bluuebunny/crossref_metadata_2025 huggingface dataset:
Participation in Communities of Women Scientists in Central America: Implications From the Science Diplomacy Perspective
Authors: Bonilla Kleinsy , Cabrera Johana , Calles-Minero Camila , Torres-Atencio Ivonne , Aquino Karina , Renderos Deysi , Alonzo Margarita
Published: July 2021

The experience of building and participating in women scientists’ communities in Central America is a multi-layered topic worthy of study. Understanding the dynamics of these women’s groups, associations, and other forms of collective participation, could assist in shedding light on why women are typically under-represented in Science, Technology, Engineering, and Mathematics (STEM) research in countries within this region. The objectives of this study are (i) to explore the experiences of participation in communities of women scientists in Guatemala, El Salvador, Honduras and Panama, and (ii) to systematize the challenges and opportunities derived from such activities. Additionally, this work elaborates on some best practices from the Science Diplomacy (SD) perspective, which could provide a helpful framework to encourage these types of collective participatory communities. The qualitative research methodology was based on the collection of primary data from semi-structured interviews and responses to an online survey sent out to Central American women scientists. The findings of this study revealed few cases of community building experiences among women scientists within the studied countries. Evidence also showed the emergence of shared patterns in terms of barriers and disincentives to participating in such communities. Meanwhile, data collected from the few existing community groups is used to identify successful incentives and motivations. The analysis of the collected data offered relevant implications for Science Diplomacy. Most respondents referred to the Organization of Women in Science for the Developing World (OWSD) as one of the main organizations that can impact and further Science Diplomacy. This organization promotes international engagement and networking among women scientists from developing countries across regions and this article shows how this has been used to foster women science community building in Central America. Exploring similar practices in-depth may offer opportunities to overcome traditional barriers and build further gender equality in science in Central America.
Environmental Services: A New Approach Toward Addressing Sustainable Development Goals in Sub-Saharan Africa
Authors: Knight Jasper
Published: July 2021

The physical environment provides resources and specific types of environmental services relevant to the maintenance of human livelihoods globally and with specific reference to sub-Saharan Africa, including soils, food, and water systems. Previous studies on the shared nexus of such resources commonly view these as self-contained systems operating independent of their physical contexts provided by landscape-scale geomorphology and its related processes. This study critically examines the viewpoints adopted by such nexus studies with specific reference to sub-Saharan Africa, arguing that these studies are reductive, considering only the shared disciplinary overlap (nexus) and not their wider contexts, and are based on only a limited understanding of the workings of physical systems. This study argues that considering the attributes of the physical landscape and its provision of environmental services provides a broader and scientifically-informed context for understanding of interlinked issues such as relationships between soil–food–water systems. Framing such “nexus” studies in this wider context can derive a better understanding of the connections between different elements such as soil, food, and water, amongst others, and with respect to the United Nations' Sustainable Development Goals. The concept of environmental services is therefore a more powerful tool to examine both the connections between physical and human environmental processes and properties in sub-Saharan Africa, and to address overarching environmental issues such as land degradation, soil erosion loss, water scarcity, and impacts of climate change.
Glial Fibrillary Acidic Protein and Ionized Calcium-Binding Adapter Molecule 1 Immunostaining Score for the Central Nervous System of Horses With Non-suppurative Encephalitis and Encephalopathies
Authors: Boos Gisele Silva , Failing Klaus , Colodel Edson Moleta , Driemeier David , Castro Márcio Botelho de , Bassuino Daniele Mariath , Diomedes Barbosa José , Herden Christiane
Published: July 2021

Like humans, horses are susceptible to neurotropic and neuroinvasive pathogens that are not always readily identified in histological sections. Instead, alterations in astrocytes and microglia cells can be used as pathological hallmarks of injured nervous tissue in a variety of infectious and degenerative diseases. On the other hand, equine glial cell alterations are poorly characterized in diseases. Therefore, in this study, we provide a statistically proved score system to classify astrogliosis and microgliosis in the central nervous system (CNS) of horses, based on morphological and quantitative analyses of 35 equine cases of encephalitis and/or encephalopathies and four non-altered CNS as controls. For this system, we used glial fibrillary acidic protein (GFAP) and ionized calcium-binding adapter molecule 1 (Iba1) immunohistochemistry, allied to statistical analysis to confirm that the scores were correctly designated. The scores of alterations ranged from 0 (non-altered) to 3 (severely altered) and provided a helpful method for describing astrocytic and microglial alterations in horses suffering from inflammatory and degenerative lesions. This system could be a template for comparative studies in other animal species and could aid algorithms designed for artificial intelligence methods lacking a defined morphological pattern.
Differential Expression Pattern of Retroviral Envelope Gene in the Equine Placenta
Authors: Stefanetti Valentina , Pascucci Luisa , Wilsher Sandra , Cappelli Katia , Capomaccio Stefano , Reale Lara , Passamonti Fabrizio , Coletti Mauro , Crociati Martina , Monaci Maurizio , Marenzoni Maria Luisa
Published: July 2021

