bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Drying Methods and Blanching on Nutrient Utilization in Black Soldier Fly Larva Meals Based on In Vitro Assays for Pigs
Authors: Son Jeonghyeon , Park Seol Hwa , Jung Hyun Jung , You Sun Jong , Kim Beob Gyun
Published: February 2023

The objective was to determine the effects of drying and blanching methods on the nutrient utilization of black soldier fly larva (BSFL; Hermetia illucens) meal by pigs using in vitro assays. Two-step and three-step in vitro assays were employed to simulate the gastrointestinal tract of pigs. Four BSFL meals were prepared using the following pretreatment methods: (1) microwave drying at 80 °C for 32 min, (2) hot-air drying at 60 °C for 17 h, (3) blanching for 5 min in boiling water and hot-air drying at 60 °C for 17 h, and (4) 2% citric acid solution blanching for 5 min in boiling solution and hot-air drying at 60 °C for 17 h. After the drying process, each BSFL was defatted and ground to obtain BSFL meals. The nitrogen (N) concentration in the test ingredients ranged from 8.5 to 9.4%, and the ether extract ranged from 6.9 to 11.5% on an as-is basis. The amino acid (AA) concentration in the BSFL meals ranged from 2.80 to 3.24% for Lys and 0.71 to 0.89% for Met on an as-is basis. Hot-air-dried BSFL meal had a greater in vitro ileal disappearance (IVID) of N compared with microwave-dried BSFL meal (p < 0.05). However, blanched BSFL meals in water or 2% citric acid solution before hot-air drying had a lower (p < 0.05) IVID of N compared with microwave-dried or hot-air-dried BSFL meal. Blanched BSFL meals in water or 2% citric acid solution before hot-air drying showed a lower (p < 0.05) in vitro total tract disappearance of dry matter and organic matter compared with microwave-dried or hot-air-dried BSFL meal. Microwave-dried BSFL meal had a lower (p < 0.05) IVID of indispensable AA, except for His, Lys, Met, and Phe, compared with hot-air-dried BSFL meals. However, blanched BSFL meals in water or 2% citric acid solution before hot-air drying showed a lower (p < 0.05) IVID of indispensable AA compared with microwave-dried or hot-air-dried BSFL meal. In conclusion, hot-air-dried BSFL meal presented greater nutrient utilization compared with microwave-dried BSFL meal for pigs. However, blanching in water or citric acid solution negatively affected the nutrient digestibility of BSFL meal based on in vitro assays.
Electrocardiographic and Echocardiographic Parameters in Pega Breed Donkeys: A Descriptive Study
Authors: Cruz-Aleixo Amanda Sarita , de Oliveira Karina Cristina , de Oliveira Ferreira Lucas Vinícius , Cedeo Quevedo Dario Alejandro , Cruz Raíssa Karolliny Salgueiro , Tsunemi Miriam Harumi , Chiacchio Simone Biagio , Lourenço Maria Lucia Gomes
Published: February 2023

Clinical, electrocardiographic and echocardiographic parameters in Pega donkeys are scarce in the literature; hence, this study was performed to describe the echocardiographic and electrocardiographic measurements in Pega breed donkeys. The objectives of this study were to describe and illustrate the clinical, electrocardiographic, and echocardiographic parameters in Pega donkeys used for reproduction. Fifty Pega breed donkeys were evaluated, with an average age of 3.4 years and with 20 males and 30 females. In each animal, the electrocardiographic examination at rest was performed using the TEB® computerized system, and the echocardiographic examination was performed using an ultrasound device with a Doppler function multifrequency sectorial transducer in 2D mode (Sonosite® M turbo). Standardizing the electrocardiographic and echocardiographic parameters for the Pega breed donkey can contribute to future assessments regarding possible changes that excessive effort can promote in these parameters to a management engrossed on animal welfare.
Serological Cross-Reactivity in Zoonotic Flaviviral Infections of Medical Importance
Authors: Gomes da Silva Priscilla , Seixas dos Reis José Augusto , Nogueira Rodrigues Marcio , da Silva Ardaya Quézia , Mesquita João Rodrigo
Published: February 2023

