Accurate Counting Research Articles

Automatic Tube Counter Monitoring System using Infrared Sensor based on NODEMCU ESP8266

Technology is needed in every industry because it can simplify the production process, improve production quality, and enhance the company's reputation in the sight of consumers. The cylinder counting activity at PT Batuah Energi Indonesia is still done manually, involving time and standardized estimation of LPG cylinder loads, which faces inaccuracy issues. In fact, PT Batuah Energi Indonesia has facilities that handle many LPG cylinders from various users and providers of LPG cylinders. While accurate cylinder counts are beneficial to the industry, companies need technology that can automatically calculate the number of filled LPG cylinders. Therefore, this study was carried out to demonstrate to students how to develop automatic tube counters using an infrared E18-D80NK as a tube detector, NodeMCU microcontrollers ESP8266 as controllers, Arduino IDE for developing programs, and IoT for remote monitoring. The developed device approach, specifically using the E18-D80NK infrared proximity sensor based on the NodeMCU ESP8266, can be coded using the Arduino IDE compiler. For the detection of the number of tubes, the E18-EN80K infrared sensor is used and data transmission wirelessly utilizes the Blynk application. The results of the automatic tube counter monitoring tool were successfully tested with a 100% accuracy rate, utilizing the E18-D80NK infrared sensor and NodeMCU microcontroller ESP8266, and can be monitored remotely using Blynk.

Tomography of a Number-Resolving Detector by Reconstruction of an Atomic Many-Body Quantum State.

The high-fidelity analysis of many-body quantum states of indistinguishable atoms requires the accurate counting of atoms. Here we report the tomographic reconstruction of an atom-number-resolving detector. The tomography is performed with an ultracold rubidium ensemble that is prepared in a coherent spin state by driving a Rabi coupling between the two hyperfine clock levels. The coupling is followed by counting the occupation number in one level. We characterize the fidelity of our detector and show that a negative-valued Wigner function is associated with it. Our results offer an exciting perspective for the high-fidelity reconstruction of entangled states and can be applied for a future demonstration of Heisenberg-limited atom interferometry.

YOLO_MRC: A fast and lightweight model for real-time detection and individual counting of Tephritidae pests

Tephritidae pests severely affect the quality and safety of various melons, fruits and vegetable crops. However, many agricultural managers lack an adequate understanding of the level of pest occurrence, resulting in the misuse of pesticides, which ultimately leads to environmental pollution and economic loss. Therefore, real-time detection and counting of Tephritidae pests are important for timely pest spotting and control. This work helps quickly determine the distribution and abundance of pests in the current environment, thus providing data on pest conditions for agricultural management to optimize pesticide use. Nevertheless, the fast speed, high accuracy, and lightweight performance of real-time detection and counting are difficult to balance. To address this problem, based on the YOLOv8n model, this paper takes Bactrocera cucurbitae pests as the detection target and proposes a fast and lightweight real-time detection and individual counting model for Tephritidae pests, named YOLO_MRC. This paper introduces three key improvements: (1) Constructing a new module called Multicat into the neck network increases the focus on the detection target by incorporating an attention mechanism; (2) Reducing the original three detection heads to two and then adjusting their sizes to decrease the number of parameters in the network model; (3) Devising a novel module, C2flite, to enhance the deep feature extraction capability of the backbone network. According to the above points, this paper conducts ablation experiments to compare the performances of different models. Experiments showed that the Multicat module significantly offsets the large increase in GFLOPs and processing time caused by reducing the detection head and can further reduce the number of parameters and improve the accuracy when combined with the C2flite module. On our Bactrocera cucurbitae pest dataset, the mAP@0.5 of the YOLO_MRC model reached 99.3%. Simultaneously, as the number of parameters decreases by 63.68%, GFLOPs is reduced by 19.75%, and the processing time is shortened by 5%. To ensure the validity of the model, YOLO_MRC is compared with four excellent detection models by using manual counting results as the benchmark. YOLO_MRC achieves an average pest counting accuracy of 94%, demonstrating superior performance in terms of model size and processing time. To further explore the performance of YOLO_MRC in multiclass insect detection tasks, we choose the public dataset Pest_24_640 for comparison with four state-of-the-art models. YOLO_MRC achieves a 3.6 ms processing time and 70.4% accuracy with only a 2.4 MB model size, which demonstrates the potential of YOLO_MRC in multiclass pest detection.

