Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City

Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.

Apomictic species of Alchemilla from the High Atlas Mountains: revision of the genus Alchemilla (Rosaceae) in Morocco

A taxonomic revision of the Alchemilla in the High Atlas Mountains has been carried out. This genus is represented in Morocco by Alchemilla atlantica, A. gourzai, A. litardieri, A. mairei and by a new taxon A. boratynskii, sp. nov., described here from the High Atlas Mountains range. Data on their chorology, ecology and phenology, as well as an identification key, are provided. The conservation status of this newly described narrow endemic and the other four taxa, also endemics previously known from the Moroccan Atlas Mountains, is provided. Alchemilla hirtipes should be excluded from the flora of Morocco.

Pathogenic potential prediction for novel fungal DNA based on a curated fungi-hosts data collection

Background: Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant, curated data remains comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone. Results: We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that the resulting database can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and show that the human pathogens are separable from non-human pathogens. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats. Conclusions: The presented data collection enables accurate detection of novel pathogens from sequencing data. It is also a comprehensive resource that can find use beyond this particular task. This can include possible applications in proteomics and genomics, employing both machine learning and direct sequence comparison. Availability: The database and models are hosted at https://zenodo.org/record/5711852 and https://zenodo.org/record/5711877. Source code is available at https://gitlab.com/dacs-hpi/deepac.

Checklist of the suborder Terebrantia (Thysanoptera): generic diversity and species composition in Xishuangbanna, Yunnan Province, China

Thysanoptera is amongst the most predominant orders of insects in different ecological zones with worldwide distribution. Due to their small size, there is a large gap in their distribution and host range data. To the best of our knowledge, there is no investigation on the thrips distribution and their host range in Xishuangbanna. Currently, a total of 566 species in 155 genera are listed in China, of which 313 species represent Terebrantia. In this study, a list of 116 species representing 55 genera within the families Aeolothripidae and Thripidae is provided. Two of these, Dichromomothrips nakahari Moud, 1976 (subfamily Thripinae) and Phibalothrips rugosus Kudo, 1979 (subfamily Panchaetothripinae) are recorded for the first time in China. Thrips species with their host ranges, habits and habitats are provided. Our study aims to contribute to the global biodiversity distribution data-gap of Thysanoptera for conservation purposes, as well as pest species targetting Integrated Pest Management tactics.

Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane

Although numerous road segmentation studies have utilized vision data, obtaining robust classification is still challenging due to vision sensor noise and target object deformation. Long-distance images are still problematic because of blur and low resolution, and these features make distinguishing roads from objects difficult. This study utilizes light detection and ranging (LiDAR), which generates information that camera images lack, such as distance, height, and intensity, as a reliable supplement to address this problem. In contrast to conventional approaches, additional domain transformation to a bird’s eye view space is executed to obtain long-range data with resolutions comparable to those of short-range data. This study proposes a convolutional neural network architecture that processes data transformed to a bird’s eye view plane. The network’s pathways are split into two parts to resolve calibration errors in the transformed image and point cloud. The network, which has modules that operate sequentially at various scaled dilated convolution rates, is designed to quickly and accurately handle a wide range of data. Comprehensive empirical studies using the Karlsruhe Institute of Technology and Toyota Technological Institute’s (KITTI’s) road detection benchmarks demonstrate that this study’s approach takes advantage of camera and LiDAR information, achieving robust road detection with short runtimes. Our result ranks 22nd in the KITTI’s leaderboard and shows real-time performance.

Biogeographic multi-species occupancy models for large-scale survey data

Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi-species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local-scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating pre-existing range information into MSOMs, yielding a 'biogeographic multi-species occupancy model' (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey, and entire avian communities in forests and pastures of Colombia's West Andes. Compared to traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species-specific inference even for never-observed species. Incorporating pre-existing range data enables principled partial pooling of information across species in large-scale MSOMs. Our biogeographic framework for multi-species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false-absences are modeled in an occupancy framework.

