events in the Lithuanian population. The Lithuanians, as the subject of this study, are of great interest as they represent a partially isolated population maintaining an ancient genetic composition and show genetic uniqueness in European comparisons. To elucidate the genetic relationships between the Lithuania and North-Eastern European and West Siberian populations, we analyzed the population structure, effective population size, and recent positive selection from genome-wide single nucleotide polymorphism (SNP) data. We identified the close genetic proximity of Lithuanians to neighboring populations (Latvians, Estonians, Belarusians) and in part with West and South Slavs (Poles, Slovaks, and Slovenians), however, with particular genetic distinctiveness. The estimated long-term Ne values ranged from ~5900 in the Estonian population to ~2400 in the South Russian population. The divergence times between the Lithuanian and study populations ranged from 240 to 12,871 YBP. We also found evidence of selection in 24 regions, 21 of which have not been discovered in previous analyses of selection. Undoubtedly, the genetic diversity analysis of geographically specific regions may provide new insights into microevolutionary processes affecting local human populations.
Precipitation Data Retrieval and Quality Assurance from Different Data Sources for the Namoi Catchment in Australia
Authors: Strehz Alexander , Einfalt Thomas
Published: October 2021

Within the Horizon 2020 Project WaterSENSE a modular approach was developed to provide different stakeholders with the required precipitation information. An operational high-quality rainfall grid was set up for the Namoi catchment in Australia based on rain gauge adjusted radar data. Data availability and processing considerations make it necessary to explore alternative precipitation approaches. The gauge adjusted radar data will serve as a benchmark for the alternative precipitation data. The two well established satellite-based precipitation datasets IMERG and GSMaP will be analyzed with the temporal and spatial requirements of the applications envisioned in WaterSENSE in mind. While first results appear promising, these datasets will need further refinements to meet the criteria of WaterSENSE, especially with respect to the spatial resolution. Inferring information from soil moisture-derived from EO observations to increase the spatial detail of the existing satellite-based datasets is a promising approach that will be investigated along with other alternatives.