Flaviviruses are enveloped RNA viruses from the family Flaviviridae that comprise many important human pathogenic arboviruses such as Yellow Fever, Dengue, and Zika viruses. Because they belong to the same genus, these viruses show sequence and structural homology among them, which results in serological cross-reactivity. Upon infection, the immune system produces both species-specific and cross-reactive antibodies, and depending on the virus, in a successive flavivirus infection, cross-reactive antibodies either enhance protection or exacerbate the disease—the latter usually due to antibody-dependent enhancement. These antigenic relationships between different flaviviruses that lead to serological cross-reactivity make them difficult to be identified through serological methods, especially when it comes to successive flavivirus infections. We present here an overview of the main structural, epidemiological, and immunological aspects of flaviviruses, highlighting the role of neutralizing antibodies in fighting viral infections and in the “original antigenic sin” problem. Finally, we draw attention to the importance of developing a rapid serological diagnostic test for flaviviruses with high sensitivity and specificity, especially when considering that cross-reactive immunity can influence the outcome of these infections.
Pilot Study Results on Antibodies to the S- and N-Proteins of SARS-CoV-2 in Paired Sera from COVID-19 Patients with Varying Severity
Authors: Desheva Yulia , Lerner Anna , Shvedova Tamara , Kopteva Olga , Kudar Polina , Koroleva Irina , Leontieva Galina , Suvorov Alexander
Published: February 2023

In this retrospective cohort study, we investigated the formation of individual classes of antibodies to SARS-CoV-2 in archived serial sera from hospitalized patients with the medium–severe (n = 17) and severe COVID-19 (n = 11). The serum/plasma samples were studied for the presence of IgG, IgM and IgA antibodies to the recombinant S- and N-proteins of SARS-CoV-2. By the 7th day of hospitalization, an IgG increase was observed in patients both with a positive PCR test and without PCR confirmation of SARS-CoV-2 infection. Significant increases in the anti-spike IgG levels were noted only in moderate COVID-19. The four-fold increase of IgM to N-protein was obtained more often in the groups with mild and moderate infections. The IgA levels decreased during the infection to both the S- and N-proteins, and the most pronounced decrease was in the severe COVID-19 patients. The serum IgG to S-protein one week after hospitalization demonstrated a high-power relationship (rs = 0.75) with the level of RBD antibodies. There was a medium strength relationship between the levels of CRP and IgG (rs = 0.43). Thus, in patients with acute COVID-19, an increase in antibodies can develop as early as 1 week of hospital stay. The SARS-CoV-2 antibody conversions may confirm SARS-CoV-2 infection in PCR-negative patients.
Addressing Antimicrobial Stewardship in Primary Care—Developing Patient Information Sheets Using Co-Design Methodology
Authors: Biezen Ruby , Ciavarella Stephen , Manski-Nankervis Jo-Anne , Monaghan Tim , Buising Kirsty
Published: February 2023

Antibiotic resistance is a threat to global health, and inappropriate antibiotic use can be associated with adverse effects. Developing tools to encourage better communication between patients and general practitioners may reduce inappropriate use of antibiotics. The aim of the study was to develop shared decision support tools on common infections using a co-design methodology to address antimicrobial stewardship (AMS) in primary care. Three co-design/interview sessions were conducted with primary care providers and consumers between October 2019–April 2020 in Melbourne, Australia. Participants critiqued existing AMS tools, identified key elements required and optimised resulting prototypes. Primary care providers and consumers prioritised information to include in the AMS tools, such as when to see a doctor, management options, disease symptoms and cause of infection differently. However, both agreed content should be communicated in a plain, concise and logical manner, using inclusive and simple language accompanied by illustrations. Information sheets should be single-sided and A4-sized, appropriate for use before, during or after consultations. Co-design provided a collaborative forum to systematically design and develop products that meet the needs of both primary care providers and consumers. This resulted in the development of seven patient information sheets on common infections that encourage discussion of these infections, conservative management options and appropriate antibiotic use in primary care.
Synergistic Antioxidant Activity of Four—Component Mixture of Essential Oils: Basil, Cedarwood, Citronella and Thyme for the Use as Medicinal and Food Ingredient
Authors: Baj Tomasz , Kowalska Grażyna , Kowalski Radosław , Szymańska Jolanta , Kai Guoyin , Coutinho Henrique Douglas Melo , Sieniawska Elwira
Published: February 2023

Mixture design is a statistical tool used to obtain the maximum desired effect using the minimum number of experiments. The aim of the presented work was the optimization of the composition of a mixture of essential oils from basil, citronella, cedarwood and thyme using simplex-lattice mixture design method. The optimized parameter was an antioxidant activity measured in DPPH assay and expressed as effective concentration (EC50). The test results showed an interesting synergy between the components of essential oils. The prepared binary and quaternary mixtures were characterized by higher activity than simple average activity. The designed mixture with approximated highest antioxidant activity was composed of: 54.4% citronella essential oil, 33.0% thyme essential oil, 9.2% cedarwood essential oil and 3.4% basil essential oil and its approximated activity was in agreement with experimental values. This work confirmed that it is possible to approximate the best antioxidant composition of four essential oils used as a potential medicinal and food ingredient.
Progranulin Deficiency Induces Mitochondrial Dysfunction in Frontotemporal Lobar Degeneration with TDP-43 Inclusions
Authors: Rodríguez-Periñán Guiomar , de la Encarnación Ana , Moreno Fermín , López de Munain Adolfo , Martínez Ana , Martín-Requero Ángeles , Alquézar Carolina , Bartolomé Fernando
Published: February 2023

