Classification success of six machine learning algorithms in radar ornithology

Radar systems have been increasingly used to monitor birds. To take full advantage of the large datasets provided by radars, researchers have implemented machine learning (ML) techniques that automatically read and attempt to classify targets. Here we used data collected from two locations in Portugal with two marine radar antennas (VSR and HSR) to apply and compare the performance of six ML algorithms that are widely used in the literature: random forests (RF), support vector machine (SVM), artificial neural networks (NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and decision trees (DT), all trained with several dataset configurations. We found that all algorithms performed well (area under the receiver operating characteristic (AUC) and accuracy > 0.80, P < 0.001) when discriminating birds from non‐biological targets such as vehicles, rain or wind turbines, but greater variance in the performance among algorithms was apparent when separating different bird functional groups or bird species (e.g. herons vs. gulls). In our case study, only RF was able to hold an accuracy > 0.80 for all classification tasks, although SVM and DT also performed well. Further, all algorithms correctly classified 86% and 66% (VSR and HSR) of the target points, and only 2% and 4% of these points were misclassified by all algorithms. Our results suggest that ML algorithms are suitable for classifying radar targets as birds, and thereby separating them from other non‐biological targets. The ability of these algorithms to correctly identify among bird species functional groups was found to be much weaker, but if properly trained and supported by a good ground truthing dataset, targeted to the relevant species groups, some of these algorithms are still able to achieve high accuracies in classification tasks. Such results indicate that ML algorithms are suitable for use in near real‐time monitoring of bird movements, and may help to mitigate collision of birds with, for example, wind turbines or airplanes.     

Ecological correlates of local extinction and colonisation in the British ladybird beetles (Coleoptera: Coccinellidae)

Five main drivers of population declines have been identified: climate change, habitat degradation, invasive alien species (IAS), overexploitation and pollution. Each of these drivers interacts with the others, and also with the intrinsic traits of individual species, to determine species’ distribution and range dynamics. We explored the relative importance of life-history and resource-use traits, climate, habitat, and the IAS Harmonia axyridis in driving local extinction and colonisation dynamics across 25 ladybird species (Coleoptera: Coccinellidae).Species were classified as continually present, continually absent, extinct, or colonising in each of 4,642 1-km² grid squares. The spatial distribution of local extinction and colonisation events (in the grid squares) across all species’ ranges were related to ecological traits, overlap with H. axyridis, climate, and habitat factors within generalised linear models (GLMs). GLMs were also used to relate species’ traits, range characteristics, and niche overlap with H. axyridis to extinction and colonisation rates summarised at the species level. Bayesian model averaging was used to account for model uncertainty, and produce reduced sets of models which were well-supported by data. Species with a high degree of niche overlap with H. axyridis suffered higher extinction rates in both analyses, while at the spatial scale extinctions were more likely and colonisations less likely in areas with a high proportion of urban land cover. In the spatial analysis, polymorphic species with large range sizes were more likely to colonise and less likely to go extinct, and sunny grid squares were more likely to be colonised. Large, multivoltine species and rainy grid squares were less likely to colonise or be colonised. In conclusion for ladybirds, extinction and colonisation dynamics are influenced by several factors. The only factor that both increased the local extinction likelihood and reduced colonisation likelihood was urban land cover, while ecological overlap with H. axyridis greatly increased extinction rates. Continued spread of H. axyridis is likely to adversely affect native species and urban areas may be particularly vulnerable.     

Photosynthetic gas exchange characteristics in three different almond species during drought stress and subsequent recovery

