Accurate Counting Research Articles

High-accuracy occupancy counting at crowded entrances for smart buildings

The Buildings Global Status Report (Buildings-GSR) highlights the significant impact of the buildings and construction sector on global climate change. This sector is responsible for approximately 34% of global energy consumption and 37% of carbon dioxide (CO2) emissions. Smart building is a promising solution to achieve Zero Net goals. High-accuracy occupancy counting plays a crucial role in enhancing building energy efficiency, improving environmental comfort, and reducing disease transmission. Occupancy information can serve as feedback in a closed-loop building control strategy, enabling occupant-centric control (OCC). Even though existing studies have made developments, they struggle to obtain high-accuracy occupancy counting because of many challenges, e.g., cumulative errors and occlusion. To address these challenges, a novel deep learning-based occupancy counting method by edge cameras at crowded entrances is introduced. We annotate the occupancy tracking dataset and train tracking neural networks to count occupants, achieving 80.4% and 69.9% MOTA in two different cameras. We fuse multi-camera information to achieve high performance. Our methods achieve an occupancy counting accuracy of approximately 98.1% with two real-scene surveillance cameras, surpassing the existing occupancy counting methods by 7.7%. Besides, we design an occupant-centric control method to save energy in buildings. Our experiments demonstrate that our occupancy counting method achieves better performance on a real-world dataset, compared to other methods. Our experiments verify that our occupant-centric control method has the potential for building energy saving about 17%-25% in a room. Our study deepens the insights into occupancy measurement, paving the way toward energy-efficient buildings and comfortable indoor environments. We make our code available at https://github.com/kailaisun/Head-tracking-for-occupancy-counting.

One-Shot Any-Scene Crowd Counting With Local-to-Global Guidance.

Due to different installation angles, heights, and positions of the camera installation in real-world scenes, it is difficult for crowd counting models to work in unseen surveillance scenes. In this paper, we are interested in accurate crowd counting based on the data collected by any surveillance camera, that is to count the crowd from any scene given only one annotated image from that scene. To this end, we firstly pose crowd counting as a one-shot learning task. Through the metric-learning, we propose a simple yet effective method that firstly estimates crowd characteristics and then transfers them to guide the model to count the crowd. Specifically, to fully capture these crowd characteristics of the target scene, we devise the Multi-Prototype Learner to learn the prototypes of foreground and density from the limited support image using the Expectation-Maximization algorithm. To learn the adaptation capability for any unseen scene, estimated multi prototypes are proposed to guide the crowd counting of query images in a local-to-global way. CNN is utilized to activate the local features. And transformer is introduced to correlate global features. Extensive experiments on three surveillance datasets suggest that our method outperforms the SOTA methods in the few-shot crowd counting.

A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits.

Rice panicle traits serve as critical indicators of both yield potential and germplasm resource quality. However, traditional manual measurements of these traits, which typically involve threshing, are not only laborious and time-consuming but also prone to introducing measurement errors. This study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length. To address the challenge of grain occlusion within an intact panicle, we define a panicle morphology index to quantify the occlusion levels among the rice grains within the panicle. By calibrating the grain number obtained directly from rice panicle images based on the panicle morphology index, we substantially improve the grain number detection accuracy. For measuring grain length and width, the EOPT selects rice grains using an intersection over union threshold of 0.8 and a confidence threshold of 0.7 during the grain detection process. The mean values of these grains were calculated to represent all the panicle grain lengths and widths. In addition, EOPT extracted the main path of the skeleton of the rice panicle using the Astar algorithm to determine panicle lengths. Validation on a dataset of 1,554 panicle images demonstrated the effectiveness of the proposed method, achieving 93.57% accuracy in panicle grain counting with a mean absolute percentage error of 6.62%. High accuracy rates were also recorded for grain length (96.83%) and panicle length (97.13%). Moreover, the utility of EOPT was confirmed across different years and scenes, both indoors and outdoors. A genome-wide association study was conducted, leveraging the phenotypic traits obtained via EOPT and genotypic data. This study identified single-nucleotide polymorphisms associated with grain length, width, number per panicle, and panicle length, further emphasizing the utility and potential of this method in advancing rice breeding.

