bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainable Bioconversion of Wetland Plant Biomass for Pleurotus ostreatus var. florida Cultivation: Studies on Proximate and Biochemical Characterization
Authors: Elbagory Mohssen , El-Nahrawy Sahar , Omara Alaa El-Dein , Eid Ebrahem M. , Bachheti Archana , Kumar Pankaj , Abou Fayssal Sami , Adelodun Bashir , Bachheti Rakesh Kumar , Kumar Pankaj , Mioč Boro , Kumar Vinod , Širić Ivan
Published: December 2022

The abundant biomass growth of aquatic macrophytes in wetlands is one of the major concerns affecting their residing biota. Moreover, the biomass degenerates within the wetlands, thereby causing a remixing of nutrients and emission of greenhouse gases. Therefore, it is crucial to find sustainable methods to utilize the biomass of aquatic macrophytes devoid of environmental concerns. The present study investigates the utilization of the biomass of three aquatic macrophytes, including the lake sedge (CL: Carex lacustris Willd.), water hyacinth (EC: Eichhornia crassipes Mart. Solms), and sacred lotus (NL: Nelumbo nucifera Gaertn.) to produce oyster (Pleurotus ostreatus var. florida) mushrooms. For this purpose, different combinations of wheat straw (WS: as control) and macrophyte’s biomass (WH) such as control (100% WH), CL50 (50% WH + 50% CL), CL100 (100% CL), EC50 (50% WH + 50% EC), EC100 (100% EC), NL50 (50% WH + 50% NL), and NL100 (100% NL) were used for P. florida cultivation under controlled laboratory conditions. The results showed that all selected combinations of wheat straw and macrophyte biomass supported the spawning and growth of P. florida. In particular, the maximum significant (p < 0.05) growth, yield, bioefficiency, proximate, and biochemical parameters were reported using the WH substrate followed by CL, NL, and EC biomass, which corresponds to the reduction efficiency of the substrate parameters. Therefore, the findings of this study reveal that the biomass of selected aquatic macrophytes can be effectively utilized for sustainable mushroom cultivation while minimizing the risk associated with their self-degeneration.
Individual and Combined Effects of High-Temperature Stress at Booting and Flowering Stages on Rice Grain Yield
Authors: Mahmood Aqib , Ali Iftikhar , Wang Wei , Ata-Ul-Karim Syed Tahir , Liu Bing , Liu Leilei , Zhu Yan , Cao Weixing , Tang Liang
Published: December 2022

Extreme temperature events as a consequence of global climate change result in a significant decline in rice yield. A two year phytotron experiment was conducted using three temperature levels and two heating durations to compare the effects of heat stress at booting (BT), flowering (FL), and combined (BT + FL) stages on rice yield and yield components. Compared with T1 (Tmean of 27 °C), heat stress at BT + FL and BT stages produced more regenerated tillers and compensated more for yield loss than heat stress at FL. Heat stress at BT + FL stages alleviated spikelet sterility and yield loss of original tillers compared to heat stress at FL. The greater variation of yield per plant (YPP) under heat stress at flowering as compared to BT and BT + FL stages was accompanied by a higher decrease in spikelet fertility, while, at BT and BT + FL stages, spikelet number per plant and 1000-grain weight also contributed well to variation in yield. Furthermore, heat stress during BT and BT + FL stages caused a significant decline in spikelet fertility of the upper part of panicles, followed by middle and lower parts, while heat stress at the FL stage responded inversely. For every 1 °C day increase in heat degree days at BT, FL, and BT + FL stages, YPPO (only original tillers) declined by 2.9%, 2.5%, and 6.0%, and YPPT (including original + regenerated tillers) decreased by 5.8%, 2.7%, and 2.2%, respectively. The projected alleviation effects under BT + FL stages of heat stress in contrast to single-stage heat stress would help to accurately estimate rice yield under extreme temperature events, as well as to develop a heat-tolerant rice cultivar.
Air Pollution Tolerance Index and Heavy Metals Accumulation of Tree Species for Sustainable Environmental Management in Megacity of Lahore
Authors: Nawaz Rab , Aslam Muhammad , Nasim Iqra , Irshad Muhammad Atif , Ahmad Sajjad , Latif Maria , Hussain Fida
Published: December 2022

Urban air and soil quality has been deteriorating during the past few years due to urbanization, industrialization and increased number of vehicles. The goal of the current study was to assess the Air Pollution Tolerance Index (APTI) and heavy metal absorption (Pb, Cd, Zn, and Ni) potential by ten selected trees planted along the roadside in the metropolitan city of Lahore, Pakistan. APTI was estimated on the basis of biochemical parameters (chlorophyll content, ascorbic acid, pH and relative water contents) of plant extract, while heavy metals (HMs) accumulation potential was measured by a digestion method. The highest APTI was estimated in P. longifolia (78.9), followed by A. scholarils (75.9) and M. indica (71.9). Overall, these three species have significant closeness among the higher pollution-tolerance results. The poor APTI result was determined in F. religiosa (19.5) and E. citriodora (14.9). The highest Pb contents were observed in P. longifolia and M. indica i.e., 135 and 132 mg/kg, respectively. Similarly, the highest Zn contents were found in P. longifolia and S. cumini with 130 and 132 mg/kg, respectively. The Ni concentration was observed highest in P. longifolia (34 mg/kg), but in the remaining species, it is almost the same trend of Ni accumulation. Combining these trees can be useful for fostering green-belt growth along roadsides to reduce air and soil pollution and achieve environmental sustainability. But unfortunately, these species are not planted well across the roadside as they have very little biodiversity index, as compared to other species. These species should be planted in urban areas to enhance biodiversity in the urban ecosystem and make them sustainable cities and communities.
Relationships between the Mini-InDel Variants within the Goat CFAP43 Gene and Body Traits
Authors: Mi Fang , Wu Xianfeng , Wang Zhen , Wang Ruolan , Lan Xianyong
Published: December 2022

