Chamelea gallina) is an important economic resource in the Mediterranean Basin; this species has exhibited a strong quantitative decline in the Adriatic Sea. The aim of this work was to provide a comprehensive view of the biological status of C. gallina to elucidate the bioecological characteristics and genetic diversity of wild populations. To the best of our knowledge, this investigation is the first to perform a multidisciplinary study on C. gallina based on two omics approaches integrated with histological, ecotoxicological, and chemical analyses and with the assessment of environmental parameters. The results obtained through RNA sequencing indicated that the striped venus has a notable ability to adapt to different environmental conditions. Moreover, the stock reduction exhibited by this species in the last 2 decades seems not to have negatively affected its genetic diversity. Indeed, the high level of genetic diversity that emerged from our ddRAD dataset analyses is ascribable to the high larval dispersal rate, which might have played a “compensatory role” on local fluctuations, conferring to this species a good adaptive potential to face the environmental perturbations. These findings may facilitate the efforts of conservation biologists to adopt ad hoc management plans for this fishery resource.The striped venus ( Commercially important marine species are frequently threatened by the effects of environmental changes, pollution, microbiological diseases, and overfishing, which may cause reductions in the size and genetic diversity of wild populations.1. The integration of data obtained from different omics approaches with ecotoxicological and environmental assessments has the advantage of creating a more holistic understanding of species, enabling researchers to develop specific tools to implement long-term management plans2.The development of omics sciences provides a remarkable contribution to species conservation biology, significantly increasing the ability of researchers to obtain insights into the molecular mechanisms adopted by species to cope environmental changeChamelea gallina  is a mollusk species that is of considerable importance, both economically and ecologically. This bivalve, which belongs to the Veneridae family, is widely distributed along the eastern Atlantic coast, ranging from, Norway and the British Isles to the Iberian Peninsula, Morocco, the Madeira Islands and the Canary Islands3. This species is also observed in the Black Sea, Mediterranean waters4 and particular in the Adriatic Sea5. C. gallina is a gonochoristic species with a long spawning period, extending from April to August, in which egg emission takes place at intervals12. In the Adriatic Sea, the fishery of C. gallina represents one of the most important resources with approximately 15,000 t reported for the year 201813, involving 670 fisheries with an annual turnover of approximately 100 million euros. For the Black Sea, the highest yield is reported in Turkey with approximately 40,000 t of annual catches10. C. gallina, similar to other marine bivalves, is also important in ecosystems, impacting nutrient cycling, creating and modifying habitats, and affecting food webs directly  and indirectly 14. Moreover, due to their filter-feeding abilities and their sessile mode of life, these bivalves play important roles as biosensors for pollution and other environmental changes in coastal waters, enabling the monitoring of the quality of the intertidal zones18. Several factors, including temperature variation, salinity, dissolved oxygen concentration in water and sediment23, presence of toxic substances of anthropogenic origin24, and overfishing25, might have induced a decrease in this fishery resource12.27 from approximately 35,000–15,000 t in the last 2 decades (FAO FishStat).In the Adriatic Sea, several mortality events due to sudden changes in the coastal environment  have strongly contributed to the decline of clam bedsC. gallina are the transcriptome published by Coppe et al.28 and gene expression analyses obtained by Milan and colleagues27, which detected differences between organisms sampled from two close sites with different local periodic mortality rates in the Italian Adriatic region of Abruzzo. Moreover, very little is known regarding the genetic substructure of C. gallina29.To date, the molecular resources available for C. gallina populations is required.Therefore, to ensure the long-term sustainability of fishery resources, further research elucidating the bioecological properties and the genetic diversity of wild 32. In particular, RNA-Seq is the favored sequencing technique to obtain knowledge on the functional response of organisms to environmental conditions. Indeed, in a multistress context, this technique enables us to depict the alteration of several genes and molecular pathways simultaneously in nonmodel organisms in response to stressful conditions35. ddRAD sequencing is one of the most powerful approaches to resolve fine-scale population structures compared with microsatellites, since ddRAD enables researchers to obtain thousands of markers37. This approach enables us to obtain a comprehensive view of genetic variability that represents a measure of the ability of species to adapt to environmental perturbations. Indeed, species showing low levels of genetic diversity are more prone to extinction. In line with these premises, this work attempted to provide, for the first time, a multidisciplinary strategy to elucidate the functional response of C. gallina to several biotic and abiotic factors and the genetic diversity level of the striped venus natural beds, as determined through RNA and ddRAD sequencing techniques. Moreover, to better contextualize the results obtained by combining omics approaches, we performed detailed histological, ecotoxicological, and chemical analyses and an assessment of environmental parameters.The utilization of high-throughput sequencing techniques, such as RNA sequencing (RNA-Seq) and double digest restriction-site associated DNA sequencing (ddRAD-Seq), is acquiring increasing recognition in the conservation biology research on sensitive and economically important speciesC. gallina to continuously changing environmental conditions. Up- and downregulated genes were functionally annotated through pathway enrichment analyses for all pairwise comparisons performed in this study , energy metabolism (29 genes), environmental information processing (21 genes), protein families: genetic information processing (21 genes), and carbohydrate metabolism (15 genes). For this sampling site, genes upregulated in autumn were more closely related to genetic information processing and protein families: genetic information processing (53 genes) , genetic information processing (18 genes), environmental information processing (17 genes), and protein families: genetic information processing (16 genes) were the most common. Furthermore, another comparison was performed between the two sampling sites in both sampling periods. Concerning the genes upregulated in spring for specimens sampled in S, genetic information processing (18 genes), energy metabolism (12 genes), environmental information processing (12 genes), and carbohydrate metabolism (11 genes) were the most heavily represented, while for the upregulated genes in the SM sampling site, genes fell primarily within genetic information processing (21 genes) and energy metabolism (3 genes). An opposite pattern was observed when analyzing the enriched pathways related to the comparison between S and SM in autumn  and Silvi Marina (SM), two sites to the north and south of Monte Conero, respectively, and belonging to two distinct biogeographic sectors. Differences in gene expression levels between the two sampling sites and sampling times were assessed by performing pairwise comparisons, as reported in Supplementary Fig. In analyzing data related to functional enrichment made using KEGG for the comparison between the two sampling sites and periods Fig. , this recellular processes included transport and catabolism, cell growth and death, cellular community (eukaryotes and prokaryotes), and cell motility.In KEGG pathway enrichment analysis, cell growth and death. In particular, the presence of the SCF complex , calmodulin, 14-3-3ɛ protein, C-Myc binding protein, Dna J subfamily member 13, and spermatogenesis associated 4 among the upregulated gametogenesis-related genes was reported. At the same time, the absence of the SCF complex within the upregulated genes in functional enrichment analyses of the autumnal period at the S site was strongly in keeping with histological analyses showing gonadal inactivity  and carbohydrate metabolism (15 entries), while in autumn, only 1 entry was in carbohydrate metabolism, and no entries were in energy metabolism.  