CTHNet: a network for wheat ear counting with local-global features fusion based on hybrid architecture.

Accurate wheat ear counting is one of the key indicators for wheat phenotyping. Convolutional neural network (CNN) algorithms for counting wheat have evolved into sophisticated tools, however because of the limitations of sensory fields, CNN is unable to simulate global context information, which has an impact on counting performance. In this study, we present a hybrid attention network (CTHNet) for wheat ear counting from RGB images that combines local features and global context information. On the one hand, to extract multi-scale local features, a convolutional neural network is built using the Cross Stage Partial framework. On the other hand, to acquire better global context information, tokenized image patches from convolutional neural network feature maps are encoded as input sequences using Pyramid Pooling Transformer. Then, the feature fusion module merges the local features with the global context information to significantly enhance the feature representation. The Global Wheat Head Detection Dataset and Wheat Ear Detection Dataset are used to assess the proposed model. There were 3.40 and 5.21 average absolute errors, respectively. The performance of the proposed model was significantly better than previous studies.

CrowdUNet: Segmentation assisted U-shaped crowd counting network

With the end of the COVID-19 pandemic, the number of pedestrians in various public places has increased dramatically. Estimating the size and density distribution of crowds accurately from images is essential for public safety. At present, there are still many factors that limit the accuracy of dense crowd counting, such as perspective distortion, background clutter and heavy occlusion. To be capable of accurately estimating the crowd size in RGB images, we propose a crowd counting network called CrowdUNet, which is assisted by a segmentation task. It applies the segmentation results to the crowd counts, making the network more focused on the prediction of foreground regions. We combine Swin Transformer Block and CNN to build a Swin Transformer Convolution(STC) module to extract deep semantic features. We analyze the characteristics of crowd images and propose a novel decoder structure called Coordinate Decoder(CD), which better aggregates low and high level features and improve the robustness of the network. In order to obtain accurate regression results, we also propose a regression head with multi-scale receptive fields, which is called Spatial Pyramid Convolution (SPC). Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech A, ShanghaiTech B, UCF p=CC 50, and UCF-QNRF have validated the proposed method.

Age estimation in tortoises: an evaluation of methods for the spur-thighed tortoise (Testudo graeca)

Abstract Age and growth-related parameters provide a better understanding of life-history adaptations and trade-offs and can be studied in the wild through capture-mark-recapture (CMR) or sclerochronology (i.e., skeletochronology and counting growth marks on hard structures). In chelonians, skeletochronology is limited to carcasses, CMR is limited by time and funding constraints, but counting shell growth annuli (SGA count) is a cheaper and faster alternative. A long-term CMR study in Testudo graeca from south-eastern Romania allowed us to compare the age estimated by the two sclerochronology methods using carcasses, and calibrate the results with data obtained from recaptured tortoises. We found that the accuracy of SGA counts significantly decreased with time (i.e., years between captures and older individuals). Skeletochronology showed the best results with long bones (i.e., ulna, radius, femur), but overall underestimated the age when compared to SGA counts; we obtained comparable results from these two methods in tortoises up to 17 years old. The oldest age estimated by skeletochronology alone was 28 years, 30 years by SGA counts, and 40 years by SGA counts when calibrated by CMR. The growth models using age estimations from both skeletochronology and SGA counts were similar, highlighting their usefulness in monitoring growth trajectories. Our study showed that while age estimation in tortoises is challenging, a combination of methods can improve its accuracy.

Optimizing Crowd Counting in Dense Environments Through Curriculum Learning Training Strategy

Counting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets; the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively

Large-scale performance assessment of the BG-Counter 2 used with two different mosquito traps