Improved Feature Fusion in YOLOv5 for Accurate Detection and Counting of Chinese Flowering Cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) Buds

Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important leaf vegetable originating from southern China. Its planting area is expanding year by year. Accurately judging its maturity and determining the appropriate harvest time are crucial for production. The open state of Chinese flowering cabbage buds serves as a crucial maturity indicator. To address the challenge of accurately identifying Chinese flowering cabbage buds, we introduced improvements to the feature fusion approach of the YOLOv5 (You Only Look Once version 5) algorithm, resulting in an innovative algorithm with a dynamically adjustable detection head, named FPNDyH-YOLOv5 (Feature Pyramid Network with Dynamic Head-You Only Look Once version 5). Firstly, a P2 detection layer was added to enhance the model’s detection ability of small objects. Secondly, the spatial-aware attention mechanism from DyHead (Dynamic Head) for feature fusion was added, enabling the adaptive fusion of semantic information across different scales. Furthermore, a center-region counting method based on the Bytetrack object tracking algorithm was devised for real-time quantification of various categories. The experimental results demonstrate that the improved model achieved a mean average precision (mAP@0.5) of 93.9%, representing a 2.5% improvement compared to the baseline model. The average precision (AP) for buds at different maturity levels was 96.1%, 86.9%, and 98.7%, respectively. When applying the trained model in conjunction with Bytetrack for video detection, the average counting accuracy, relative to manual counting, was 88.5%, with class-specific accuracies of 90.4%, 80.0%, and 95.1%. In conclusion, this method facilitates relatively accurate classification and counting of Chinese flowering cabbage buds in natural environments.

The Effect of the Wild Boar Hunting System on Agricultural Damages: The North-East of Italy as a Case Scenario.

Hunting is a method commonly used in several European countries to reduce crop damages by wild boar Sus scrofa. However, results are still controversial and poorly treated. Using data on official claims (i.e., damages to crops) and wild boar local counts and hunting bags collected from 2019 to 2022, the purpose of this work was to evaluate the effect of the hunting system (divided into eradication and non-eradication areas) carried out in the north-east of Italy on the number of agricultural damages. The spatio-temporal distribution of wild boar hunting, density and damages as well as the effect of hunting, were evaluated through the hotspot analysis and the zero-inflated models, respectively. The results obtained revealed no clear spatial overlap among wild boar hunting, wild boar density and damages to agriculture in both the eradication and non-eradication areas. Moreover, the current level of harvesting did not significantly affect the number of agricultural damages. A multifaceted approach focused on the numeric control of the species based on accurate local counts and oriented towards more age classes in a similar percentage should be preferred to mitigate damages to cultivars.

Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis

Yield forecasting depends on accurate tree fruit counts and mean size estimation. This information is generally obtained manually, requiring many hours of work. Artificial vision emerges as an interesting alternative to obtaining more information in less time. This study aimed to test and train YOLO pre-trained models based on neural networks for the detection and count of pears and apples on trees after image analysis; while also estimating fruit size. Images of trees were taken during the day and at night in apple and pear trees while fruits were manually counted. Trained models were evaluated according to recall, precision and F1score. The correlation between detected and counted fruits was calculated while fruit size estimation was made after drawing straight lines on each fruit and using reference elements. The precision, recall and F1score achieved by the models were up to 0.86, 0.83 and 0.84, respectively. Correlation coefficients between fruit sizes measured manually and by images were 0.73 for apples and 0.80 for pears. The proposed methodologies showed promising results, allowing forecasters to make less time consuming and accurate estimates compared to manual measurements. Highlights: The number of fruits in apple and pear trees, could be estimated from images with promising results. The possibility of estimating the fruit numbers from images could reduce the time spent on this task, and above all, the costs. This allow growers to increase the number of trees sampled to make yield forecasts.