Endogenous retroviruses (ERVs) are proviral phases of exogenous retroviruses, which have coevolved with vertebrate genomes for millions of years. The conservation of ERV genes throughout evolution suggests their beneficial effects on their hosts' survival. An example of such positive selection is demonstrated by the syncytin gene, which encodes a protein with affinity for various mammalian placentas that is involved in the formation of syncytiotrophoblasts. Although the horse has an epitheliochorial placenta, in which the fetal trophoblasts are simply apposed to the intact uterine epithelium, we have previously demonstrated that the equine ERV (EqERV) env RNA is unexpectedly expressed in placental tissue. In the present study, we investigated the mRNA expression pattern of the EqERV env gene in different parts of the equine placenta, to gain more insight into its putative role in the fetal–maternal relationship. To this end, we used reverse transcription–quantitative PCR (RT–qPCR) and in situ hybridization assays to analyze different target areas of the equine placenta. The retroviral env gene is expressed in the equine placenta, even though there is no syncytium or erosion of the uterine endometrium. The gene is also expressed in all the sampled areas, although with some quantitative differences. We suggest that these differences are attributable to variations in the density, height, and degree of morphological complexity of the chorionic villi forming the microcotyledons. The involvement of the EqERV env gene in different functional pathways affecting the fetus–mother relationship can be hypothesized.
Mycobacterial Infection of Precision-Cut Lung Slices Reveals Type 1 Interferon Pathway Is Locally Induced by Mycobacterium bovis but Not M. tuberculosis in a Cattle Breed
Authors: Remot Aude , Carreras Florence , Coupé Anthony , Doz-Deblauwe Émilie , Boschiroli Maria L. , Browne John A. , Marquant Quentin , Descamps Delphyne , Archer Fabienne , Aseffa Abraham , Germon Pierre , Gordon Stephen V. , Winter Nathalie
Published: July 2021

Tuberculosis exacts a terrible toll on human and animal health. While Mycobacterium tuberculosis (Mtb) is restricted to humans, Mycobacterium bovis (Mb) is present in a large range of mammalian hosts. In cattle, bovine TB (bTB) is a noticeable disease responsible for important economic losses in developed countries and underestimated zoonosis in the developing world. Early interactions that take place between mycobacteria and the lung tissue early after aerosol infection govern the outcome of the disease. In cattle, these early steps remain poorly characterized. The precision-cut lung slice (PCLS) model preserves the structure and cell diversity of the lung. We developed this model in cattle in order to study the early lung response to mycobacterial infection. In situ imaging of PCLS infected with fluorescent Mb revealed bacilli in the alveolar compartment, in adjacent or inside alveolar macrophages, and in close contact with pneumocytes. We analyzed the global transcriptional lung inflammation signature following infection of PCLS with Mb and Mtb in two French beef breeds: Blonde d'Aquitaine and Charolaise. Whereas, lungs from the Blonde d'Aquitaine produced high levels of mediators of neutrophil and monocyte recruitment in response to infection, such signatures were not observed in the Charolaise in our study. In the Blonde d'Aquitaine lung, whereas the inflammatory response was highly induced by two Mb strains, AF2122 isolated from cattle in the UK and Mb3601 circulating in France, the response against two Mtb strains, H37Rv, the reference laboratory strain, and BTB1558, isolated from zebu in Ethiopia, was very low. Strikingly, the type I interferon pathway was only induced by Mb but not Mtb strains, indicating that this pathway may be involved in mycobacterial virulence and host tropism. Hence, the PCLS model in cattle is a valuable tool to deepen our understanding of early interactions between lung host cells and mycobacteria. It revealed striking differences between cattle breeds and mycobacterial strains. This model could help in deciphering biomarkers of resistance vs. susceptibility to bTB in cattle as such information is still critically needed for bovine genetic selection programs and would greatly help the global effort to eradicate bTB.
Reconfigurable Slip Vectoring Control in Four In-Wheel Drive Electric Vehicles
Authors: Amato Gerardo , Marino Riccardo
Published: July 2021