The cilia- and flagella-associated protein 43 (CFAP43) gene encodes a member of the cilia- and flagellum-associated protein family. Cilia on the cell surface influence intercellular signaling and are involved in biological processes such as osteogenesis and energy metabolism in animals. Previous studies have shown that insertion/deletion (InDel) variants in the CFAP43 gene affect litter size in Shaanbei white cashmere (SBWC) goats, and that litter size and body traits are correlated in this breed. Therefore, we hypothesized that there is a significant relationship between InDel variants within the CFAP43 gene and body traits in SBWC goats. Herein, we first investigated the association between three InDel variant loci (L-13, L-16, and L-19 loci) within CFAP43 and body traits in SBWC goats (n = 1827). Analyses revealed that the L-13, L-16, and L-19 loci were significantly associated with chest depth, four body traits, and three body traits, respectively. The results of this study are in good agreement with those previously reported and could provide useful molecular markers for the selection and breeding of goats for body traits.
Role of Guinea Pigs (Cavia porcellus) Raised as Livestock in Ecuadorian Andes as Reservoirs of Zoonotic Yeasts
Authors: Buela Lenys , Cuenca Mercy , Sarmiento Jéssica , Peláez Diana , Mendoza Ana Yolanda , Cabrera Erika Judith , Yarzábal Luis Andrés
Published: December 2022

Guinea pigs (Cavia porcellus) have been reared for centuries in the Andean region for ceremonial purposes or as the main ingredient of traditional foods. The animals are kept in close proximity of households and interact closely with humans; this also occurs in western countries, where guinea pigs are considered pets. Even though it is acknowledged that domestic animals carry pathogenic yeasts in their tissues and organs that can cause human diseases, almost nothing is known in the case of guinea pigs. In this work we used traditional microbiological approaches and molecular biology techniques to isolate, identify, and characterize potentially zoonotic yeasts colonizing the nasal duct of guinea pigs raised as livestock in Southern Ecuador (Cañar Province). Our results show that 44% of the 100 animals studied were colonized in their nasal mucosa by at least eleven yeast species, belonging to eight genera: Wickerhamomyces, Diutina, Meyerozyma, Candida, Pichia, Rhodotorula, Galactomyces, and Cryptococcus. Noticeably, several isolates were insensitive toward several antifungal drugs of therapeutic use, including fluconazole, voriconazole, itraconazole, and caspofungin. Together, our results emphasize the threat posed by these potentially zoonotic yeasts to the farmers, their families, the final consumers, and, in general, to public and animal health.
A High-Precision Method for 100-Day-Old Classification of Chickens in Edge Computing Scenarios Based on Federated Computing
Authors: Huang Yikang , Yang Xinze , Guo Jiangyi , Cheng Jia , Qu Hao , Ma Jie , Li Lin
Published: December 2022

Due to the booming development of computer vision technology and artificial intelligence algorithms, it has become more feasible to implement artificial rearing of animals in real production scenarios. Improving the accuracy of day-age detection of chickens is one of the examples and is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. Due to the huge amount of data and the different computing power of different devices in practical application scenarios, it is important to maximize the computing power of edge computing devices without sacrificing accuracy. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. In order to accommodate different computing power in different scenarios, this paper proposes a dual-ended adaptive federated learning framework; in order to adapt to low computing power scenarios, this paper performs lightweighting operations on the mainstream model; and in order to verify the effectiveness of the model, this paper conducts a number of targeted experiments. Compared with AlexNet, VGG, ResNet and GoogLeNet, this model improves the classification accuracy to 96.1%, which is 14.4% better than the baseline model and improves the Recall and Precision by 14.8% and 14.2%, respectively. In addition, by lightening the network, our methods reduce the inference latency and transmission latency by 24.4 ms and 10.5 ms, respectively. Finally, this model is deployed in a real-world application and an application is developed based on the wechat SDK.
Antimicrobial Nanomaterials Based on Halloysite Clay Mineral: Research Advances and Outlook
Authors: Massaro Marina , Ciani Rebecca , Cinà Giuseppe , Colletti Carmelo Giuseppe , Leone Federica , Riela Serena
Published: December 2022

