bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge, Attitude, and Behavior of Parents Regarding Early Childhood Caries Prevention of Preschool Children in Western Region of Saudi Arabia: A Cross-Sectional Study
Authors: Nassar Afnan A. , Fatani Buthaina A. , Almobarak Othoob T. , Alotaibi Shahad I. , Alhazmi Reem A. , Marghalani Abdullah A.
Published: November 2022

The knowledge and beliefs of parents have an impact on their children’s general and oral health. The objective of this study is to assess knowledge, attitude, and behavior of parents and identify possible associated factors of early childhood caries (ECC) in the western region of Saudi Arabia. Parents aged 18 and older were invited to complete a self-administered, validated questionnaire. Questions covered demographics, knowledge, attitude, and behavior regarding ECC etiology and prevention. Of the 644 parents who completed the questionnaire, 51.4% were not aware of the early signs of tooth decay, such as white lines. Pain and trauma were reported as the major reasons for a child to visit a dentist (42.4%). Only 37% of parents were aware of the importance of fissure sealant, and only 23% knew how it is applied. Compared with mothers, fathers were significantly less aware of hidden sugar and the relation between the frequency of consuming breast milk or bottled milk and caries development (p = 0.001). Similarly, fathers were more likely to believe that children could achieve effective teeth cleaning without parental assistance (p = 0.001). Preventive guidance should be provided by oral healthcare providers in Saudi Arabia to improve parents’ knowledge of ECC prevention.
Vegetation Cover Estimation in Semi-Arid Shrublands after Prescribed Burning: Field-Ground and Drone Image Comparison
Authors: Pérez-Luque Antonio J. , Ramos-Font María Eugenia , Tognetti Barbieri Mauro J. , Tarragona Pérez Carlos , Calvo Renta Guillermo , Robles Cruz Ana Belén
Published: November 2022

The use of drones for vegetation monitoring allows the acquisition of large amounts of high spatial resolution data in a simple and fast way. In this study, we evaluated the accuracy of vegetation cover estimation by drones in Mediterranean semi-arid shrublands (Sierra de Filabres; Almería; southern Spain) after prescribed burns (2 years). We compared drone-based vegetation cover estimates with those based on traditional vegetation sampling in ninety-six 1 m2 plots. We explored how this accuracy varies in different types of coverage (low-, moderate- and high-cover shrublands, and high-cover alfa grass steppe); as well as with diversity, plant richness, and topographic slope. The coverage estimated using a drone was strongly correlated with that obtained by vegetation sampling (R2 = 0.81). This estimate varied between cover classes, with the error rate being higher in low-cover shrublands, and lower in high-cover alfa grass steppe (normalized RMSE 33% vs. 9%). Diversity and slope did not affect the accuracy of the cover estimates, while errors were larger in plots with greater richness. These results suggest that in semi-arid environments, the drone might underestimate vegetation cover in low-cover shrublands.
The Influence of Venture Capital on Growth through Innovation and the Use of Internet Technology in Micro and Small Industries (MSIs) in Indonesia
Authors: Sofia Myrna , Tan Firwan , Bachtiar Nasri , Putra Febriandi Prima
Published: November 2022

One challenge that Micro and Small Industries (MSIs) face is financing. Venture capital is one of the financing options to overcome capital problems by providing financial and non-financial support. This study aims to analyze the influence of venture capital on the growth of Micro and Small Industries (MSIs) in Indonesia through innovation and internet technology as the intervening variables. The research data were collected from a survey (Statistics Indonesia) in 2019 with 83,616 Micro and Small Industries (MSIs) as the samples. This research used the STATA logistic regression method and the Sobel test. The results showed that venture capital had a direct positive but insignificant influence on growth. Meanwhile, innovation could mediate the effect of venture capital on growth, yet the use of internet technology could not mediate the impact of venture capital on growth. This study proves that venture capital increases innovation activities and accelerates growth.
Supporting Institutional Change through Interracial Dialogue among Leaders
Authors: D’Costa Stephanie , Mascarenhas Mridula , Lovell Jennifer
Published: November 2022

This paper elucidates how using a dialogic approach to interracial conversations supported two cohorts of campus leaders to engage in organizational change. Dialogue centralizes relationship-building as a key mechanism for addressing organizational problems collaboratively. This paper describes the processes undertaken and lessons learned in the interracial dialogue program, which could serve as a guide for institutions of higher education (IHE) interested in anti-racism work. Findings include the ways relationships supported growth in understanding of the racialized experiences of BIPOC participants, differences in emotional taxation for participants given their racial identities, and the importance of acknowledging pain before moving towards change.
A Lightweight Border Patrol Object Detection Network for Edge Devices
Authors: Yue Lei , Ling Haifeng , Yuan Jianhu , Bai Linyuan
Published: November 2022