Three different drought stress levels (water potential of the nutrient solution, Ψ_s = −0.6, −1.2 and −1.8 MPa, respectively), and a control treatment (Ψ_s = −0.1 MPa), were applied during 2 weeks to three almond species, followed by 3 weeks of recovery. The selected test species were Prunus dulcis (Miller) D.Webb (bitter almond) and two wild almond species, P. lycioides (Spach) C.K. Schneider and P. scoparia (Spach) C.K. Schneider. All three are species native to Iran, and can be used as rootstock, but only P. dulcis is actually used for commercial almond production.In the absence of drought stress, maximum net assimilation rate (A_max) is highest for P. scoparia and lowest for P. dulcis. For all species A_max was above 16 μmol CO₂ m⁻² s⁻¹. A similar relationship between A_max and dark respiration rate (R_d), was observed for all species. This relationship suggests that optimisation of the carbon budget is independent of species.The three investigated species seem to have a different reaction to a similar stress, indicating different drought stress coping strategies. P. scoparia lost all its leaves during the experiment, while P. lycioides only kept some leaves, however, the remaining leaves were almost totally wilted and did not allow for any photosynthesis measurement. P. scoparia did not recover during the experiment, as no new leaves were developed once Ψ_s was restored to pre-drought stress levels. However, this species has green stems, indicating that stem photosynthesis might play an important role in the plants’ overall carbon balance. This species is an opportunistic one (sensu [Higgins, S.S., Larsen, F.E., Bendel, R.B., Radamaker, G.K., Bassman, J.H., Bidlake, W.R., Alwir, A., 1992. Comparative gas-exchange characteristics of potted, glasshouse-grown almond, apple, fig, grape, olive, peach and Asian pear. Sci. Hortic. 52 (4), 313–329]), where assimilation is seriously limited by non-stomatal processes as evidenced by measurements of intercellular CO₂ concentration, eventually resulting in total leaf loss. All P. lycioides leaves almost completely wilted during the experiment, but this species recovered rather quickly. Leaves, newly formed at the end of the experiment, obtained maximal assimilation rates under control Ψ_s levels, equivalent to those measured in the control treatment. Finally, P. dulcis did keep at least part of its leaves during drought stress. However, assimilation rates after 2 weeks of drought treatment and 3 weeks of recovery were only about half of those measured in the control treatment. Of the three investigated species, non-stomatal limitation of assimilation seems to be the least important in P. dulcis.Intrinsic water use efficiency, defined as the ratio of assimilation rate over stomatal conductance, increased for P. dulcis with increasing drought stress, while a different pattern was observed for P. lycioides and P. scoparia, indicating non-stomatal processes prevail over stomatal limitations of the assimilation process. It was concluded that P. dulcis is the species most tolerant to drought. P. scoparia tries to avoid drought, whereas P. lycioides has an intermediate behaviour. Besides P. dulcis, also P. lycioides seems to have some potential for use as rootstock for commercial almond production.     

Effects of simulated acid rain on the foliar micromorphology and anatomy of tree tropical species

In order to correlate sintomatology with anatomical alterations caused by acid rain in leaves of tropical species, seedlings and saplings of Spondias dulcis Forst. F., Mimosa artemisiana Heringer and Paula and Gallesia integrifolia (Spreng.) Harms were exposed to simulated low-pH acid rain (pH 3.0). Control plants were submitted only to rain with distilled water (pH 6.0). The plants were exposed daily to the acid rain for 20 min for 10 consecutive days. Necrotic spots on the leaf blade occurred and most of the injuries onset on the epidermis in all species studied. S. dulcis displayed epicuticular wax erosion and rupture of epidermis. The abaxial surface of M. artemisiana was colonized by a mass of fungi hyphae and stomatal outer ledge rupture occurred. Some epidermal cells of G. integrifolia showed appearance similar to plasmolysis. The plants accumulated phenolic compounds in necrotic areas. Afterwards, leaves presented injuries in the mesophyll and collapsed completely. Cells surrounding the injured areas accumulated starch grains in S. dulcis. M. artemisiana showed more drastic symptom intensity in response to acidic rain. S. dulcis displayed visual symptoms similar to G. integrifolia, however, anatomical alterations were more severe.     

Toxicity of Insecticides and Metabolism of Deltamethrin in Helicoverpa assulta Guenéee and Spodoptera litura Fabricius