Bacterial infections represent one of the major causes of mortality worldwide. Therefore, over the years, several nanomaterials with antibacterial properties have been developed. In this context, clay minerals, because of their intrinsic properties, have been efficiently used as antimicrobial agents since ancient times. Halloysite nanotubes are one of the emerging nanomaterials that have found application as antimicrobial agents in several fields. In this review, we summarize some examples of the use of pristine and modified halloysite nanotubes as antimicrobial agents, scaffolds for wound healing and orthopedic implants, fillers for active food packaging, and carriers for pesticides in food pest control.
Use of a Novel Anti-Infective Noble Metal Alloy-Coated Titanium Orthopedic Nail in Patients with Open Fractures: A Case Series from Malaysia
Authors: Karupiah Thevarajan , Yong Aik Peng , Ong Ze Wee , Tan Heng Keat , Tang Wei Chern , Salam Hishamuddin Bin
Published: December 2022

Fracture-related infection is a serious complication in orthopedic surgery with severe consequences for the patient. We evaluated whether a novel noble metal nail-coating technology can prevent bacterial adhesion and biofilm formation without interfering with bony union. In this retrospective, single-center case series, we described the incidence of fracture-related infections and bony union achievement in patients who had Gustilo type IIIa or IIIb femoral or tibial fractures treated with noble metal alloy-coated titanium nails. Patients were treated between January 2017 and January 2019 at the Sultanah Aminah Hospital, Johor Bahru, Malaysia. Information on fracture-related infections and bone healing assessments was collected from patient records. Additionally, three independent experts retrospectively reviewed patient X-ray images from follow-up visits to further evaluate bony union achievement. Thirty-five patients were included. Infection developed in 3/35 (8.6%) patients; all cases were resolved by antibiotic therapy. Radiographs were available for 32 patients; these confirmed the presence of bone healing in 30/32 (93.8%) patients. However, according to patient records, bony union was achieved in all patients. No safety issues were recorded. This case series suggests that a noble metal alloy-coated titanium nail can prevent infection and facilitate bony union achievement in patients undergoing surgery for severe open fractures.
First Report on the Chemical Composition, Antioxidant Capacity, and Preliminary Toxicity to Artemia salina L. of Croton campinarensis Secco, A. Rosário & PE Berry (Euphorbiaceae) Essential Oil, and In Silico Study
Authors: da Costa Leonardo Souza , de Moraes Ângelo Antônio Barbosa , Cruz Jorddy Neves , Mali Suraj N. , Almeida Lorena Queiroz , do Nascimento Lidiane Diniz , Ferreira Oberdan Oliveira , Varela Everton Luiz Pompeu , Percário Sandro , de Oliveira Mozaniel Santana , Andrade Eloisa Helena de Aguiar
Published: December 2022

Croton campinarensis Secco, A. Rosário & PE Berry is an aromatic species recently discovered in the Amazon region. This study first reports the chemical profile, antioxidant capacity, and preliminary toxicity to A. salina Leach of the essential oil (EO) of this species. The phytochemical profile of the essential oil was analyzed by gas chromatography (GC/MS) and (GC-FID). The antioxidant capacity of the EO was measured by its inhibition of ABTS•+ and DPPH• radicals. Molecular modeling was used to evaluate the mode of interaction of the major compounds with acetylcholinesterase (AChE). The results indicate that the EO yield was 0.24%, and germacrene D (26.95%), bicyclogermacrene (17.08%), (E)-caryophyllene (17.06%), and δ-elemene (7.59%) were the major compounds of the EO sample. The EO showed a TEAC of 0.55 ± 0.04 mM·L−1 for the reduction of the ABTS•+ radical and 1.88 ± 0.08 mM·L−1 for the reduction of the DPPH• radical. Regarding preliminary toxicity, the EO was classified as toxic in the bioassay with A. salina (LC50 = 20.84 ± 4.84 µg·mL−1). Through molecular docking, it was found that the majority of the EO components were able to interact with the binding pocket of AChE, a molecular target related to toxicity evaluated in A. salina models; the main interactions were van der Waals and π-alkyl interactions.
Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea
Authors: Kwon Tae Ho , Kim Jaehwan , Park Ki-Tae , Jung Kyu-San
Published: December 2022

Reinforced concrete slab (RCS) bridges deteriorate because of exposure to environmental factors over time, resulting in reduced durability. Particularly, the carbonation of RCS bridges corrodes the rebars and reduces the strength. However, carbonation models derived from short-term experiments exhibit low reliability with respect to existing bridges. Therefore, a long short-term memory (LSTM)-based methodology was developed in this study for generating carbonation models using existing bridge inspection reports. The proposed methodology trains the LSTM model by combining data extracted from reports and local environmental data. The learning process uses padding and masking methods to consider the history of environmental data. A case study was performed to validate the proposed method in three different regions of Korea. The results verified that the coefficient of determination of the proposed method was higher than those of the existing carbonation models and other regression analyses. Therefore, the developed methodology can be used for predicting regional carbonation models using the data from existing bridges.
Deep Electrochemical Etching of Stainless Steel Using a Deposited Copper Layer
Authors: Shin Hong-Shik
Published: December 2022