Monitoring wader breeding productivity

A robust understanding of the mechanisms driving demographic change in wild animal populations is fundamental to the delivery of effective conservation interventions. Demographic change can be driven by variation in adult survival, recruitment of juveniles into the breeding population or breeding productivity – the number of fledglings produced per breeding pair. Across Europe, low breeding productivity in wader populations has been a significant driver of population decline, increasing the importance of gathering accurate data on breeding productivity. Monitoring wader breeding productivity is challenging because finding nests can be time‐consuming and requires experienced fieldworkers; wader chicks are nidifugous and difficult to count due to their cryptic behaviour; and waders often have high re‐laying rates following nest failure, meaning that hatching or fledging can be highly asynchronous. This paper reviews approaches to estimating breeding productivity where fieldworkers either record the agitation or alarm‐calling behaviour of adults with dependent young, make direct observations of broods on survey visits, or both. Using a systematic literature search (restricted to Europe where most of these studies have taken place) we identified 38 peer‐reviewed papers which used this approach. The productivity metrics produced can be divided into the following categories: (i) ‘Hatching Success’ (HS), (ii) ‘Fledging Success’ (FS) and (iii) ‘Young Fledged Per Pair’ (YFP), from the coarsest to the most precise. The first two metrics are most often used when direct observations of broods are not possible due to the behaviour of broods or vegetation structure; YFP is preferred if brood counts are possible. Design of an appropriate metric depends on (i) whether accurate brood counts are possible; (ii) whether adults exhibit diagnostic agitation behaviour when young are present; (iii) whether individual breeding territories are separable; (iv) whether re‐nesting rates are assumed to be high; and (v) the availability of experienced surveyors (particularly where behavioural observations are required). Globally there are many wader species for which the methods described here could provide valuable information and we hope this review encourages further development or adoption of these methods.

Accuracy of Umbilical Cord Complete Blood Count in Detecting Early-Onset Neonatal Thrombocytopenia.

Neonatal thrombocytopenia (NTCP) is a common hematological disorder whose platelet count falls below the normal limitof 150 x 109/L. NTCP can causelate complications if left untreated. The current study aimed to evaluate the accuracy of the umbilical cord complete blood count (UC CBC) in detecting early-onsetneonatal thrombocytopenia(EO-NTCP). Further, the prevalence of NTCP was also investigated. Methods: A cross-sectional study with a matched control was conducted on all newborns delivered at a tertiary care center in Jeddah, Saudi Arabia, between May 2016 and 2019. After exclusions, 40 neonateswith EO-NTCP (cases) and 80 without EO-NTCP (controls) were included. The case-to-control ratio was 1:2. The results of UC CBC were compared with those of follow-up CBC, performed within 72 hours.A p-value of <0.05 was considered statistically significant. All data were analyzed using IBM SPSS version 28 for Windows (IBM Corp., Armonk, NY). The prevalence of NTCP was approximately 1.02% (111/10,936). Lack of antenatal care was found in12 (30%) neonates with EO-NTCP vs. 10 (12.5%)neonates without EO-NTCP (p = 0.02). Neonates with EO-NTCP were more likely to have experiencedintrauterine growth restriction (5 (37.5%) vs. 5 (6.3%), p < 0.001)and oligohydramnios (5 (12.5%) vs. 0 (0%), p = 0.003). Neonates who developed EO-NTCP were more likely to be admitted to the NICU (34 (85%) vs. 35 (43.8%), p < 0.001) and receiveantibiotics (22 (55%) vs. 25 (31.3%), p = 0.012). Also, neonates with EO-NTCP were more frequently diagnosed with neonatal sepsis (7 (17.5%) vs. 3 (3.8%), p = 0.015)and more likely to receiveplatelet transfusions (15 (37.5% vs. 1 (1.3%), p < 0.001). They also had a higher median length of hospital stay(13 (interquartile range (IQR) 3-28) vs. 4(IQR 2-9) days, p = 0.006). The mortality rates of neonates with EO-NTCP and those without were 6 (15%) vs. 2 (2.5%) neonates (p = 0.016). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of UC CBC were 62.50%, 97.50%, 20.40%, and 99.61%, respectively. The prevalence of EO-NTCP in King Abdulaziz Medical City is comparable to international and national figures, and itis associated with preceding maternal comorbidities, serious neonatal morbidity, and even mortality. Therefore, proper antenatal care is vital in preventing maternal and neonatal morbidities, including the risks of NTCP and itsrelatedcomplications. With high NPV, using UC CBC as a universal screening method could assist in safely discharging newborns. However, because of its low sensitivity, a comprehensive clinical examination with confirmatory laboratory tests are stillthe cornerstone in diagnosing EO-NTCP. Future trials should aim to study the cost-effectiveness of universal UC CBC and the long-term outcomes of infants diagnosed with EO-NTCP.