than those sampled in autumn when a decrease was observed at SM compared to S Fig. A. The lyThe activity of acetylcholinesterase (AChE), as measured with a hemocytes, showed a nonsignificant increase in autumn compared to spring at both sites Fig. C.A significant difference in metallothionein content in the digestive glands was observed in clams collected at SM in spring compared to the values detected in the other sampling groups Fig. D.Antioxidants, measured in the digestive glands, showed fluctuations primarily related to the season. A slight increase was observed for CAT activity in autumn compared to spring, independent of the sampling site Fig. E. The clThe bioaccumulation analyses in clam tissues revealed limited variations in the different analyzed parameters , with the measured levels being similar to those typically observed in organisms from reference areas  between the two sampling stations in terms of seasonal temperature  for salinity in all seasons and for chlorophyll concentration in winter, spring and autumn. It can also be noted  were de novo assembled and filtered by Stacks2.4 algorithms. Only ca. three hundred loci found a match in the GenBank nt database (1e−04), primarily with sequences identified as Bivalvia Figure . After fSea Fig. E–H. In aSea Fig. ; the aveion Fig. . Even ifThe striped venus is an important economic resource in the Mediterranean Basin. However, it is known that in the Adriatic Sea, this species experienced a quantitative decline, and several factors have been suggested to be responsible for the reduction of natural clam beds.C. gallina in relation to several biotic and abiotic factors, and ddRAD sequencing was employed to assess if the genetic diversity level was enough to ensure the adaptive potential of the species to cope environmental perturbations to which it is continuously subjected. Moreover, ecotoxicological and chemical analyses, assessment of environmental parameters, and histological observations were performed to better contextualize the obtained data.In this study, for the first time, we employed two omics approaches based on high-throughput sequencing, the integration of which may be able to provide a comprehensive view of the biological status of the striped venus: RNA sequencing was performed to obtain information on the functional status of 43. The promontory at this location influences water circulation patterns and the diffusion of runoff waters coming from the main northern Adriatic river (the Po River). The functional enrichment analyses on the DEGs obtained from the performed comparisons demonstrated a different representation of some pathways between specimens of the two sites and within the same site between specimens of the two sampling periods  proteins, which are necessary for the progression of meiosis I and II, respectively44. Another upregulated gene identified in the same pathway was calmodulin, a mediator of cell cycle progression from meiosis I to meiosis II. In addition, 14-3-3ɛ protein was found among the upregulated genes. This biomolecule belongs to a highly conserved protein family that is involved in a wide range of roles in cellular processes in all eukaryotes45, including the control of the cell cycle and the regulation of apoptosis46. Interestingly, the schematic representation of the oocyte meiosis-enriched pathway shows the 14-3-3ɛ isotype in its dissociated form, essential for the progression of meiosis I. Indeed, the 14-3-3ɛ isotype binds the cell division cycle 25C (CDC25C) protein, thereby preventing cell cycle progression47.Of significant interest was the presence of the 48, such as C-Myc binding protein, DnaJ subfamily member 13, and spermatogenesis associated 4, were observed. All these findings suggest a timeline discrepancy in gonadal development in SM specimens compared to those collected at the S sampling site.Among the upregulated genes of sampling site S in the spring period and in SM in the autumnal period, male-specific genes involved in bivalve species gametogenesis49 have affirmed that a low value for this index may be the consequence of an intense biological effort that might be due to the gamete production process of the clams in this study.These observations were consistent with histological analyses, showing ongoing gametogenesis in SM specimens collected in the autumnal period in contrast to S clams. This finding is also in line with values observed for the Condition Index, a general indicator of the physiological status of organisms. In our case, lower values were recorded for specimens collected in the sampling site of SM in autumn. Lucas and Beninger (1985)50 reported an increase in the metabolic rate of animals, which was strictly related to glycolysis. In mollusks, an upregulation of the glycolytic pathway has been described during thermal acclimation in providing additional energy resources52. However, in our case study, the absence of substantial variations regarding surface water temperature enabled us to exclude the possibility that differences in metabolic rates observed between specimens of SM and S might be related to this type of environmental stressor. Therefore, variations in metabolic rates for C. gallina might be linked to the values observed for phytoplankton availability and, consequently, to food intake that could, in turn, have influenced the reproductive state in an opportunistic species.Artigaud and colleagues in 201538 reported that in Portugal, gametogenesis in C. gallina occurs at the same time as the increase in food availability and is able to provide energy useful for this process54. Moreover, recent results38 challenge the canonical evidence reported for the reproductive cycle of C. gallina, in which two peaks of gamete emission in April and October were described9. Indeed, analyses performed on natural populations of C. gallina living in the Gulf of Cadíz, as reported by Delgado and colleagues11, demonstrated a long reproductive period between March and September with a recovery of gametogenesis in November related to the high peak of chlorophyll-a availability observed in October. In addition, an intragonadal and interindividual asynchrony has been reported for the striped venus. These observations are in keeping with the timeline discrepancy in the reproduction of C. gallina specimens between the S and SM sites. This timeline incongruence might be consistent with two possible scenarios: gametogenesis in SM clams is delayed and thus covers a shifted forward period compared to S specimens; either SM clams experienced an additional peak of gamete emission in the autumnal period. Ecotoxicological and chemical analyses supported our interpretation of transcriptomic data. Although it is known that cellular responses can be affected by environmental contaminants, the determination of chemical parameters in the clam tissues enabled us to exclude pollutants as possibly causes of observed variations in both transcriptomics and in biomarker responses. Indeed, detected bioaccumulation values were below regulatory limits for bivalve mollusks (where present) and similar to those found in C. gallina inhabiting nonpolluted areas 55.In 2014, Joaquim and coworkers58. The results obtained in the present study highlighted that in C. gallina, the biological responses utilized as biomarkers were modulated by biotic and abiotic seasonal factors, including salinity, temperature, food availability and reproductive status, confirming again the influence of these factors on biomarker modulation in sentinel marine species60. In particular, in S clams, the higher levels of glutathione and of the activities of all glutathione-dependent antioxidant enzymes  in the spring period can be related to an increased metabolic rate, as also indicated by the upregulation of energy metabolism genes, the higher phytoplankton availability and the active gonadic status. In contrast, only small variations in biological responses  were observed in SM clams between spring and autumn seasons, although a certain difference was observed at the transcriptomic level (upregulation of energy metabolism genes in autumn). This finding could be interpreted as an indication that transcriptional responses occur earlier, and they are translated into a functional effect later.It is known for several bivalve species that biological parameters undergo seasonal variations in natural populations not exposed to contaminated conditions; hence, the differences shown from biomarker analyses in marine organisms employed as bioindicators of environmental quality might be related to a different physiological stateC. gallina in the last 2 decades did not negatively affect the genetic variability and, consequently, its ability to respond to modified environments.The findings discussed to this point indicate an intriguing ability of the striped venus to adapt to different environmental conditions. It is known that the adaptive potential of species to cope environmental perturbations is positively correlated with genetic diversity. Therefore, the assessment of the genetic diversity level of the striped venus natural beds has been performed to confirm that the quantitative decline experienced by 63 and in species with higher adult dispersal rates, such as squid and cuttlefish64. Moreover, the FST between Adriatic  and Tyrrhenian localities is high compared to the differentiation recorded in the Mediterranean Basin for other marine mollusks, such as the wedge clam65, the octopus67 and the bivalve Pecten jacobaeus37, or more mobile fish species70.The results showed that striped venus individuals sampled at five different localities in the Adriatic Sea, spanning over 600 km  to SM), are all part of a largely panmictic population Fig. , as also73. Genetic interconnection due to larval dispersion between Adriatic sites represents a fundamental counterforce against local population size decline, thereby increasing the ability of population persistence74. Larval dispersion seems to have played a less important role due to the species’ limited and patchy distribution along the Italian Tyrrhenian coast and to the separation from the Adriatic population. Accurate comparisons on diversity and structure are, however, limited because of the scarcity of genomic studies in Mediterranean marine invertebrates75.The high genetic diversity recorded together with the existence of a unique population in the Adriatic Sea confers to this species a higher responsiveness to local environmental perturbations , as reported for invertebrate marine species76, our demographic reconstruction showed no signature of recent decline in the Adriatic population size , a circular current of the upper water masses represents a possible constraint of C. gallina pelagic larval migration, enabling the isolation of the Adriatic one from the other Mediterranean