Controllability, maneuverability, fault-tolerance/isolation and safety are significantly enhanced in electric vehicles (EV) equipped with the redundant actuator configuration of four-in-wheel electric motors (4IWM). A highly reconfigurable architecture is proposed and illustrated for the adaptive, nonmodel-based control of 4IWM-EVs. Given the longitudinal force, yaw-moment requests and the reconfiguration matrix, each IWM is given a slip reference according to a Slip Vectoring (SV) allocation strategy, which minimizes the overall slip vector norm. The distributed electric propulsion and the slip vector reference allow for a decentralized online estimation of the four-wheel torque-loads, which are uncertain depending on loading and road conditions. This allows for the allocation of four different torques depending on individual wheel conditions and to determine in which region (linear/nonsaturated or nonlinear/saturated) of the torque/slip characteristics each wheel is operating. Consequently, the 4IWMs can be equalized or reconfigured, including actuator fault-isolation as a special case, so that they are enforced to operate within the linear tire region. The initial driving-mode selection can be automatically adjusted and restored among eighteen configurations to meet the safety requirements of linear torque/slip behavior. Three CarSim realistic simulations illustrate the equalization algorithm, the quick fault-isolation capabilities and the importance of a continuous differential action in a critical double-lane-change maneuver.
Using Aggregated Farm Location Information to Predict Regional Structural Change of Farm Specialisation, Size and Exit/Entry in Norway Agriculture
Authors: Neuenfeldt Sebastian , Gocht Alexander , Heckelei Thomas , Mittenzwei Klaus , Ciaian Pavel
Published: July 2021

In this paper, we extend the analysis of farm structural change with respect to farm specialisation, size and exit in Norway by, first, explicitly incorporating the location information of farms generating a number of neighbouring farms within a certain range and, second, by predicting farm numbers in addition to farm group shares, which allows for consideration of the exit farm group. We use Norwegian single-farm full census data for the period 1996–2015. Four production specialisations and seven size classes represent farm groups, as well as a residual and an exit farm group at the regional level. The estimates indicate the explanatory power and importance of aggregated farm location information in the model. Simulation analysis showed that the farm groups develop differently, given a change in the number of neighbouring farms with respect to the farm numbers and farm group shares.
Effects of Plant Protection Products on Biochemical Markers in Honey Bees
Authors: Murawska Agnieszka , Migdał Paweł , Roman Adam
Published: July 2021

Plant protection products (PPPs) are pesticides that protect crops and ornamental plants. PPPs include primarily insecticides, herbicides, and fungicides. Bees’ contact with PPPs can cause immediate death or, in sublethal dose, may affect their physiology and/or behavior. Understanding the effect of PPPs’ sublethal doses is especially important. Contact with a sublethal dose of PPPs generally allows the bee to return to the hive, which may expose the whole colony to the harmful substance. Biochemical changes may affect colony condition, health, and performance. Most of the research on the biochemical effects of PPP in honey bees focuses on insecticides and among them neonicotinoids (especially imidacloprid). The vast majority of research is carried out on Apis mellifera workers. A small part of the research has been conducted on drones and queens. Pesticides, including fungicides and herbicides, may alter antioxidant defense, detoxification, gene expression, and immune response of the bee. They affect the drones’ semen quality and metabolic rate of the queen. In this review, the biochemical effect of PPP products in the honey bee was examined, with a focus on the effect on cytochrome P450 monooxygenases, glutathione transferases, and carboxylesterases, which take part in toxin metabolism or the detoxification process. PPPs effects on the activity of glutathione peroxidase (GPX), catalase (CAT), superoxide dismutase (SOD), proteases, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and phenoloxidase (PO) are also presented.
The Buzz Changes within Time: Native Apis mellifera mellifera Honeybee Subspecies Less and Less Popular among Polish Beekeepers Since 1980
Authors: Bieńkowska Małgorzata , Splitt Aleksandra , Węgrzynowicz Paweł , Maciorowski Robert
Published: July 2021

Socio-cultural research might address anthropocentric reasons for honeybee (Apis mellifera) conservation. In some regions, particular honeybee subspecies are considered to be native; A. mellifera mellifera (“dark bee”) in the north-east and A. mellifera carnica in the Island Beskids in Poland. Additionally, A. mellifera caucasia (often incorrectly called A. mellifera caucasica) and Buckfast are reported across Poland. In order to verify the actual choice of beekeepers, a survey on honeybee subspecies kept in apiaries was conducted annually from 1980 to 2018. This is a way to verify if conservation management towards the dark bee influenced its maintenance at a sufficient level for their restoration. The analysis revealed that Polish beekeepers know what is “buzzing” in their hives, and the awareness of which subspecies/types of honeybee they maintain has grown through the years. Initially, they kept up to four different subspecies per apiary, but now most have only one (maximum of two). Currently, Polish apiaries approach a homogeneous share with the exclusive presence of A. mellifera carnica subspecies. The popularity of indigenous A. mellifera mellifera has declined over time and is low now. It seems that new solutions should be considered to increase the effectiveness of dark European bee conservation management efforts.
InDel Markers Based on 3K Whole-Genome Re-Sequencing Data Characterise the Subspecies of Rice (Oryza sativa L.)
Authors: Yuan Huanran , Yang Weilong , Zou Jianing , Cheng Mingxing , Fan Fengfeng , Liang Ting , Yu Yajie , Qiu Ronghua , Li Shaoqing , Hu Jun
Published: July 2021