Improving Turn Movement Count Using Cooperative Feedback.

In this paper, we propose a new cooperative method that improves the accuracy of Turn Movement Count (TMC) under challenging conditions by introducing contextual observations from the surrounding areas. The proposed method focuses on the correct identification of the movements in conditions where current methods have difficulties. Existing vision-based TMC systems are limited under heavy traffic conditions. The main problems for most existing methods are occlusions between vehicles that prevent the correct detection and tracking of the vehicles through the entire intersection and the assessment of the vehicle's entry and exit points, incorrectly assigning the movement. The proposed method intends to overcome this incapability by sharing information with other observation systems located at neighboring intersections. Shared information is used in a cooperative scheme to infer the missing data, thereby improving the assessment that would otherwise not be counted or miscounted. Experimental evaluation of the system shows a clear improvement over related reference methods.

A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0260 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0260 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model

The objective of this project was to automate the detection and counting process of stacked eucalypt (hybrid Eucalyptus urophylla x Eucalyptus grandis) timber in the forestry industry using the YOLOv8 model. The dataset consists of 230 diverse images of eucalypt roundwood, including images of roundwood separated on a rail and stacked timber. The annotations were made using LabelImg, ensuring accurate delineation of target objects on the log surfaces. The YOLOv8 model is customized with a CSPDarknet53 backbone, C2f module, and SPPF layer for efficient computation. The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. The best results were achieved at epoch 261, with a precision of 0.814, recall of 0.812, and mAP50 of 0.844 on the training set and a precision of 0.778, recall of 0.798, and mAP50 of 0.839 on the validation set. The model’s generalization was tested on separate images, demonstrating robust detection and accurate counting. The model effectively identified roundwood that was widely spaced, scattered, and overlapping. However, when applied to stacked timber, the automatic counting was not very accurate, especially when using images. In contrast, when using video, the relative percentage error for automatic counting significantly decreased to −12.442%. In conclusion, video proved to be more effective than images for counting stacked timber, while photographs should be reserved for the counting of individual roundwood pieces.

Automated Object Counting System in Digital Images

In inventories, the context of object counting and speed of response becomes essential. The process is more straightforward when there are only a few items, but its complexity lies when there are many close or overlapping items. In addition, the pressure to meet tight inventory deadlines adds another challenge. This work aimed to develop a program that automates the accurate counting of similar objects in digital images, regardless of their quantity or arrangement. For this purpose, an algorithm has been implemented in Python. The main results show efficiency in analyzing images with similar objects, a significant step towards separating and accurately counting adjacent objects in various scientific fields. This solution promises to simplify and speed up the counting process in digital images, with potential beneficial applications in multiple scientific disciplines.

DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression

With the development of artificial intelligence, automatically and accurately counting chickens has become a reality. However, insufficient lighting, irregular sizes, and dense flocks make this a challenging task. The existing methods cannot perform accurate and stable counting. In this article, a dense flock of chickens counting network (DFCCNet) is proposed based on density map regression, where features from different levels are merged using feature fusion to obtain more information for distinguishing chickens from the background, resulting in more stable counting results. Multi-scaling is used to detect and count chickens at various scales, which can improve the counting accuracy and ensure stable performance for chickens of different sizes. Feature convolution kernels are adopted to convolve feature maps, which can extract more accurate target information, reduce the impact of occlusion, and achieve more reliable and precise results. A dataset of dense flocks of chickens (namely Dense-Chicken) has been collected and constructed, which contains 600 images of 99,916 chickens, with labeled points and boxes. It can be accessed by researchers as benchmark data. The proposed method was compared with some state-of-the-art algorithms, to validate its effectiveness. With its robustness being verified by counting in three kinds of density situations, with the mean absolute error being 4.26, 9.85, and 19.17, respectively, and a speed of 16.15 FPS. DFCCNet provides an automatic and fast approach to counting chickens in a dense farming environment. It can be easily embedded into handheld devices for application in agricultural engineering.

DFENet: Double Feature Enhanced Class Agnostic Counting Methods

Object counting is a basic computer vision task, which can estimate the number of each object in an image, thus providing valuable information. In dense scenes, there are huge differences in target individual scale, and the different target individual scale leads to low accuracy of target count. In addition, most of the existing target count datasets in the field require a lot of manual creation and annotation, which increases the cost and difficulty of the dataset, lack of ease of use and portability. To solve these problems, this paper proposes a class agnostic counting method Double Feature Enhancement Net based on improved Bilinear Matching Network+ (BMNet+). By introducing the feature enhancement module based on the principle of conditional random field and the adaptively spatial feature fusion module, combined with the feature similarity measurement strategy of bilinear matching network, the method can effectively extract the target features of different scales, enhance the adaptability to the targets with large scale changes, and improve the counting performance of the network. Experiments were carried out on FSC-147 data set, and the experimental results show that the proposed model has been further improved in counting accuracy. The MAE and MSE of the verification set are 15.03 and 54.53 respectively. In the test set, MAE reaches 13.65, MSE reaches 89.54, and the counting performance is at the advanced level in the field.

Few-shot sheep counting based on density map regression

With the continuous expansion of breeding scale, the automatic counting of sheep has attracted a lot of research attention in recent years, which aims to count the number of sheep in real time to rationally use grassland resources and design appropriate agricultural production policies. The traditional method of counting sheep mainly relies on manual work, which is time-consuming and labor-intensive. Thanks to the rapid development of image acquisition equipment and pattern recognition technology, the recognition and counting of sheep based on deep learning has become the current mainstream counting framework. However, for dense flocks of sheep, such as occlusions between sheep, the accuracy of existing methods cannot meet the needs of practical applications. In this paper, we suggest FamNet, a few-shot sheep counting method based on UAV images and density map regression. Specifically, we first collected 190 sheep images with different resolutions, aspect ratios, backgrounds, and sheep numbers; secondly, by selecting count samples to extract objects in the images, and using density map regression to predict the number of sheep, this enables sheep counting in small numbers of samples. A large number of experimental results have verified the effectiveness of the method in this paper, which can achieve an average counting accuracy of 91.02%. In addition, we discuss the impact of sample selection, backbone selection, and feature scaling on model accuracy. All the results show that the FamNet flock counting method proposed in this paper can be applied to flock counting, has good adaptability to flock counting under different background conditions and can count the number of flocks in a timely and effective manner.

A nested generalized sidelobe canceller for source counting, localization, and signal separation in reverberant fields

This paper proposes a nested generalized sidelobe canceller (NGSC) for typical array signal processing tasks, including source counting, localization, and signal separation. Multiple blocking matrices are arranged in a nested structure to successively eliminate dominant sources until the remaining signal is predominantly incoherent noise. The microphone signals are dereverberated using a multichannel weighted prediction error algorithm. In source counting, the number of sound sources is determined by tracking the average power of the blocked signal. In source localization, the direction-of-arrival (DOA) is estimated via cosine similarity in conjunction with the golden section search. In signal separation, the estimated DOA enables speech separation in a linearly constrained minimum variance beamformer with postfiltering (LCMV-PF). Monte Carlo simulations are performed to compare the proposed NGSC approach with four baselines, minimum description length, second order statistic of the eigenvalue, multistage Wiener filtering, and multiple signal classification. The results show that NGSC achieves at least 28.80% higher source counting accuracy with a 1.04° lower root mean square degree error than the baselines. The signal-to-distortion ratio achieved by LCMV-PF is 1.52 dB higher than that achieved by the linearly constrained minimum power beamformer and the multichannel Wiener filter.

