Field Identification Research Articles

Genetic Analysis of Autosomal Short Tandom Repeats Loci in The Libyan Western Mountain Population

There is a little investigation of the genetic diversity of the Libyan population. This study aimed to explore STR loci in the Libyan western mountain population to analyse its genetic landscape. Allele frequency for 15 autosomal STR loci (D8S1179, D21S11, D7S820, CSF1PO, D3S1358, THO1, D13S317, D16S539, D2S1338, D19S433, VWA, TPOX, D18S51, D5S818, FGA) included in the Identifiler®Plus kit were investigate in 120 random unrelated individuals from the Westerner Mountain area of Libya. No deviations from Hardy-Weinberg equilibrium were seen, with the exception of D8S1179, D21S11, D3S1358, TH01 and FGA loci. A total of 129 alleles were observed with the corresponding allelic frequencies ranging between 0.00417 and 0.47917 of the studied sites were observed, and that alleles 8 and 12 are the highest frequency for all studied areas for the TPOX and D5S818 sites respectively had a value of (0.47917). D18S51 was also observed to be the most diverse site (Expected heterozygosity: 0.88312 and probability of matching: 0.0354). The results indicate that the 15 STR loci have a significant genetic diversity among studied individuals and are suitable for personal identification in the criminal field, and the importance of this work lies in the creation of a database of frequency distribution of allelic frequencies to be used for criminal identification, identification of missing persons and paternity tests.

Rediscovery and redescription of Ixalus garo Boulenger, 1919, and Ixalus kempiae Boulenger, 1919, with a reassessment of the taxonomic status of Raorchestes cangyuanensis Wu, Suwannapoom, Xu, Murphy & Che, 2019 and the description of a new species from the Garo Hills of Meghalaya

Bush frogs from Garo and Khasi Hills were collected, and a thorough examination based on morphology and geographic distribution revealed that two of the populations sampled from Garo Hills during this study agree with the holotypes and descriptions of the taxa Ixalus garo and I. kempiae, described more than a century ago. The original description lacked information on several morphological characters, genetic material, and photographs of the species in life. The absence of such information posed challenges in field identification of these species. Our phylogeny shows them to be nested within the Raorchestes clade, and therefore, we allocate these two species to this genus and formally announce their rediscovery. We also redescribe these two species here based on additional adult vouchers and report geographic range extensions from new localities. Based on molecular and morphological analysis, we reassess the status of the recently described Raorchestes cangyuanensis, resulting in its placement under the subjective junior synonymy of R. kempiae. We also describe a morphologically distinct new species from this region. This paper bridges an important gap in the knowledge of the genus Raorchestes in this part of India and highlights the importance of systematic surveys in documenting and understanding amphibian diversity in the region.

Analyzing the performance of deep convolutional neural network models for weed identification in potato fields

Weeds pose a significant and fundamental challenge in agriculture, competing with crops for vital resources such as water, nutrients, and sunlight. This competition often leads to reduced crop yields and diminished quality of produce. Additionally, weeds can host pests and diseases that further harm crops, increasing the risk of infestation and reducing farm productivity. Accurate weed identification through deep learning offers a solution, enabling farmers to implement site-specific herbicide spraying, thus lowering herbicide usage and minimizing environmental impact. This study introduces a benchmark crop and weed classification dataset and evaluates seven state-of-the-art deep-learning models for weed identification. The dataset was obtained from potato fields in Punjab, India, over two consecutive growth seasons (2022 and 2023). Seven deep learning models, Convolution Neural Network (CNN)-11, CNN-14, Inceptionv3, AlexNet, VGG16, ResNet50, and the YOLOv8 were trained and tested on this dataset for potato and weed classification. Among these models, YOLOv8 emerges as the top performer, achieving flawless accuracy of 100% with 37.5 million parameters. The custom CNN-11 model, despite having the fewest parameters (2.2 million), achieves 52% accuracy, making it suitable for resource-constrained environments. ResNet50, with its residual networks, also demonstrates exceptional performance with 99% accuracy and a moderate number of parameters (23 million), which can be a significant consideration in environments with limited resources or when deploying models on edge devices. These findings guide researchers and practitioners in selecting optimal models to reduce herbicide usage, minimize environmental impact, and enhance precision agriculture practices. Ultimately, this study advances weed management strategies, supporting sustainable crop management and improving agricultural productivity.