Border patrol object detection is an important basis for obtaining information about the border patrol area and for analyzing and determining the mission situation. Border Patrol Staffing is now equipped with medium to close range UAVs and portable reconnaissance equipment to carry out its tasks. In this paper, we designed a detection algorithm TP-ODA for the border patrol object detection task in order to improve the UAV and portable reconnaissance equipment for the task of border patrol object detection, which is mostly performed in embedded devices with limited computing power and the detection frame imbalance problem is improved; finally, the PDOEM structure is designed in the neck network to optimize the feature fusion module of the algorithm. In order to verify the improvement effect of the algorithm in this paper, the Border Patrol object dataset BDP is constructed. The experiments show that, compared to the baseline model, the TP-ODA algorithm improves mAP by 2.9%, reduces GFLOPs by 65.19%, reduces model volume by 63.83% and improves FPS by 8.47%. The model comparison experiments were then combined with the requirements of the border patrol tasks, and it was concluded that the TP-ODA model is more suitable for UAV and portable reconnaissance equipment to carry and can better fulfill the task of border patrol object detection.
Recognition of Emotions in Speech Using Convolutional Neural Networks on Different Datasets
Authors: Zielonka Marta , Piastowski Artur , Czyżewski Andrzej , Nadachowski Paweł , Operlejn Maksymilian , Kaczor Kamil
Published: November 2022

Artificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extraction method better represents emotions and how big the differences in efficiency are in this context. The conducted studies demonstrated that mel-spectrograms are a better-suited data type for training CNN-based speech emotion recognition (SER). The research experiments employed five popular datasets: Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAVEE), Toronto Emotional Speech Set (TESS), and The Interactive Emotional Dyadic Motion Capture (IEMOCAP). Six different classes of emotions were used: happiness, anger, sadness, fear, disgust, and neutral. However, some experiments were prepared to recognize just four emotions due to the characteristics of the IEMOCAP dataset. A comparison of classification efficiency on different datasets and an attempt to develop a universal model trained using all datasets were also performed. This approach brought an accuracy of 55.89% when recognizing four emotions. The most accurate model for six emotion recognition was trained and achieved 57.42% accuracy on a combination of four datasets (CREMA-D, RAVDESS, SAVEE, TESS). What is more, another study was developed that demonstrated that improper data division for training and test sets significantly influences the test accuracy of CNNs. Therefore, the problem of inappropriate data division between the training and test sets, which affected the results of studies known from the literature, was addressed extensively. The performed experiments employed the popular ResNet18 architecture to demonstrate the reliability of the research results and to show that these problems are not unique to the custom CNN architecture proposed in experiments. Subsequently, the label correctness of the CREMA-D dataset was studied through the employment of a prepared questionnaire.
GPGCN: A General-Purpose Graph Convolution Neural Network Accelerator Based on RISC-V ISA Extension
Authors: Tang Wenkai , Zhang Peiyong
Published: November 2022

In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, each with their own characteristics, but their common disadvantage is that the hardware architecture is not programmable and it is optimized for a specific network and dataset. They may not support acceleration for different GCNs and may not achieve optimal hardware resource utilization for datasets of different sizes. Therefore, given the above shortcomings, and according to the development trend of traditional neural network accelerators, this paper proposes and implements GPGCN: a general-purpose GCNs accelerator architecture based on RISC-V instruction set extension, providing the software programming freedom to support acceleration for various GCNs, and achieving the best acceleration efficiency for different GCNs with different datasets. Compared with traditional CPU, and traditional CPU with vector expansion, GPGCN achieves above 1001×, 267× speedup for GCN with the Cora dataset. Compared with dedicated accelerators, GPGCN has software programmability and supports the acceleration of more GCNs.
Deep-COVID: Detection and Analysis of COVID-19 Outcomes Using Deep Learning
Authors: Khalil Muhammad Ibrahim , Rehman Saif Ur , Alhajlah Mousa , Mahmood Awais , Karamat Tehmina , Haneef Muhammad , Alhajlah Ashwaq
Published: November 2022

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The proposed framework achieves an accuracy of 97%. Our model’s experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification.
Artificial Intelligence Algorithms for Detecting and Classifying MQTT Protocol Internet of Things Attacks
Authors: Alzahrani Ali , Aldhyani Theyazn H. H.
Published: November 2022

The Internet of Things (IoT) grew in popularity in recent years, becoming a crucial component of industrial, residential, and telecommunication applications, among others. This innovative idea promotes communication between physical components, such as sensors and actuators, to improve process flexibility and efficiency. Smart gadgets in IoT contexts interact using various message protocols. Message queuing telemetry transfer (MQTT) is a protocol that is used extensively in the IoT context to deliver sensor or event data. The aim of the proposed system is to create an intrusion detection system based on an artificial intelligence algorithm, which is becoming essential in the defense of the IoT networks against cybersecurity threats. This study proposes using a k-nearest neighbors (KNN) algorithm, linear discriminant analysis (LDA), a convolutional neural network (CNN), and a convolutional long short-term memory neural network (CNN-LSTM) to identify MQTT protocol IoT intrusions. A cybersecurity system based on artificial intelligence algorithms was examined and evaluated using a standard dataset retrieved from the Kaggle repository. The dataset was injected by five attacks, namely brute-force, flooding, malformed packet, SlowITe, and normal packets. The deep learning algorithm achieved high performance compared with the developing security system using machine learning algorithms. The performance accuracy of the KNN method was 80.82%, while the accuracy of the LDA algorithm was 76.60%. The CNN-LSTM model attained a high level of precision (98.94%) and is thus very effective at detecting intrusions in IoT settings.
A Differential-to-Single-Ended Converter Based on Enhanced Body-Driven Current Mirrors Targeting Ultra-Low-Voltage OTAs
Authors: Della Sala Riccardo , Centurelli Francesco , Scotti Giuseppe , Tommasino Pasquale , Trifiletti Alessandro
Published: November 2022