Loss-of-function (LOF) mutations in GRN gene, which encodes progranulin (PGRN), cause frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP). FTLD-TDP is one of the most common forms of early onset dementia, but its pathogenesis is not fully understood. Mitochondrial dysfunction has been associated with several neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Here, we have investigated whether mitochondrial alterations could also contribute to the pathogenesis of PGRN deficiency-associated FTLD-TDP. Our results showed that PGRN deficiency induced mitochondrial depolarization, increased ROS production and lowered ATP levels in GRN KD SH-SY5Y neuroblastoma cells. Interestingly, lymphoblasts from FTLD-TDP patients carrying a LOF mutation in the GRN gene (c.709-1G > A) also demonstrated mitochondrial depolarization and lower ATP levels. Such mitochondrial damage increased mitochondrial fission to remove dysfunctional mitochondria by mitophagy. Interestingly, PGRN-deficient cells showed elevated mitochondrial mass together with autophagy dysfunction, implying that PGRN deficiency induced the accumulation of damaged mitochondria by blocking its degradation in the lysosomes. Importantly, the treatment with two brain-penetrant CK-1δ inhibitors (IGS-2.7 and IGS-3.27), known for preventing the phosphorylation and cytosolic accumulation of TDP-43, rescued mitochondrial function in PGRN-deficient cells. Taken together, these results suggest that mitochondrial function is impaired in FTLD-TDP associated with LOF GRN mutations and that the TDP-43 pathology linked to PGRN deficiency might be a key mechanism contributing to such mitochondrial dysfunction. Furthermore, our results point to the use of drugs targeting TDP-43 pathology as a promising therapeutic strategy for restoring mitochondrial function in FTLD-TDP and other TDP-43-related diseases.
Antioxidant Carboxymethyl Chitosan Carbon Dots with Calcium Doping Achieve Ultra-Low Calcium Concentration for Iron-Induced Osteoporosis Treatment by Effectively Enhancing Calcium Bioavailability in Zebrafish
Authors: Yu Lidong , Li Xueting , He Mingyue , Wang Qingchen , Chen Ce , Li Fangshun , Li Bingsheng , Li Li
Published: February 2023

Iron overloads osteoporosis mainly occurs to postmenopausal women and people requiring repeated blood transfusions. Iron overload increases the activity of osteoclasts and decreases the activity of osteoblasts, leading to the occurrence of osteoporosis. Conventional treatment options include calcium supplements and iron chelators. However, simple calcium supplementation is not effective, and it does not have a good therapeutic effect. Oxidative stress is one of the triggers for osteoporosis. Therefore, the study focuses on the antioxidant aspect of osteoporosis treatment. The present work revealed that antioxidant carboxymethyl chitosan-based carbon dots (AOCDs) can effectively treat iron overload osteoporosis. More interestingly, the functional modification of AOCDs by doping calcium gluconate (AOCDs:Ca) is superior to the use of any single component. AOCDs:Ca have the dual function of antioxidant and calcium supplement. AOCDs:Ca effectively improve the bioavailability of calcium and achieve ultra-low concentration calcium supplement for the treatment of iron-induced osteoporosis in zebrafish.
Influence of Pile Foundation Stiffness on Column Design in One-Story Reinforced Concrete Frames
Authors: Dyka Ireneusz , Klempka Krzysztof
Published: February 2023

When designing reinforced slender columns for the lowest story of buildings, all factors that may affect the load-bearing capacity should be considered. One is the increase in the bending moment value caused by the rotation of the supporting foundation. The geotechnical parameters of subsoil stiffness and the method of calculating foundation displacements are the main influences. This problem applies to both shallow foundations and pile foundations. The article presents this issue in relation to pile foundations in multilayered subsoils and also the method and examples of second-order calculations that take into account the influence of pile support and columns’ nominal stiffness. The results showed that taking into account the stiffness of the pile foundation has an impact on the increase in bending moments in the columns, and this cannot be ignored in the design. The presented method allows for a precise, safe and optimal design of concrete columns supported by foundations on multilayered subsoils.
Fermentation of Chenopodium formosanum Leaf Extract with Aspergillus oryzae Significantly Enhanced Its Physiological Activities
Authors: Lin Yi-Min , Chung Ying-Chien , Chen Pei-Yu , Chang Yu-Chi , Chen Wen-Liang
Published: February 2023