A molecular marker is a valuable tool in genetic research. Insertions–deletions (InDels) are commonly used polymorphisms in gene mapping, analysing genetic diversity, marker-assisted breeding, and phylogenetics. The 3000 Rice Genome Project, a re-sequencing project, discovered millions of genome-wide InDels. We found that the proportion of >50-bp long InDels (699,475) of the total (1,248,503) is 56.02%. The number of InDels on each chromosome was consistent with the corresponding chromosome length. The maximum InDels were on chromosome 1 (78,935), and the minimum InDels were on chromosome 9 (41,752), with an average density of 1.87 InDels/kb (range: 1.50–2.36 InDels/kb). Furthermore, 96 InDels of about 3.98 Mb/InDel were selected to detect the polymorphism. The results exhibited ideal performance in 2% agarose gel electrophoresis. Phylogenetic analysis exhibited that InDel markers had excellent polymorphisms between rice varieties of japonica and indica, and varieties could be classified based on the statistical results of their polymorphisms. The InDel markers could be applied to identify the recombinant inbred lines in a population. These results reveal that the high-density long InDel markers could help us examine the functional diversity, species variation, and map-based cloning.
Effects of Organic Energy Crop Rotations and Fertilisation with the Liquid Digestate Phase on Organic Carbon in the Topsoil
Authors: Levin Karin S. , Auerswald Karl , Reents Hans Jürgen , Hülsbergen Kurt-Jürgen
Published: July 2021

Combining organic farming and biogas production from agricultural feedstocks has been suggested as a way of achieving carbon (C) neutrality in Europe. However, as the long-term effects of C removal for methane production on soil organic carbon (SOC) are unclear, organic farmers in particular have questioned whether farm biogas production will have a positive effect on soil fertility. Eight years of data from an organic long-term field trial involving digestate fertilisation and various crop rotations (CRs) with differing proportions of clover-grass leys were used to calculate C inputs based on the CANDY model, and these modelled changes compared with measured changes in SOC content (SOCc) over the same period. Measured SOCc increased by nearly 20% over the eight years. Digestate fertilisation significantly increased SOCc. Fertilised plots with the highest proportion of clover-grass in the CR had the highest SOCc. The C inputs from clover-grass leys, even if they only made up 25% of the CR, were high enough to increase SOCc, even with the removal of all aboveground biomass and without fertilisation. Our results show that biogas production based on clover-grass leys could be an important part of sustainable farming, improving or maintaining SOCc and improving nutrient flows, particularly in organic farming, while simultaneously providing renewable energy.
Evaluation of miRNA as Biomarkers of Emotional Valence in Pigs
Authors: Marsh Laura , Hutchinson Mark R. , McLaughlan Clive , Musolino Stefan T. , Hebart Michelle L. , Terry Robyn , Verma Paul J. , Hiendleder Stefan , Whittaker Alexandra L.
Published: July 2021

The ability to assess the welfare of animals is dependent on our ability to accurately determine their emotional (affective) state, with particular emphasis being placed on the identification of positive emotions. The challenge remains that current physiological and behavioral indices are either unable to distinguish between positive and negative emotional states, or they are simply not suitable for a production environment. Therefore, the development of novel measures of animal emotion is a necessity. Here we investigated the efficacy of microRNA (miRNA) in the brain and blood as biomarkers of emotional state in the pig. Female Large White × Landrace pigs (n = 24) were selected at weaning and trained to perform a judgment bias test (JBT), before being exposed for 5 weeks to either enriched (n = 12) or barren housing (n = 12) conditions. Pigs were tested on the JBT once prior to treatment, and immediately following treatment. MiRNA and neurotransmitters were analyzed in blood and brain tissue after euthanasia. Treatment had no effect on the outcomes of the JBT. There was also no effect of treatment on miRNA expression in blood or the brain (FDR p > 0.05). However, pigs exposed to enriched housing had elevated dopamine within the striatum compared to pigs in barren housing (p = 0.02). The results imply that either (a) miRNAs are not likely to be valid biomarkers of a positive affective state, at least under the type of conditions employed in this study, or (b) that the study design used to modify affective state was not able to create differential affective states, and therefore establish the validity of miRNA as biomarkers.
Influence of Alternative Lifestyles on Antibiotic Use during Pregnancy, Lactation and in Children
Authors: Eras Pien , Simões-Wüst Ana Paula , Thijs Carel
Published: July 2021