This experiment was carried out to evaluate the effects of synergists on toxicity of deltamethrin and to investigate the pharmacokinetics of deltamethrin in Helicoverpa assulta Guenéee and Spodoptera litura Fabricius. Susceptibilities of H. assulta and Haman-collected S. litura to 7 insecticides revealed different responses between the 2 species. The different susceptibility of the 2 species to deltamethrin was correlated with the differences in the penetration and metabolism. Larvae of H. assulta pretreated with synergists (piperonyl butoxide (PB), S-benzyl-O, O-diisopropyl phosphorothioate (IBP), and diethyl maleate (DM) were more sensitive to deltamethrin, with the highest synergism ratio (SR = 6.8) being observed with PB. Significant differences in synergism among synergists were not observed. For S. litura, the highest SR(=8.8) was observed with PB. At all time periods, the penetration of ¹⁴C-deltamethrin was relatively faster to S. litura than to H. assulta. Time (hrs) required for 20% (t₂₀) of the applied doses to be excreted from treated S. litura was 1.5-fold faster than that of H. assulta. Higher synergism and longer retention in larval body of S. litura by the addition of PB suggest that the mixed function oxidase be a major factor in detoxification of deltamethrin. The combined use of PB with deltamethrin, and possibly other pyrethroids, might indicate a potential for controlling field populations resistant to deltamethrin and other pyrethroids.     

The role of protected area zoning in invasive plant management

As anthropogenic pressure on the landscape increases, invasive alien species (IAS) pose a growing threat to areas designed to protect high biodiversity habitats. In order to assess the present danger of IAS spread, we examined 23 Czech sites of community importance (SCI) within Natura 2000 protected areas (PA) over 2015 and mapped the occurrence of four IAS: Solidago spp. (goldenrod), Impatiens glandulifera (Himalayan balsam), Heracleum mantegazzianum (giant hogweed) and Fallopia spp. (Japanese knotweed). The model areas were divided into five monitoring zones, graded by conservation importance and habitat disturbance level (core area [A], broader core area [B], semi-natural habitat [C], anthropogenically affected habitat [D], anthropogenically degraded habitat [E]). Despite a high number of IAS occurrences (3222 localities), habitats of European importance (zone A) showed a relatively low level of invasion (< 0.3% total area). Highest IAS occurrence number was in SCI border areas and disturbed habitats (zones C and E). There was a significant positive correlation between level of invasion inside and outside SCIs, related to human activities such as logging and urbanisation. A strong effect for watercourse vicinity was noted for the occurrence of I. glandulifera and Fallopia spp.; but not for H. mantegazzianum and Solidago spp. A stratified management approach, employing zones delimitation to assess what threat pose IAS to the PA objects of conservation, can be useful to prioritize control measures in IAS local action plans.     

Invasive alien predator causes rapid declines of native European ladybirds

Aim Invasive alien species (IAS) are recognized as major drivers of biodiversity loss, but few causal relationships between IAS and species declines have been documented. In this study, we compare the distribution (Belgium and Britain) and abundance (Belgium, Britain and Switzerland) of formerly common and widespread native ladybirds before and after the arrival of Harmonia axyridis, a globally rapidly expanding IAS. Location Europe Methods We used generalized linear mixed‐effects models (GLMMs) to assess the distribution trends of eight conspicuous and historically widespread and common species of ladybird within Belgium and Britain before and after the arrival of H. axyridis. The distribution data were collated largely through public participatory surveys but verified by a recognized expert. We also used GLMMs to model trends in the abundance of ladybirds using data collated through systematic surveys of deciduous trees in Belgium, Britain and Switzerland. Results Five (Belgium) and seven (Britain) of eight species studied show substantial declines attributable to the arrival of H. axyridis. Indeed, the two‐spot ladybird, Adalia bipunctata, declined by 30% (Belgium) and 44% (Britain) over 5 years after the arrival of H. axyridis. Trends in ladybird abundance revealed similar patterns of declines across three countries. Main conclusion Together, these analyses show H. axyridis to be displacing native ladybirds with high niche overlap, probably through predation and competition. This finding provides strong evidence of a causal link between the arrival of an IAS and decline in native biodiversity. Rapid biotic homogenization at the continental scale could impact on the resilience of ecosystems and severely diminish the services they deliver.     