Deep electrochemical etching (DEE) is proposed in this paper. DEE is a process that repeats steps consisting of electrodeposition, laser patterning, and electrochemical etching. In the electrodeposition step, a deposited layer is formed on the surface of the workpiece. This deposited layer serves as a protective layer to protect the surface of the workpiece until it is completely dissolved in the electrochemical etching step. Laser patterning is the step of patterning a deposited layer by irradiating a laser beam while minimizing surface damage to the workpiece. The etching of the workpiece proceeds through the electrochemical etching step. If these three steps are performed sequentially, one cycle of the DEE process is completed. By repeating this cycle, an improved etch factor can be obtained in the DEE process. The processing characteristics according to DEE process conditions were analyzed through an SEM and 3D surface profiler. Through the DEE process, a microstructure with a deep etching depth was fabricated, and the micropatterns successfully penetrated a one-hundred-um-thick stainless steel specimen. Through the DEE process, it was confirmed that it is possible to fabricate a micro with a high aspect ratio structure pattern by improving the etch factor.
A Mathematical Model for the Treatment of Melanoma with the BRAF/MEK Inhibitor and Anti-PD-1
Authors: Nave OPhir , Sigron Moriah
Published: December 2022

Skin cancer treatment is a combination of BRAF and MEK kinase inhibitors administered as tablets, along with immunotherapy treatment (treatment into the vein) with a group of drugs that inhibit the activity of the immune barrier proteins PD-1 and PDL1. Here, we propose a new approach to the therapy for melanoma with the BRAF/MEK inhibitor and anti-PD-1. With the help of explicit analytical functions, we were able to model this combined treatment and present the treatment in a mathematical model described by a system of differential equations including variables, such as Treg, IL12, Il10, TGF-β, and cytokine, which are significant variables that are all critical factors which determine the effectiveness of therapies. The most significant advantage of a treatment described by a mathematical model with explicit analytical functions is the control of parameters, such as time and dose, which are variable critical parameters in the treatment, that is, these parameters can be adapted to the patient’s personalized treatment. In the current study, we showed that by simultaneously changing and combining these two parameters, we could decrease the tumor volume. To validate the numerical results, we computed the relative error between the results obtained from the mathematical model and clinical data.
Influence of Paraprobiotics-Containing Moisturizer on Skin Hydration and Microbiome: A Preliminary Study
Authors: Chaiyasut Chaiyavat , Sivamaruthi Bhagavathi Sundaram , Tansrisook Chawin , Peerajan Sartjin , Chaiyasut Khontaros , Bharathi Muruganantham
Published: December 2022

The skin is a physical barrier to protect the human body and is rich in microbial niches. Skin is damaged due to several factors, including poor nutrition and exposure to harsh environments resulting in dryness, acidic skin, and infections. Studies have shown that probiotics and their derivatives could protect the skin. Skin care products with probiotic components are the latest approach to developing cosmetic products with health benefits. The current study aimed to examine the moisturizing effect of paraprobiotics (moist heat-inactivated Bifidobacterium lactis (B. lactis), Lactobacillus plantarum (L. plantarum))-containing moisturizer (APM) and its influences on the skin microbiome of healthy subjects. Fifty healthy subjects were randomly divided into treatment (n = 25) and control (n = 25) groups. The APM or placebo (without paraprobiotics; PM) was applied on the skin of the right forehand of subjects, and the changes in transepidermal water loss (TEWL) and stratum corneum moisture (SCM) levels every 1 h for 4 h and after 4 weeks of treatment were observed. Skin swab samples were collected before and after the treatments (4 weeks) and subjected to microbiome analysis through next-generation sequencing technology. The results indicated that the APM treatment significantly reduced the TEWL and increased the SCM values compared to the respective baseline values and controls. The sequencing study showed significant changes in Cutibacterium (p = 0.0431), Corynebacterium (p = 0.0431), and Acinetobacter (p = 0.0431) in the treatment group. The changes in phylum were not statistically significant. Still, based on the relative frequency, the abundance of phylum Proteobacteria and Firmicutes and Cyanobacterial was decreased, and the abundance of Planctomycetes, Chloroflexi, Verrucomicrobia, and Gemmatimonadetes was increased after treatment. Additionally, the APM treatment suppressed C. tuberculostearicum in healthy subjects. The results suggested that APM could improve skin hydration and skin-beneficial microbial composition. The study has limitations such as a small sample size and treatment period, so further extensive studies are required to confirm the findings of the current study, which could aid in developing paraprobiotics-based skin care formulations.
Fresher Experience Plays a More Important Role in Prioritized Experience Replay
Authors: Ma Jue , Ning Dejun , Zhang Chengyi , Liu Shipeng
Published: December 2022

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the sampling probability of experiences. Although PER can sample valuable experiences according to the TD-error, freshness is also an important character of experiences. It implicitly reflects the potential value of experiences. Fresh experiences are produced by virtue of the current networks and they are more valuable for updating the current network parameters than the past. The sampling of fresh experiences to train the neural networks can increase the learning speed of the agent, but few algorithms can perform this job efficiently. To solve this issue, a novel experience replay method is proposed in this paper. We first define that the experience freshness is negatively correlated with the number of replays. A new hyper-parameter, the freshness discounted factor μ, is introduced in PER to measure the experience freshness. Further, a novel experience replacement strategy in the replay buffer is proposed to increase the experience replacement efficiency. In our method, the sampling probability of fresh experiences is increased by raising its priority properly. So the algorithm is more likely to choose fresh experiences to train the neural networks during the learning process. We evaluated this method in both discrete control tasks and continuous control tasks via OpenAI Gym. The experimental results show that our method achieves better performance in both modes of operation.
Flexural Behavior of One-Way Slab Reinforced with Grid-Type Carbon Fiber Reinforced Plastics of Various Geometric and Physical Properties
Authors: Kim Kyung-Min , Cheon Ju-Hyun
Published: December 2022