In this work, an ultra-low-voltage (ULV) technique to improve body-driven current mirrors is proposed. The proposed technique is employed to improve the performance of conventional differential-to-single-ended (D2S) converters which at these low voltages suffer from a low common-mode rejection ratio (CMRR). In addition, the technique aims to improve the performance of the conventional D2S also under a large signal swing and with respect to the process, voltage and temperature (PVT) variations, resulting in a very low distortion, high current mirror accuracy and robust performance. An enhanced body-driven current mirror was designed in a 130 nm CMOS technology from STMicroelectronics and an exhaustive campaign of simulations was conducted to confirm the effectiveness of the strategy and the robustness of the results. The enhanced D2S was also employed to design a ULV operational transconductance amplifier (OTA) and a comparison with an OTA based on a conventional D2S was provided. The simulation results have shown that the proposed enhanced D2S allows achieving the ULV OTAs with a CMRR and a PSRR which are 18 and 9 dB higher than the ones obtained with the conventional D2S topology, respectively. Moreover, the linearity performance is also improved as shown by the THD, whose value is decreased of about 5 dB.
Updates of Genomics and Proteomics of Parathyroid Carcinoma
Authors: Kong Sung Hye
Published: November 2022

Parathyroid carcinoma is a rare disease that needs an additional diagnostic tool and wide therapeutic options. The genomics and proteomics approach may help to find the tools to improve the prognosis of the disease by early detection and metastatic control. The findings from genomics were mainly CDC73, PRUNE2, CCND1, and genes related to PI3K/AKT/mTOR and Wnt pathways. CDC73, PRUNE2, and CCND1 were closely related to each other, and PRUNE2 and CCND1 genes are related to expression levels of parafibromin protein, which may aid in supporting the definite diagnosis of the disease. PI3K/AKT/mTOR and Wnt pathways could be a potential therapeutic target for the disease, which needs further basket trials to prove the concept. In this review, current findings from genomics and proteomics studies in parathyroid carcinoma were reviewed.
Impact of Environmental Gradients on Phenometrics of Major Forest Types of Kumaon Region of the Western Himalaya
Authors: Dugesar Vikas , Satish Koppineedi V. , Pandey Manish K. , Srivastava Prashant K. , Petropoulos George P. , Anand Akash , Behera Mukunda Dev
Published: November 2022

Understanding ecosystem functional behaviour and its response to climate change necessitates a detailed understanding of vegetation phenology. The present study investigates the effect of an elevational gradient, temperature, and precipitation on the start of the season (SOS) and end of the season (EOS), in major forest types of the Kumaon region of the western Himalaya. The analysis made use of the Normalised Difference Vegetation Index (NDVI) time series that was observed by the optical datasets between the years 2001 and 2019. The relationship between vegetation growth stages (phenophases) and climatic variables was investigated as an interannual variation, variation along the elevation, and variation with latitude. The SOS indicates a delayed trend along the elevational gradient (EG) till mid-latitude and shows an advancing pattern thereafter. The highest rate of change for the SOS and EOS is 3.3 and 2.9 days per year in grassland (GL). The lowest rate of temporal change for SOS is 0.9 days per year in mixed forests and for EOS it is 1.2 days per year in evergreen needle-leaf forests (ENF). Similarly, the highest rate of change in SOS along the elevation gradient is 2.4 days/100 m in evergreen broadleaf forest (EBF) and the lowest is −0.7 days/100 m in savanna, and for EOS, the highest rate of change is 2.2 days/100 m in EBF and lowest is −0.9 days/100 m in GL. Winter warming and low winter precipitation push EOS days further. In the present study area, due to winter warming and summer dryness, despite a warming trend in springseason or springtime, onset of the vegetation growth cycle shows a delayed trend across the vegetation types. As vegetation phenology responds differently over heterogeneous mountain landscapes to climate change, a detailed local-level observational insight could improve our understanding of climate change mitigation and adaptation policies.
Unveiling the Microbial Ecology behind Mezcal: A Spirit Drink with a Growing Global Demand
Authors: Becerra-Lucio Patricia Alejandra , Diego-García Elia , Guillén-Navarro Karina , Peña-Ramírez Yuri Jorge
Published: November 2022

The advent of omics has expanded our knowledge of microbial ecology behind Mezcal, a fermented spirit made from the juices of cooked Agave plants (Agave spp., Asparagaceae). Mezcal has been produced in Mexico for over 200 years, however, has been in high demand since its discovery by international markets in the last decade. Mezcal is appreciated for its diverse and complex sensory profile, which is tied to the geographic and environmental diversity of the different Mezcal-producing regions. This regional typicity is brought about by spontaneous fermentation consortia that act in loosely controlled artisanal fermentation processes. Previous works have mainly concentrated on microorganisms involved in the biosynthesis of alcohol and other volatile compounds, or from a different perspective, on culturable microorganisms (mainly yeasts) influencing the taste profile. Attention has been aimed at the richness of microbial populations in point events or under laboratory conditions, which leaves much of the biological richness out of account. Omics techniques have become powerful tools for characterizing the composition of autochthonous fermentation microbiota, regional or endemic features, and ecological processes that determine the dynamics of Mezcal fermentation. The analyses of genetic material, proteins, and metabolites allow disentangling the biological complexity of Mezcal production. This review presents the reader with an up-to-date overview of publications that discuss microbial communities in Mezcal fermentation, metabolic pathways regulated by microbial interactions, and the application of omics to characterize the spontaneous fermenting microbiota conformation and dynamics considering the subjacent ecological processes.
Study of the Effect of Dietary Agavin Supplementation in Blood Parameters and Antioxidant Enzymes of Juvenile Nile Tilapia (Oreochromis niloticus) under Stress Conditions
Authors: Flores-Méndez Lizeth Carolina , Lizárraga-Velázquez Cynthia E. , Sánchez-Gutiérrez Erika Y. , Arrizon Javier , Leyva-López Nayely , Hernández Crisantema
Published: November 2022

High-density aquaculture produces oxidative stress that affects fish welfare and leads to poor growth. Agavin is a fructan that regulates the antioxidant response and inhibits stress-related alterations in blood metabolites (cortisol and blood lipids), preventing oxidative damage in fish. This study evaluated the effect of dietary agavin on the hepatic antioxidant response and blood metabolites in Nile tilapia under high rearing density. A negative control, a positive control, and experimental diets supplemented with 20 and 40 g/kg agavin were formulated. Nile tilapia (1.04 g ± 0.01 g initial body weight) were fed for 80 days to evaluate the antioxidant response and blood metabolites. After 90 days, fish were exposed to high density (63 kg/m3) for twenty days, except for the negative control (low-density). Under high-density stress, cortisol, cholesterol, and triglyceride levels decreased in fish fed diets with agavin compared with the positive control (high density). CAT and SOD activity significantly increased in fish fed agavin diets; malondialdehyde levels decreased under high density compared with both controls. These results suggest that dietary agavin supplementation promotes the antioxidant response and prevents stress-related alterations in blood parameters in Nile tilapia under high rearing density through its antioxidant properties and, probably, has a prebiotic effect.
Revisiting Protein Quality Assessment to Include Alternative Proteins
Authors: Monsonego Ornan Efrat , Reifen Ram
Published: November 2022

The high demand for novel and existing sustainable protein sources (e.g., legumes, insects, algae, and cultured meat) to replace the animal-based sources is becoming crucial. This change in protein consumption calls for the re-evaluation of the current methods to assess their quality and bioavailability. The two conventional scores for PDCAAS (protein digestibility-corrected AA score) and DIAAS (Digestible Indispensable AA Score) have their limitations and have not been re-evaluated and updated to address plant and novel proteins’ quality. We suggest a sensitive physiological preclinical model that can rapidly and confidently address proteins from different sources. Our model is based on the postnatal growth, a major parameter for development and health in children, that influenced by environmental nutritional and lifestyle factors. Our results demonstrate that, with an appropriate amount of protein in the diet, almost all tested proteins performed as well as casein, the animal source. However, upon restriction (10% of calories), all alternative sources did not accomplish normal growth performance. Surprisingly, when compared to PDCAAS and DIAAS parameters obtained from the literature, no correlations were found between growth performance and these parameters, demonstrating their limitations.
Ultrasound-Assisted Extraction of Polyphenols from Maritime Pine Residues with Deep Eutectic Solvents
Authors: Duarte Hugo , Gomes Valentim , Aliaño-González María José , Faleiro Leonor , Romano Anabela , Medronho Bruno
Published: November 2022

Deep eutectic solvents represent an important alternative in the field of green solvents due to their low volatility, non-toxicity, and low synthesis cost. In the present investigation, we propose the production of enriched polyphenolic extracts from maritime pine forest residues via an ultrasound-assisted approach. A Box–Behnken experimental design with a response surface methodology was used with six variables to be optimized: solid-to-solvent ratio, water percentage, temperature and time of extraction, amplitude, and catalyst concentration. The mixture of levulinic and formic acids achieved the highest extraction yield of polyphenols from pine needle and bark biomass. In addition, the solid-to-solvent ratio was found to be the only influential variable in the extraction (p-value: 0.0000). The optimal conditions were established as: 0.1 g of sample in 10 mL of LA:FA (70:30%, v/v) with 0% water and 0 M H2SO4 heated to 30 °C and extracted during 40 min with an ultrasound amplitude of 80% at 37 kHz. The bioactive properties of polyphenol-enriched extracts have been proven with significant antioxidant (45.90 ± 2.10 and 66.96 ± 2.75 mg Trolox equivalents/g dw) and antimicrobial activities. The possibility to recycle and reuse the solvent was also demonstrated; levulinic acid was successfully recovered from the extracts and reused in novel extractions on pine residues. This research shows an important alternative to obtaining polyphenol-enriched extracts from forest residues that are commonly discarded without any clear application, thus opening an important window toward the valorization of such residues.
Boundary Value Problem for Multi-Term Nonlinear Delay Generalized Proportional Caputo Fractional Differential Equations
Authors: Agarwal Ravi P. , Hristova Snezhana
Published: November 2022

A nonlocal boundary value problem for a couple of two scalar nonlinear differential equations with several generalized proportional Caputo fractional derivatives and a delay is studied. The exact solution of the scalar nonlinear differential equation with several generalized proportional Caputo fractional derivatives with different orders is obtained. A mild solution of the boundary value problem for the multi-term nonlinear couple of the given fractional equations is defined. The connection between the mild solution and the solution of the studied problem is discussed. As a partial case, several results for the nonlocal boundary value problem for the linear and non-linear multi-term Caputo fractional differential equations are provided. The results generalize several known results in the literature.
Investigating the Effect of Volatiles on Sub-23 nm Particle Number Measurements for a Downsized GDI Engine with a Catalytic Stripper and Digital Filtering
Authors: Pfau Sebastian A. , Haffner-Staton Ephraim , La Rocca Antonino , Cairns Alasdair
Published: November 2022

Recent efforts of both researchers and regulators regarding particulate emissions have focused on the contribution and presence of sub-23 nm particulates. Despite being previously excluded from emissions legislation with the particle measurement programme (PMP), the latest regulatory proposals suggest lowering the cut-off sizes for counting efficiencies and the use of catalytic strippers to include solid particles in this size range. This work investigated particulate emissions of a 1.0 L gasoline direct injection (GDI) engine using a differential mobility spectrometer (DMS) in combination with a catalytic stripper. Direct comparison of measurements taken with and without the catalytic stripper reveals that the catalytic stripper noticeably reduced variability in sub-23 nm particle concentration measurements. A significant portion of particles in this size regime remained (58–92%), suggesting a non-volatile nature for these particles. Digital filtering functions for imposing defined counting efficiencies were assessed with datasets acquired with the catalytic stripper; i.e., particle size distributions (PSDs) with removed volatiles. An updated filtering function for counting efficiency thresholds of d65 = 10 nm and d90 = 15 nm showed an increase in particulate numbers between 1.5% and up to 11.2%, compared to the closest previous digital filtering function. However, this increase is highly dependent on the underlying PSD. For a matrix of operating conditions (1250 to 2250 rpm and fast-idle to 40 Nm brake torque), the highest emissions occurred at fast-idle 1250 rpm with 1.93 × 108 #/cm3 using the updated filtering function and catalytic stripper. This setup showed an increase in particulate number of +27% to +390% over the test matrix when compared to DMS measurements without the catalytic stripper and applied counting efficiency thresholds of d50 = 23 nm and d90 = 41.
Suicide-Related Single Nucleotide Polymorphisms, rs4918918 and rs10903034: Association with Dementia in Older Adults
Authors: Abramova Olga , Soloveva Kristina , Zorkina Yana , Gryadunov Dmitry , Ikonnikova Anna , Fedoseeva Elena , Emelyanova Marina , Ochneva Aleksandra , Andriushchenko Nika , Pavlov Konstantin , Pavlova Olga , Ushakova Valeriya , Syunyakov Timur , Andryushchenko Alisa , Karpenko Olga , Savilov Victor , Kurmishev Marat , Andreuyk Denis , Gurina Olga , Chekhonin Vladimir , Kostyuk Georgy , Morozova Anna
Published: November 2022

Dementia has enormous implications for patients and the health care system. Genetic markers are promising for detecting the risk of cognitive impairment. We hypothesized that genetic variants associated with suicide risk might significantly increase the risk of cognitive decline because suicide in older adults is often a consequence of cognitive impairment. We investigated several single-nucleotide polymorphisms that were initially associated with suicide risk in dementia older adults and identified the APOE gene alleles. The study was performed with subjects over the age of 65: 112 patients with dementia and 146 healthy volunteers. The MMSE score was used to assess cognitive functions. Study participants were genotyped using real-time PCR (APOE: rs429358, rs7412; genes associated with suicide: rs9475195, rs7982251, rs2834789, rs358592, rs4918918, rs3781878, rs10903034, rs165774, rs16841143, rs11833579 rs10898553, rs7296262, rs3806263, and rs2462021). Genotype analysis revealed the significance of APOEε4, APOEε2, and rs4918918 (SORBS1) when comparing dementia and healthy control groups. The association of APOEε4, APOEε2, and rs10903034 (IFNLR1) with the overall MMSE score was indicated. The study found an association with dementia of rs4918918 (SORBS1) and rs10903034 (IFNLR1) previously associated with suicide and confirmed the association of APOEε4 and APOEε2 with dementia.
ABCG2 Polymorphisms and Predictive Fluoroquinolone Phototoxicity in Nondomestic Felids
Authors: Gochenauer Alexandria E. , Dreger Dayna L. , Davis Brian W. , Cook Shawna , Barber Katie E. , Ekenstedt Kari J.
Published: November 2022

Fluoroquinolones are a widely used class of chemotherapeutics within veterinary medicine, prized for their broad-spectrum bactericidal activity. These drugs present a known risk of retinal phototoxicity in domestic cats (Felis catus); therefore, using lower doses and alternative antibiotic classes is encouraged in this species. This adverse drug effect of fluoroquinolones, and enrofloxacin specifically, has been determined to be species-specific in domestic felids. Four feline-specific missense variants in ABCG2 result in four amino acid changes (E159M, S279L, H283Q, and T644I) that are unique to the domestic cat compared with multiple other nonfeline mammalian species. These changes alter the ABCG2 protein involved with the cellular transmembrane transport of drugs, including fluoroquinolones, making the protein functionally defective in domestic cats. The predisposition to fluoroquinolone-mediated phototoxicity in nondomestic felids was explored in this study. At least eight nondomestic felids share the four ABCG2 missense variants with domestic cats, and eleven other felids shared at least three of the four domestic cat variants. Taken together, these results suggest the genetic potential for nondomestic felids to also experience fluoroquinolone-induced retinal phototoxicity; therefore, cautions similar to those for domestic cats should be followed for these drugs in the entire feline taxon.
Uricase-Deficient Larval Zebrafish with Elevated Urate Levels Demonstrate Suppressed Acute Inflammatory Response to Monosodium Urate Crystals and Prolonged Crystal Persistence
Authors: Linnerz Tanja , Sung Yih Jian , Rolland Leah , Astin Jonathan W. , Dalbeth Nicola , Hall Christopher J.
Published: November 2022

Gout is caused by elevated serum urate leading to the deposition of monosodium urate (MSU) crystals that can trigger episodes of acute inflammation. Humans are sensitive to developing gout because they lack a functional urate-metabolizing enzyme called uricase/urate oxidase (encoded by the UOX gene). A hallmark of long-standing disease is tophaceous gout, characterized by the formation of tissue-damaging granuloma-like structures (‘tophi’) composed of densely packed MSU crystals and immune cells. Little is known about how tophi form, largely due to the lack of suitable animal models in which the host response to MSU crystals can be studied in vivo long-term. We have previously described a larval zebrafish model of acute gouty inflammation where the host response to microinjected MSU crystals can be live imaged within an intact animal. Although useful for modeling acute inflammation, crystals are rapidly cleared following a robust innate immune response, precluding analysis at later stages. Here we describe a zebrafish uox null mutant that possesses elevated urate levels at larval stages. Uricase-deficient ‘hyperuricemic’ larvae exhibit a suppressed acute inflammatory response to MSU crystals and prolonged in vivo crystal persistence. Imaging of crystals at later stages reveals that they form granuloma-like structures dominated by macrophages. We believe that uox−/− larvae will provide a useful tool to explore the transition from acute gouty inflammation to tophus formation, one of the remaining mysteries of gout pathogenesis.
Probabilistic Forecasts of Flood Inundation Maps Using Surrogate Models
Authors: Zanchetta Andre D. L. , Coulibaly Paulin
Published: November 2022

The use of data-driven surrogate models to produce deterministic flood inundation maps in a timely manner has been investigated and proposed as an additional component for flood early warning systems. This study explores the potential of such surrogate models to forecast multiple inundation maps in order to generate probabilistic outputs and assesses the impact of including quantitative precipitation forecasts (QPFs) in the set of predictors. The use of a k-fold approach for training an ensemble of flood inundation surrogate models that replicate the behavior of a physics-based hydraulic model is proposed. The models are used to forecast the inundation maps resulting from three out-of-the-dataset intense rainfall events both using and not using QPFs as a predictor, and the outputs are compared against the maps produced by a physics-based hydrodynamic model. The results show that the k-fold ensemble approach has the potential to capture the uncertainties related to the process of surrogating a hydrodynamic model. Results also indicate that the inclusion of the QPFs has the potential to increase the sharpness, with the tread-off also increasing the bias of the forecasts issued for lead times longer than 2 h.
The Alonissos Study: Cross-Sectional Study of the Community Respiratory Health Status in a Greek Healthcare Access Underprivileged Island
Authors: Kassas Petros , Gogou Eudoxia , Varsamas Charalampos , Vogiatzidis Konstantinos , Psatha Aggeliki , Pinaka Maria , Siachpazidou Dimitra , Sistou Alexandra , Papazoglou Eleftherios D. , Kalousi Despoina , Vatzia Konstantina , Astara Kyriaki , Tsiouvakas Nikolaos , Zarogiannis Sotirios G. , Gourgoulianis Konstantinos I.
Published: November 2022