Chenopodium formosanum Koidz (CF) is an indigenous cereal plant of Taiwan. Its high content of secondary metabolites and nutrients has attracted attention for its use in skin care products and functional foods. However, most studies have focused on the extract of CF seeds, which are relatively expensive, and none have investigated the effects of combining extraction and fermentation. In this study, we evaluated the utility of using extracts of different parts of CF, i.e., the leaves, stems, and unhulled and hulled seeds. We first made aqueous, ethanolic, methanolic, and ethyl acetate extracts of the four parts. After assessing their biological activities, we selected only unfermented and fermented CF leaf methanolic extracts for subsequent analysis. None of the concentrations of fermented CF leaf extract (≤400 mg/L) were cytotoxic, and all exhibited antioxidative, anti-inflammatory, antimicrobial, skin-whitening, moisturizing, and antiaging activities. The concentrations of protocatechuic acid, epicatechin, gallic acid, and quercetin increased the most after fermentation. Therefore, they were subjected to a molecular docking analysis, which revealed that quercetin and epicatechin may contribute the most to skin-whitening and antiaging properties, respectively. In conclusion, fermented CF leaf methanolic extracts can be useful as a functional ingredient in health foods, botanical drugs, and cosmetic products.
DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
Authors: Seo Youngmin , Kook Joongjin
Published: February 2023

In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net for learning the visual content of images and image acquisition artifacts, and a JPEG artifact learning module for learning compression artifacts in the discrete cosine transform (DCT) domain. The backbone model of a network based on pre-training, such as CAT-Net, a representative network for image forgery detection, has a relatively large number of parameters, resulting in overfitting in a small dataset, which hinders generalization performance. Therefore, in this paper, the learning module is designed to learn the characteristics according to the DCT domain in real time without pre-training. In the experiments, the proposed network architecture and training method of DRRU-Net show that the network parameters are smaller than CAT-Net, the forgery detection performance is better than that of RRU-Net, and the generalization performance for various datasets can be improved.
Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
Authors: Mihálik Ján , Gago Filip , Vlček Jozef , Drusa Marián
Published: February 2023

Analysis of pile bearing capacity is an important task in the investigation of soil-structure interaction. The paper is dedicated to the prediction methods for the pile bearing capacity calculation based on the cone penetration test (CPTu) results, namely UniCone method, Laboratoire Central des Ponts et Chaussées method (LCPC), and the method involved in the Eurocode 7—2. A set of CFA piles was tested to obtain reference bearing capacity. The ability of the prediction methods to determine the bearing capacity of the pile was investigated. In each evaluation criteria using statistical tools, the methods were ranked based on their performance. The results of the study indicate that the UniCone method is most applicable for the given conditions. The EC 7—2 method showed the largest variability of results, and we do not recommend its application without a deeper analysis. The applicability of any presented method cannot be considered final or universal. It is advisable to use more modern and updated methods which have been developed from a larger database of pile tests. The development of these methods should continue by expanding the database of tested piles together with the application of more advanced rock environment testing procedures.
Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images
Authors: Kim Donggeun , Kim Taejin , Jeon Jihun , Son Younghwan
Published: February 2023

For appropriate managing fields and crops, it is essential to understand soil properties. There are drawbacks to the conventional methods currently used for collecting a large amount of data from agricultural lands. Convolutional neural network is a deep learning algorithm that specializes in image classification, and developing soil property prediction techniques using this algorithm will be extremely beneficial to soil management. We present the convolution neural network models for estimating water content and dry density using soil surface images. Soil surface images were taken with a conventional digital camera. The range of water content and dry density were determined considering general upland soil conditions. Each image was divided into segmented images and used for model training and validation. The developed model confirmed that the model can learn soil features through appropriate image argumentation of few of original soil surface images. Additionally, it was possible to predict the soil water content in a situation where various soil dry density conditions were considered.
Shaping 1,2,4-Triazolium Fluorinated Ionic Liquid Crystals
Authors: Rizzo Carla , Fiduccia Ignazio , Buscemi Silvestre , Palumbo Piccionello Antonio , Pace Andrea , Pibiri Ivana
Published: February 2023

The synthesis and thermotropic behaviour of some di-alkyloxy-phenyl-1,2,4-triazolium trifluoromethane-sulfonate salts bearing a seven-carbon atom perfluoroalkyl chain on the cation is herein described. The fluorinated salts presenting a 1,2,4-triazole as a core and differing in the length of two alkyloxy chains on the phenyl ring demonstrated a typical liquid crystalline behaviour. The mesomorphic properties of this set of salts were studied by differential scanning calorimetry and polarized optical microscopy. The thermotropic properties are discussed on the grounds of the tuneable structures of the salts. The results showed the existence of a monotropic, columnar, liquid crystalline phase for the salts tested. An increase in the temperature mesophase range and the presence of two enantiotropic mesophases for the sixteen-atom alkyloxy chain salt can be observed by increasing the length of the alkyloxy chain on the phenyl ring.
Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
Authors: Nguyen Nghia-Hieu , Le Bao-Chi , Bui Thanh-Trung
Published: February 2023