BackgroundMosquitoes are important vectors of pathogens. They are usually collected with CO2-baited traps and subsequently identified by morphology. This procedure is very time-consuming. Automatic counting traps could facilitate timely evaluation of the local risk for mosquito-borne pathogen transmission or decision-making on vector control measures, but the counting accuracy of such devices has rarely been validated in the field.MethodsThe Biogents (BG)-Counter 2 automatically counts mosquitoes by discriminating the size of captured objects directly in the field and transmits the data to a cloud server. To assess the accuracy of this counting device, 27 traps were placed at 19 sampling sites across Germany and used in daily, weekly or bimonthly intervals from April until October 2021. The BG-Counter 2 was attached to a CO2-trap (BG-Pro trap = CO2-Pro) and the same trap was converted to also attract gravid mosquitoes (upside-down BG-Pro trap with a water container beneath = CO2-Pro-gravid). All captured mosquitoes were identified by morphology. The number of females (unfed and gravid), mosquito diversity and the number of identified specimens in relation to the counting data of the BG-Counter were compared between the two trapping devices to evaluate sampling success and counting accuracy.ResultsIn total 26,714 mosquitoes were collected during 854 trap days. The CO2-Pro-gravid trap captured significantly more mosquitoes per trap day for all specimens, gravid females and non-gravid females, while there was no difference in the mosquito diversity. The linear model with the captured mosquitoes as a response and the counted specimens as a predictor explained only a small degree of the variation within the data (R2 = 0.16), but per individual trap the value could reach up to 0.62 (mean R2 = 0.23). The counting accuracy for the daily samples had a significant positive correlation with sample size, resulting in higher accuracy for the CO2-Pro-gravid trap and higher accuracy for sites and sampling months with high mosquito abundance.ConclusionsWhile the accuracy of the BG-Counter 2 is quite low, the device is able to depict mosquito phenology and provide information about local population dynamics.Graphical

CrowdAlign: Shared-weight dual-level alignment fusion for RGB-T crowd counting

The combination of visible and thermal images has been proven to be effective in improving accuracy for crowd counting in illumination-unconstrained scenes. However, the challenging problem of misalignment in RGB-T image pairs has not been extensively explored in this context. This study aims to address the issue of misalignment between RGB and thermal image pairs to enhance the counting accuracy of cross-modal models. Specifically, we propose CrowdAlign, a cross-modal feature alignment fusion network that utilizes a shared-weight strategy for efficient feature extraction. Additionally, CrowdAlign addresses alignment adjustments through two stages: pre-fusion and post-fusion alignment. For pre-fusion feature extraction, we design a dual-level spatial-semantic parallel alignment module, while for post-fusion feature extraction, a low-frequency feature attention filtering module is developed. This two-stage alignment approach enables cross-modal feature alignment without requiring additional supervision. Experiments on the public benchmarks demonstrate that our method is effective under RGB-T misalignment or dark conditions. We hope CrowdAlign will inspire researchers to focus on and explore the issue of misalignment between RGB image and thermal image for cross-modal crowd counting.

Intelligently Counting Agricultural Pests by Integrating SAM with FamNet

The utilization of the large pretrained model (LPM) based on Transformer has emerged as a prominent research area in various fields, owing to its robust computational capabilities. However, there remains a need to explore how LPM can be effectively employed in the agricultural domain. This research aims to enhance agricultural pest detection with limited samples by leveraging the strong generalization performance of the LPM. Through extensive research, this study has revealed that in tasks involving the counting of a small number of samples, complex agricultural scenes with varying lighting and environmental conditions can significantly impede the accuracy of pest counting. Consequently, accurately counting pests in diverse lighting and environmental conditions with limited samples remains a challenging task. To address this issue, the present research suggests a unique approach that integrates the outstanding performance of the segment anything model in class-agnostic segmentation with the counting network. Moreover, by intelligently utilizing a straightforward TopK matching algorithm to propagate accurate labels, and drawing inspiration from the GPT model while incorporating the forgetting mechanism, a more robust model can be achieved. This approach transforms the problem of matching instances in different scenarios into a problem of matching similar instances within a single image. Experimental results demonstrate that our method enhances the accuracy of the FamNet baseline model by 69.17% on this dataset. Exploring the synergy between large models and agricultural scenes warrants further discussion and consideration.

Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference

3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a 4-cm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4~\ ext {-cm}^3$$\\end{document} portion of the Broca’s area. We aim at helping users in selecting appropriate techniques depending on their research objectives. To this end, we compare methods along various dimensions of analysis, including correctness of the predicted density and localization, computational efficiency, and human annotation effort. Our results suggest that deep learning approaches are very effective, have a high throughput providing each cell 3D location, and obtain results comparable to the estimates of the adopted stereological design.