Textile-reinforced concrete (TRC) has many advantages, including corrosion resistance, but TRC is a novel composite material and there is limited experimental research on the flexural behavior of TRC members. This paper aims to experimentally evaluate the flexural behavior of TRC slabs reinforced with nine types of grid-type carbon fiber-reinforced plastic (CFRP) (hereafter referred to as carbon grid) with varying cross-sectional areas, spacings, tensile strengths, and elastic moduli of longitudinal strands. The experimental results show that the maximum load tends to be higher in specimens reinforced with carbon grids with small cross-sectional areas and spacings of strands but high tensile strength. Cross-sectional area and spacing were also revealed to influence the crack-formation stage behavior. On the other hand, stiffness decreased to approximately 8% or lower than the initial stiffness, with cracking in all carbon grid-reinforced specimens; post-peak behavior also exhibited dependency on tensile stress acting on the carbon grids under the maximum load, based on 80% of the tensile strength.
STALITA: Innovative Platform for Bank Transactions Analysis
Authors: Jesenko David , Kohek Štefan , Žalik Borut , Brumen Matej , Kavran Domen , Lukač Niko , Živec Andrej , Pur Aleksander
Published: December 2022

Acts of fraud have become much more prevalent in the financial industry with the rise of technology and the continued economic growth in modern society. Fraudsters are evolving their approaches continuously to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. To overcome and investigate financial frauds, this paper presents STALITA, which is an innovative platform for the analysis of bank transactions. STALITA enables graph-based data analysis using a powerful Neo4j graph database and the Cypher query language. Additionally, a diversity of other supporting tools, such as support for heterogeneous data sources, force-based graph visualisation, pivot tables, and time charts, enable in-depth investigation of the available data. In the Results section, we present the usability of the platform through real-world case scenarios.
The Effect of Milk Thistle (Silybum marianum L.) Fortification, Rich in Dietary Fibre and Antioxidants, on Structure and Physicochemical Properties of Biscuits
Authors: Krystyjan Magdalena , Gumul Dorota , Adamczyk Greta
Published: December 2022

In the present study, an attempt was made to enrich cookies with ground seeds of milk thistle (Silybum marianum L.) and to determine the effect of this addition on the quality of cookies. The content of nutrients, fibre, and calorific value, as well as, texture, spreadability factor, volume, colour and sensory parameters were tested. The biscuits were characterized by lower caloric value, hardness and volume, but higher spreadability. The cookies with 5% of milk thistle gained high consumer acceptance, but greater than 20% addition caused a worsening quality of the product. The biscuit fortification with ground milk thistle seeds seems to be justified, through enrichment of the products with fibre and the antioxidant compounds having health-promoting properties.
An Adaptive Partitioning and Multi-Granularity Network for Video-Based Person Re-Identification
Authors: Huang Bailiang , Piao Yan , Tang Yanfeng
Published: December 2022

Person re-identification (Re-ID) is a key technology used in the field of intelligent surveillance. The existing Re-ID methods are mainly realized by using convolutional neural networks (CNNs), but the feature information is easily lost in the operation process due to the down-sampling structure design in CNNs. Moreover, CNNs can only process one local neighbourhood at a time, which makes the global perception of the network poor. To overcome these shortcomings, in this study, we apply a pure transformer to a video-based Re-ID task by proposing an adaptive partitioning and multi-granularity (APMG) network framework. To enable the pure transformer structure better at adapting to the Re-ID task, we propose a new correlation-adaptive partitioning (CAP) of feature embedding modules that can adaptively partition person images according to structural correlations and thus retain the structure and semantics of local feature information in the images. To improve the Re-ID performance of the network, we also propose a multi-granularity (MG) module to better capture people feature information at different levels of granularity. We performed validation trials on three video-based benchmark datasets. The results show that the network structure based on the pure transformer can adapt to Re-ID tasks well, and our APMG network outperforms other state-of-the-art methods.
Lactoplantibacillus plantarum KG Lysates Inhibit the Internalization of Staphylococcus aureus by Human Keratinocytes through the Induction of Human Beta-Defensin 3
Authors: Jang Kyoung-Ok , Yu Chaeyeon , Kim Hangeun , Chung Dae-Kyun
Published: December 2022