In this study, we investigated the self-reported (questionnaire-based) prevalence of Obstructive Sleep Apnoea Syndrome (OSAS) and the prevalence of Chronic Obstructive Pulmonary Diseases (COPD) in the context of demographics and adherence to the Mediterranean diet in the general population of Alonissos, a non-profit line island in Greece (i.e., with scarce boat transportation to the mainland). In this cross-sectional study, 236 inhabitants of Alonissos participated (circa 10% of the island’s population), and 115 males and 121 females were evaluated with appropriate questionnaires for OSAS, COPD, and adherence to the Mediterranean diet and subsequently underwent spirometry testing to establish COPD diagnosis. The self-reported prevalence of OSAS and COPD was 9.44% and 18.8%, respectively. However, only 8.99% of the participants were diagnosed with COPD based on their spirometry testing. Adherence to the Mediterranean Diet was moderate. The high prevalence of COPD and OSAS in this underprivileged island in terms of healthcare access highlights the need for improvements in health promotion and primary healthcare provision in non-profit line Greek islands.
Contemporary Collecting in a Pandemic: Challenges and Solutions for Documenting the COVID-19 Pandemic in Memory Organizations
Authors: Zuanni Chiara
Published: November 2022

This paper draws on a survey of contemporary collecting projects aiming to document the COVID-19 pandemic in museums and other memory organizations. The survey focused on European memory organizations and investigated the practices they adopted in collecting, accessioning, recording, preserving, and displaying material and immaterial witnesses of the pandemic. This paper presents the results of the survey, with a particular attention on the challenges faced by these projects in relation to born-digital objects. It discusses how organizations were able to quickly launch initiatives aimed at collecting memories of the pandemic, drawing on different collection methods, adapting to the circumstances, and using a proactive attitude to reach out to different communities. The paper highlights the solutions adopted to ensure legal compliance in these projects and discusses the need for ethical considerations in relation to the collection of traumatic memories. It suggests that these collecting projects are likely to face significant challenges in the subsequent processing of this material—due to its volume and the need for new digital curation and preservation workflows. Therefore, the paper argues that these projects could also lead to a renewed attention and collaboration across the heritage sector on issues of digital curation and preservation.
Post-Bloom CPPU Application Is Effective at Improving Fruit Set and Suppressing Coloration but Ineffective at Increasing Fruit Size in Litchi
Authors: Liu Xinsheng , Luo Yecheng , Wang Huicong , Huang Xuming
Published: November 2022

This study aimed to provide a reference for the practical use of forchlorfenuron (CPPU) in the improvement of fruit set and quality (especially size) in litchi (Litchi chinensis Sonn.). CPPU at 5 mg/L was sprayed to fruit clusters of ‘Feizixiao’ at 2, 4, 6 and 8 weeks after female bloom (WAFB) in 2017 and at 2, 4, 6, 8, 9 or 10 WAFB in 2018, with spraying water as the control. The treatments were all effective at suppressing fruit abscission, resulting in higher fruit retention at harvest. Except for treatment at 2 WAFB, which caused stunted fruit and significantly reduced fruit weight, all the treatments had no significant influence on fruit weight and flesh (aril) weight, although CPPU tended to increase pericarp weight and strongly suppressed fruit coloration, and such effects were most significant in treatments at 4, 6 and 8 WAFB. Content of total soluble solids (TSS) in commercially ripe fruit (11WAFB) was not significantly influenced by CPPU applied at any of the stages. Ripe fruit continued to gain redness but lost TSS as it became overripe. CPPU suppressed redness gaining, the effect being stronger in later application. The treatments did not influence TSS loss, but treatments at 4, 6 and 8 WAFB significantly reduced the increase in fruit membrane leakage. In treatment at 6 WAFB, CPPU residue in the pericarp decreased sharply with time but relatively constant in the aril, where it maintained lower than 10 μg/kg, the maximal residue limitation in Australia and the United States. The results suggest that 5 mg/L CPPU is effective at improving fruit set and suppressing fruit pigmentation but has no significant effect on fruit size and TSS accumulation or maintenance in litchi.
Impact of Carbon Tax and Environmental Regulation on Inbound Cross-Border Mergers and Acquisitions Volume: An Evidence from India
Authors: Raghavendra Chandrika , Rampilla Mahesh , Thanikella Venkata Ramana , Gupta Isha
Published: November 2022

Climate change, global warming, and carbon emission are global issues. Countries are strengthening their environmental regulations to mitigate the emission problem. According to the pollution haven hypothesis, rich countries invest in emerging economies where the institutional framework is weak to migrate the emissions. With this background, this study examines the impact of the introduction of the carbon tax in India and environmental regulation restriction distance on India’s inbound cross-border mergers and acquisitions (a form of foreign direct investment) volume using a 979 country-pair-year observation sample. The Tobit regression model findings suggest that carbon tax introduction and environmental regulation distance negatively impact India’s inbound cross-border mergers and acquisitions volume. Furthermore, control of corruption intensifies its impact by effectively moderating them. The results indicate that India can avoid becoming a pollution haven by strengthening its environmental policies and controlling corruption. These results provide insight into strengthening the policies relating to environmental regulations and continuing the efforts required to control corruption in India.
Cosmetic Preservatives: Hazardous Micropollutants in Need of Greater Attention?
Authors: Nowak-Lange Marta , Niedziałkowska Katarzyna , Lisowska Katarzyna
Published: November 2022

In recent years, personal care products (PCPs) have surfaced as a novel class of pollutants due to their release into wastewater treatment plants (WWTPs) and receiving environments by sewage effluent and biosolid-augmentation soil, which poses potential risks to non-target organisms. Among PCPs, there are preservatives that are added to cosmetics for protection against microbial spoilage. This paper presents a review of the occurrence in different environmental matrices, toxicological effects, and mechanisms of microbial degradation of four selected preservatives (triclocarban, chloroxylenol, methylisothiazolinone, and benzalkonium chloride). Due to the insufficient removal from WWTPs, cosmetic preservatives have been widely detected in aquatic environments and sewage sludge at concentrations mainly below tens of µg L-1. These compounds are toxic to aquatic organisms, such as fish, algae, daphnids, and rotifers, as well as terrestrial organisms. A summary of the mechanisms of preservative biodegradation by micro-organisms and analysis of emerging intermediates is also provided. Formed metabolites are often characterized by lower toxicity compared to the parent compounds. Further studies are needed for an evaluation of environmental concentrations of preservatives in diverse matrices and toxicity to more species of aquatic and terrestrial organisms, and for an understanding of the mechanisms of microbial degradation. The research should focus on chloroxylenol and methylisothiazolinone because these compounds are the least understood.
DFT Study of the Direct Radical Scavenging Potency of Two Natural Catecholic Compounds
Authors: Amić Ana , Mastiľák Cagardová Denisa
Published: November 2022

To ascertain quercetin’s and rooperol’s potency of H-atom donation to CH3OO• and HOO•, thermodynamics, kinetics and tunnelling, three forms of chemical reaction control, were theoretically examined. In lipid media, H-atom donation from quercetin’s catecholic OH groups via the proton-coupled electron transfer (PCET) mechanism, is more relevant than from C-ring enolic moiety. Amongst rooperol’s two catecholic moieties, H-atom donation from A-ring OH groups is favored. Allylic hydrogens of rooperol are poorly abstractable via the hydrogen atom transfer (HAT) mechanism. Kinetic analysis including tunnelling enables a more reliable prediction of the H-atom donation potency of quercetin and rooperol, avoiding the pitfalls of a solely thermodynamic approach. Obtained results contradict the increasing number of misleading statements about the high impact of C–H bond breaking on polyphenols’ antioxidant potency. In an aqueous environment at pH = 7.4, the 3-O− phenoxide anion of quercetin and rooperol’s 4′-O− phenoxide anion are preferred sites for CH3OO• and HOO• inactivation via the single electron transfer (SET) mechanism.
Trigeminal Neuralgia Treatment Outcomes Following Gamma Knife Stereotactic Radiosurgery
Authors: Jarrahi Abbas , Cantrell Rebecca , Norris Cynthia , Dhandapani Krishnan , Barrett John , Vender John
Published: November 2022

Trigeminal neuralgia (TN) is a chronic pain condition causing lancinating pain in the distribution of one or more divisions of the trigeminal nerve. Gamma knife stereotactic radiosurgery (GKSRS) is a surgical option for TN refractory to medical therapy. To report our experience and to analyze the reasons for the variance in radiosurgery outcomes between patients in our diverse population, we conducted a retrospective analysis of a prospectively created database. The 178 patients completed a pain assessment questionnaire before surgery, and at 1 and 2 year follow-ups. We used the “Trigeminal Neuralgia Gamma Knife Outcome Scale” (TN GKOS) to report the response. At 1-year, 35.4% of patients had grade 1A outcome (pain-free and off all pain medications), 24.7% had grade 1B (pain-free on pain medications), 24.2% had grade 1C (some pain but improved with radiosurgery), 12.9% had grade 2 (same as before radiosurgery) and 2.8% had grade 3 (worse pain compared to before radiosurgery). At 2 years, 42.3% had grade 1A, 20.5% had grade 1B, 19.2% had grade 1C, 14.1% had grade 2 and 3.8% had grade 3 outcome. Remarkably, a statistically significant association was found between GKOS and age, racial background and obesity.
Atmospheric Muon Flux Measurement near Earth’s Equatorial Line
Authors: Borja Cristian , Ávila Carlos , Roque Gerardo , Sánchez Manuel
Published: November 2022

We report measurements of muon flux over the sky of the city of Bogotá at 4°35′56′′ north latitude, 74°04′51′′ west longitude, and an altitude of 2657 m above sea level, carried out with a hodoscope composed of four stations of plastic scintillators located equidistant over a distance of 4.8 m. Measurements were taken at different zenith (θ) angles within the range 1.5° ≤ θ ≤90°, the muon flux data is statistically consistent with a cos2θ dependence, with a χ2 per degree of freedom near unity. If instead, we fit to a cosnθ we obtain n = 2.145±0.046 with a lower χ2 per degree of freedom. Integrating the muon flux distribution as a function of the zenith angle over the solid angle of the upper Earth’s hemisphere allows an estimation of the atmospheric vertical muon rate at the altitude and latitude of Bogota obtaining a value of 255.1 ± 5.8m−2s−1. This estimate is consistent with an independent direct measurement of the vertical muon flux with all detectors stacked horizontally. These measurements play a key role in the further development of detectors, aimed to perform muon imaging of Monserrate Hill, located in Bogotá, where the detectors will be placed at similar locations to those used in the present study.