Diagnostic validity of early proximal caries detection using near-infrared imaging technology on 3D range data of posterior teeth

Objectives This in vitro study analysed potential of early proximal caries detection using 3D range data of teeth consisting of near-infrared reflection images at 850 nm (NIRR). Materials and methods Two hundred fifty healthy and carious permanent human teeth were arranged pairwise, examined with bitewing radiography (BWR) and NIRR and validated with micro-computed tomography. NIRR findings were evaluated from buccal, lingual and occlusal (trilateral) views according to yes/no decisions about presence of caries. Reliability assessments included kappa statistics and revealed high agreement for both methods. Statistical analysis included cross tabulation and calculation of sensitivity, specificity and AUC. Results Underestimation of caries was 24.8% for NIRR and 26.4% for BWR. Overestimation was 10.4% for occlusal NIRR and 0% for BWR. Trilateral NIRR had overall accuracy of 64.8%, overestimation of 15.6% and underestimation of 19.6%. NIRR and BWR showed high specificity and low sensitivity for proximal caries detection. Conclusions NIRR achieved diagnostic results comparable to BWR. Trilateral NIRR assessments overestimated presence of proximal caries, revealing stronger sensitivity for initial caries detection than BWR. Clinical relevance NIRR provided valid complement to BWR as diagnostic instrument. Investigation from multiple angles did not substantially improve proximal caries detection with NIRR.

Effects of lead exposure on blood electrical impedance spectroscopy of mice

Background Lead is a nonessential heavy metal, which can inhibit heme synthesis and has significant cytotoxic effects. Nevertheless, its effect on the electrical properties of red blood cells (RBCs) remains unclear. Consequently, this study aimed to investigate the electrical properties and the electrophysiological mechanism of lead exposure in mouse blood using Electrical Impedance Spectroscopy (EIS) in 0.01–100 MHz frequency range. Data characteristic of the impedance spectrum, Bodes plot, Nyquist plot and Nichols plot, and Constant Phase Element (CPE) equivalent circuit model were used to explicitly analyze the differences in amplitude–frequency, phase–frequency, and the frequency characteristics of blood in electrical impedance properties. Results Compared with the healthy blood in control mice, the changes in blood exposed to lead were as follows: (i) the hematocrit decreased; (ii) the amplitude–frequency and phase–frequency characteristics of electrical impedance decreased; (iii) the characteristic frequencies (f0) were significantly increased; (iv) the electrical impedance of plasma, erythrocyte membrane, and hemoglobin decreased, while the conductivity increased. (v) The pseudo-capacitance of cell membrane (CPE_Tm) and the intracellular pseudo-capacitance (CPE-Ti) were decreased. Conclusions Therefore, EIS can be used as an effective method to monitor blood and RBC abnormalities caused by lead exposure. The electrical properties of the cells can be applied as an important observation in the evaluation of the toxic effects of heavy metals.

Does the Garra population (Teleostei: Cyprinidae: Labeoninae) from the Kol River drainage, Persian Gulf basin merit formal description?

Recognizing and defining a species has been a controversial concern for a long time. To define the variation and the limitation between different species, especially closely related taxa in a complex species group, several concepts have been proposed which may lead to different taxonomic decisions. When a taxonomist studies a specific taxon, she/he should adopt a species concept and provide a species limitation to define the studied taxa. Garra population from the Kol River drainage, Persian Gulf basin has already been considered as Garra sp., based on molecular data, and to date no taxonomic decision has been made to provide a specific name for it. The Kol population presents several morphological characters that distinguish it from congeners: fully scaled breast; 7–8 ½ branched dorsal-fin rays; caudal fin with 16–17 branched rays and well-developed mental disc with free lateral and posterior margins. It is also distinguished from all other congeners in the Garra rufa group in Iran, by having two fixed, diagnostic nucleotide substitutions in the mtDNA COI barcode region. Furthermore, the Kol population demonstrates some distinct osteological characteristics in comparison to its closest species G. mondica. Based on the integrative molecular phylogenetic and species delimitation analyses, and morphological, osteological and distribution range data presented here, we think that the Kol River population merits formal description and can be considered as a distinct taxonomic unit (species).