Multi-species identification and number counting of fish passing through fishway at hydropower stations with LigTraNet

Fishway monitoring can verify the effectiveness of the fishway, optimise the operation mode, and achieve scientific management of fishway operations. Traditional fishway monitoring approaches, hindered by their inefficiency and substantial disruption of fish, are ill-suited for long-term surveillance; thus, employing video monitoring coupled with object detection technology presents an alternative or complementary solution. However, challenges such as the constrained computational capacity of onsite equipment in fishways, complexities involved in model deployment, and sluggish pace of detection are significant hurdles. In this study, by utilising the YOLOv8n model as a benchmark, we engineered a cross-stage partial module with a single convolution (C1) module to replace the existing C2f module with the aim of enhancing performance. We replaced the conventional 2D convolutions in the bottleneck configuration with depthwise separable convolutions and integrated the SimAM module to extract the detailed characteristics of the fish species. By amalgamating LigObNet detection with the DeepSORT algorithm, we established LigTraNet, which is designed to enable precise tracking, identification, and counting of individual fish. The results showed that LigObNet exhibited the lowest complexity and fastest detection speed for underwater fish among similar object recognition and detection models. Compared with the benchmark YOLOv8n model, there were reductions of 8.9% in the network layers, 40.5% in the parameter count, 39.3% in the memory footprint, and 35.8% in the giga floating-point operations and a 38.1% improvement in the inference speed. LigTraNet achieved a total count accuracy rate of 91.8%, demonstrating superior species quantification capabilities over other models with minimal resource usage and rapid inference capabilities, thus offering enhanced practicality for deployment on devices in real-world engineering contexts. This represents a departure from traditional manual monitoring methods for assessing fishway effectiveness, revolutionising aquatic ecological monitoring tools and methodologies and fostering the collaborative advancement of water resource project operations and ecological conservation.

Extremely high spatiotemporal resolution microscopy for live cell imaging by single photon counting, noise elimination, and a novel restoration algorithm based on probability calculation.

Optical microscopy is essential for direct observation of dynamic phenomena in living cells. According to the classic optical theories, the images obtained through light microscopes are blurred for about half the wavelength of light, and therefore small structures below this "diffraction limit" were thought unresolvable by conventional optical microscopy. In reality, accurately obtained optical images contain complete information about the observed objects. Temporal resolution is also important for the observation of dynamic phenomena. A challenge exists here to overcome the trade-off between the time required for measurement and the accuracy of the measurement. The present paper describes a concrete methodology for reconstructing the structure of an observed object, based on the information contained in the image obtained by optical microscopy. It is realized by accurate single photon counting, complete noise elimination, and a novel restoration algorithm based on probability calculation. This method has been implemented in the Super-resolution Confocal Live Imaging Microscopy (SCLIM) we developed. The new system named SCLIM2M achieves unprecedented high spatiotemporal resolution. We have succeeded in capturing sub-diffraction-limit structures with millisecond-level dynamics of organelles and vesicles in living cells, which were never observed by conventional optical microscopy. Actual examples of the high-speed and high-resolution 4D observation of living cells are presented.

SLCOBNet: Shrimp larvae counting network with overlapping splitting and Bayesian-DM-count loss

Estimating the number of shrimp larvae plays a critical role for achieving reasonable feeding in aquaculture. However, previous shrimp larvae counting models failed to accurately distinguish between shrimp larvae and other objects due to the loss of shrimp larvae continuity information. Also, shrimp larvae counting has the challenges of multi-scale changes, transparent body overlap, and background noise. To solve the above problems, a novel shrimp larvae counting network called SLCOBNet is proposed. First, overlapping splitting image is devised, with each patch sharing half of its area, ensuring the continuity of information regarding shrimp larvae between patches. Then, shrimp larvae feature of different scales and multi-scale density maps are obtained through the feature pyramid aggregation and the regression head with multi-scale receptive fields, respectively. Moreover, a novel loss function called Bayesian-DM-Count loss, composed of DM-Count loss and Bayesian loss, was designed to address the existing transparent body overlap and background noise problems. Experiments were performed by collecting shrimp larvae data from the breeding trays of a real aquaculture farm. The extensive experimental results on three shrimp larvae datasets have shown that SLCOBNet achieves 3.27, 3.61 and 1.28 in Mean Absolute Error. Hence, the proposed method exhibits a better counting accuracy compared to other counting methods. Furthermore, the predicted results were consistent with the true values.

Mapping and quantifying unique branching structures in lentil (Lens culinaris Medik.)

BackgroundLentil (Lens culinaris Medik.) is a globally-significant agricultural crop used to feed millions of people. Lentils have been cultivated in the Australian states of Victoria and South Australia for several decades, but efforts are now being made to expand their cultivation into Western Australia and New South Wales. Plant architecture plays a pivotal role in adaptation, leading to improved and stable yields especially in new expansion regions. Image-based high-throughput phenomics technologies provide opportunities for an improved understanding of plant development, architecture, and trait genetics. This paper describes a novel method for mapping and quantifying individual branch structures on immature glasshouse-grown lentil plants grown using a LemnaTec Scanalyser 3D high-throughput phenomics platform, which collected side-view RGB images at regular intervals under controlled photographic conditions throughout the experiment. A queue and distance-based algorithm that analysed morphological skeletons generated from images of lentil plants was developed in Python. This code was incorporated into an image analysis pipeline using open-source software (PlantCV) to measure the number, angle, and length of individual branches on lentil plants.ResultsBranching structures could be accurately identified and quantified in immature plants, which is sufficient for calculating early vigour traits, however the accuracy declined as the plants matured. Absolute accuracy for branch counts was 77.9% for plants at 22 days after sowing (DAS), 57.9% at 29 DAS and 51.9% at 36 DAS. Allowing for an error of ± 1 branch, the associated accuracies for the same time periods were 97.6%, 90.8% and 79.2% respectively. Occlusion in more mature plants made the mapping of branches less accurate, but the information collected could still be useful for trait estimation. For branch length calculations, the amount of variance explained by linear mixed-effects models was 82% for geodesic length and 87% for Euclidean branch lengths. Within these models, both the mean geodesic and Euclidean distance measurements of branches were found to be significantly affected by genotype, DAS and their interaction. Two informative metrices were derived from the calculations of branch angle; ‘splay’ is a measure of how far a branch angle deviates from being fully upright whilst ‘angle-difference’ is the difference between the smallest and largest recorded branch angle on each plant. The amount of variance explained by linear mixed-effects models was 38% for splay and 50% for angle difference. These lower R2 values are likely due to the inherent difficulties in measuring these parameters, nevertheless both splay and angle difference were found to be significantly affected by cultivar, DAS and their interaction. When 276 diverse lentil genotypes with varying degrees of salt tolerance were grown in a glasshouse-based experiment where a portion were subjected to a salt treatment, the branching algorithm was able to distinguish between salt-treated and untreated lentil lines based on differences in branch counts. Likewise, the mean geodesic and Euclidean distance measurements of branches were both found to be significantly affected by cultivar, DAS and salt treatment. The amount of variance explained by the linear mixed-effects models was 57.8% for geodesic branch length and 46.5% for Euclidean branch length.ConclusionThe methodology enabled the accurate quantification of the number, angle, and length of individual branches on glasshouse-grown lentil plants. This methodology could be applied to other dicotyledonous species.

Multi-branch progressive embedding network for crowd counting

Crowd counting is essential for video surveillance and public safety. The performance of counting models has been greatly improved with the rapid development of Convolution neural networks (CNN), while it still suffers interference from complex background and large-scale variation. To relieve the above challenges, this paper proposes a novel Multi-branch Progressive Embedding Network (MPENet) for crowd counting. Specifically, the proposed network mainly consists of two modules named Background Area Filter (BAF) and Sequential Multi-scale Modules (SMM), which are embedded with each other to generate higher-quality density maps. Firstly, the BAF model base on attention mechanism is proposed to distinguish crowd from background, which effectively avoids the model outputting positive predictions in the background region. Meanwhile, a multi-level supervision mechanism is proposed to generate more accurate attention maps. Besides, the SMM module is designed to be progressively embedded with scale context information so that the scale feature will be smooth and continuous. Finally, a novel multi-scale consistency structural loss is proposed to avoid pixel-level isolation due to Euclidean loss. The proposed method significantly improves counting accuracy, achieving Mean Absolute Error (MAE) of 57.6 and 6.9 on ShanghaiTechA and ShanghaiTechB respectively.