populations. This situation has also been observed in other marine species69. The cause of the differentiation of the Adriatic C. gallina population from that of the Tyrrhenian Sea might also be attributed to the geodynamic evolution of the Mediterranean Basin. The oldest fossil records of the venus clam date to the Oligocene80 and the Messinian Stage in the late Miocene. Most likely, the deep biota underwent extinction, while the shallow-water biota may have survived this drastic event84.Moreover, genetic structure in sessile marine organisms is expected to be related to the length and dispersal capacity of the larval stageC. gallina specimens collected in two different sites of the Italian middle Adriatic coast showed a substantial temporal difference in gonadal development and gamete emission. This characteristic is likely due to the “opportunistic” behavior of C. gallina, which is able to exploit local fluctuations in nutrient availability. Ecotoxicological and chemical investigations excluded the effects of pollution and anthropic factors, suggesting a good healthy status of the striped venus. The high genetic diversity level that emerged from our ddRAD dataset analyses suggests that stock reduction has not negatively affected C. gallina natural beds. Indeed, the high larval dispersal rate might have played a “compensatory role” on local fluctuations, conferring a good adaptive potential of this species to face the environmental perturbations to which it is continuously subjected.This study provides a multidisciplinary approach that is able to provide an exhaustive view of the biological status of striped venus. Comparisons of transcriptomic and histological data between conditio sine qua non in the field of conservation biology to adopt ad hoc management plans of fishery resources, which may also result in positive effects on the fishing economy.Therefore, elucidating the bioecological properties and the genetic diversity of wild populations is a This study was conducted on invertebrates (molluscs), not subjected to specific permissions since the sampling area is not privately owned or protected and did not involve endangered or protected species.Chamelea gallina of similar size and weight  were collected by hydraulic dredge, in different sites and periods.Clams For population genetics analyses, 155 specimens were sampled from five sites  were frozen in liquid nitrogen and stored at − 80 °C for ecotoxicological analyses (biomarkers); 3 pools of whole tissues (50 organisms per pool) were collected for chemical analyses of bioaccumulation.Concerning transcriptomic, ecotoxicological, chemical, and histological analyses, specimens of the two sampling sites, S and SM, were collected in late spring and autumn 2018. In particular, for transcriptomic analyses, three pools of digestive glands dissected from 10 specimens were analysed for each sampling site and period; for histological analyses 20 specimens for each sampling site were examined to determine the stage of gonadal development. Ecotoxicological and chemical analyses required a total of 235 specimens for each sampling group: whole tissue was collected from 30 individuals for the calculation of Condition Index (CI = [dry tissue weight (g)/dry shell weight (g)] × 100)https://igatechnology.com) where TruSeq Stranded mRNA Sample Prep kit  was used for library preparation following the manufacturer’s instructions, starting with 1 µg of good quality RNA (R.I.N > 7) as input. The poly-A mRNA was fragmented 3 min at 94 °C and every purification step was performed by using 1 × Agencourt AMPure XP beads. Libraries were then processed with Illumina cBot for cluster generation on the flowcell, following the manufacturer’s instructions and sequenced on 2 × 125 bp paired-end mode on HiSeq2500 .Total RNA was extracted using Trizol reagent  and purity and quantity were checked using Qubit 4.0 fluorometer (ThermoFisher Scientific). RNA integrity was evaluated by Agilent 2100 Bioanalyzer . RNAs were sent to IGA Technology Service  was then used to perform domain search in PFAM database v3292. Functional enrichment analysis was performed with KEGG BlastKOALA95 .Contigs obtained from comparative analyses conducted using DESeq2 were used to perform functional annotation and pathway enrichment analyses. From each comparison, genes were filtered using the Benjamini–Hochberg adjusted Gonadal tissues of specimens from S and SM were histologically examined to determine the stage of gonadal development. After embedding in Killik  medium, sections of 8–10 μm were obtained using Leica cryostat . The histology prepared slides were stained with haematoxylin and eosin method and inspected using the optical light microscope .97; metallothioneins level98.Ecotoxicological analyses (biomarker) analyses were carried out through standardized procedures and included: Condition Index (CI) calculation; analysis of lysosomal membrane stability in the hemocytes; spectrophotometric determination of acetylcholinesterase activity in the hemolymph; spectrophotometric determination of single antioxidants  and gas-chromatographic assay of total antioxidant scavenging capacity toward peroxyl and hydroxyl radicals99.Chemical analyses of trace metals  and polycyclic aromatic hydrocarbons (PAHs), both low molecular weight  and high molecular weight  were performed on whole clam tissues by conventional procedures base on gas-chromatography with flame ionization detector, HPLC with fluorimetric detection, and atomic absorption spectrophotometryDetails of analytical procedures for both ecotoxicological and chemical analyses are given in Supplementary File p < 0.05, and the post hoc comparison Newman–Keuls. The analyses were performed with the R software.Statistical comparison of biological parameters was performed through the analysis of variance (ANOVA) with a level of significance set at https://marine.copernicus.eu). Temperature and salinity were extracted from the Mediterranean Sea Physics Analysis and Forecast dataset100 which nowadays represents the best available estimate of the physical environment and dynamics of the whole Mediterranean Sea. Chlorophyll timeseries were extracted from the Mediterranean Sea Biogeochemistry Analysis and Forecast dataset101 which is produced by a biogeochemical fluxes model, named MedBFM, driven by physical forcing fields from the aforementioned analysis and forecast dataset. Extracted timeseries span the year 2018. Data were provided as daily means for temperature, salinity, and chlorophyll. The model grid sea-points nearest to S and SM sampling stations were chosen and data were extracted at the sea-floor level. Seasonal means of the year 2018 were calculated and further processed with the aim to check if the two sampling stations exhibit significant differences in terms of monthly and seasonal means of the parameters taken into account. The assessment was performed by applying a one-way analysis of variance (ANOVA) and a Tukey’s test as post-hoc pairwise comparison. The null hypothesis stated that there were no differences in terms of temperature, salinity or chlorophyll concentration between the two sampling stations. The level of significance was set at 0.05. The software Unistat 10 was used to perform the analysis.Historical timeseries of temperature, salinity and chlorophyll concentration are taken from the output of hydrodynamics and biogeochemical models of the Mediterranean Sea, publicly available on E.U. Copernicus Marine Service Information (https://igatechnology.com) where ddRAD libraries were produced using IGATech custom protocol, with minor modifications of Peterson’s and colleagues (2012)102. Libraries were constructed using SphI and HindIII restriction enzymes and sequenced with V4 chemistry paired end 125 bp mode on HiSeq2500 instrument .Total DNA extraction from foot tissue was performed for 48 individuals of each sampling site Fig.  using DNprocess_radtags in Stacks 2.4103 with default settings. Paired-end reads from all individuals were de novo assembled using the script denovo_map.pl in the Stacks package, allowing for maximum two mismatches between stacks to call a locus within and among individuals (-M 2 and -n 2), disabling haplotype calling from secondary reads (–H) and setting the maximum number of stacks at locus to 2 (–max_locus_stacks). The script populations in the Stacks package was employed to discard loci found in less than 40% of individuals across all populations (− R = 0.4) and with observed heterozygosity higher than 0.8 (–max-obs-het). Before any downstream analysis, our dataset for contamination by non-endogenous DNA was tested. Blast104 was used to map the consensus sequence of each locus to nt database (downloaded on 14/09/19) applying an epsilon threshold of 1e−04. Blast results were taxonomically summarized using MEGAN105  was run, selecting only loci with less than 45 SNPs (918 loci), selecting individuals with less than 25% missing data , and the combination of both filters .The scripts fineRADstructure using a custom python script  was calculated in each of the two populations inferred by 108, a model-free method to infer the variation in past coalescent rate within a population through the maximization of the composite likelihood of the site frequency spectrum. The total number of observed sites was calculated as the number of loci times an average locus length of 230 bp and all bins of the SFS were used given the high confidence in heterozygous sites calling due to the high coverage per locus. As mutation rate is not known in this species, time and population size of the demographic reconstruction are given as scaled by the mutation rate.To separately infer the past demographic histories of the two populations, we used the SFS as calculated before in Stairway PlotSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.


Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers. The number of deaths from traffic accidents is decreasing. However, according to the data on the reduction rate of deaths in traffic accidents by vehicle type in the last 10 years, the reduction rate of noncommercial vehicles was 39%, while that of commercial vehicles was 31% ,3. AccorAlthough there have been various efforts to improve the quality of life and working environments of taxi drivers, continuous management plans for taxi drivers to reduce accidents are still insufficient. As human factors account for about 94% of the major reasons of traffic accidents, it is essential to understand and manage drivers’ intrinsic factors to reduce traffic accidents . TherefoThe purpose of this study is to predict high-risk taxi drivers based on a deep learning method using wellness data. This study consists of Stage 0 through Stage 3. Stage 0 is the data construction phase. In this phase, in-depth interviews are conducted to identify the wellness of commercial vehicle drivers, and this wellness data is matched with an accident dataset collected from the commercial vehicle driver management system operated by the Korea Transport Safety Authority (KOTSA). In Stage 1, the priority of factors affecting the severity of accidents is defined, and the random forest analysis is applied as the methodology. Stage 2 involves constructing an optimized model based on an artificial neural network (ANN), which classifies the severity level of an accident. In this phase, by using the priority of variables derived from Stage 1, combinations of input data are set as scenarios, and optimized classifiers for each scenario are derived. Stage 3 aims to choose an optimized model for predicting high-risk taxi drivers. In this phase, the selection criteria for the optimized model are established and applied. This derived model is expected to be useful in determining high-risk taxi drivers. With the model, it should be possible to support customized consulting systems for commercial vehicle drivers and manage wellness factors that are highly related to accidents. This study can be used to induce active improvements in traffic safety through drivers’ self-diagnosis and feedback.The rest of this paper is organized as follows. In this study, a deep learning method is applied to predict high-risk taxi drivers through driver wellness evaluation, and the process of the study is presented in As of July 2020, there were 254,490 taxi drivers in Korea, of which 89,650, or 35%, belonged to taxi companies. Moreover, private taxi drivers account for 65% of taxi drivers, and ride-hailing services such as Uber do not operate in Korea. In Stage 0, to identify the wellness of taxi drivers, in-depth interviews were conducted by a professional investigator. In the in-depth interviews, professional investigators were matched one-to-one to increase the reliability of the data. The subjects of the in-depth interviews were drivers belonging to taxi companies; these drivers caused serious traffic accidents from the third quarter of 2018 to the second quarter of 2019. Drivers belonging to taxi companies  were selected as the subject of the survey because corporate taxi companies in which serious accidents occurred are obligated to undergo safety inspections and have high access to investigations. Interviews were conducted with 993 drivers at 89 taxi companies from September to October 2019. The survey items consist of 246 variables, including wellness, the characteristics of the driver, and company data. The 20 wellness variables related to traffic safety were selected based on the literature review presented in The investigation items consist of 20 categories related to wellness and traffic safety, which were collected through the interviews, and one accident characteristic factor, which was collected through the commercial vehicle driver management system. The items and scale of the survey are presented in In order to analyze the accident characteristics of the interviewed taxi drivers, we used the dataset of the number of casualties per traffic accident from August 2016 to July 2019; this was obtained from the commercial vehicle driver management system. Accident data were also considered only when the taxi driver was the perpetrator. Among the 993 interviewed drivers, accident data were collected for 781 drivers. From the collected data, the accident severity is classified into three levels based on the severity of the accident, and this is defined as the risk level of accident severity. High-risk (class 3) refers to a driver who has experienced more than a serious injury, medium-risk (class 2) refers to a driver who has experienced a minor injury, and low-risk (class 1) refers to a driver with no accident experience. The numbers of drivers for the different risk levels of accident severity are 66, 164, and 551, respectively.k, is calculated using Equation (1).The random forest model is an ensemble machine learning technique, and it uses an advanced decision tree analysis methodology to overcome the drawbacks of decision tree analysis . In the i is an indicator function that is set as 1 when the value in the parentheses is true and set to 0 when the value is false. Additionally, jt means the jth decision tree in the generated decision tree (t).Here, iOOB is a group of decision trees that is not used in the learning process, by bagging as an observed attribute. If a set of decision trees does not include i) is j, that is,  pk^(xi) . The ver pk^(xi) ,27. Thisif is the ratio of classifying i to the i category correctly, and 1 − if is the ratio of classifying i to another category. If the model perfectly classifies every category, if is 1 and the value of the GI index becomes 0. A higher MDG value for a particular variable indicates that this value is suitable for correct classification of a certain category, meaning that it decreases the degree of impurity. The MDG value varies from 0 to 100. When an MDG value of one variable is 0, the variable will not be used for classification. However, if the MDG is closer to 100, the observation can be completely classified by the variable.Here, An ANN is a data processing system resulting from the mathematical modeling of the learning process inspired by humans. It consists of an input layer that accepts input data, a hidden layer that processes input values and produces the result, and an output layer that calculates an output value . Each laThe pattern recognition network applied in this study is a feed-forward network, which can be trained to classify inputs according to the classes of outputs. The feed-forward network is a method in which the signal from the input layer is forwarded to the hidden layer and the signal from the hidden layer is forwarded to the output layer. The hyperparameters that need to be optimized in neural networks are the number of hidden layers and neurons, as well as the transfer function that calculates the output value of the neurons. The descriptions of each hyperparameter are shown in x that maximizes the objective function f(x). This is defined as shown in Equation (3) [x*: Optimized hyperparametersx: Hyperparameterf(x): Correct classification rate (CCR) of the models.For neural network optimization, some studies have applied the Bayesian optimization method, which effectively solves the global optimization problem ,32,33. Btion (3) . This stf(x). This process is outlined as follows: (i) With the assumption that f(x) follows the Gaussian process (GP) prior, learn the model by using the given data D, and (iii) include the data point D that has the largest acquisition function value. The acquisition function is a measure used to find the global optimum, which is the hyperparameter affecting the maximum classification accuracy. In this study, the expected improvement (EI) function is selected as the acquisition function. EI minimizes the error of the predicted f(x), and it is defined as shown in Equation (4) [G)z: Normal cumulative distribution function(g)z: Probability density function for z.(Bayesian optimization constructs a probabilistic model for tion (4) ,35,36,37tion (4) ,39,40. The F1-score is 0.77 or higher.Criterion (2) The classification accuracies of each class are 53% or higher.Criterion (3) Scenarios that satisfy criteria 1 and 2, with fewer input variables.From Stage 2, it is confirmed that the performances of the ANN model in each scenario are different. Therefore, in Stage 3, three criteria are designed to select the best model, as follows. Criterion 1 is based on the F1-score, and the 75th percentile value of 0.77 is set as the threshold for satisfying this criterion. Through criterion 1, a model considering the prediction performances of all classes can be determined. However, as the F1-score of the model is highly influenced by the classification accuracy of Class 1 (low-risk) with a large number of samples, criterion 1 is limited in that it does not accurately reflect the classification accuracy of Classes 2 (medium-risk) and 3 (high-risk). In order to address the limitation of criterion 1, the classification accuracies of the three classes are checked as the second criterion. In criterion 2, 53% is defined as the threshold, which is the 75th percentile value of the classification accuracy for Class 3, which shows the lowest average accuracy value. If the classification accuracies of each class are 53% or more, criterion 2 is satisfied. Finally, criterion 3 determines whether the model can show a similar performance with the minimum dataset. In this study, to expand and easily use the classification results in the future, it is expected that a model working with a minimal data collection would be practical. Accordingly, in criterion 3, models satisfying criteria 1 and 2 show similar performance. When the performances of the models are similar, the model with the smaller number of input variables is determined as the better model. A summary of these three criteria is shown below, and the models that satisfy these criteria are shown in As a result of selecting the best model according to the three criteria mentioned above, the model with 16 input variables is selected as the best model. Effective traffic accident prevention is possible when taxi drivers’ working environment, life patterns, and active management of health characteristics, and the degree of interest in taxi companies’ driver management, are improved. The analysis results of this study will be used as basic data to improve the effect of preventing traffic accidents in commercial vehicles. For example, the traffic safety manager of a taxi company may investigate 16 items derived from the analysis results of a taxi driver’s traffic-safety survey. It is possible to perform customized traffic safety consulting for taxi drivers using the factors derived from the best model. Taxis account for the largest share at 40.1% of the traffic accidents in commercial vehicles. Moreover, the reduction rate of deaths in taxi accidents was low compared to other commercial vehicles. Therefore, efforts should be made to improve the traffic safety of taxis by preparing effective measures. Many taxi drivers are exposed to overwork due to inadequate working environments and long working hours. Therefore, a traffic safety management system for taxis based on underlying human factors is required. However, existing studies investigating the relationship between accidents and drivers have focused on physical factors and demographic characteristics, such as accident sites and vehicle factors. Few studies have looked into intrinsic aspects of the drivers.This study develops a risk level of accident severity classifiers to predict high-risk taxi drivers based on a deep learning method with wellness data. The study is broken into several stages. In Stage 0, wellness data are collected, including information related to drivers’ working environments, living environments, and health characteristics, through in-depth interviews conducted with 993 taxi drivers. In addition, high-risk drivers are classified based on the severity of the accidents they experienced, which is derived from the accident dataset of the drivers collected from a driver management system. High-risk drivers were classified based on the severity of the accidents they experienced. In Stage 1, a random forest analysis is used to identify the priorities of factors affecting the risk level of accident severity that taxi drivers experienced. As a result, a driver’s age, living satisfaction, level of job satisfaction, amount of sleeping time, and working hours per week are identified as the top five variables that have the greatest influence on the risk level of accident severity. In Stage 2, optimized ANN classifiers are derived to predict the risk level of accident severity for 16 scenarios using the priority of variables derived in Stage 1. Finally, in Stage 3, the best model for predicting high-risk taxi drivers is selected based on three criteria considering the classification accuracy, F1-score, and number of input variables. As a result, the scenario with input variables up to the 16th priority is selected as the best model; this showed a classification accuracy of 86% and an F1-score of 0.77. With the optimal model derived in this study, high-risk taxi drivers can be identified. Based on these results, it is expected that traffic safety measures can be established to reflect the wellness of individual drivers, which can be used to manage high-risk drivers.In order to increase the reliability of the high-risk taxi driver classifier developed in this study, further studies should be undertaken in the future. First, this study identifies 20 items related to the wellness of taxi drivers through an interview, but it is necessary to consider more variables that can represent wellness, such as the job-related affective well-being scale (JAWS), physical symptoms inventory (PSI), and so on. In the case of health-related variables, the reliability of data can be improved through objective data collection, such as data from medical institutions. In addition, while this study predicts the risk level of accident severity, it is also necessary to consider other variables related to traffic safety, such as the number of accidents and dangerous driving behavior. Additionally, it is necessary to consider the driving distance and the number of working days as exposure to traffic accidents. Second, it is necessary to secure the reliability of the prediction model by collecting additional data. In order to increase the accuracy of the collected data, this study applies an interview method. However, face-to-face interviews take a lot of time. Therefore, a way to collect more data while increasing the accuracy of the response is necessary in the future. Furthermore, it is necessary to predict high-risk taxi drivers with consideration for the types of taxi drivers by expanding the survey groups to individual taxi drivers. Finally, we may be able to increase the reliability of the model by considering various deep learning methods, such as k-fold validation.This study derives classifiers for predicting high-risk taxi drivers based on the driver’s wellness, which goes beyond the physical factors of traffic accidents. It is expected that the results of this study can be used to prepare plans for changing the paradigm of taxi traffic safety measures based on wellness. Additionally, it is also expected that these results can be used as basic data for establishing an effective traffic safety policy.


