Search Results (73)

Resolving the eukaryotic tree of life (eToL) remains a fundamental challenge in biology. Much of eukaryotic phylogenetic diversity is occupied by unicellular microbial eukaryotes (i.e., protists). Among these, the phylogenetic positions of a significant number of lineages remain unresolved due to limited data and ambiguous traits. To address this issue, we introduce the term “PUPAs” (protists with uncertain phylogenetic affiliations) to collectively describe these lineages, instead of using vague or inconsistent labels, such as incertae sedis or orphan taxa. Historically, protists were classified based solely on morphological features, and many with divergent cell structures were left unplaced in the eToL. With the advent of sequence-based approaches, the phylogenetic affiliations of some PUPAs have been clarified using molecular markers, such as small subunit ribosomal DNA. The combination of technological progress and continuous efforts to cultivate diverse protists, including PUPAs and novel protists, now enables phylogenetic analyses based on hundreds of proteins, providing their concrete placements in the eToL. For example, these advances have led to the discovery of new deep-branching lineages (e.g., Hemimastigophora), the resolution of relationships among major groups (e.g., Microheliella, which linked Cryptista and Archaeplastida), and insights into evolutionary innovations within specific clades (e.g., Glissandra). In this review, we summarize current consensus in eukaryotic phylogeny and highlight recent findings on PUPAs whose phylogenetic affiliations have been clarified. We also discuss a few lineages for which the phylogenetic homes remain unsettled, the evolutionary implications of these discoveries, and the remaining challenges in resolving the complete eToL. Full article

This research assesses the impacts of five cleaner production (CP) profiles of soap products and the overall equipment effectiveness (OEE) of liquid detergent production lines. A simulation model was built to depict the “As-Is” condition, and then the overall equipment effectiveness was calculated. Results showed high rejection rates and bottlenecks, resulting in long average cycle times and waiting times, as well as low production rates and machine utilization. Consequently, lean, agile, and resilient actions were utilized to enhance the OEE of the line’s processes. The improvement results showed that the bottle placement, filling, labeling, and taping processes were enhanced by 237%, 4.67%, 5.41%, and 26.02%, respectively. Moreover, the smallest percentages of availability, quality, and performance were 97.46%, 99.82%, and 81.56%, respectively, indicating a considerable enhancement in the performance of the line’s processes. Further, cleaner production assessment was performed on soap products to estimate environmental profile indices for raw material, energy, product, waste, and packaging. The estimated overall environmental index was 107.93, with liquid waste contributing the largest index value. Therefore, a proposal for a water treatment system was suggested and then assessed. In conclusion, lean, agile, and resilient actions were found to be effective in enhancing the OEE of production processes. Moreover, cleaner production provided valuable support to decision-makers in determining the appropriate actions for improving the OEE and environmental performance of the technological processes and products of detergent production lines. Full article

The purpose of this study was to investigate the taxonomic and phylogenomic placement of the proposed genus ‘Solwaraspora’ within the context of other marine genera using a dual-omics approach. Initially, we isolated bacteria from marine tunicates, squirts, and sponges, which were morphologically similar to an emerging genus (identified as ‘Micromonospora_E’ by the GTDB-tk2 database using whole genome sequence data) by colony shape, size, and clustering pattern, but only found five strains in our dataset belonging to this distinction. Due to the minimally explored nature of this genus, we sought to identify more bacterial strains with similar morphology to Micromonospora‘Micromonospora_E’ by whole genome sequencing (WGS). Within our collection, we noted 35 strains that met this criterion and extracted genomic information to perform WGS on these strains. With this information, we studied taxonomic and phylogenomic relationships among these organisms. Using the data gathered from WGS, we were able to identify an additional five strains labeled by the GTDB-tk2 database as Micromonospora‘Micromonospora_E’, as well as construct phylogenomic trees to examine the evolutionary relationships between these strains. ANI values were calculated between strains from our dataset and type strains of Micromonospora and Plantactinospora as well as against an outgroup Streptomyces strain. No type strains are available for ‘Solwaraspora’. Using MALDI-TOF MS, we positively identified ‘Solwaraspora’, which was supported by the phylogenomic tree showing Micromonospora’Micromonospora_E’ (‘Solwaraspora’) in a distinct clade from Plantactinospora and Micromonospora. Additionally, we discovered gene cluster families (GCFs) in alignment with genera, as well as a large representation of biosynthetic gene clusters (BGCs) coming from the ‘Solwaraspora’ strains. These findings suggest significant potential to discover novel chemistry from ‘Solwaraspora’, adding to the importance of investigating this new genus of bacteria. Full article