机器学习在微生物组宿主表型预测中的应用

随着大数据时代的到来,如何将生物组学海量数据转化为易理解及可视化的知识是当前生物信息学面临的重要挑战之一。为了处理复杂、高维的微生物组数据,目前机器学习算法已被应用于人体微生物组研究,以揭示疾病背后的复杂机制。本文首先简述了微生物组数据处理方法及常用的机器学习算法,如支持向量机(SVM)、随机森林(RF)和人工神经网络(ANN)等,然后对机器学习的工作流程及其要点进行阐述,并探讨了机器学习算法在基于微生物组数据预测宿主表型方面的应用。最后以唾液微生物组数据预测口腔异味为例,实现了机器学习算法的模型构建与评估分析,并提供了可用于微生物组研究实践的R/Python代码(https://github.com/LiLabZSU/microbioML)。     

阿根廷滑柔鱼两个群体间耳石和角质颚的形态差异

头足类硬组织具有稳定的形态特征、良好的信息储存以及抗腐蚀性等特点。根据2007年2—5月和2010年1—3月我国鱿钓船采集的阿根廷滑柔鱼样品,提取出625对耳石和787对角质颚,测量耳石10个形态数据和角质颚12个形态数据,通过除以胴长(Mantle Length,ML)校正后,对南巴塔哥尼亚群体(South Patagonic Stock,SPS)和布宜诺斯艾利斯-巴塔哥尼亚群体(Bonaerensis-Northpatagonic Stock,BNS)形态差异进行分析,并建立不同群体的判别函数。结果表明,BNS群体耳石和角质颚外形参数雌性大于雄性,而SPS群体则为雄性大于雌性。均数差异性检验认为,同一群体不同性别的耳石总长(Total StatolithLength,TSL)、最大宽度(Maximum Width,MW)、侧区长(Lateral Dome Length,LDL)、翼区长(Wing Length,WL)、翼区宽(WingWidth,WW)存在显著差异(P<0.05),同性别不同群体的MW、背侧区长(Dorsal Lateral Length,DLL)、吻侧区长(RostrumLateral Length,RLL)和WW存在显著差异(P<0.05)。而同一群体不同性别间角质颚的上头盖长(Upper Hood length,UHL)、上脊突长(Upper Crest length,UCL)、上喙长(Upper Rostrum length,URL)、上喙宽(Upper rostrum width,URW)、上侧壁长(UpperLateral wall length,ULWL)、下喙长(Lower Rostrum length,LRL)存在显著差异(P<0.01),同一性别不同群体角质颚的下头盖长(Lower Hood length,LHL)、下脊突长(Lower crest length,LCL)、LRL、下喙宽(Lower Rostrum width,LRW)、下侧壁长(LowerLateral wall length,LLWL)、下翼长(Lower Wing length,LWL)存在显著差异(P<0.01)。耳石形态参数经主成分分析,认为BNS群体雌雄的主成分因子主要集中在TSL/ML、DLL/ML、RW/ML和MW/ML,SPS群体主要集中于TSL/ML、RW/ML、WW/ML和DDL/ML;角质颚形态参数经主成分分析,认为BNS群体主成分因子主要集中在UHL/ML、UCL/ML、ULWL/ML和LRW/ML,SPS群体主要集中在UHL/ML、UCL/ML、ULWL/ML、URL/ML、LWL/ML和LRL/ML。利用角质颚和耳石对两群体样本分性别建立了判别函数,判别正确率均高于60%,所划分群体在部分形态指标上差异明显,具有一定的可信度。今后应加强样本采集个体大小和时间跨度,以更好的分析其群体变化规律。     

湖南珍稀特有植物喜雨草的重新发现及补充描述

喜雨草,湖南珍稀特有植物,属唇形科单型属喜雨草属,自1918年8月韩马迪在湖南西南部武冈云山采到模式标本,并于1936年作为一个新属、新种发表后,其踪迹消失全无。该次报道为喜雨草被发现近100年后,在湖南宁远、通道、道县、新宁被重新发现。根据采集的标本和拍摄的照片,更正修改和补充了原形态描述,重新确定了该种花萼为3/2式,花冠颜色为白色。     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

Studies on genetic diversity and phylogenetic relationships of limpograss (Hemarthria altissima) and related species based on combined chloroplast DNA intergenic spacer data