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2=0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Unobserved mortality occurs early in larval walleye (Sander vitreus) aquaculture

One of the most persistent challenges in walleye larviculture is the high rate of unobserved mortality occurring within the first 30 days post-hatch. Walleye larviculture is characterized by high mortality rates, but oftentimes, only a fraction of the total mortality in walleye larviculture is observed. Unobserved mortality is frequently attributed to a high rate of cannibalism, but decomposition of larvae and other factors may also be important contributors. Unobserved mortality can make walleye larviculture especially challenging as accurately estimating daily feed rations and forecasting larval production rely on accurate counts of surviving fish. Despite the importance of unobserved mortality, there is little empirical evidence available that assesses the temporal occurrence and causes of unobserved mortality in walleye larviculture. This study assessed when unobserved mortality occurs during walleye larviculture by examining survival at three timepoints (8, 15, and 30 days post-stocking). Tanks were cleaned daily and dead fry were enumerated to account for observed mortality. At the end of each treatment period, the tanks were drained, all remaining fry were enumerated, and the unobserved mortality rate was calculated. While observed mortality trended higher as trial duration increased, unobserved mortality did not increase significantly from the 8-day treatment suggesting that the bulk of unobserved mortality occurs during the first 8 days of walleye larviculture.

Plastic takeaway food containers may cause human intestinal damage in routine life usage: Microplastics formation and cytotoxic effect

The microplastics and organic additives formed in routine use of plastic takeaway food containers may pose significant health risks. Thus, we collected plastic containers made of polystyrene, polypropylene, polyethylene terephthalate, polylactic acid and simulated two thermal usages, including hot water (I) and microwave treatments (M). Nile Red fluorescence staining was developed to improve accurate counting of microplastics with the aid of TEM and DLS analysis. The quantity of MPs released from thermal treatments was determined ranging from 285.7 thousand items/cm2 to 681.5 thousand items/cm2 in containers loaded with hot water with the following order: IPS>IPP>IPET>IPLA, while microwave treatment showed lower values ranging from 171.9 thousand items/cm2 to 301.6 thousand items/cm2. In vitro toxicity test using human intestinal epithelial Caco-2 cells indicated decrease of cell viability in raw leachate, resuspended MPs and supernatants, which might further lead to cell membrane rupture, ROS production, and decreased mitochondrial membrane potential. Moreover, the leachate inhibited the expression of key genes in the electron transport chain (ETC) process, disrupted energy metabolism. For the first time, we isolate the actually released microplastics and organic substances for in vitro toxicity testing, and demonstrate their potential impacts to human intestine. SynopsisPlastic take-out containers may release microplastics and organic substances during daily usage, both of which can cause individual and combined cytotoxic effects on human colon adenocarcinoma cells Caco-2.

Application of machine learning in a rodent malaria model for rapid, accurate, and consistent parasite counts.

Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can be inconsistent between individuals and labs. We have developed an easy-to-use machine learning (ML)-based software, Malaria Screener R, to expedite and standardize such studies by automating the counting of Plasmodium iRBCs in rodents. This software can process Giemsa-stained blood smear images captured by any camera-equipped microscope. It features an intuitive graphical user interface that facilitates image processing and visualization of the results. The software has been developed as a desktop application that processes images on standard Windows and Mac OS computers. A previous ML model created by the authors designed to count P. falciparum -infected human RBCs did not perform well counting Plasmodium -infected mouse RBCs. We leveraged that model by loading the pre-trained weights and training the algorithm with newly collected data to target P. yoelii and P. berghei mouse iRBCs. This new model reliably measured both P. yoelii and P. berghei parasitemia (R 2 = 0.9916). Additional rounds of training data to incorporate variances due to length of Giemsa staining, microscopes etc, have produced a generalizable model, meeting WHO Competency Level 1 for the sub-category of parasite counting using independent microscopes. Reliable, automated analyses of blood-stage parasitemia will facilitate rapid and consistent evaluation of novel vaccines and antimalarials across labs in an easily accessible in vivo malaria model.