The dorsomedial caudate–putamen (dmCPu), a key input structure of the basal ganglia, plays a crucial role in goal-directed behaviors and the transition to habits. The functional specialization of the dmCPu along its anteroposterior axis suggests that distinct prefrontal cortex (PFC) subregions may differentially contribute to these processes. However, the precise topographical organization of PFC and adjacent areas projections to the anterior and posterior dmCPu remains poorly understood. We employed retrograde tracing using Fluoro-Gold to map the projections from PFC subregions and adjacent areas to the anterior and posterior dmCPu in male Sprague Dawley rats. Histological verification and immunohistochemical labeling were conducted to confirm injection sites and neuronal labeling. Quantitative analyses were performed to assess the effects of injection site placement (anterior vs. posterior dmCPu), laterality (ipsilateral vs. contralateral), and cortical subregion on projection density. The posterior dmCPu received significantly higher projection densities than the anterior dmCPu, with a pronounced ipsilateral dominance across all cortical subregions. Among the subregions examined, the cingulate cortex exhibited the highest number of labeled neurons projecting to the dmCPu, with distinct patterns of connectivity between anterior and posterior injection sites. Notably, motor and somatosensory cortical projections were more prominent in the posterior dmCPu, whereas cingulate projections demonstrated robust anteroposterior and lateralized differences. These findings provide a comprehensive map of the topographical organization of cortical inputs to the dmCPu, highlighting differential connectivity patterns that may underlie distinct functional roles in goal-directed and habitual behaviors. This work advances our understanding of corticostriatal circuits and their relevance to adaptive behaviors and neuropsychiatric disorders. Full article

This article introduces a novel methodological framework—the “spatial reading of inventories”—to reconstruct domestic interiors in seventeenth-century Amsterdam. By integrating probate inventories with architectural floor plans, this study establishes three house typologies with schematic 3D drawings that resolve ambiguities in room labels and spatial organization, bridging the gap between architectural history and material culture studies. Focusing on methodological innovation, this article both reveals how house size and structure created distinct spatial context and breathes new life into the well-researched probate inventories by using its untapped spatial information. While using seventeenth-century Amsterdam as a case study, this approach offers a model for studying historical domestic spaces across contexts and provides a foundation for future analyses of object placement, sensory experience, and cultural practices at home. Full article

Each living organism thrives best in a habitat that provides optimal conditions for flourishing, reproduction, and distribution within a certain area. This study aims to investigate the seasonal variation in insect biodiversity across different sites of the King Abdulaziz Royal Reserve (KARR), located between E 45.19–46.57 and N 25.15–27.41, with a focus on assessing biodiversity, density and seasonal variation using active and passive methods, over the period from January to November 2023. A total of 68 sites within the study area were randomly selected for trap placement. The trapped specimens were labeled and transferred to plastic bottles half filled with 70% ethanol and then taken to the laboratory for counting and identification. Identification was based on morphological characteristics and appropriate identification keys, with the assistance of entomological expertise, and a list of local species. Simpson’s diversity index (D) was also calculated. The results revealed that, out of 6320 trapped insects, species were identified across six orders: Blattodea (termites), represented by 2 families and 2 species; Coleoptera, comprising 12 families and 38 species, of which 11 belonged to the family Tenebrionidae; Hemiptera, comprising 7 families and 9 species, 3 of which belonged to the family Lygaeidae; Hymenoptera, comprising 5 families and 15 species, 9 of which were from Formicidae; Lepidoptera, comprising 2 families and 3 species; and Orthoptera, comprising 3 families and 7 species, 4 of which were from family Acrididae. Insect biodiversity and abundance were observed to be relatively low during the winter (January–March) and autumn (October–November) seasons, while relatively higher densities were recorded during spring (May) and summer (August–September). Full article

Point feature cartographic label placement is a key problem in the automatic configuration of map labeling. Prior research on it only addresses label conflict or overlap issues; it does not fully take into account and resolve both types of issues. In this study, we attempt to apply machine learning techniques to the automatic placement of point feature labels since label placement is a task that heavily relies on expert expertise, which is very congruent with neural networks’ ability to mimic the human brain’s thought process. We trained ResNet using large amounts of well-labeled picture data. The label’s proper location for a given unlabeled point feature was then predicted by the trained model. We assessed the outcomes both quantitatively and qualitatively, contrasting the ResNet model’s output with that of the expert manual placement approach and the conventional Maplex automatic placement method. According to the evaluation, the ResNet model’s test set accuracy was 97.08%, demonstrating its ability to locate the point feature label in the right place. This study offers a workable solution to the label overlap and conflict issue. Simultaneously, it has significantly enhanced the map’s esthetic appeal and the information’s clarity. Full article