Performance evaluation of PLT-H (hybrid-channel platelet) under various interferences and application studies for platelet transfusion decisions

The hybrid-channel platelet counting method (PLT-H) is a new platelet counting technique proposed by Mindray of China. In this study, we aimed to evaluate the accuracy of this technique in various situations and its reliability in platelet transfusion decision-making. A total of 378 venous blood samples were tested. Using the immunological PLT counting method recommended by the International Council for Standardization in Hematology as the reference method (PLT-IRM), Passing-Bablok regression and Bland-Altman analysis were performed on the PLT-H results. The anti-interference performance of PLT-H under different interference levels was explored using intergroup comparisons, and confusion matrices were analyzed at various transfusion cutoff values. In the absence of interference, there was a strong correlation between PLT-H and PLT-IRM (r = 0.993, 95% CI: 0.990–0.996). Under various interference conditions, the correlation between PLT-H and PLT-IRM was between 0.963 and 0.992, with an average deviation of −14.56 to −2.02. The performance of PLT-H against interference did not change significantly with increasing levels of small RBCs, large PLTs, and RBC fragments (P = .5704, 0.0832, 0.9893). In low-value samples (PLT <100 × 109/L), the coefficient of variation (CV) for PLT-H was less than 7.6%, regardless of the presence or absence of interfering substances. In addition, there was a high agreement between PLT-H and PLT-IRM (ICC = 0.972). Confusion matrice analysis at each medical decision level showed similarity to methods using the fluorescence channel (PLT-O) and superiority to the impedance channel (PLT-I). Compared with PLT-I, PLT-H has higher accuracy in PLT counting, stronger anti-interference ability, better performance in low-value samples at no extra economic cost and can be more useful for platelet transfusion decision-making. PLT-H is a novel method for platelet counting that offers higher accuracy, providing physicians with the ability to make better medical decisions, particularly in cases where values are low, or interference is present. As it does not require additional reagents, it is highly likely to replace PLT-I and become the mainstream method for platelet counting in the future.

Prioritized imputed sequence variants from multi-population GWAS improve prediction accuracy for sea lice count in Atlantic salmon (Salmo salar)

Sea lice infestation is one of the major fish health problems that occurs during the grow-out phase in Atlantic salmon (Salmo salar) aquaculture. In this study, we integrated different genomic approaches, including whole-genome sequencing (WGS), genotype imputation and meta-analysis of genome-wide association studies (GWAS), to identify single-nucleotide polymorphisms (SNPs) associated with sea lice count in Atlantic salmon. Different sets of trait-associated SNPs were prioritized and compared against randomly chosen markers, based on the accuracy of genomic predictions for the trait. Lice count phenotypes and dense genotypes of five breeding populations challenged with sea lice were used. Genotype imputation was applied to increase SNP density of challenged animals to WGS level. The summary statistics from GWAS of each population were then combined in a meta-analysis to increase the sample size and improve the statistical power of associations. Eight different genotyping scenarios were considered for genomic prediction: 70K_array: 70 K standard genotyping panel; 70K_priori: 70 K SNPs with the highest p-values identified in the meta-analysis; 30K_priori: 30 K SNPs with the highest p-values identified in the meta-analysis; WGS: SNPs imputed to whole-genome sequencing level. The remaining four scenarios were the same SNP sets with a linkage disequilibrium (LD) pruning filter: 70K_array_LD; 70K_priori_LD; 30K_priori_LD and WGS_LD. Genomic prediction accuracy was evaluated using a five-fold cross-validation scheme in two different populations which were excluded from the meta-analysis to remove possible validation-reference bias. Results showed significant genetic variation for the number of sea lice in Atlantic salmon across populations, with heritabilities ranging from 0.06 to 0.24. The meta-analysis identified several SNPs associated with sea lice resistance, mainly in Ssa03 and Ssa09 chromosomes. Genomic prediction using the GWAS-based prioritized SNPs showed higher accuracy compared to the standard SNP array in most of scenarios; achieving up to 57% increase in accuracy. Accuracy of prioritized scenarios was higher for the 70K_priori than the 30K_priori. The use of WGS data in genomic prediction presented marginal or negative accuracy gain compared to the standard SNP array. The LD-pruning filter presented no benefits, reducing accuracy in most scenarios. Overall, our study demonstrated the potential of prioritized imputed sequence variants from multi-population GWAS meta-analysis to improve prediction accuracy for sea lice counts in Atlantic salmon. The findings suggest that incorporating WGS data and prioritized SNPs from GWAS meta-analysis can accelerate the genetic progress of selection for polygenic traits in salmon aquaculture.