In the step-by-step roadmap for limiting and eliminating power sources that use fossil fuel, especially coal-fired power, as well as for setting the Vietnamese government targets for developing renewable energy for replacement, solar farms and rooftop solar power, followed by floating photovoltaic (PV) power, are considered the best candidates for meeting the goals of land space, PV system operation efficiency, and meeting environmental goals. In addition to the rapid development of PV cell technology are the government’s incentives and financial support for PV plants on the water surface area, which is still very large. Floating PV plants will strongly attract investors in the near future. This study could help stakeholders in the market understand the economic–technical aspects from analyzing economic–financial indicators of floating PV plants with a capacity of 47.5 MW connected to the national utility grid 110 kV at Da Mi hydropower reservoir in Binh Thuan province, Vietnam, in May 2019. This is the first floating PV plant on the reservoir in Vietnam, and is the property of a third party.
Investigation of Chaboche and Bouc–Wen Parameters of Quenched and Tempered Steel and Comparison of Model Predictive Capabilities
Authors: Santus Ciro , Romanelli Lorenzo , Grossi Tommaso , Bertini Leonardo , Neri Paolo , Le Bone Luca , Chiesi Francesco , Tognarelli Leonardo
Published: February 2023

The aim of this paper is to model the elastic–plastic uniaxial behaviour of a quenched and tempered steel. The common Chaboche isotropic kinematic hardening model (CIKH) is introduced, and a physics-based procedure is proposed to determine its parameters. This procedure is based on strain- and stress-controlled tests and is focused on the stabilized cycles. The imposed cycle properties are the hysteresis area, the stress range, the slope at the inversion points, obtained from the stabilized cycles of strain-controlled tests, and the ratcheting rate extracted from a stress-controlled test. The novelty of the algorithm is to determine the hardening parameters from the global properties of the cycle rather than imposing a pointwise fitting, which is also implemented to calculate the parameters for a comparison. The Bouc–Wen model showed great flexibility in describing nonlinear behaviours, corresponding to different physical phenomena, through an appropriate tuning of its parameter values. In this paper, another optimization approach is developed to estimate the Bouc–Wen coefficients and accurately describe the same experimental cycles. The performances of the Bouc–Wen model are compared with the predictions of the Chaboche model, and a discussion comparing the techniques used to reproduce cyclic plastic behaviour is provided.
Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
Authors: Ding Hao , He Xuhui , Jing Haiquan , Wu Xiaoping , Weng Xiaojun
Published: February 2023

Cable-supported photovoltaic systems (CSPSs) are a new technology for supporting structures that have broad application prospects owing to their cost-effectiveness, light weight, large span, high headroom, few pile foundations, short construction period, and symbiosis with fisheries and farms. Recently, a new CSPS with a much smaller settlement and stronger wind resistance was proposed. The new CSPS, with a 10% lower cost compared with traditional fix-tilted PV support, is a better alternative to traditional photovoltaic (PV) support systems. In this study, the failure models and bearing capacity of the primary structures of the new CSPS were investigated in detail using the FEM method, and a design method for the new structure was proposed based on the limit state design method. The results showed that the structure had a strong load-bearing capacity. Failure of the cables and triangular brackets are the two main types of failure of the primary structure. The cross-sectional area of the cables is the most important factor affecting the load-bearing capacity of the structure and directly affecting the failure modes of the CSPS. The numerical case verified that the proposed design method works well and that the designed structure has sufficient loading capacity and is cost-effective.
Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
Authors: Wan Xiaoyu , Yang Xiongqing , Wang Zhengqiang , Fan Zifu , Duo Bin
Published: February 2023

In the unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) networks, the practical hardware impairments (HIs) and resource allocation is still a challenging problem. Most existing research on resource allocation algorithms for UAV communication is considered with the ideal hardware condition. However, the impact of HIs on system performance cannot be ignored, especially in the case of high bit rates. Considering the HIs, most studies are from the perspective of performance analysis. The resource allocation of UAV relay-assisted NOMA systems is investigated in this paper with HIs. We aim to maximize the sum rate by jointly optimizing the deployment of UAV and transmit power. To address this problem, we first transformed the mixed integer programming problem (MIPP) into a standard convex optimization problem based on successive convex approximation (SCA) technology. Then, we introduced the Lagrangian dual transformation and quadratic transform methods to solve the power allocation problem. Finally, we propose an effective iterative algorithm to achieve an approximate optimal solution. Numerical results demonstrate that the proposed algorithm achieved better performance in terms of the sum rate compared with other benchmark schemes.
Exploring Maintainability Index Variants for Software Maintainability Measurement in Object-Oriented Systems
Authors: Heričko Tjaša , Šumak Boštjan
Published: February 2023