More recently, we demonstrated a possibility to use thymol to overcome the resistance of a P. nodorum strain able to grow on difenoconazole-containing media. However, potential for thymol to serve as a chemosensitizing agent in seed or plant treatments, to provide an effective suppression of the above-mentioned plant pathogens by triazole fungicides applied in lowered dosages, had yet to be tested. In the work presented here, we showed combined treatments of naturally infected barley seeds with thymol and difenoconazole (Dividend® 030 FS) synergistically exacerbated the protective effect against common root rot agent, B. sorokiniana, and other fungi (Fusarium spp. and Alternaria spp.). Similarly, co-applied treatment of wheat seeds, artificially inoculated with Fusarium culmorum, resulted in equivalent reduction of disease incidence on barley seedlings as application of Dividend®, alone, at a ten-fold higher dosage. In foliar treatments of wheat seedlings, thymol combined with Folicur® 250 EC (a.i. tebuconazole) enhanced sensitivity of P. nodorum, a glume/leaf blotch pathogen, to the fungicide and provided a significant mitigation of disease severity on treated seedlings, compared to controls, without increasing Folicur® dosages. Folicur® co-applied with thymol was also significantly more effective against a strain of P. nodorum tolerant to Folicur® alone. No additional deoxynivalenol or zearalenone production was found when a toxigenic F. culmorum was cultured in a nutrient medium containing thymol at a concentration used for chemosensitization of root rot agents. Accordingly, F. culmorum exposure to thymol at the sensitizing concentration did not up-regulate key genes associated with the biosynthesis of trichothecene or polyketide mycotoxins in this pathogen. Further studies using field trials are necessary to determine if thymol-triazole co-applications result in sensitization of seed- and foliar-associated plant pathogenic fungi, and if thymol affects production of fusarial toxins under field conditions.Thymol, a secondary plant metabolite possessing antifungal and chemosensitizing activities, disrupts cell wall or membrane integrity and interferes with ergosterol biosynthesis. Thymol also functions as a redox-active compound inducing generation of reactive oxygen species and lipid peroxidation in fungal cells. Previously, we showed thymol significantly enhanced the  Redox-active secondary metabolites of various plants attract continuing interest with regard to the diversity of their biological activity . Since mAn important specific feature of a number of redox-active compounds rendering them as promising agents for control of various pathogenic microorganisms, including some plant pathogenic fungi, is their ability to induce oxidative stress in pathogens or impair fungal antioxidation systems . In addiThymus vulgaris found also in other Thymus and Lamiaceae species. Thymol attacks several cellular and metabolic targets in pathogenic fungi. It disrupts cell wall or membrane integrity and interferes with ergosterol biosynthesis (Bipolaris sorokiniana and Parastagonospora nodorum (P. nodorum strain that was able to grow on difenoconazole-containing media (® 030 FS) against B. sorokiniana, F. culmorum and also accompanying Fusarium and Alternaria species on seedlings grown from barley and wheat seeds treated with this fungicide in combination with thymol. In another experimental series, protection efficacy of wheat seedlings sprayed with thymol-combined tebuconazole (Folicur® 250 EC) against P. nodorum was assessed. We found significant synergistic augmentation of the protective effect of both difenoconazole and tebuconazole when co-applied with thymol. In addition, a possibility to control a tebuconazole-tolerant P. nodorum mutant strain by co-application of thymol with Folicur® was showed. We also analyzed deoxynivalenol (DON) and zearalenone (ZEN) production in a toxigenic F. culmorum exposed to thymol and profiled expression of key genes associated with the biosynthesis of trichothecene or polyketide mycotoxins in this pathogen. These experiments demonstrated thymol at sensitizing concentrations did not stimulate DON and ZEN biosynthesis. Collectively, our findings confirm the promise of using chemosensitizing agents as an approach to controlling fungal pathogens in agriculture and possibly contributing to development of better environmentally friendly integrated crop protection systems.The damaging and regulatory effects of redox-active molecules are realized through their interaction with lipids, proteins and DNA. These molecules cause lipid peroxidation, oxidative injury of the cell plasma membrane, protein, and DNA oxidation, as well as nitrosylation and S-glutathionylation of proteins . Thymol ynthesis . Thymol ynthesis . Earlier nodorum . Recentlng media . HoweverFusarium culmorum (OR-02-37) and Parastagonospora nodorum (B-9/47) pathogenic to wheat were provided by the State Collection of Plant Pathogenic Microorganisms at the All-Russian Research Institute of Phytopathology (ARRIP) and the ARRIP Department of Mycology, respectively. F. culmorum stock cultures of the fungi maintained on potato dextrose agar slants were resumed by culturing for 10–14 days in Petri plates on the same medium to obtain spore-producing colonies. Suspensions of fungal conidia for inoculations of wheat seeds with F. culmorum and detached wheat leaves with P. nodorum were prepared according to Strains of Bipolaris sorokiniana, a common root rot agent, were collected from plants grown on the ARRIP experimental plots.Samples of barley grain naturally infected with ® 030 FS  and Folicur® 250 EC , which are commonly applied for seed and foliar treatments of various crops, including cereals.Since thymol (CAS 89-83-8) is slightly soluble in water, solutions of 99% commercial thymol  in dimethyl sulfoxide, (CAS 67-68-5) 99.9%  were used for seed and leaf treatments. Commercial fungicides tested included DividendTo select non-phytotoxic dimethyl sulfoxide (DMSO) and thymol concentrations, spring wheat (cv. Zlata) and barley (cv. Zazersky 85) seeds (200 seeds per each treatment) were soaked overnight in aqueous 0.5, 1.0, or 10% DMSO or in thymol dissolved in the DMSO solutions to final concentrations 10, 100, or 1000 ppm. After such treatments seeds were placed on paper towels , which were rolled-up and put into beakers with distillated water. Plants were grown at 22°C (day) and 16°C (night), 60% relative humidity and 16-h light period for 7 days. Seeds treated with distillated water were used as controls. The number of germinated seeds, number of seedlings and their length were recorded after 7 days of plant growth.B. sorokiniana and wheat seeds artificially inoculated with F. culmorum were treated by a 3-h agitation in a minimal volume of aqueous Dividend® 030 FS allowing complete seed wetting. This was followed by 16–18 h incubation with the fungicide formulation at room temperature, without agitation. Prior to treatment with the fungicide, wheat seeds were disinfected  at slow stirring, and then slightly dried. The highest difenoconazole concentration (500 ppm) used for fungicidal treatments of barley and wheat grain samples corresponded to Dividend® 030 FS dose rate recommended for agricultural practice  were tested. After these treatments, the Dividend-exposed and water-exposed (control) seeds were assayed using rolled-towel test, as described above. After 12–14 days post-inoculation, the number of diseased seedling were counted to evaluate root rot incidence, and disease symptoms on were roots and root necks were visually estimated according to a five-score rating scale  of common root rot and Fusarium root rot, on barley and wheat seedlings, respectively, were assayed using the rolled-towel test in parallel with the treatments with the fungicide alone. The incidence of Fusarium spp. and Alternaria spp. accompanying the predominant B. sorokiniana infection of barley seeds was also evaluated.Following the determination of sub-fungicidal DividendP. nodorum spores were suspended to a final concentration of 106 conidia/ml in 1% aqueous DMSO or thymol (from 10 to 5000 ppm) dissolved in 1% DMSO. Control conidial suspensions (106 per ml) were prepared in sterile distilled water (SDW). Eight centimeter long leaf fragments were cut from central parts of detached wheat leaves and placed in Petri plates atop of 1% water agar supplemented with benzimidazole (40 μg/ml). Aliquots (10 μl) of the conidial suspensions in DMSO or thymol solutions were dropped on upper  parts of 8-cm leaf fragments. Drops (10-μl) of conidial suspensions (106 conidia/ml) in SDW (control for DMSO-treated detached leaves) or 1% DMSO solutions (control for thymol-treated leaves) were applied to lower  parts of the same 8-cm leaf fragments. In situ disease development was recorded on 10 leaf sections per each treatment. An additional plate, in which both distal and basal locations on each of 10 leaf fragment were inoculated with pathogenic conidia in SDW, was prepared to confirm that they was able to cause the disease. Disease symptoms were scored at 5 days post-inoculation according to a five-score rating. Sub-fungicidal dosages of Folicur® 250 EC, tested at concentrations ranging from 0.05 to 0.5 ppm (based on tebuconazole) were determined using the same assay  supplemented with Folicur® alone or DMSO-dissolved thymol alone, each taken at two sub-fungicidal (0.1 and 0.25 ppm of tebuconazole) or two marginally fungitoxic (50 and 100 ppm of thymol) dosages. In parallel, another seedling series was sprayed with fungal conidia suspended in solutions of the fungicide at a concentration of 0.1 ppm combined with thymol at 50 or 100 ppm. To promote infection, pots with treated and inoculated seedlings were maintained in an inocubation chamber at 80–85% relative humidity and 18°C for 24 h. Thereafter plant growing was continued under above-mentioned controlled conditions for 2 weeks. The disease development was scored according to the commonly approved international James’scale (Spring wheat seedlings (cv. Khakasskaya) were grown in pots  to the two-three leaf stage under controlled conditions . Young seedlings were sprayed with es’scale  at 14–15es’scale  as recomes’scale .F. culmorum strains, Fc-M-01-55/3, was grown in submerged culture for 7–9 days at 25°Ñ and 220 rpm  in 250-ml shaker flasks with toxigenesis-promoting liquid Myro medium (6 conidia/ml) or by placing a piece of fungal mycelium grown on PDA into the center of the plates. Fungal cultures grown in/on thymol-free media supplemented with corresponding aliquots of 1.0% DMSO under the same conditions served as controls.To estimate production of deoxynivalenol (DON) and zearalenone (ZEN), a toxigenic o medium  suppleme2SO4 and evaporated on a rotary evaporator at 40°C. Dry residues were dissolved in a mixture of acetonitrile, methanol and water (1:1:0.75) or (1:1:4.0) used as mobile phases in RP-HPLC analyses of ZEN or DON, respectively. Aliquots (10 μL) of the prepared solutions were applied on a temperature-controlled Symmetry C18  column followed by toxin elution, using the above-mentioned mobile phases, and detected at 254 nm. Mycotoxin concentrations (μg/mg) were calculated using a calibration curve based on commercial DON and ZEN preparations , which were also added in extracts obtained from control fungal cultures as external standards. All samples were analyzed twice. The limits of detection were 0.5 (DON) and 0.2 (ZEN) ng/mg; the recovery levels averaged 81% and 77% for DON and ZEN, respectively.Upon completion of the cultivation, DON and ZEN were quantified by reverse phase HPLC using a Waters 1525 Breeze HPLC system equipped with a Waters 2487 UV detector, applying isolation procedures slightly modified from those described previously . Briefly® Plant Mini Kit  according to manufacturer’s protocol. Ten microgram of total RNA were used for cDNA synthesis carried out by the MMLV RT kit .Total RNA was extracted from fungal mycelium using RNeasyTRI5 (trichodiene synthase), TRI6 (transcription factor), PKS4 and PKS13 (polyketide synthesis) were analyzed by qPCR. The translation elongation factor 1 alpha gene (TEF1α) served as a control as the most suitable housekeeping gene according to q slope method. QGene96 software was used for analyzing the qPCR results.Expression levels of the following four genes responsible for different steps of trichothecene and polyketide biosynthesis were quantified by qPCR: t-test for independent variables at p ≤ 0.05. Experiments involving spraying of wheat seedlings (100–120 ones for each treatment) or assessment of Alternaria incidence on barley seeds were replicated four times. Other experiments were run in triplicate. Each independent experiment with barley or wheat gain included 200 seeds per treatment.Data obtained in the experiments involving seed treatments and spraying of wheat seedlings were analyzed using a STATISTICA 6.1 software (StatSoft Inc.). Mean values, standard deviations and standard errors were calculated. Significant difference between treatments and controls were determined using a Protection efficacy (PE) was calculated using formula PE% = Dc – Dt/Dc × 100, where Dc is the average incidence or disease severity in controls, while Dt represents these parameters for treated seeds or seedlings.p ≤ 0.05), was determined  – X% × Y%/100, where X% and Y% are PE% of thymol or the fungicide.To reveal a putative synergy between thymol and tebuconazole or difenoconazole in co-applications on seeds or seedlings, the Limpel criterion, Er ≥ Ee . These four dosages are indicated below as Dividend/1 (the highest), Dividend/05, Dividend/01, and Dividend/001, respectively. Results of roll-towel assays showed Dividend/1 and Dividend/05 were effective against common root rot infection on barley seedling, while Dividend/01 was the lowest dosage still inhibiting development of B. sorokiniana, a predominant seed pathogen, and did not affect seed germination , but no effect was found in two other ones. Compared to controls, the average incidence of Fusarium rot agents on seedlings decreased to 6.4% or 3.2% after seed treatments with Dividend/01, alone, or Dividend/01 + thymol at 50 ppm, respectively.In addition to the predominant root rot pathogen, ® on artificially inoculated wheat seeds also resulted in augmentation of the suppressive activity of this triazole fungicide against F. culmorum  after application of fungicide, alone  that was lethal to a sensitive wild strain  localized in the trichothecene biosynthetic cluster and two (PKS4 and PKS13) belonging to the polyketide biosynthetic cluster. Gene expression was analyzed 2 days before the maximal accumulation of DON or ZEN in thymol-containing culture media inoculated with the pathogen. Levels of TRI5, TRI6, PKS4, and PKS13 expression were compared to those in control fungal cultures grown without thymol under the same conditions. Expression of the control gene (TEF1α) was stable in all the samples tested. Expression levels of four target genes in thymol-exposed samples were the same or slightly lower those of the control, in both submerged and agar cultures  pointing to the absence of a calculated synergism  used against a zoophilic yeast Malassezia pachydermatis  and Fusarium crown-foot-root rot, head blight (F. culmorum), and glume/leaf blotch of wheat (P. nodorum).Previously, we found thymol synergistically augmented colonies . In thisngicides . These fngicides  and caus® and Folicur®, selected for our experiments to show sensitization by thymol, were at levels that did not protect seedlings or only provided a minor protective effect, and were far lower than dosages recommended for agricultural use  in all experiments.At certain concentrations, thymol is known to be phytotoxic and was reported to inhibit seed germination and growth of some weed plants . Howeverural use , 4, 5. Rde alone , 3. Moreectively , 3. The via ROS generation with the added