Human β-defensin 3 (hBD3) is an antimicrobial peptide and a chemoattractant molecule expressed in human keratinocytes. Lysates prepared from Lactoplantibacillus plantarum KG (KG lysates), a fermented lactobacillus strain with Ginseng extracts, significantly induced hBD3 in human keratinocytes. hBD3 induced by KG lysates affected the internalization of Staphylococcus aureus by human keratinocytes. The intracellular pathway for the induction of hBD3 by KG lysates was examined using inhibitors. Briefly, KG lysates stimulated the late signaling pathway including JAK1/2, which might be induced by the EGFR- or IFN-γ-mediated pathway, and affected hBD3 induction. Through this study, we confirmed that KG lysates have the ability to control bacterial infections, especially S. aureus, in human keratinocytes. Thus, KG lysates can be provided in various formulations for the development of food and pharmaceutical materials to control bacterial infection.
Association between Cough and Ambient Polycyclic Aromatic Hydrocarbons in Patients with Chronic Cough: An Observational Study in Two Regions of Japan
Authors: Hara Akinori , Odajima Hiroshi , Matsuzaki Hiroshi , Fujimura Masaki , Toma Tomoko , Wada Taizo , Ohkura Noriyuki , Zhao Jiaye , Pham Kim-Oanh , Suzuki Keita , Tsujiguchi Hiromasa , Takami Akinori , Hayakawa Kazuichi , Nakamura Hiroyuki
Published: December 2022

Ambient polycyclic aromatic hydrocarbons’ (PAHs) specific components are likely involved in respiratory disease development and exacerbation in children and adults. Airborne PAH exposure’s effects on cough symptoms in children and adults with chronic coughs in Kanazawa and Fukuoka, Japan, were investigated in this longitudinal study. A total of 98 patients with chronic coughs were enrolled and followed up between 1 April and 31 May 2020. The enrolled patients were non-smoking adults and children aged 3–83 years. Cough diaries were used to record and collect daily cough symptoms. High-performance liquid chromatography coupled with a fluorescence detector was used to determine the particulate PAH content in daily total suspended particles collected on quartz fiber filters. Ambient concentrations of fine particulate matter, nitrogen dioxide, and sulfur dioxide were obtained from local monitoring sites. Generalized estimated equations were used to estimate the association between daily PAHs and cough symptoms. Among nine PAHs measured, benz[a]anthracene (BaA) was significantly associated with cough symptoms for both lag4 and lag5 PAH exposure. These findings suggest that airborne specific PAHs, especially BaA, affect cough symptoms in children and adults with chronic cough. Further studies are needed to develop effective measures to prevent respiratory diseases against specific PAHs.
Complementary Barrier Infrared Detector Architecture for Long-Wavelength Infrared InAs/InAsSb Type-II Superlattice
Authors: Ting David Z. , Soibel Alexander , Khoshakhlagh Arezou , Fisher Anita M. , Pepper Brian J. , Keo Sam A. , Hill Cory J. , Rafol Sir B. , Gunapala Sarath D.
Published: December 2022

We describe the challenges for long- and very long-wavelength InAs/InAsSb type-II strained-layer superlattice infrared detectors, and provide an overview of progress in device architecture development for addressing them. Specifically, we have explored the complementary barrier infrared detector (CBIRD) that contains p-type InAs/InAsSb T2SLS absorbers for enhancing quantum efficiency, while also suppressing surface shunt current. We describe selected device results, and also provide references to additional results and more in-depth discussions.
Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
Authors: Kim Yeji , Song Chanyoung , Song Gyuseon , Kim Sol Bi , Han Hyun-Wook , Han Inbo
Published: December 2022

A natural language processing (NLP) pipeline was developed to identify lumbar spine imaging findings associated with low back pain (LBP) in X-radiation (X-ray), computed tomography (CT), and magnetic resonance imaging (MRI) reports. A total of 18,640 report datasets were randomly sampled (stratified by imaging modality) to obtain a balanced sample of 300 X-ray, 300 CT, and 300 MRI reports. A total of 23 radiologic findings potentially related to LBP were defined, and their presence was extracted from radiologic reports. In developing NLP pipelines, section and sentence segmentation from the radiology reports was performed using a rule-based method, including regular expression with negation detection. Datasets were randomly split into 80% for development and 20% for testing to evaluate the model’s extraction performance. The performance of the NLP pipeline was evaluated by using recall, precision, accuracy, and the F1 score. In evaluating NLP model performances, four parameters—recall, precision, accuracy, and F1 score—were greater than 0.9 for all 23 radiologic findings. These four scores were 1.0 for 10 radiologic findings (listhesis, annular fissure, disc bulge, disc extrusion, disc protrusion, endplate edema or Type 1 Modic change, lateral recess stenosis, Schmorl’s node, osteophyte, and any stenosis). In the seven potentially clinically important radiologic findings, the F1 score ranged from 0.9882 to 1.0. In this study, a rule-based NLP system identifying 23 findings related to LBP from X-ray, CT, and MRI reports was developed, and it presented good performance in regards to the four scoring parameters.
Analysis of the Cooling and Humidification Effect of Multi-Layered Vegetation Communities in Urban Parks and Its Impact
Authors: Zhang Yu , Dai Meiqi
Published: December 2022