During maintenance, software systems undergo continuous correction and enhancement activities due to emerging faults, changing environments, and evolving requirements, making this phase expensive and time-consuming, often exceeding the initial development costs. To understand and manage software under development and maintenance better, several maintainability measures have been proposed. The Maintainability Index is commonly used as a quantitative measure of the relative ease of software maintenance. There are several Index variants that differ in the factors affecting maintainability (e.g., code complexity, software size, documentation) and their given importance. To explore the variants and understand how they compare when evaluating software maintainability, an experiment was conducted with 45 Java-based object-oriented software systems. The results showed that the choice of the variant could influence the perception of maintainability. Although different variants presented different values when subjected to the same software, their values were strongly positively correlated and generally indicated similarly how maintainability evolved between releases and over the long term. Though, when focusing on fine-grained results posed by the Index, the variant selection had a larger impact. Based on their characteristics, behavior, and interrelationships, the variants were divided into two distinct clusters, i.e., variants that do not consider code comments in their calculation and those that do.
In Vitro Activity of Octenidine Dihydrochloride-Containing Lozenges against Biofilm-Forming Pathogens of Oral Cavity and Throat
Authors: Dudek Bartłomiej , Tymińska Justyna , Szymczyk-Ziółkowska Patrycja , Chodaczek Grzegorz , Migdał Paweł , Czajkowska Joanna , Junka Adam
Published: February 2023

The disruption of the balance in the composition of oral and throat microbiota due to overgrowth of infectious biofilms may lead not only to severe infections, but also to serious, systemic complications resulting in a permanent loss of health or even in the death of the patient. Therefore, a number of hygienic measures are applied to counter-act such a threat, including the provision of locally active antiseptic agents. In this work, the antimicrobial activity of a well-established drug, octenidine dihydrochloride, in a new formulation of lozenges toward the biofilms formed in vitro by Staphylococcus aureus, Streptococcus pyogenes, Candida albicans, Pseudomonas aeruginosa and Aggregatibacter actinomycetemcomitans was assessed. The metabolic activity and quantitative culturing were performed and also scanning electron and confocal microscopies with regard to these biofilms exposed to the activity of octenidine in lozenges vs. a comparator (octenidine dihydrochloride dissolved in liquid). Biofilms were cultured in different experimental settings, including one in which hydroxyapatite served as the biofilm growth surface and using artificial saliva as the biofilm milieu. The obtained results indicated that the tested formulation of octenidine-containing lozenges displayed a high efficacy towards the analyzed biofilms, regardless of the in vitro setting applied. The current work is of a strictly in vitro nature, and the analyses were performed on reference microbial strains and not on the spectrum of clinical strains. Nevertheless, considering the fact that the time of exposition of biofilm to the octenidine released from the lozenge is significantly longer than the contact time of an antiseptic dissolved in liquid also tested in this study, it can be assumed that stable carriers of octenidine may find its broad application in the maintenance of oral and throat hygiene and the eradication of biofilm-based infections.
Automatic Quantitative Coronary Analysis Based on Deep Learning
Authors: Liu Xuqing , Wang Xiaofei , Chen Donghao , Zhang Honggang
Published: February 2023

As a core technique to quantitatively assess the stenosis severity of coronary arteries, quantitative coronary analysis (QCA) is urgently supposed to become more automated and intelligent, especially for regions lacking expertise and technology. The existing QCA methods highly depend on manual operation, which is time-consuming and subject to personal experience. This study innovatively proposes a fully automatic QCA workflow based on artificial intelligence (AI-QCA), which can quickly and accurately make a quantitative assessment of stenosis severity. The whole AI-QCA workflow mainly consists of three parts: the boundary-aware segmentation on the coronary angiogram (CAG) images, the AI-enabled coronary artery tree construction, and the diameter fitting and stenosis detection. Experiments show that the precision, recall, and F1 score of the segmentation, evaluated on 1322 CAGs, are 0.866, 0.897, and 0.879, respectively. Furthermore, the RMSE between diameter stenosis assessed by AI-QCA and manual QCA served by senior experts, evaluated on 249 CAGs, is 0.064, and the Pearson coefficient is 0.765. Meanwhile, the operation time can be reduced from tens of minutes to several seconds by AI-QCA. As a conclusion, the proposed AI-QCA is able to quickly quantify stenosis parameters as accurately as senior experts, which is significant for the intelligent diagnosis and treatment of coronary artery disease.
Solvent-Dispersible Nanostructured MIMI: An Experimental and Computational Study
Authors: Candreva Angela , Di Maio Giuseppe , Palermo Giovanna , Guglielmelli Alexa , Strangi Giuseppe , La Deda Massimo
Published: February 2023