Monitoring Infected Reticulocytes During Murine Malaria Infection

Abstract Malaria infection is a major cause of mortality worldwide. According to the 2022 World Health Organization Malaria Report, malaria caused approximately 247 million infections and 619,000 deaths in 2021, with more than 470,000 children dying of malaria. The gold-standard for measuring parasitemia is counting infected red blood cells (RBCs) by eye using Giemsa staining whole blood smears. This method is time consuming and is subject to error by both who is counting, the number of cells counted, and which frames are selected for counting. Automating the process of measuring malaria infection by flow cytometry decreases both the time required to obtain accurate parasitemia counts and the possible error on behalf of the researcher. Hundreds of thousands of cells can be counted by flow cytometry in a shorter period of time. Animal models of malaria infection are widespread and well-characterized. The rodent malaria strain P. berghei has shown preferential infection of reticulocytes over mature RBCs. Here we outline a method of measuring P. berghei blood stage infection in reticulocytes and mature RBCs and comparing the results to Giemsa stained whole blood smears. Using commercially available P. berghei ANKA expressing green-fluorescent protein (GFP), we infected wild type mice with 1x106 parasitized RBCs by intraperitoneal injection. Whole blood samples were collected daily starting day 3 post infection through day 7 by tail vein collection. Random and representative fields of Giemsa stained peripheral smears were selected to measure the percentage of parasitemia in mature RBCs and reticulocytes for a total cell count of approximately 1000. For flow cytometry, whole blood was incubated in 500uL of 1X SYBR Green I Nucleic Acid Gel Stain in PBS at 37°C for 20 minutes in the dark, washed with PBS, and stained with anti-CD71 to label reticulocytes. During the early stages of infection, the reticulocyte percentage of infected animals is not significant from uninfected animals, approximately 1.86%. As parasitemia increases over time, although the percentage of total reticulocytes decreases, the percentage of infected reticulocytes increases. On day 3 through 7 for one experimental replicate, day 3 demonstrated 1.86% total reticulocytes, and of these 2.56% reticulocytes were infected; day 4 had 0.66% total reticulocytes, and of these 32.92% were infected; day 5 showed 0.18% reticulocytes with 71.81% infected reticulocytes; on day 6 there were 0.04% reticulocytes with 75.44% infected reticulocytes; and day 7 had 0.01% reticulocytes with 69.56% infected reticulocytes. There was a strong correlation (R2 = 0.9147) when comparing flow cytometry to measure parasitemia with counting parasitemia by peripheral smear. This flow cytometry panel is an excellent tool to assess malarial infection of reticulocytes compared to mature RBCs and can be applied to other malarial species while decreasing bias, error and time needed to conduct the experiment.

Artificial intelligent identification of apatite fission tracks based on machine learning

Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth’s uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the TensorFlow Object Detection API, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research.