Hemarthria R. Br. is a genus which includes important forage grasses. However, there is currently a lack of data analysis on the chloroplast DNA (cpDNA) of Hemarthria species. This study is to use three cpDNA intergenic spacers (trnL-F, trnC-ycf6 and psbC-trnS) to obtain phylogenetic information in 36 Hemarthria samples including four Hemarthria species: Hemarthria altissima (Poir.) Stapf et C. E. Hubb., Hemarthria compressa (L. f.) R. Br., Hemarthria uncinata R. Br., and Hemarthria japonica (Hack.) Roshev. Data analysis revealed that non-significant genetic diversity existed in our samples, which was implied by nucleotide sequences information and the results of haplotypic and nucleotide diversity. The results of phylogenetic trees based on maximum likelihood (ML) and Bayesian inference (BI) revealed that H. altissima and H. compressa samples were not entirely distinct, suggesting that the two species share an intimate genetic relationship. A haplotype median-joining (MJ) network revealed broadly similar results to those derived from the ML and BI trees and implied that haplotype H3 may represent an ancient haplotype. Analysis of the population statistic F_ST revealed little genetic differentiation among the seven populations of H. altissima in Africa.     

Further Evidence of an Association between the Presence of Leishmania RNA Virus 1 and the Mucosal Manifestations in Tegumentary Leishmaniasis Patients

Tegumentary Leishmaniasis (TL) is endemic in Latin America, and Brazil contributes approximately 20 thousand cases per year. The pathogenesis of TL, however, is still not fully understood. Clinical manifestations vary from cutaneous leishmaniasis (CL) to more severe outcomes, such as disseminated leishmaniasis (DL), mucosal leishmaniasis (ML) and diffuse cutaneous leishmaniasis (DCL). Many factors have been associated with the severity of the disease and the development of lesions. Recent studies have reported that the presence of Leishmania RNA virus 1 infecting Leishmania (Leishmania RNA virus 1, LRV1) is an important factor associated with the severity of ML in experimental animal models. In the present study, 156 patients who attended Rondonia''s Hospital of Tropical Medicine with both leishmaniasis clinical diagnoses (109 CL; 38 ML; 5 CL+ML; 3 DL and 1 DCL) and molecular diagnoses were investigated. The clinical diagnosis were confirmed by PCR by targeting hsp70 and kDNA DNA sequences and the species causing the infection were determined by HSP70 PCR-RFPL. The presence of LVR1 was tested by RT-PCR. Five Leishmania species were detected: 121 (77.6%) samples were positive for Leishmania (Viannia) braziliensis, 18 (11.5%) were positive for Leishmania (V.) guyanensis, 3 (1.8%) for Leishmania (V.) lainsoni, 2 (1.3%) for Leishmania (Leishmania) amazonensis and 2 (1.3%) for Leishmania (V.) shawi. Six (3.9%) samples were positive for Leishmania sp. but the species could not be determined, and 4 (2.6%) samples were suggestive of mixed infection by L. (V.) braziliensis and L. (V.) guyanensis. The virus was detected in L. braziliensis (N = 54), L. guyanensis (N = 5), L. amazonensis (N = 2), L. lainsoni (N = 1) and inconclusive samples (N = 6). Patients presenting with CL+ML, DL and DCL were excluded from further analysis. Association between the presence of the virus and the disease outcome were tested among the remaining 147 patients (CL = 109 and ML = 38). Of them, 71.1% (n = 27) mucosal lesions were positive for LRV1, and 28.9% (n = 11) were negative. In cutaneous lesions, 36.7% (n = 40) were positive and 63.3% (n = 69) were negative for LRV1. The ratio P(ML|LRV1+)/P(ML|LRV1-) was 2.93 (CI95% 1.57…5.46; p<0.001), thus corroborating the hypothesis of the association between LRV1 and the occurrence of mucosal leishmaniasis, as previously described in animal models; it also indicates that LRV1 is not the only factor contributing to the disease outcome.     

Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms

Machine learning (ML) along with high volume of satellite images offers an alternative to agronomists in crop yield predictions for decision support systems. This research exploited the possibility of using monthly image composites from Sentinel-2 imageries for rice crop yield predictions one month before the harvesting period at the field level using ML techniques in Taiwan. Three ML models, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), were designed to address the research question of yield predictions in four consecutive growing seasons from 2019 to 2020 using field survey data. The research findings of yield modeling and predictions showed that SVM slightly outperformed RF and ANN. The results of model validation, obtained from SVM models using the data from transplanting to ripening, showed that the root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) values were 5.5% and 4.5% for the 2019 second crop, and 4.7% and 3.5% for the 2020 first crop, respectively. The results of yield predictions (obtained from SVM) for the 2019 second crop and the 2020 first crop evaluated against the government statistics indicated a close agreement between these two datasets, with the RMSPE and MAPE values generally smaller than 11.2% and 9.2%. The SVM model configuration parameters used for rice crop yield predictions indicated satisfactory results. The comparison results between the predicted yields and the official statistics showed slight underestimations, with RMSPE and MAPE values of 9.4% and 7.1% for the 2019 first crop (hindcast), and 11.0% and 9.4% for the 2020 second crop (forecast), respectively. This study has successfully proven the validity of our methods for yield modeling and prediction from monthly composites from Sentinel-2 imageries using ML algorithms. The research findings from this research work could useful for agronomists to timely formulate action plans to address national food security issues.     