provocation of osmotic stress, are main mechanisms augmenting sensitivity of fungi to triazoles and other antimycotics : U22464, AY130290, MN313473-313507 (TRI5); MH514940-514957, MH001614-MH001648 (TRI5 and TRI6); AY429625, XM009259983 (PKS4), AY495638, JF966273 (PKS13); DQ019316 (PKS4 and PKS13); KY205745-205748, DQ382164-382166 (TEF1a).The primers and fluorogenic probes were designed based on the alignment of the following sequences retrieved from GenBank (LS and VD conceived the concept of the research and designed the experiments. OM, LA, and DE performed the main experiments, prepared the reagents, materials, and analysis tools. AS performed the experiments on expression of genes associated with mycotoxigenesis. LS and AS wrote the original draft and prepared the figures and tables. LS, VD, and SZ reviewed and edited the drafts of the manuscript. All authors reviewed and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Myelodysplastic syndromes (MDS) like many other blood cancers is a disease of the bone marrow, in which the spongy part of the bone is not able to produce enough healthy blood cells. MDS is primarily a disease of the elderly, but it can affect people at a younger age as well. The disease arises as a result of a combination of complex processes that is thought to be primarily driven by accumulation of genetic mutations in the stem cells. However, there is an increasing evidence implicating the bone marrow environment as a fertile milieu where these mutated stem cells thrive and give rise to the disease. In this review, we have discussed the role of blood stem cells and how other cell types in the bone marrow environment interact with each other, therefore contributing to MDS. In addition, we discuss the therapeutic strategies that can be exploited to treat MDS. Myelodysplastic syndrome (MDS) are clonal haematopoietic stem cell (HSC) disorders driven by a complex combination(s) of changes within the genome that result in heterogeneity in both clinical phenotype and disease outcomes. MDS is among the most common of the haematological cancers and its incidence markedly increases with age. Currently available treatments have limited success, with <5% of patients undergoing allogeneic HSC transplantation, a procedure that offers the only possible cure. Critical contributions of the bone marrow microenvironment to the MDS have recently been investigated. Although the better understanding of the underlying biology, particularly genetics of haematopoietic stem cells, has led to better disease and risk classification; however, the role that the bone marrow microenvironment plays in the development of MDS remains largely unclear. This review provides a comprehensive overview of the latest developments in understanding the aetiology of MDS, particularly focussing on understanding how HSCs and the surrounding immune/non-immune bone marrow niche interacts together. Myelodysplastic syndrome (MDS) represents a collection of clonal haematopoietic stem cell (HSC) disorders primarily diagnosed in elderly, with a median age of diagnosis at 70 years of age. MDS is driven by a complex combination(s) of genetic changes, that results in heterogeneity in both clinical phenotype as well as the disease outcomes, with a high propensity to develop acute myeloid leukaemia (AML) ,2. SigniLike any major metropolitan city in the world, a cell teams with surrounding ‘specialized workers’ to carry out its daily operations in a highly regulated manner. Detailed reviews on the bone marrow hematopoiesis and niche have been extensively covered elsewhere ,22,23,24Bone marrow niches are also home to immune cells, such as myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), dendritic cells, natural killer cells, monocytes, macrophages, T-cells, B-cells and plasma cells that themselves primarily form the ‘immuno-microenvironment’ . Approxi+CD38−CD45RA−CD90+/CD49f+), providing the strongest evidence for the existence of Myelodysplastic syndrome-initiating cells (MDS-ICs) . I. I124]. Macrophages are another cell type that are essential component of the innate immune system and their main function is to ingest as well as degrade abnormal cells (or cellular debris), and drive inflammatory processes. Macrophages are also known to be directly regulating HSCs via CD234/CD82 (to inhibit cell cycle progression), CD169 (promote erythroblast differentiation into reticulocytes and destroy the aging erythrocyte) and VCAM-1 (HSC homing) ,130,131.MDSCs , an immature innate immune cell type, known to accumulate in cancer patients, are effector immunosuppressive cells that contribute to cancer progression ,138,139.Recent studies have identified another crucial player in immune regulation, namely FoxP3+ regulatory T cells (Tregs), that are drawing attention for their heterogeneity and noncanonical functions, such as HSC quiescence and engraftment ,144. TreAltered hematopoiesis and associated immunological changes, predominantly cytokines, in the BM milieu are hallmarks of not only MDS  but alsoRecent data have suggested that damage-associated molecular pattern (DAMP)-induced inflammation  in the BM of MDS patients can lead to the uncontrolled activation of inflammasome machinery, causing HPSC lytic cell death mediated via pyroptosis, and this can act as a pathogenetic driver of ineffective haematopoiesis in MDS . The levDeveloping treatment for patients with MDS needs a complex and personalized array of therapeutic approaches. There is a significant lack of progress for treatment options and the only FDA approved treatment that is currently been used in the clinics include the thalidomide analogue lenalidomide and the DNA methyltransferase inhibitors azacytidine and decitabine, and stem cell transplantation . All theRecently, Magrolimab, a monoclonal antibody that specifically interferes with the signal regulatory protein alpha (SIRPa) receptor present on macrophages, which is overexpressed on malignant cells, was tested in a clinical trial alongside azacytidine in high-risk MDS patients. Azacytidine increases the expression of CD47, which leads to the increased binding of magrolimab to CD47, which ultimately results in the increased phagocytosis of tumour cells by macrophages. Notably, this novel combination therapy led to high response rates (complete remission in 42%) and an acceptable toxicity  in high-Despite the initial theoretical optimism; however, results from clinical trials targeting BMME  involving AML patients have generated disappointing results. Hedgehog signalling, which is highly active in endothelial cells, has also been targeted in MDS and other leukaemia using small molecule inhibitors with limited success . FinallyAlthough recent developments in MDS HSC genetics have been instrumental in gaining knowledge of disease pathophysiology and has led to the idea of combined targeted therapies, limited understanding of how all of the components in the bone marrow interact remains a major bottleneck. There is an urgent need to understand the role of various specialized niches where these clonal HSCs reside, especially during the early stage of clonal development. The juxtaposition of malignant HSCs carrying multiple mutation(s) within various niches might have an influence on the function as well as the evolution of these clones, resulting in significant variation in the path it takes during the course of the disease. Dissecting the signalling routes that MDS clonal HSCs use to either remodel its environment and directly or indirectly suppress their normal cohabitating HSCs will be of great therapeutic potential. Furthermore, it shall be of future interest to delineate the interaction between the HSC disease and ‘inflammaging’ bone marrow memory. Gaining an understanding of how intrinsic and extrinsic inflammation factors contribute to HSC aging and the development of hematological cancers, in general, might provide novel therapeutic options that can be potentially used alongside existing treatments.