A MIMI (metal–insulator-metal–insulator) nanoparticle was conceived and synthesized. It consists of a core of gold nanoparticles of different shapes, covered by a silica shell in turn covered by a layer of gold and finally by another silica shell. This hybrid nano-matryoshka, completely dispersed in water, was characterized by UV–Vis and TEM spectroscopy, comparing the architecture and photophysical properties of each synthetic step. Through a numerical simulation, it was possible to study in depth the absorption and extinction cross sections, determining the role of the various layers. This is an example of architecture used in the construction of metamaterials, the first in the form of a water-dispersed nanoparticles.
Application of Quantum–Chemical Methods in the Forensic Prediction of Psychedelic Drugs’ Spectra (IR, NMR, UV–VIS, and MS): A Case Study of LSD and Its Analogs
Authors: Džodić Jelica , Milenković Dejan , Marković Milica , Marković Zoran , Dimić Dušan
Published: February 2023

Lysergic acid diethylamide (LSD) and its analogs are commonly encountered substances at crime scenes due to their misuse as hallucinogenic compounds. Modern methods have led to synthesizing different LSD analogs with pronounced physiological effects. Theoretical methods can be a valuable tool for predicting the spectra and stability of novel substances, especially when experimental data are partially available. The current work describes the application of theoretical methods in predicting IR, NMR, UV–VIS, and MS spectra of LSD based on the optimized structure at the M05-2X/6-311++G(d,p) level of theory. A suitable functional has been determined by comparison of the theoretically obtained geometrical parameters with the experimental ones based on the crystallographic structure. The MAE values for the structure optimized at M05-2X/6-311++G(d,p) level of theory were 0.0436 Å (bond lengths) and 2.70° (bond angles). The IR spectra of LSD and LSD tartrate have been described in detail, with the prominent bands being well reproduced (the difference between experimental and theoretical C=O stretching vibration wavenumbers was lower than 11 cm−1). Detailed assignment of 13C NMR spectra led to a high correlation factor (0.999) and low mean absolute error (2.0 ppm) between experimental and theoretical chemical shifts. Optimizing the ground and excited states allowed for the calculation of the energy difference of 330 nm, which reproduced the observed band position in the UV–VIS spectrum of LSD. The most abundant fragments in the experimental mass spectrum (at 323, 221, 207, 181, and 72 m/z) have been optimized, and their stability has been discussed from the structural point of view. This methodology has been validated by comparison with the experimental GC-MS spectra of sample seized at the crime screen and by structure optimization and computation of NMR spectra of common LSD analogs. The theoretical methods for the structure determination and prediction of spectra show great potential in the fast-developing world of new psychedelics.
Variable Selection Using Deep Variational Information Bottleneck with Drop-Out-One Loss
Authors: Pan Junlong , Li Weifu , Liu Liyuan , Jia Kang , Liu Tong , Chen Fen
Published: February 2023

The information bottleneck (IB) model aims to find the optimal representations of input variables with respect to the response variable. While it has been widely used in the machine-learning community, research from the perspective of the information-theoretic method has been rarely reported regarding variable selection. In this paper, we investigate DNNs for variable selection through an information-theoretic lens. To be specific, we first state the rationality of variable selection with IB and then propose a new statistic to measure the variable importance. On this basis, a new algorithm based on a deep variational information bottleneck is developed to calculate the statistic, in which we consider the Gaussian distribution and the exponential distribution to estimate the Kullback–Leibler divergence. Empirical evaluations on simulated and real-world data show that the proposed method performs better than classical variable-selection methods. This confirms the feasibility of the variable selection from the perspective of IB.
Refined Answer Selection Method with Attentive Bidirectional Long Short-Term Memory Network and Self-Attention Mechanism for Intelligent Medical Service Robot
Authors: Wang Deguang , Liang Ye , Ma Hengrui , Xu Fengqiang
Published: February 2023

Answer selection, as a crucial method for intelligent medical service robots, has become more and more important in natural language processing (NLP). However, there are still some critical issues in the answer selection model. On the one hand, the model lacks semantic understanding of long questions because of noise information in a question–answer (QA) pair. On the other hand, some researchers combine two or more neural network models to improve the quality of answer selection. However, these models focus on the similarity between questions and answers without considering background information. To this end, this paper proposes a novel refined answer selection method, which uses an attentive bidirectional long short-term memory (Bi-LSTM) network and a self-attention mechanism to solve these issues. First of all, this paper constructs the required knowledge-based text as background information and converts the questions and answers from words to vectors, respectively. Furthermore, the self-attention mechanism is adopted to extract the global features from the vectors. Finally, an attentive Bi-LSTM network is designed to address long-distance dependent learning problems and calculate the similarity between the question and answer with consideration of the background knowledge information. To verify the effectiveness of the proposed method, this paper constructs a knowledge-based QA dataset including multiple medical QA pairs and conducts a series of experiments on it. The experimental results reveal that the proposed approach could achieve impressive performance on the answer selection task and reach an accuracy of 71.4%, MAP of 68.8%, and decrease the BLUE indicator to 3.10.
Factors Associated with Musculoskeletal Injuries in Pre-Professional Modern Dancers before and after the COVID-19 Pandemic
Authors: Kishon Lital , Sarig Bahat Hilla
Published: February 2023