Alternative lifestyles are likely to be associated with distinct usage of specific medicinal products. Our goal was to find out whether the intake of antibiotics during pregnancy and by children differs according to whether the mothers have alternative or conventional lifestyles. Therefore, we investigated the use of antibiotics by pregnant women and by children up to 11 years of age participating in the KOALA Birth Cohort Study. This cohort comprises two recruitment groups of mother–infant pairs, one with alternative lifestyles (selected via organic food shops, anthroposophic clinicians and midwives, anthroposophic under-five clinics, Rudolf Steiner schools and relevant magazines, n = 491) the other with conventional lifestyles (no selection based on lifestyle, n = 2343). Mothers in the alternative lifestyle group more frequently adhered to specific living rules and identified themselves with anthroposophy more than mothers in the conventional lifestyle group. The results revealed significant differences in antibiotic use during pregnancy and in children from 3 months to 10 years of age between the two groups. The rate of antibiotic use in children was consistently lower in the alternative lifestyle group than in the conventional lifestyle group. Antibiotic use in pregnancy was higher in low educated women, and maternal antibiotic use during lactation was higher after an instrumented delivery in hospital. Antibiotic use in the infant was higher when they had older sibs or were born in hospital, and lower in those who had been longer breastfed. After adjustment for these factors, the differences in antibiotic use between the alternative and conventional groups remained. The results suggest that an alternative lifestyle is associated with cautious antibiotic use during pregnancy, lactation and in children.
Exudative Epidermitis in Combination with Staphylococcal Pyoderma in Suckling Piglets
Authors: Schwarz Lukas , Loncaric Igor , Brunthaler Rene , Knecht Christian , Hennig-Pauka Isabel , Ladinig Andrea
Published: July 2021

A case of generalized exudative epidermitis (EE) is described, which occurred in a very small piglet producing farm in Austria. The antimicrobial treatment prescribed by the herd veterinarian did not improve the clinical problem. Therefore, the University Clinic for Swine intervened in the case. Lab investigations were initiated in which Staphylococcus hyicus (SH) and Staphylococcus aureus (SA), both methicillin-resistant and susceptible strains, could be isolated from the skin of affected piglets. Poor hygiene and management practices were identified as predisposing factors on site. Adaptation of antimicrobial treatment according to results of the in vitro susceptibility testing and the implementation of proper hygiene measures resolved the clinical problem. Here, we describe a fatal coinfection of SH and SA in suckling piglets.
Surveillance of Antibacterial Usage during the COVID-19 Pandemic in England, 2020
Authors: Andrews Amelia , Budd Emma , Hendrick Aoife , Ashiru-Oredope Diane , Beech Elizabeth , Hopkins Susan , Gerver Sarah , Muller-Pebody Berit ,   the AMU COVID-19 Stakeholder Group
Published: July 2021

Changes in antibacterial prescribing during the COVID-19 pandemic were anticipated given that the clinical features of severe respiratory infection syndrome caused by SARS-CoV-2 mirror bacterial respiratory tract infections. Antibacterial consumption was measured in items/1000 population for primary care and in Defined Daily Doses (DDDs)/1000 admissions for secondary care in England from 2015 to October 2020. Interrupted time-series analyses were conducted to evaluate the effects of the pandemic on antibacterial consumption. In the community, the rate of antibacterial items prescribed decreased further in 2020 (by an extra 1.4% per month, 95% CI: −2.3 to −0.5) compared to before COVID-19. In hospitals, the volume of antibacterial use decreased during COVID-19 overall (−12.1% compared to pre-COVID, 95% CI: −19.1 to −4.4), although the rate of usage in hospitals increased steeply in April 2020. Use of antibacterials prescribed for respiratory infections and broad-spectrum antibacterials (predominately ‘Watch’ antibacterials in hospitals) increased in both settings. Overall volumes of antibacterial use at the beginning of the COVID-19 pandemic decreased in both primary and secondary settings, although there were increases in the rate of usage in hospitals in April 2020 and in specific antibacterials. This highlights the importance of antimicrobial stewardship during pandemics to ensure appropriate prescribing and avoid negative consequences on patient outcomes and antimicrobial resistance.
Bacterial Natural Product Drug Discovery for New Antibiotics: Strategies for Tackling the Problem of Antibiotic Resistance by Efficient Bioprospecting
Authors: Schneider Yannik K.
Published: July 2021