Automated rice mapping using multitemporal Sentinel-1 SAR imagery using dynamic threshold and slope-based index methods

Rice cultivation plays a crucial role in food security and economic development, particularly in regions like India, due to its vast population and position as the top rice producer globally. This work introduces a novel framework, the Rice Mapping Method (RMM), which leverages Multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery for automated rice mapping. Contrary to the traditional approaches, RMM combines the Dynamic Threshold Method (DTM) for robust rice field identification and a slope-based index for classifying single and double cropping practices. By analyzing VH backscatter patterns and employing specific thresholds, DTM separates rice pixels from the other background pixels. The DTM, which relies on VH backscatter values during the growing season, has been tested across various rice cultivation landscapes, demonstrating high accuracy upto 0.95. DTM is also tested on different rice-growing areas such as the hilly Kodagu district, with an F1 Score of 0.96, and in the flooded delta region of Kuttanad, achieving an F1 Score of 0.93. The Slope-based Index I(r,c) is introduced to differentiate the single and double cropping pixels by calculating the index for the second season of cropping and gives F1 Score of 0.81. The DTM’s effectiveness in rice field identification is evaluated by comparing it to the classification of the Bi-directional Gated Recurrent Unit (Bi-GRU) network. Similarly, the Slope-based Index is compared with other established automated rice mapping methods to assess its accuracy in distinguishing cropping patterns. RMM was successfully applied in mapping rice-growing areas in the Udupi district for 2021, estimating Kharif and Rabi season areas, the estimated rice area is compared to official statistics by the Directorate of Economics and Statistics, Karnataka State. The proposed RMM approach offers a robust solution for mapping rice fields, particularly in regions with complex cropping landscapes, and enhances agricultural monitoring and decision-making processes contributing to sustainable rice production and food security initiatives.

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers

Accurately estimating high-dimensional permeability (k) fields through data assimilation is critical for minimizing uncertainties in groundwater flow and solute transport simulations. However, designing an effective monitoring network to obtain diverse system responses in heterogeneous aquifers for data assimilation presents significant challenges. To investigate the influence of different measurement types (hydraulic heads, solute concentrations, and permeability) and monitoring strategies on the accuracy of permeability characterization, this study integrates a deep learning-based surrogate modeling approach and the entropy-based maximum information minimum redundancy (MIMR) monitoring design criterion into a data assimilation framework. An ensemble MIMR-optimized method is developed to provide more comprehensive monitoring information and avoid missing key information due to the randomness of stochastic response datasets in entropy analysis. A numerical case of solute transport with log-Gaussian permeability fields is presented, with twelve scenarios designed by combining different measurement types and monitoring strategies. The results demonstrated that the proposed ensemble MIMR-optimized method significantly improved the k-field estimates compared to the conventional MIMR method. Additionally, high prediction accuracy in forward modeling is essential for ensuring reliable inversion results, especially for observation data with strong nonlinearity. The findings of this study enhance our understanding and management of k-field estimation in heterogeneous aquifers, contributing to the development of more robust inversion frameworks for general data assimilation tasks.

Successional changes in aphyllophorales macromycetes at different stages of coniferous xylolysis

The state of modern protective plantations in coniferous-deciduous forests of the Non-Chernozem zone is characterized by great heterogeneity. Currently, both protective forest plantations and protective tree plantations are managed by an extensive method, often without taking into account biological relationships in the agrophytocenosis. Successional changes in Aphyllophorales macromycetes (AFMM) at different stages of xylolysis of large tree debris represent a complex biochemical process. The limiting conditions for the active growth of fungal mycelium inside wood include light, access to moisture and air, violations in water transport. As mycogenic xylolysis develops, there is an increase in the biodiversity of AFMM species, which reaches a maximum at stage III–IV. On a large data set of 332 model trees and 3,543 basidiomes of xylotrophic basidiomycetes, the presence of correlations between the settlement on the substrate of various ecological groups of AFMM involved in xylolysis was established. It has also been shown that the fruit bodies of fungi are actively formed under conditions of abiotic stress and their number increases in subsequent seasons. When using AFMM as indicators of the xylolysis stage of coniferous species, it is necessary to take into account that the development of mycelium depends on the structural features of the wood. The course of mycelium development in the trunk cross-section is associated with both the action of external environmental factors and interference processes between species. At the same time, the frequency of basidioma formation and their age are of great importance in field identification.

Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN

In Thailand, two snail-eating turtle species in the genus Malayemes (M. subtrijuga and M. macrocephala) are protected animals in which smuggling and trading are illegal. Recently, a new species M. khoratensis has been reported and it has not yet been considered as protected animal species. To enforce the law, species identification of Malayemes is crucial. However, it is quite challenging and requires expertise. Therefore, a simple tool, such as image analysis, to differentiate these three snail-eating species would be highly useful. This study proposes a novel ensemble multiview image processing approach for the automated classification of three turtle species in the genus Malayemys. The original YOLOv8 architecture was improved by utilizing a convolutional neural network (CNN) to overcome the limitations of traditional identification methods. This model captures unique morphological features by analyzing Malayemys species images from various angles, addressing challenges such as occlusion and appearance variations. The ensemble multiview strategy significantly increases the YOLOv8 classification accuracy using a comprehensive dataset, achieving an average mean average precision (mAP) of 98% for the genus Malayemys compared with the nonensemble multiview and single-view strategies. The species identification accuracy of the proposed models was validated by comparing genetic methods using mitochondrial DNA with morphological characteristics. Even though the morphological characteristics of these three species are ambiguous, the mitochondrial DNA sequences are quite distinct. Therefore, this alternative tool should be used to increase confidence in field identification. In summary, the contribution of this study not only marks a significant advancement in computational biology but also supports wildlife and turtle conservation efforts by enabling rapid, accurate species identification.

Empirical error field control at JET in preparation of ITER start-up

This work reports on error field identification and control studies carried out at JET and insights on the development of the empirical EF controller for ITER. The empirical EF controller has been included in the JET real-time central controller following the execution of the non-disruptive compass scan method (Paz-Soldan C. et al., Nuclear Fusion 62 (2022) 126007), which allowed the identification of the EF source and the currents for error field compensation. When testing the empirical EF controller, born locked n = 1 modes have been observed to spin-up and a lower density regime has been explored in the 1.8 MA plasma current, 2.1 T toroidal magnetic field scenario than otherwise achievable. These experimental results demonstrate the benefits of EF correction. In preparation of EF correction studies in ITER, the empirical EF controller for ITER has been developed and integrated in the plasma control system database.

A new Boulenophrys species (Anura, Megophryidae) from the coastal hills of eastern Fujian Province, China.

A new species of the genus Boulenophrys is described from the coastal hills of eastern Fujian Province, China. The new taxon can be distinguished from all recognized congeners by a combination of discrete morphological character state differences and genetic divergences in the combined mitochondrial 16S + CO1 genes. We also provide a map showing the distribution pattern of Boulenophrys species in Fujian and a provincial-specific key, which will aid their conservation by helping the local authorities accurately identify species during field identifications and data collection efforts.

Development of molecular diagnostic protocols for simultaneous identification of common bed bugs (Cimex lectularius) and tropical bed bugs (Cimex hemipterus)

BackgroundThe resurgence of two bed bug species, the common bed bug (Cimex lectularius Linnaeus, 1758) and tropical bed bug (Cimex hemipterus Fabricius, 1803), in the same geographical regions has been frequently reported recently. Consequently, the rapid identification of these species is crucial for implementing targeted capture traps and tailored pyrethroid resistance diagnosis, due to differences in genetic and physiological traits.MethodsTo develop molecular diagnostic methods, distinct protocols were established for multiplex PCR and loop-mediated isothermal amplification (LAMP) using species-specific primers based on species-specific segments of internal transcribed spacer 2 sequences. These methods were optimized for rapid and accurate identification of the two bed bug species.ResultsBoth multiplex PCR and LAMP protocols were effective in simultaneously identifying the two bed bug species, even when utilizing DNA released from dead specimens. Notably, the straightforward procedure and minimal time commitment of LAMP suggest its potential for rapid and accurate diagnosis of bed bugs in the field. The diagnostic accuracy of these methods was validated through a blind test.ConclusionsThe multiplex PCR and LAMP protocols lay the foundation for rapid and accurate field identification of bed bug species, enabling the use of appropriate traps and the detection of species-specific pyrethroid resistance mutations. This approach ensures effective management tailored to the unique characteristics of each bed bug species.Graphical

A Novel Method for Identifying Landslide Surface Deformation via the Integrated YOLOX and Mask R-CNN Model