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

Background

Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA.

Methods

The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models.

Results

The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001).

Conclusions

The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.     

An octopaminergic system in the CNS of the snails,Lymnaea stagnalis and Helix pomatia

Octopamine (OA) levels in each ganglion of the terrestrial snail, Helix pomatia, and the pond snail, Lymnaea stagnalis, were measured by using the HPLC technique. In both species an inhomogeneous distribution of OA was found in the central nervous system. The buccal ganglia contained a concentration of OA (12.6 pmol mg^-1 and 18.8 pmol mg^-1) that was two to three times higher than the pedal (4.93 pmol mg^-1 and 9.2 pmol mg^-1) or cerebral (4.46 pmol mg^-1 and 4.9 pmol mg^-1) ganglia of Helix and Lymnaea, respectively, whereas no detectable amount of OA could be assayed in the visceroparietal complex. In Lymnaea ganglia, the OA uptake into the synaptosomal fraction had a high (K_m1 = 4.07 ± 0.51 μM, V_max1 = 0.56 ± 0.11 pmol mg^-1 per 20 min), and a low (K_m2 = 47.6 ± 5.2 μM, V_max2 = 4.2 ± 0.27 pmol mg^-1 per 20 min), affinity component. A specific and dissociable ³H-OA binding to the membrane pellet prepared from the CNS of both Helix and Lymnaea was demonstrated. The Scatchard analysis of the ligand binding data showed a one-binding site, representing a single receptor site. The K_d and B_max values were found to be 33.7 ± 5.95 nM and 1678 ± 179 fmol g^-1 tissue in Helix and 84.9 ± 17.4 nM and 3803 ± 515 fmol g^-1 tissue in Lymnaea preparation. The pharmacological properties of the putative molluscan OA receptor were characterized in both species and it was demonstrated that the receptor resembled the insect OA₂ rather than to the cloned Lymnaea OA receptor. Immunocytochemical labelling demonstrated the presence of OA-immunoreactive neurons and fibres in the buccal, cerebral and pedal ganglia in the central nervous system of both species investigated. Electrophysiological experiments also suggested that the Lymnaea brain possessed specific receptors for OA. Local application of OA onto the identified buccal B2 neuron evoked a hyperpolarization which could selectively be inhibited by the OAergic agents phentolamine, demethylchlordimeform and 2-chloro-4-methyl-2-(phenylimino)-imidazolidine. Among the dopamine antagonists, ergotamine reversibly inhibited the OA response, whereas sulpiride had no effect. Based on our findings, a neurotransmitter-modulator role of OA is suggested in the gastropod CNS.     

Phylogenetic relationships of cultivated Prunus species from an analysis of chloroplast DNA variation

Chloroplast DNA (cpDNA) restriction-site mutations in seven cultivated Prunus species were compared to establish the phylogenetic relationships among them. Mutations were detected in 3.2-kb and 2.1-kb amplified regions of variable cpDNA, cut with 21 and 10 restriction endonucleases, respectively, to reveal polymorphisms. Parsimony and cluster analyses were performed. The species pairs P. persica-P. dulcis, P. domestica-P. salicina, and P.cerasus-P. fruticosa were completely monophyletic. All of the species were grouped with conventional subgenus classifications. The subgenus Cerasus was the most diverged. Cerasus ancestors separated from the remainder of Prunus relatively early in the development of the genus. P. persica-P. dulcis, P. domestica-P. salicina and P. armeniaca formed a second monophyletic group. Prunophora species were less diverged than Amygdalus species. The results also suggest that the rate of mutation in Cerasus spp. chloroplast genomes is significantly greater than for the other subgenera sampled.     

Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces,Turkey

In recent years, the number of wildfires has increased all over the world. Therefore, mapping wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire management decisions. This study aims to generate Machine Learning (ML) based wildfire susceptibility maps for Adana and Mersin provinces, which are located in the Mediterranean Region of Turkey. To generate a wildfire inventory, this study uses active fire pixels derived from MODIS monthly MCD14ML composites. Furthermore, as a sub aim, the performance of seven ML approaches, namely, stand-alone Logistic Regression (LR), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and ensemble algorithms, namely Random Forest (RF), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and AdaBoost (AB), was evaluated based on wildfire susceptibility mapping. The capabilities of the corresponding ML methods were assessed using thirteen wildfire conditioning factors, which can be grouped into four main categories: topographical, meteorological, vegetation, and anthropogenic factors. The Information Gain (IG) approach was used to assess their importance scores. A multicollinearity analysis was also performed to assess the relationship between conditioning factors. To compare the predictive performances of ML algorithms, five performance metrics, namely average accuracy, precision, recall, F1 score, and area under the curve, were used. To test the significance of the generated wildfire susceptibility maps and to detect similarities and differences among the output of these ML algorithms, McNemar's test was implemented. In the end, the ML-based models were locally interpreted using the Shapley Additive exPlanations (SHAP) technique. The AUC values of seven methods varied from 0.817 to 0.879, and the accuracy scores ranged between 0.734 and 0.812. The results showed that the RF model provided the best results considering all performance metrics. The accuracy score and AUC values of the RF model were equal to 0.812 and 0.879, respectively. On the other hand, stand-alone algorithms (LDA, SVM, and LR) represented lower performance than tree-based ensemble methods. Both the IG and SHAP analyses showed that elevation, temperature, and slope factors were the most contributing factors. The RF model classifier found that 7.20% of the study area has very high wildfire susceptibility, and the majority of the wildfire samples (68.84%) correspond to the very high susceptible areas in the RF model. The outcomes of this study are likely to provide decision-makers with a better understanding of wildfires in the Eastern Mediterranean Region of Turkey.     

Postural control and thigh muscle activity in men with knee osteoarthritis

The aim of this study was to examine the standing balance and the function of vastus medialis (VM) and biceps femoris (BF) muscles with surface electromyography (EMG). Fifty-four subjects with uni- or bilateral knee osteoarthritis (OA) (aged 50–69 years) and 53 age-matched randomly selected clinically and radiologically healthy men participated in this study. Postural control was assessed on a force platform with a bipedal stance with eyes open (EO) and closed (EC) and a monopedal stance with EO. The balance parameters, mean sway velocity, velocity along AP and ML axes, elliptical area, standard deviation of center of pressure, average radial displacement, mean frequency and frequency domain balance parameters and different power spectral density frequency bands were determined. Root mean square (RMS) for EMG amplitude, mean EMG frequency (f_EMG,mean) and median EMG frequency (f_EMG,med) of motor unit activity were calculated from the normalized EMG data. During bipedal stance with EC and EO, there were no significant differences in balance parameters between groups, but during bipedal stance with EO, the RMS in VM was about 56% higher (p < 0.05) in subjects with knee OA than in the control subjects and the values of f_EMG,mean and f_EMG,med were about 48% higher (p < 0.05) in control subjects than subjects with knee OA. It is concluded that subjects with knee OA do not have any standing balance deficit, but they do exhibit increased muscle activity in VM muscle compared to control subjects.     

Constituents from the pseudofruits of Hovenia dulcis and their chemotaxonomic significance

Sixteen compounds, including six flavonoids (1–6), one lignan (7), three megastigmanes (8–10), three triterpenoids (11–13), and three benzoic acid derivatives (14–16) were isolated and structurally elucidated from the pseudo-fruits of Hovenia dulcis. Their structures were analyzed by NMR spectroscopic and data comparison. Among them, compounds 4, 7–11, 13, 15, and 16 were isolated from the Hovenia genus for the first time. The chemotaxonomic significance of the isolates was also described, which revealed a relationship between H. dulcis and H. acerbar as well as other species belonging to the Rhamnaceae family.