The problem of antibiotic resistance has become a challenge for our public health and society; it has allowed infectious diseases to re-emerge as a risk to human health. New antibiotics that are introduced to the market face the rise of resistant pathogens after a certain period of use. The relatively fast development of resistance against some antibiotics seems to be closely linked to their microbial origin and function in nature. Antibiotics in clinical use are merely products of microorganisms or derivatives of microbial products. The evolution of these antimicrobial compounds has progressed with the evolution of the respective resistance mechanisms in microbes for billions of years. Thus, antimicrobial resistance genes are present within the environment and can be taken up by pathogens through horizontal gene transfer. Natural products from bacteria are an important source of leads for drug development, and microbial natural products have contributed the most antibiotics in current clinical use. Bioprospecting for new antibiotics is a labor-intensive task as obstacles such as redetection of known compounds and low compound yields consume significant resources. The number of bacterial isolates one can theoretically investigate for new secondary metabolites is, on the other hand, immense. Therefore, the available capacity for biodiscovery should be focused on the most promising sources for chemical novelty and bioactivity, employing the appropriate scientific tools. This can be done by first looking into under- or unexplored environments for bacterial isolates and by focusing on the promising candidates to reduce the number of subjects.
N-Acetylcysteine Reduces the Pro-Oxidant and Inflammatory Responses during Pancreatitis and Pancreas Tumorigenesis
Authors: Minati Marie-Albane , Libert Maxime , Dahou Hajar , Jacquemin Patrick , Assi Mohamad
Published: July 2021

Pancreatitis, an inflammation of the pancreas, appears to be a main driver of pancreatic cancer when combined with Kras mutations. In this context, the exact redox mechanisms are not clearly elucidated. Herein, we treated mice expressing a KrasG12D mutation in pancreatic acinar cells with cerulein to induce acute pancreatitis. In the presence of KrasG12D, pancreatitis triggered significantly greater redox unbalance and oxidative damages compared to control mice expressing wild-type Kras alleles. Further analyses identified the disruption in glutathione metabolism as the main redox event occurring during pancreatitis. Compared to the wild-type background, KrasG12D-bearing mice showed a greater responsiveness to treatment with a thiol-containing compound, N-acetylcysteine (NAC). Notably, NAC treatment increased the pancreatic glutathione pool, reduced systemic markers related to pancreatic and liver damages, limited the extent of pancreatic edema and fibrosis as well as reduced systemic and pancreatic oxidative damages. The protective effects of NAC were, at least, partly due to a decrease in the production of tumor necrosis factor-α (TNF-α) by acinar cells, which was concomitant with the inhibition of NF-κB(p65) nuclear translocation. Our data provide a rationale to use thiol-containing compounds as an adjuvant therapy to alleviate the severity of inflammation during pancreatitis and pancreatic tumorigenesis.
Deep Learning for Information Triage on Twitter
Authors: Ptaszynski Michal , Masui Fumito , Fukushima Yuuto , Oikawa Yuuto , Hayakawa Hiroshi , Miyamori Yasunori , Takahashi Kiyoshi , Kawajiri Shunzo
Published: July 2021

In this paper, we present a Deep Learning-based system for the support of information triaging on Twitter during emergency situations, such as disasters, or other influential events, such as political elections. The system is based on the assumption that a different type of information is required right after the event and some time after the event occurs. In a preliminary study, we analyze the language behavior of Twitter users during two kinds of influential events, namely, natural disasters and political elections. In the study, we analyze the credibility of information included by users in tweets in the above-mentioned situations, by classifying the information into two kinds: Primary Information (first-hand reports) and Secondary Information (second-hand reports, retweets, etc.). We also perform sentiment analysis of the data to check user attitudes toward the occurring events. Next, we present the structure of the system and compare a number of classifiers, including the proposed one based on Convolutional Neural Networks. Finally, we validate the system by performing an in-depth analysis of information obtained after a number of additional events, including an eruption of a Japanese volcano Ontake on 27 September 2014, as well as heavy rains and typhoons that occurred in 2020. We confirm that the methods works sufficiently well even when trained on data from nearly 10 years ago, which strongly suggests that the model is well-generalized and sufficiently grasps important aspects of each type of classified information.
An Artificial Intelligence Solution for Predicting Short-Term Degradation Behaviors of Proton Exchange Membrane Fuel Cell
Authors: Yang Zijun , Wang Bowen , Sheng Xia , Wang Yupeng , Ren Qiang , He Shaoqing , Xuan Jin , Jiao Kui
Published: July 2021