The detection of landslide areas and surface characteristics is the prerequisite and basis of landslide hazard risk assessment. The traditional method relies mainly on manual field identification, and discrimination is based on the lack of unified quantitative standards. Thus, the use of neural networks for the quantitative identification and prediction of landslide surface deformation is explored. By constructing an integrated model based on YOLO X-CNN and Mask R-CNN, a deep learning-based feature detection method for landslide surface images is proposed. First, the method superimposes Unmanned Aerial Vehicle (UAV) oblique photography data (UOPD) and Internet heterosource image data (IHID) to construct a landslide surface image dataset and landslide surface deformation database. Second, an integrated model suitable for small- and medium-scale target detection and large-scale target edge extraction is constructed to automatically identify and extract landslide surface features and to achieve rapid detection of landslide surface features and accurate segmentation and deformation recognition of landslide areas. The results show that the detection accuracy for small rock targets is greater than 80% and that the speed is 57.04 FPS. The classification and mask segmentation accuracies of large slope targets are approximately 90%. A speed of 7.89 FPS can meet the needs of disaster emergency response; this provides a reference method for the accurate identification of landslide surface features.

Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision

Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, image processing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machine vision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machine vision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challenges and outlining future opportunities. By Integrating state-of-the-art technology with sustainable agricultural practices, implementing an intelligent system offers a viable approach toward efficient and environmentally friendly weed management in modern agriculture.

Mentha choujunensis, a new species from Indian Trans-Himalayan region of Ladakh

Mentha choujunensis (Lamiaceae), a new species from Suru valley of district Kargil in Ladakh, Trans-Himalaya, India is described here. The new species can be distinguished from its closest congeneric M. longifolia in possessing prostate stem, leaves petiolate and ovate to ovate-lanceolate, sparsely pubescent, trichomes 1-celled on adaxial side and 2-celled on abaxial side, base oblique, margin serrate, calyx less than half the length of corolla tube, 8–10 veined, calyx teeth reddish brown with distal trichomes 3–4 celled long and corolla white. Photo-illustrations, comparison of diagnostic characters of the new species with M. longifolia and taxonomic key for the Indian species under section Mentha are provided to facilitate field identification.

Crop Pest Detection using Convolutional Neural Network

Pests in plants can cause significant losses in agricultural production. As a result, various technologies are used nowadays to improve agriculture's efficiency and make it more sustainable. This research highlights the contribution of machine learning algorithms and image recognition technologies for pest identification. Farmers can use the system to recognize pests and take the necessary actions to reduce them. Convolutional Neural Networks (CNN) is used in this study for image recognition tasks, including pest identification in agricultural fields. The algorithm is trained using the Agricultural Pests Dataset acquired from Kaggle. The experiment results showed that the CNN performed better than the other state-of-the-art machine learning models, with a much lower false rejection rate of 0.12% and an accuracy of 99%.

Emilia fosbergii (Asteraceae) – A new record for India

Emilia fosbergii Nicolson of the family Asteraceae is reported here as new record for Indian flora from Jharkhand state. Brief description, illustration and photographs are provided in this article to aid field identification.

Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets

Urbanization has exerted considerable impacts on urban water systems and ecological environments, yet its effects on local meteorological drought remain under-explored. The primary challenge to local-scale drought analysis is the scarcity of meteorological datasets with sufficient spatial and temporal resolution. To address the research gap, we initially proposed a two-step fusion framework, integrating both surface (i.e., gridded data)-surface and point (i.e., in-situ data)-surface fusion. The framework was applied to generate daily precipitation and average/maximum/minimum air temperature at a 1 km resolution through the integration of high-resolution remotely sensed datasets across the Yangtze River Basin (YRB), China. The final fused data demonstrated excellent performance, achieving a PCC (RMSE) of 0.806 (5.414 mm/day), 0.993 (1.138 °C), 0.987 (1.443 °C), and 0.988 (1.376 °C) for precipitation and average/maximum/minimum air temperature, respectively. A comparison of our fused data with CPC, ERA5-Land, CMFD, CHIRPS, IMERG, and TMPA products confirmed its capability in capturing local-scale meteorological dynamics by improving spatial resolution from 0.1°-0.25° to 1 km. Utilizing these high-resolution datasets, we quantified urbanization's impacts on local drought across 52 major cities in the YRB. We found that urbanization significantly magnified extreme Standardized Precipitation Evapotranspiration Index (SPEI) and drought severity in 69.2% and 61.5% of these cities, respectively. The effects of urbanization on extreme SPEI were amplified by the increase of urbanization rates, with a slope of −0.24 (p < 0.05). To further examine the spatial patterns of urbanization-induced local drought, we proposed a drought spatial field identification method, utilizing it in three representative urban regions: Chengdu, Wuhan, and the Yangtze River Delta. Our findings revealed that urbanization led to more intense peak drought intensity and average drought severity. In addition, urban drought fields showed lower effective radius, indicating more concentrated drought towards urban regions. While urbanization is projected to continue alongside rapid population growth in the future, the advanced application of remote sensing data and technology in this study not only improves our understanding of urban water resource challenges but also equips urban planners with the necessary data to develop effective drought mitigation strategies.