This study’s primary goal is to use computer vision and ultra-wideband (UWB) localisation techniques to automatically mark numerals in cow photos. In order to accomplish this, we created a UWB-based cow localisation system that involves installing tags on cow heads and placing several base stations throughout the farm. The system can determine the distance between each base station and the cow using wireless communication technology, which allows it to determine the cow’s current location coordinates. The study employed a neural network to train and optimise the ranging data gathered in the 1–20 m range in order to solve the issue of significant ranging errors in conventional UWB positioning systems. The experimental data indicates that the UWB positioning system’s unoptimized range error has an absolute mean of 0.18 m and a standard deviation of 0.047. However, when using a neural network-trained model, the ranging error is much decreased, with an absolute mean of 0.038 m and a standard deviation of 0.0079. The average root mean square error (RMSE) of the positioning coordinates is decreased to 0.043 m following the positioning computation utilising the optimised range data, greatly increasing the positioning accuracy. This study used the conventional camera shooting method for image acquisition. Following image acquisition, the system extracts the cow’s coordinate information from the image using a perspective transformation method. This allows for accurate cow identification and number labelling when compared to the location coordinates. According to the trial findings, this plan, which integrates computer vision and UWB positioning technologies, achieves high-precision cow labelling and placement in the optimised system and greatly raises the degree of automation and precise management in the farming process. This technology has many potential applications, particularly in the administration and surveillance of big dairy farms, and it offers a strong technical basis for precision farming. Full article

Physics-informed neural networks (PINNs) are gaining traction as surrogate models for fluid dynamics problems, combining machine learning with physics-based constraints. This study investigates the impact of labeled data on the performance of parameterized physics-informed neural networks (PINNs) for surrogate modeling and design optimization. Different training approaches, including physics-only, data-only, and several combinations of both, are evaluated using fully connected (FCNN) and Fourier neural network (FNN) architectures. The test case focuses on reducing drag over a forward-facing step through optimal placement and sizing of an upstream obstacle. Results demonstrate that the inclusion of labeled data significantly enhances the accuracy and convergence rates of FCNNs, particularly in predicting flow separation and recirculation regions, and improves the stability of design optimization outcomes. Conversely, FNNs exhibit inconsistent responses to parameter changes when trained with labeled data, suggesting limitations in their applicability for certain design optimization tasks. The findings reveal that FCNNs trained with a balanced integration of data and physics constraints outperform both data-only and physics-only models, highlighting the importance of optimizing the training approach based on the specific requirements of fluid mechanics applications. Full article

Background/Objectives: Systematic quality review of ultrasound exams is recommended to ensure accurate diagnosis. Our primary objectives were to develop a quantitative method for quality review of estimated fetal weight (EFW) and to assess the accuracy of EFW for an entire practice and for individual personnel. A secondary objective was to evaluate the accuracy of fetal sex determination. Methods: This is a retrospective cohort study. Eligible ultrasound exams included singleton pregnancies with live birth and known birth weight (BW). A published method was used to predict BW from EFW for exams with ultrasound-to-delivery intervals of up to 12 weeks. Mean error and median absolute error (AE) were compared between different personnel. Image audits were performed for exams with AE > 30% and exams with reported fetal sex different than newborn sex. Results: We analyzed 1938 exams from 890 patients. In the last exam before birth, the median AE was 5.9%, and the predicted BW was within ±20% of the actual BW in 97.2% of patients. AE was >30% in 28 exams (1.4%); image audit found correct caliper placement in all 28. Only two patients (0.2%) had AE > 30% on the last exam before birth. One sonographer systematically over-measured head and abdominal circumferences, leading to EFWs that were overestimated. Reported fetal sex differed from newborn sex in seven exams (0.4%) and five patients (0.6%). Images in four of these patients were annotated with the correct fetal sex, but a clerical error was made in the report. In one patient, an unclear image was labeled “probably female”, but the newborn was male. Conclusions: The accuracy of EFW in this practice was similar to literature reports. The quantitative analysis identified a sonographer with outlier measurements. Time-consuming image audits could be focused on a small number of exams with large errors. We suggest some enhancements to ultrasound reporting software that may help to reduce clerical errors. We provide tools to help other practices perform similar quality reviews. Full article

In dietary assessment using a single-view food image, an object of known size, such as a checkerboard, is often placed manually in the camera’s view as a scale reference to estimate food volume. This traditional scale reference is inconvenient to use because of the manual placement requirement. Consequently, utensils, such as plates and bowls, have been suggested as alternative references. Although these references do not need a manual placement procedure, there is a unique challenge when a dining bowl is used as a reference. Unlike a dining plate, whose shallow shape does not usually block the view of the food, a dining bowl does obscure the food view, and its shape may not be fully observable from the single-view food image. As a result, significant errors may occur in food volume estimation due to the unknown shape of the bowl. To address this challenge, we present a novel method to premeasure both the size and shape of the empty bowl before it is used in a dietary assessment study. In our method, an image is taken with a labeled paper ruler adhered to the interior surface of the bowl, a mathematical model is developed to describe its shape and size, and then an optimization method is used to determine the bowl parameters based on the locations of observed ruler makers from the bowl image. Experimental studies were performed using both simulated and actual bowls to assess the reliability and accuracy of our bowl measurement method. Full article