The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function.
Selection of a PCM for a Vehicle’s Rooftop by Multicriteria Decision Methods and Simulation
Authors: Nicolalde Juan Francisco , Cabrera Mario , Martínez-Gómez Javier , Salazar Rodger Benjamín , Reyes Evelyn
Published: July 2021

The automotive industry is one of the most contaminant; for this reason, solutions in efficient matter has been proposed over the years. This research contributes to this subject by evaluating the thermal comfort in the internal air of a vehicle by using a 20 mm layer of a phase-change material attached to the rooftop interior of a car. The phase-change material selection is based on a list of other materials proposed in previous research and chosen by multicriteria decision methods. In this sense, the material savENRG PCM-HS22P proved to be the best. Moreover, a simulation using the finite elements method showed how the PCM reduced the temperature of the air by 9 °C when heating and by 4 °C when the temperature drops. To conclude, the multicriteria selection methods chose the best material to absorb energy during the charging process and released it during the discharging event in this automotive application.
Development of Sensing Algorithms for Object Tracking and Predictive Safety Evaluation of Autonomous Excavators
Authors: Rasul Abdullah , Seo Jaho , Khajepour Amir
Published: July 2021

This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement.
Research on Long Short-Term Decision-Making System for Excavator Market Demand Forecasting Based on Improved Support Vector Machine
Authors: Zhang Bin , Yang Teng , Hong Haocen , Cheng Guozan , Yang Huayong , Wang Tongman , Cao Donghui
Published: July 2021

Future demand forecasting of the excavators is of great significance to guide the supply and marketing plan. For a long time, market forecasting of the construction machinery is regarded as short-term forecasting, which lacks the analysis of macro-marketing law and cannot reflect the true law of market development. In this paper, a decision-making system based on both long-term and short-term features was proposed. The interval classification and recursive feature elimination were used to select the main factors that affect the demand of excavators. Then a support vector regression model based on decomposition synthesis (DS-SVR) was developed to forecast the long-term features, and a model combined with a seasonal autoregressive integrated moving average model (SARIMA) was developed to forecast the short-term features. Finally, the differential evolution algorithm (DE) was applied to optimize model parameters. The performance of the forecasting model was tested using the marketing data of a typical enterprise. The results showed that the total error rate of the forecasting model for the one-year long-term forecasting is 26.61%, and the classification error of forecasting of the three-month short-term forecasting are 13.65%, 18.83%, and 19.62%, respectively, which are superior to the SVR forecasting model and the SARIMA forecasting model.
Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
Authors: Zhu Haoyu , Woo Jong Hun
Published: July 2021

In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it only references past records. This paper proposes a new self-organizing hierarchical particle swarm algorithm (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC)-support vector machine (SVM) regression model to increase the accuracy of lead-time prediction by combining the advanced PSO and SVM models. Moreover, this paper compares the prediction results of each SVM-based model with those of other conventional machine-learning algorithms. The results demonstrate that the proposed NHPSO-JTVAC-SVM model can achieve further meaningful enhancements in terms of prediction accuracy. The prediction performance of the NHPSO-JTVAC-SVM model is also better than that of the other SVM-based models or other machine learning algorithms. Overall, the NHPSO–JTVAC-SVM model is feasible for predicting the lead time in shipbuilding.
Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
Authors: Aziz Tamoor , Ilesanmi Ademola E. , Charoenlarpnopparut Chalie
Published: July 2021

Diabetic retinopathy (DR) is one of the diseases that cause blindness globally. Untreated accumulation of fat and cholesterol may trigger atherosclerosis in the diabetic patient, which may obstruct blood vessels. Retinal fundus images are used as diagnostic tools to screen abnormalities linked to diseases that affect the eye. Blurriness and low contrast are major problems when segmenting retinal fundus images. This article proposes an algorithm to segment and detect hemorrhages in retinal fundus images. The proposed method first performs preprocessing on retinal fundus images. Then a novel smart windowing-based adaptive threshold is utilized to segment hemorrhages. Finally, conventional and hand-crafted features are extracted from each candidate and classified by a support vector machine. Two datasets are used to evaluate the algorithms. Precision rate (P), recall rate (R), and F1 score are used for quantitative evaluation of segmentation methods. Mean square error, peak signal to noise ratio, information entropy, and contrast are also used to evaluate preprocessing method. The proposed method achieves a high F1 score with 83.85% for the DIARETDB1 image dataset and 72.25% for the DIARETDB0 image dataset. The proposed algorithm adequately adapts when compared with conventional algorithms, hence will act as a tool for segmentation.
Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana
Authors: Barradas Ana , Correia Pedro M.P. , Silva Sara , Mariano Pedro , Pires Margarida Calejo , Matos Ana Rita , da Silva Anabela Bernardes , Marques da Silva Jorge
Published: July 2021