As urbanization continues to accelerate, the urban heat island effects have become one of the most important issues affecting the urban environment and people’s living experience. Numerous studies have shown that urban parks and green spaces can effectively alleviate the problem of the urban heat island effect and provide cooling and humidifying effects. Vegetation communities are a fundamental part of urban parklands, and multi-layered vegetation communities are considered to have better cooling and humidifying effects. Previous studies have focused on comparative analyses between different cover types of vegetation communities but have not explored the differences in the cooling and humidifying effects of multi-layered vegetation communities of the same cover type. Therefore, the Olympic Forest Park in Beijing was selected as the subject of this study, and multi-layered vegetation-covered (tree-shrub-grass) with different degrees of densities and uncovered squares were selected for the control and comparison. The cooling and humidifying effects of multi-layered vegetation communities with different canopy densities at different times of the day through field measurements were studied, and the influencing factors for this were analyzed. The results show that the tree cover is the core factor affecting temperature; the degree of the canopy density of multi-layered vegetation communities is significantly and positively correlated with the intensity of cooling and humidification, and the cooling and humidifying effect of multi-layered vegetation communities increases as the degree of canopy density increases. The results of this study can provide some references for the planning and design of urban parks and green spaces.
A Novel Hybrid Model Combining the Support Vector Machine (SVM) and Boosted Regression Trees (BRT) Technique in Predicting PM10 Concentration
Authors: Shaziayani Wan Nur , Ahmat Hasfazilah , Razak Tajul Rosli , Zainan Abidin Aida Wati , Warris Saiful Nizam , Asmat Arnis , Noor Norazian Mohamed , Ul-Saufie Ahmad Zia
Published: December 2022

The PM10 concentration is subject to significant changes brought on by both gaseous and meteorological variables. The aim of this research was to explore the performance of a hybrid model combining the support vector machine (SVM) and the boosted regression trees (BRT) technique in predicting the PM10 concentration for 3 consecutive days. The BRT model was trained by utilizing maximum daily data in the cities of Alor Setar, Klang, and Kuching from the years 2002 to 2017. The SVM–BRT model can optimize the number of predictors and predict PM10 concentration; it was shown to be capable of predicting air pollution based on the models’ performance with NAE (0.15–0.33), RMSE (10.46–32.60), R2 (0.33–0.70), IA (0.59–0.91), and PA (0.50–0.84). This was accomplished while saving training time by reducing the feature size given in the data representation and preventing learning from noise (overfitting) to improve accuracy. This knowledge establishes the foundation for the development of efficient methods to prevent and/or minimize the health effects of PM10 exposure on one’s health.
Calculation of the Free Energy of the Ising Model on a Cayley Tree via the Self-Similarity Method
Authors: Akın Hasan
Published: December 2022

In this study, an interactive Ising model having the nearest and prolonged next-nearest neighbors defined on a Cayley tree is considered. Inspired by the results obtained for the one-dimensional Ising model, we will construct the partition function and then calculate the free energy of the Ising model having the prolonged next nearest and nearest neighbor interactions and external field on a two-order Cayley tree using the self-similarity of the semi-infinite Cayley tree. The phase transition problem for the Ising system is investigated under the given conditions.
A Two-Stage Model Based on EFQM, FBWM, and FMOORA for Business Excellence Evaluation in the Process of Manufacturing
Authors: Petrović Tijana , Vesić Vasović Jasmina , Komatina Nikola , Tadić Danijela , Klipa Đuro , Đurić Goran
Published: December 2022

In recent decades, many researchers and practitioners have believed that reaching a high level of business excellence leads to the continuous realization of a set of business goals. In the literature, a vast number of models for business excellence evaluation that contain different criteria depending on the cultural, technological, organizational, and socio-economic factors can be found. The aims of the proposed fuzzy two-stage model are to address some of the main shortcomings of the EFQM2020 model and to adapt it to the needs of process manufacturing. The relative importance of quality criteria and their values are presented by pre-defined linguistic expressions modeled by the triangular fuzzy numbers. The determination of the weight vector of criteria is stated as a fuzzy group decision-making problem and determined by using the fuzzy best-worst method. The proposed fuzzy multi-objective optimization by ratio analysis is implemented for determining the rank of enterprises. The management initiatives that should lead to the improvement of business excellence should be based on the business practices of enterprises that are highly placed in the rank. Testing and verification of the proposed model are performed on real data originating from enterprises operating in the same economic sector.
Yolov5 Series Algorithm for Road Marking Sign Identification
Authors: Dewi Christine , Chen Rung-Ching , Zhuang Yong-Cun , Christanto Henoch Juli
Published: December 2022

Road markings and signs provide vehicles and pedestrians with essential information that assists them to follow the traffic regulations. Road surface markings include pedestrian crossings, directional arrows, zebra crossings, speed limit signs, other similar signs and text, and so on, which are usually painted directly onto the road surface. Road markings fulfill a variety of important functions, such as alerting drivers to the potentially hazardous road section, directing traffic, prohibiting certain actions, and slowing down. This research paper provides a summary of the Yolov5 algorithm series for road marking sign identification, which includes Yolov5s, Yolov5m, Yolov5n, Yolov5l, and Yolov5x. This study explores a wide range of contemporary object detectors, such as the ones that are used to determine the location of road marking signs. Performance metrics monitor important data, including the quantity of BFLOPS, the mean average precision (mAP), and the detection time (IoU). Our findings shows that Yolov5m is the most stable method compared to other methods with 76% precision, 86% recall, and 83% mAP during the training stage. Moreover, Yolov5m and Yolov5l achieve the highest score, mAP 87% on average in the testing stage. In addition, we have created a new dataset for road marking signs in Taiwan, called TRMSD.
Pulsed Electric Field Processing of Red Wine: Effect on Wine Quality and Microbial Inactivation
Authors: Akdemir Evrendilek Gulsun
Published: December 2022

Pulsed electric field (PEF) treatment of red wine samples with energies changing from 2.4 to 13.2 kJ to inactivate Saccharomyces cerevisiae, Hansenula anomala, Candida lipolytica, Lactobacillus delbrueckii ssp. bulgaricus, and Escherichia coli O157:H7 with the determination of the changes in the quality and sensory properties in addition to metal ion concentration (Na, Mg, K, and Mn) were explored. Increased applied energy resulted in a significant increase in pH, conductivity, lightness (L*), yellowness (b*), and total phenolic substance content with significant inactivation of all microorganisms with no significant change in metal ion concentration. Sensory properties of particle status, sour taste, and aftertaste were significantly decreased, whereas the other measured properties were significantly increased by 13.2 kJ PEF treatment (p < 0.05). Joint optimization studies for the most optimal processing parameters for the measured properties were 488 s, 0.13 kJ, and 0.22 kV; 488 s, 13.2 kJ, and 31 kV; 348 s, 9.39 kJ, and 31 kV/cm; and 488 s, 13.2 kJ, and 0 kV EFS, with 0.79, 0.69, 1.00, and 0.72 composite desirability, respectively.
Conjugation Mechanism for Pneumococcal Glycoconjugate Vaccines: Classic and Emerging Methods
Authors: Morais Victor , Suarez Norma
Published: December 2022

Licensed glycoconjugate vaccines are generally prepared using native or sized polysaccharides coupled to a carrier protein through random linkages along the polysaccharide chain. These polysaccharides must be chemically modified before covalent linking to a carrier protein in order to obtain a more defined polysaccharide structure that leads to a more rational design and safer vaccines. There are classic and new methods for site-selective glycopolysaccharide conjugation, either chemical or enzymatic modification of the polysaccharide length or of specific amino acid residues of the protein carrier. Here, we discuss the state of the art and the advancement of conjugation of S. pneumoniae glycoconjugate vaccines based on pneumococcal capsular polysaccharides to improve existing vaccines.
A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph
Authors: Chuo Yueh , Lin Wen-Ming , Chen Tsung-Yi , Chan Mei-Ling , Chang Yu-Sung , Lin Yan-Ru , Lin Yuan-Jin , Shao Yu-Han , Chen Chiung-An , Chen Shih-Lun , Abu Patricia Angela R.
Published: December 2022

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.
Phyto-Synthesis, Characterization, and In Vitro Antibacterial Activity of Silver Nanoparticles Using Various Plant Extracts
Authors: Ahmad Bilal , Chang Li , Satti Usama , Rehman Sami , Arshad Huma , Mustafa Ghazala , Shaukat Uzma , Wang Fenghua , Tong Chunyi
Published: December 2022

Aloe vera, Mentha arvensis (mint), Coriandrum sativum (coriander), and Cymbopogon citratus (lemongrass) leaf extracts were used to synthesize stable silver nanoparticles (Ag-NPs) by green chemistry. UV–vis spectrophotometry, X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy dispersive X-ray (EDX) spectroscopy techniques were used to characterize these biosynthesized nanoparticles. The data indicated that the silver nanoparticles were successfully synthesized, and the narrower particle size distribution was at 10–22 nm by maintaining a specific pH. As a short-term post-sowing treatment, Ag-NP solutions of different sizes (10 and 50 ppm) were introduced to mung bean seedlings, and the overall increase in plant growth was found to be more pronounced at 50 ppm concentration. The antibacterial activity of Ag-NPs was also investigated by disc diffusion test, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) test. The zones of inhibition (ZOI) were shown by Escherichia coli (E. coli) (1.9, 2.1, 1.7, and 2 mm), followed by Staphylococcus aureus (S. aureus) (1.8, 1.7, 1.6, and 1.9 mm), against coriander, mint, Aloe vera, and lemongrass, respectively. MIC and MBC values of E. coli, and S. aureus ranged from 7 to 8 µg/mL. Overall, this study demonstrates that Ag-NPs exhibit a strong antimicrobial activity and thus might be developed as a new type of antimicrobial agent for the treatment of bacterial infection.
The Glacier Ice Worm, Mesenchytraeus solifugus, Elevates Mitochondrial Inorganic Polyphosphate (PolyP) Levels in Response to Stress
Authors: Osorio Teresa , Scoma Ernest R. , Shain Daniel H. , Melissaratos Diana S. , Riggs Lindsey M. , Hambardikar Vedangi , Solesio Maria E.
Published: December 2022

The inorganic polymer, polyphosphate (polyP), is present in all organisms examined to date with putative functions ranging from the maintenance of bioenergetics to stress resilience and protein homeostasis. Bioenergetics in the glacier-obligate, segmented worm, Mesenchytraeus solifugus, is characterized by a paradoxical increase in intracellular ATP levels as temperatures decline. We show here that steady-state, mitochondrial polyP levels vary among species of Annelida, but were elevated only in M. solifugus in response to thermal stress. In contrast, polyP levels decreased with temperature in the mesophilic worm, Enchytraeus crypticus. These results identify fundamentally different bioenergetic strategies between closely related annelid worms, and suggest that I worm mitochondria maintain ATP and polyP in a dynamic equilibrium.