On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps. Full article

The distribution of shallow subsurface quality factors (Q) is a crucial indicator for assessing the integrity of subsurface structures and serves as a primary parameter for evaluating the attenuation characteristics of seismic waves propagating through subsurface media. As the complexity of underground spaces increases, regions expand, and testing environments diversify, the survivability of test nodes is compromised, resulting in sparse effective seismic data with a low signal-to-noise ratio (SNR). Within the confined area defined by the source and sensor placement, only the Q factor along the wave propagation path can be estimated with relative accuracy. Estimating the Q factor in other parts of the area is challenging. Additionally, in recent years, deep neural networks have been employed to address the issue of missing values in seismic data; however, these methods typically require large datasets to train networks that can effectively fit the data, making them less applicable to our specific problem. In response to this challenge, we have developed a supervised learning method for the restoration of shallow subsurface Q factor distributions. The process begins with the construction of an incomplete labeled data volume, followed by the application of a block-based data augmentation technique to enrich the training samples and train the network. The uniformly partitioned initial data are then fed into the trained network to obtain output data, which are subsequently combined to form a complete Q factor data volume. We have validated this training approach using various networks, all yielding favorable results. Additionally, we compared our method with a data augmentation approach that involves creating random masks, demonstrating that our method reduces the mean absolute percentage error (MAPE) by 5%. Full article

While policies targeting education in schools, marketing campaigns, and taxation strategies are of great importance to tackle our population’s malnutrition, there is growing concern about enhancing the supermarket environment to promote healthier food consumption. Public and private policies targeting the food retail sector can contribute to achieving this goal. Supermarkets play a significant role in presenting food options. As gatekeepers of the food system’s impact on consumers’ health, they have the power to help people make better food choices. Examples include making healthy checkouts and nudging consumption of vegetables through infographics. Currently, few policies specify how supermarkets’ environments could be set up to improve healthy purchases (e.g., the “Partnership for a healthy diet” in Norway and “hange4Life Convenience Stores” in the UK). Furthermore, we see recent shifts in the policy agendas of countries like Germany and Argentina, with new nutrition behavioural policies being legislated and implemented. However, we lack general guidelines that unify the available information on this topic. Addressing this gap has the potential to guide future supermarket policy recommendations. We will perform a review and delve deeper to search for governmental policies and policies of major supermarket brands in selected countries (i.e., Germany and Argentina) to taxonomize the policy initiatives, examining their projects and campaigns designed to improve supermarket environments and encourage consumers to choose healthy options. We will harvest sources published since the release of the SDGs (2015), from ScienceDirect, the FAO policy search engine Informas, and the NOURISHING platform. We will identify the grey literature by searching related websites and databases as well as national government pages or private supermarket policy documents. We will extract the information relevant to policymaking to improve environments in terms of product reformulation, labelling strategies, placement, product processes, and sweet beverage representation. While spotting incentive, procurement, and regulative policies, we will group them in terms of interventions and further taxonomize the different approaches/initiatives. Our objective is to provide unified policy recommendations by leveraging existing experiences and policies and designing applicable recommendations to improve the food environment in supermarkets. Furthermore, we aim to promote the creation of science-based policies that consider these recommendations. Full article

Growing pumpkins in controlled environments, such as greenhouses, has become increasingly important due to the potential to optimise yield and quality. However, achieving optimal environmental conditions for pumpkin cultivation requires precise monitoring and control, which can be facilitated by modern sensor technologies. The objective of this study was to determine the optimal placement of sensors to determine the influence of external parameters on the maturity of pumpkins. The greenhouse used in the study consisted of a plastic film for growing pumpkins. Five different sensors labeled from A1 to A5 measured the air temperature, humidity, soil temperature, soil humidity, and illumination at five different locations. We used two methods, error-based sensor placement and entropy-based sensor placement, to evaluate optimisation. We selected A3 sensor locations where the monitored data were close to the reference value, i.e., the average data of all measurement locations and parameters. Using this method, we selected sensor positions to monitor the influence of external parameters on the maturity of pumpkins. These methods enable the determination of optimal sensor locations to represent the entire facility environment and detect areas with significant environmental disparities. Our study provides an accurate measurement of the internal environment of a greenhouse and properly selects the base installation locations of sensors in the pumpkin greenhouse. Full article