PathoDuet: Foundation models for pathological slide analysis of H&E and IHC stains

Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.

Genetic profile and ancestral polymorphism research of the Guizhou Shui and Dong ethnic groups using a novel self-developed AIM-InDel panel

Insertion or deletion (InDel), a genetic marker with short insertion/deletion fragment length polymorphism, is widely used in the field of forensic biological research. The Guizhou Shui (Shui) ethnic group and Guizhou Dong (Dong) ethnic group are located in the southwestern region of China, with rich historical and cultural background. In this study, a self-developed panel included 56 ancestry informative marker (AIM)-InDel loci on the autosomes, three InDel loci on the Y chromosome, and one sex-determined Amelogenin locus. Firstly, we used the 56 autosomal loci to assess the forensic individual identification and paternity testing abilities in both the Shui and Dong groups. The cumulative probability of match and probability of exclusion for the Shui and Dong groups were 2.228×10−15 and 0.991518139; 7.604×10−16 and 0.992253273, respectively. In addition, we also conducted in-depth analyses for the genetic backgrounds and structures of the Shui and Dong groups based on 56 AIM-InDel loci. This research has found that the Shui and Dong groups have close genetic relationships with the East Asian populations. Meanwhile, we also found that the Shui group has a close genetic distance with Chinese Dai in Xishuangbanna (CDX). These insights provide vital information for the genetic structures of the Shui and Dong groups, as well as basic population data and molecular biological evidence support for individual identification and biogeographic ancestry inference in forensic genetic field.

Identification Of Liquefaction Slide Planes Using The Geoelectric Resistance Type Method In Balaroa Village

Introduction: Research has been conducted with the title "Identification of Liquefaction Sliding Fields Using the Type Resistance Geoelectric Method in Balaroa Village". This study aims to determine the subsurface constituent rock layers and the position of the sliding plane based on the type resistance data. Method: This study consists of 6 measurement tracks with a track length of 150 meters using the Vertical Electrical Sounding (VES) method with the Schlumberger configuration. Data processing uses the software program Progress version 3.0 and ipi2win. Results and Discussion: The results obtained indicate the presence of subsurface constituent layers, namely: Passive clay, clay sand, and gravel, and the presence of an inclined plane of 25° to 55.5% with a steep slope. Conclusion: Based on data analysis and interpretation, it can be concluded that the rock lithology at the study site consists of passive clay, clay sand, and gravel, based on the value of specific resistance. The slope of the sliding plane layers that have the potential to trigger landslides and liquefaction at the site tends to be west-east with a slope angle of 25°, or 55.6%, which is included in the steep slope category

NOTES ABOUT THE SCOPE OF THE PRESENT INVESTIGATION ON THE LATIN AMERICAN AUTHORITARIAN STATE AND ITS SCHOOL

The purpose of this paper is to provide a preliminary overview of the state of research on education as an instrument of domination during the «exceptional» moments assumed by the State throughout the 20th century in Latin America. This is a summary review mainly aimed at recovering part of the knowledge debated in the Ibero-American Congresses on the History of Latin American Education (CIHELA) in the last thirty years. Certainly, the universe of connotations that open up in this call outlined by the concepts «authoritarianism, violence, war, vulnerability and school», forces a limited theoretical-methodological operation and, for instance, affordable. In this sense, this work results from the search, identification, selection and analysis of a broad field of research and reflection on this subject, problems and academic objectives. Following the works presented in the CIHELA, this selection focuses on the transformations of the educational field, in general, and the school, in particular, in periods of restriction or closure of democratic participation. More specifically, it addresses the devices, uses and scope of authoritarian power according to the many variants assumed in each geography and unique history (foreign occupations, military and civil-military dictatorships, etc.).