The expeditious and world pandemic viral disease of new coronavirus (SARS-CoV-2) has formed a prompt urgency to discover auspicious target-based ligand for the treatment of COVID-19. Symptoms of novel coronavirus disease (COVID-19) typically include dry cough, fever, and shortness of breath. Recent studies on many COVID-19 patients in Italy and the United Kingdom found increasing anosmia and ageusia among the COVID-19-infected patients. SARS-CoV-2 possibly infects neurons in the nasal passage and disrupts the senses of smell and taste, like other coronaviruses, such as SARS-CoV and MERS-CoV that could target the central nervous system. Developing a drug based on the T2Rs might be of better understanding and worth finding better molecules to act against COVID-19. In this research, we have taken a taste receptor agonist molecule to find a better core molecule that may act as the best resource to design a drug or corresponding derivatives. Based on the computational docking studies, the antibiotic tobramycin showed the best interaction against 6LU7 COVID-19 main protease. Aromatic carbonyl functional groups of the molecule established intermolecular hydrogen bonding interaction with GLN189 amino acid and it showed the two strongest carbonyl interactions with receptor protein resulting in a glide score of −11.159. To conclude, depending on the molecular recognition of the GPCR proteins, the agonist molecule can be recognized to represent the cell secondary mechanism; thus, it provides enough confidence to design a suitable molecule based on the tobramycin drug. A new strain of single-stranded RNA virus, belonging to the Coronoviridae family, brought the world to a halt, presenting 2020 with the coronavirus pandemic. HCoV-229E, HCoV-OC43, SARS-CoV or SARS-CoV-1, SARS-CoV-2, MERS-CoV (Middle East respiratory syndrome coronavirus), HCoV-NL63, and HCoV-HKU1 are seven members known (to date) to be part of the coronavirus family . The entHowever, studies have already demonstrated that bitter taste receptors have a significant correlation with COVID-19 infection . Barham Unfortunately, drug designing and drug development initiatives to fight against COVID-19 involves several challenges. Despite the urgency of effective drug designing, the processes involved in this regard are quite lengthy and complicated. Frequent mutation in the viral genome is one of the major challenges in such drug designing. The pace at which the SARS-CoV-2 is mutating cannot be made sense by concurrent scientific understanding . Such muHowever, considering the corresponding drug development-related challenges and association of bitter taste receptors with SARS-CoV-2, this study aims to predict a suitable structure for a drug molecule that may have suitable molecular recognition with GPCR proteins.The study used Schrödinger docking software to seek core molecules that can efficiently inhibit the pathway of COVID-19 pathogen as it invades cellular membranes, spreading within the body. For T2R, a mechanism that was experimentally validated was used . The docNeutrophils, monocytes, and lymphocytes can express bitter taste receptors (T2Rs), being involved in immune response , and wasAnalysis revealed that among all the different molecules, tobramycin has strong interactions between the aromatic carbonyl functional groups and GLN189 amino acid. Two strong carbonyl interactions with receptor protein had a glide score of −11.159, making the drug tobramycin a potential model to design drug molecules or create drug derivatives. The drug azithromycin demonstrated two side chain bonding (between aromatic carbonyl groups and LEU141 and GLY143), along with the presence of a hydrogen bond (GLU166 and carbonyl group) . The docOther drugs and molecular complexes tested, such as diphenhydramine, levofloxacin, HHQ, 3-oxo-C12-HSL, cromolyn, and C8-HSL, respectively, also contained side chain, backbone, and hydrogen bonding interactions. However, the total number of bonding interactions was relatively less than that in tobramycin. Although the drugs had the same binding pocket with different secondary interactions depending on their orientation, the drug tobramycin is speculated to be promising to design a molecule based on its structure. Azithromycin, levofloxacin, C4-HSL, and structure-2D also showed prominent binding interaction with T2R agonist, which accounted to study the number of hit molecules, perform virtual screening, and optimize lead compounds. Studying the docking properties and drug’s molecular dynamics, based on the interactions of the taste receptor and spike protein, upon modifying the complex (the drug–ligand interaction) the pathway where COVID-19 invades adjacent host cells, via cytoplasmic membranes, can be altered and prevent the spread of the virus.The docking results yielded various interactions with the T2Rs agonist molecules, where some ligands were more favorable than others. The drug, tobramycin, had the highest affinity for T2Rs, followed by azithromycin. Depending on the molecular recognition, the GPCR proteins are highly recognized as the agonist molecule to represent the cell secondary mechanism to give confidence in designing a new derivative of the tobramycin drug, to biochemically inhibit the prominent spread of COVID-19.


Mammals are a highly diverse group, with body mass ranging from 2 g to 170 t, and encompassing species with terrestrial, aquatic, aerial, and subterranean lifestyles. The skeleton is involved in most aspects of vertebrate life history, but while previous macroevolutionary analyses have shown that structural, phylogenetic, and functional factors influence the gross morphology of skeletal elements, their inner structure has received comparatively little attention. Here we analysed bone structure of the humerus and mid-lumbar vertebrae across mammals and their correlations with different lifestyles and body size.We acquired bone structure parameters in appendicular and axial elements (humerus and mid-lumbar vertebra) from 190 species across therian mammals . Our sample captures all transitions to aerial, fully aquatic, and subterranean lifestyles in extant therian clades. We found that mammalian bone structure is highly disparate and we show that the investigated vertebral structure parameters mostly correlate with body size, but not lifestyle, while the opposite is true for humeral parameters. The latter also show a high degree of convergence among the clades that have acquired specialised  lifestyles.In light of phylogenetic, size, and functional factors, the distribution of each investigated structural parameter reveals patterns explaining the construction of appendicular and axial skeletal elements in mammalian species spanning most of the extant diversity of the clade in terms of body size and lifestyle. These patterns should be further investigated with analyses focused on specific lifestyle transitions that would ideally include key fossils. In the context of comparative analyses, however, it is impossible to disentangle the effect of phenotypic plasticity from evolutionary acquisitions. Furthermore, it is likely that the reaction norm  of bone structure is under selective pressure and can itself evolve  ~ body mass [g]a, the coefficient denoting isometry would be − 1 [x-, y-, and z-components of the eigenvector defining the main orientation of the anisotropy; Additional file Several traits were acquired to capture the bone structure properties of the whole humerus and whole mid-lumbar vertebra (in consistency with ). Micro-J v. 1.4 ). DE wasJ v. 1.4  Fig. 6)Optimise J v. 1.4 . But it J v. 1.4 . BoneJ wJ v. 1.4 . VOIs heurements , 90. We d be − 1  from theBoth humerus and lumbar vertebrae were not available for each sampled specimen. Furthermore, some of the CT scans of Dumont et al.  only capIn order to avoid potential bias affecting bone structure, specimens were selected to be adult , devoid of apparent disease, and coming from the wild. But as marsupials and many small-sized mammals maintain unfused humeral epiphyses well into adulthood (if not throughout life), humeral epiphyses VOI parameters were not included in this analysis. Species body mass were taken from the AnAge  and MOM All data analyses were performed with R  OVAs were performed with generalised least squares linear models comprising a within-group correlation structure based on the optimised lambda value of the model’s residuals  (gls fun package ). For AN package ). Variabp values < 0.014; see also Additional file p values > 0.194). Furthermore, the terrestrial taxa also feature a greater disparity in their sizes, their range encompassing all other lifestyles but the largest aquatic taxa. Subsets of the datasets for which the terrestrial class content was pruned to match the size and disparity of the other classes were therefore additionally examined: to compare subterranean and aerial lifestyles to the terrestrial one, the latter was pruned to 60 taxa for the humeral dataset , and to 60 and 62 taxa for the vertebral datasets ; to compare the aquatic and terrestrial lifestyles, the latter was pruned to 7 taxa for the humeral dataset, and 9 and 14 taxa for the vertebral datasets.Body mass and specimen-specific size proxies were used to test the effect of body size on the parameters. For the latter, we used mean total cross-sectional area  see aboC, as theWe investigated the effect of size and lifestyle on each studied bone structure parameters using ANCOVAs. When a significant correlation between the size proxy and the parameter under study was recovered, residuals of the corresponding regression were computed. The latter were regarded as the size-corrected version of these parameters , which wp value (convratsig function), which gives an overall assessment of how much the phenotypes of converging clades evolved towards one another [github.com//eliamson/convevol1d.We quantified the convergent acquisition of each specialised lifestyle using the framework of the convevol package  for R. F another . Because another ). ModifiTo avoid the potential influence of lifestyles, only terrestrial species were included in this analysis. Pagel’s lambda  was comAdditional file 1. Additional figures 1-4.Additional file 2. Distribution of body size according to lifestyle.Additional file 3. Supplementary results. A, Specimen list; B, Descriptive statistics; C, AN(C)OVAs with body mass; D, AN(C)OVAs with specimen-specific body size proxies; Phylogenetic signal.Additional file 4. Phenograms depicting the reconstructed evolution of each trait among specialised clades.Additional file 5. Additional trabecular parameters of the vertebral centrum: a, Trabecular Thickness (Tb.Th); b, Trabecular Spacing (Tb.Sp); c, Degree of Anisotropy (DA), and d, the main direction of the trabeculae (MDT).