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.
Computation of Optimized Electrode Arrays for 3-D Electrical Resistivity Tomography Surveys
Authors: Simyrdanis Kleanthis , Papadopoulos Nikos , Oikonomou Dimitrios
Published: July 2021

The present study explores the applicability and effectiveness of an optimization technique applied to electrical resistivity tomography data. The procedure is based on the Jacobian matrix, where the most sensitive measurements are selected from a comprehensive data set to enhance the least resolvable parameters of the reconstructed model. Two existing inversion programs in two and three dimensions are modified to incorporate this new approach. Both of them are selecting the optimum data from an initial comprehensive data set which is comprised of merged conventional arrays. With the two-dimensional (2-D) optimization approach, the most sensitive measurements are selected from a 2-D survey profile and then a clone of the resulting optimum profile reproduces a three-dimensional (3-D) optimum data set composed of equally spaced parallel lines. In a different approach, with the 3-D optimization technique, the optimum data are selected from a 3-D data set of equally spaced individual parallel lines. Both approaches are compared with Stummer’s optimization technique which is based on the resolution matrix. The Jacobian optimization approach has the advantage of selecting the optimum data set without the need for the solution of the inversion problem since the Jacobian matrix is calculated as part of the forward resistivity problem, thus being faster from previous published approached based on the calculation of the sensitivity matrix. Synthetic 3-D data based on the extension of previous published works for the 2-D optimization case and field data from two case studies in Greece are tested, thus verifying the validity of the present study, where fewer measurements from the initial data set (about 15–50%) are able to reconstruct a model similar with the one produced from the original comprehensive data set.
Smart Cities’ Applications to Facilitate the Mobility of Older Adults: A Systematic Review of the Literature
Authors: Rocha Nelson Pacheco , Bastardo Rute , Pavão João , Santinha Gonçalo , Rodrigues Mário , Rodrigues Carlos , Queirós Alexandra , Dias Ana
Published: July 2021

This study aimed to identify: (i) the relevant applications based on information technologies and requiring smart cities’ infrastructure to facilitate the mobility of older adults in URBAN SPACES; (ii) the type of data being used by the proposed applications; (iii) the maturity level of these applications; and (iv) the barriers TO their dissemination. An electronic search was conducted on Web of Science, Scopus, and IEEE Xplore databases, combining relevant keywords. Then, titles and abstracts were screened against inclusion and exclusion criteria, and the full texts of the eligible articles were retrieved and screened for inclusion. A total of 28 articles were included. These articles report smart cities’ applications to facilitate the mobility of older adults using different types of sensing devices. The number of included articles is reduced when compared with the total number of articles related to smart cities, which means that the mobility of older adults it is still a not significant topic within the research on smart cities’. Although most of the included studies aimed the implementation of specific applications, these were still in an early stage of development, without the assessment of potential end-users. This is an important research gap since it makes difficult the creation of market-oriented solutions. Another research gap is the integration of knowledge generated by other research topics related to smart cities and smart mobility. Consequently, important issues (e.g., user privacy, data standardization and integration, Internet of Things implementation, and sensors’ characteristics) were poorly addressed by the included studies.
Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell
Authors: Zore Aleš , Čerin Robert , Munih Marko
Published: July 2021

In our study a robot was used to deliver objects for measurement into the Equator gauging system. To investigate the robot’s manipulation influence on dimensional measurements, the robot’s tasks were divided into basic functions. Based on these basic functions, nine different robot-manipulation scenarios were defined, i.e., from zero to full robot manipulation, for two measuring objects (named Magnet and PKR) and six measurement characteristics (rectangular and spherical). The robot’s manipulation influence was determined on the basis of the statistical parameters Cp, R, and the 6σ obtained from a measurement system analysis (MSA) type-1 study. The results show that the degree of implemented manipulation of the robot affects the scattering of the measurement data. However, the effect is much more pronounced in the case of length measurements than with spherical geometries. Different measuring methods (touch-triggering or scanning measurement mode, number of sampling points) were used, which showed similar measurement data. This directly indicated the influence of the robot’s manipulation on Cp, R and 6σ. Increasing the degree of the robot’s manipulation decreases the Cp value and increases the R and 6σ values for the length measurements. There is no such pronounced course in the spherical geometries, where the values of Cp, R and 6σ remain approximately the same. The main influential factor for decreasing the Cp value with increasing robot manipulation was the angular misalignment of the object’s orientation in the fixture.