The prevalence of dance-related injuries is high, yet risk factors remain unclear in modern dance. The COVID-19 pandemic has affected the continuity and nature of dance programs. The objectives were to investigate factors associated with musculoskeletal injuries in pre-professional modern dancers before and after the pandemic and to qualitatively describe the dancers’ experiences during COVID-19. We examined 31 modern dancers (19.42 ± 1.57 years) pre-COVID-19 and re-examined 21 participants after one-year post-COVID-19, using objective physical tests for upper and lower body strength, aerobic fitness, hip ranges, injury history, and training hours’ data. The injury rate remained mostly unaffected (0.87 ± 0.88 pre-, 0.76 ± 0.83 post-COVID-19), but weekly dance hours were reduced (33 pre-, 21.40 post-COVID-19). Dancers significantly improved their push-up score (p = 0.016) and decreased their compensated turnout (p = 0.022). Notably, non-injured dancers scored higher in the pre-COVID-19 Chester Step Test (p = 0.033) and practiced more strength training independently post-COVID-19 (p = 0.024). No significant differences were found between injured and non-injured dancers in the other parameters. Dancers reported negative thoughts and difficulty maintaining their fitness during the pandemic. In conclusion, strength training and aerobic fitness were identified as potentially important factors related to injury risk in modern dance and should be examined further for their injury prevention value.
Prediction of Diabetes Complications Using Computational Intelligence Techniques
Authors: Alghamdi Turki
Published: February 2023

Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%.
Predictive Energy Management Strategy for Hybrid Electric Air-Ground Vehicle Considering Battery Thermal Dynamics
Authors: Li Zhe , Jiao Xiaohong , Zha Mingjun , Yang Chao , Yang Liuquan
Published: February 2023

Hybrid electric air-ground vehicles (HEAGVs), which can run on the land and fly in the air, are considered a promising future transportation. The operation of HEAGVs, accompanied by high energy consumption, could lead to increasing battery temperature, which may affect the lifespan of the battery. To make the battery last longer and improve energy efficiency, an effective energy management strategy (EMS) is necessary for the operation of HEAGVs. In this regard, this paper proposes a predictive EMS based on model predictive control (MPC). Firstly, speed information is obtained by intelligent network technology to achieve a prediction of power demand, and then the state of charge (SOC) reference trajectory is planned. Secondly, a Pontryagin’s minimum principle-based model predictive control (PMP-MPC) framework is proposed, including battery thermal dynamics. Under the framework, fuel efficiency is improved by reducing the temperature of the battery. Finally, the proposed method is compared to PMP, dynamic programming (DP), and rule-based (RB) methods. The effect of different preview horizon sizes on fuel economy and battery temperature is analyzed. Verification results under two driving cycles indicate that compared with the rule-based method, the proposed method improves fuel economy by 5.14% and 5.2% and decreases the temperature by 5.9% and 4.9%, respectively. The results demonstrate that the proposed PMP-MPC method can effectively improve fuel economy and reduce temperature.
The Effects of Daubechies Wavelet Basis Function (DWBF) and Decomposition Level on the Performance of Artificial Intelligence-Based Atrial Fibrillation (AF) Detection Based on Electrocardiogram (ECG) Signals
Authors: Mandala Satria , Pratiwi Wibowo Annisa Rizki , Adiwijaya  , Suyanto  , Zahid Mohd Soperi Mohd , Rizal Ardian
Published: February 2023

This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of training and testing data for the SVM uses the 5-fold cross-validation (CV) principle to produce optimum detection performance. In this study, AF detection performance is measured and analyzed based on accuracy, sensitivity, and specificity metrics. The results of the analysis show that accuracy tends to decrease with increases in the decomposition level. In addition, it becomes stable in various types of DWBF. For both sensitivity and specificity, the results of the analysis show that increasing the decomposition level also causes a decrease in both sensitivity and specificity. However, unlike the accuracy, changing the DWBF type causes both two metrics to fluctuate over a wider range. The statistical results also indicate that the highest AF accuracy detection (i.e., 94.17%) is obtained at the Daubechies 2 (DB2) function with a decomposition level of 4, whereas the highest sensitivity, 97.57%, occurs when the AF detection uses DB6 with a decomposition level of 2. Finally, DB2 with decomposition level 4 results in 96.750% for specificity. The finding of this study is that selecting the appropriate DL has a more significant effect than DWBF on AF detection using WCF.
Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
Authors: Alhmiedat Tareq
Published: February 2023

The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy.