Interaction of acetamiprid, Varroa destructor, and Nosema ceranae in honey bees.

Health of honey bees is threatened by a variety of stressors, including pesticides and parasites. Here, we investigated effects of acetamiprid, Varroa destructor, and Nosema ceranae, which act either alone or in combination. Our results suggested that interaction between the three factors was additive, with survival risk increasing as the number of stressors increased. Although exposure to 150 µg/L acetamiprid alone did not negatively impact honey bee survival, it caused severe damage to midgut tissue. Among the three stressors, V. destructor posed the greatest threat to honey bee survival, and N. ceranae exacerbated intestinal damage and increased thickness of the midgut wall. Transcriptomic analysis indicated that different combinations of stressors elicited specific gene expression responses in honey bees, and genes involved in energy metabolism, immunity, and detoxification were altered in response to multiple stressor combinations. Additionally, genes associated with Toll and Imd signalling, tyrosine metabolism, and phototransduction pathway were significantly suppressed in response to different combinations of multiple stressors. This study enhances our understanding of the adaptation mechanisms to multiple stressors and aids in development of suitable protective measures for honey bees. ENVIRONMENTAL IMPLICATION: We believe our study is environmentally relevant for the following reasons: This study investigates combined effects of pesticide, Varroa destructor, and Nosema ceranae. These stressors are known to pose a threat to long-term survival of honey bees (Apis mellifera) and stability of the ecosystems. The research provides valuable insights into the adaptive mechanisms of honey bees in response to multiple stressors and developing effective conservation strategies. Further research can identify traits that promote honey bee survival in the face of future challenges from multiple stressors to maintain the overall stability of environment.

[Effect of Vegetation Restoration on Soil Organic Carbon Storage in Coal Mining Areas Based on Meta-analysis].

Coal mining is the world's primary means of coping with an increasing energy demand. However, with the mining of coal, the regional ecosystem has been damaged to varying degrees, resulting in a decrease in the "carbon sink" capacity. Vegetation restoration is the basis for the restoration of degraded ecosystems and carbon sequestration functions in mining areas. However, no systematic studies have been conducted on the effects of vegetation restoration on soil organic carbon in coal mining areas on a global scale. Therefore, it is not possible to accurately predict the response of the global SOC pool to vegetation restoration. In this study, soil physicochemical properties of vegetation restoration were collected from 112 peer-reviewed articles to assess the effects of vegetation restoration type, soil depth, restoration year, mean annual temperature, annual precipitation, and elevation on soil organic carbon in coal mining areas and to identify relevant key drivers. The results showed that the damaged coal mine area could significantly improve the physicochemical properties of the soil through vegetation restoration. The restored soils had 39.02% higher SOC reserves compared to that in unrestored or naturally restored soils. When environmental factors were not considered, the vegetation restoration types that were favorable for SOC stock accumulation were cropland > woodland > grassland > shrubland. All four types of vegetation restoration significantly increased the SOC storage in the surface layer (0-20 cm). Grassland and shrubs significantly increased SOC storage at depth (>40 cm), whereas SOC storage at depth under woodland and farmland types was not significantly different from SOC storage after unrestored or natural restoration. The increasing trend of SOC storage after vegetation restoration decreased with increasing soil depth. The specific vegetation restoration strategy should select the appropriate vegetation type according to the climatic conditions. The types of vegetation restoration with higher carbon sequestration effects in damaged coal mining areas with mean annual temperature <0℃ and mean annual precipitation <500 mm were grassland or shrubland. In contrast, woodland and cropland restoration types could better increase SOC storage in environments with mean annual temperature >15℃ and annual precipitation >800 mm. TN, BD, AN, and AK were the main factors influencing the ability to affect soil carbon sequestration. This study can provide a theoretical reference for quantifying the carbon sequestration effects of different vegetation restoration measures in damaged coal mining areas and the restoration and reconstruction of degraded ecosystems.

A repertoire of single nucleotide polymorphisms (SNPs) of major fecundity BMPR1B gene among 75 sheep breeds worldwide.

The BMPR1B gene is a major determinant of sheep reproductive capacity. Previous studies revealed that Q249R (FecB) is a profound variant of BMPR1B that influences the ovulation rate and litter size in sheep. However, unlike Q249R locus, the full spectrum of single nucleotide polymorphisms (SNPs) within BMPR1B has not been extensively studied. A systematic screen of SNPs in BMPR1B would facilitate the discovery of novel variants that are associated with litter size. This study aimed to investigate SNPs in the BMPR1B gene via whole genome sequence (WGS) data from 2409 individuals of 75 sheep breeds worldwide. Herein, a total of 9688 variants were screened, among which 15 were coding variants and 8 were novel changes. Specifically, we presented the most comprehensive frequency distribution map of the well-known FecB mutation to date. Besides, among the above-mentioned SNPs, one synonymous mutation (g.30050773C > T) was found to be likely under selection and is potentially associated with fecundity in Duolang sheep. Thus, our study greatly expands the variation repertoire of the ovine BMPR1B gene and provides a valuable resource for exploring causative mutations and genetic markers associated with litter size.

Perinatal exposure to bisphenol A impairs cognitive function via the gamma-aminobutyric acid signaling pathway in male rat offspring.

Bisphenol A (BPA) is a common synthetic endocrine disruptor that can be utilized in the fabrication of materials such as polycarbonates and epoxy resins. Numerous studies have linked BPA to learning and memory problems, although the precise mechanism remains unknown. Gamma-aminobutyric acid (GABA) is the most abundant inhibitory neurotransmitter in the vertebrate central nervous system, and it is intimately related to learning and memory. This study aims to evaluate whether altered cognitive behavior involves the GABA signaling pathway in male offspring of rats exposed to BPA during the prenatal and early postnatal periods. Pregnant rats were orally given BPA (0, 0.04, 0.4, and 4 mg/kg body weight (BW)/day) from the first day of pregnancy to the 21st day of breastfeeding. Three-week-old male rat offspring were selected for an open-field experiment and a new object recognition experiment to evaluate the effect of BPA exposure on cognitive behavior. Furthermore, the role of GABA signaling markers in the cognition affected by BPA was investigated at the molecular level using western blotting and real-time polymerase chain reaction (RT-PCR). The research demonstrated that BPA exposure impacted the behavior and memory of male rat offspring and elevated the expression of glutamic acid decarboxylase 67 (GAD67), GABA type A receptors subunit (GABAARα1), and GABA vesicle transporter (VGAT) in the hippocampus while decreasing the expression levels of GABA transaminase (GABA-T) and GABA transporter 1 (GAT-1). These findings indicate that the alteration in the expression of GABA signaling molecules may be one of the molecular mechanisms by which perinatal exposure to BPA leads to decreased learning and memory in male rat offspring.

Whole-genome resequencing reveals the genomic diversity and signatures of selection in Romanov sheep.

Romanov sheep are adapted to the extremely cold and harsh environment and display a distinctive grey color. Herein, we analyzed the population structure, genetic diversity, and selection signatures of Romanov sheep based on whole-genome sequencing data of 17 Romanov sheep, 114 individuals from other 10 European breeds. The results of PCA, ADMIXTURE, and NJ-tree showed that the Romanov sheep was closely related to other northern European breeds. A relative high level of genetic diversity, low inbreeding coefficient, and large effective population size was observed in Romanov sheep when compared with other European breeds. We then screened the genomic selection signatures of Romanov sheep using FST, XP-XLP, and XP-EHH methods. The most significant region under selection (CHR14:14.2 to 14.3 Mb) harbored a haplotype that contained MC1R gene. Furthermore, this haplotype was also found in other grey-body breeds including Gotland sheep, Grey Tronder Sheep, and German grey heath sheep, suggesting that it was associated with the unique coat color of these breeds. We also found one region (CHR10:40.8Mb- 41.0Mb) harboring PCDH9 gene which was potentially associated with cold environmental adaptation. In summary, this study identified candidate genes that were associated with the unique grey color and environmental adaptation in Romanov sheep, which provided a basis for understanding the genetic background and utilization of this breed.

Romanov sheep is one of the most famous sheep breeds in the word, characterized by adaptability to harsh environment, high fertility, and unique coat color. Understanding its genetic architecture and signatures is of great value for its conservation and utilization. In this study, we analyzed whole-genome sequences of Romanov sheep as compared with 11 other European breeds, to explore for the population structure, genetic diversity, and selection signatures. We discovered a series of candidate genes that likely play a role in the grey coat color and cold adaptation of the Romanov sheep. In particular, we identified MC1R as a strong candidate gene that determines the grey coat color.

Interaction of chlorothalonil and Varroa destructor on immature honey bees rearing in vitro.

Under realistic environmental conditions, bees are often exposed to multiple stressors, especially Varroa destructor and pesticides. In this study, the effects of exposure to NOAEC of chlorothalonil during the larval stage, in the presence or absence of V. destructor, was examined in terms of survival, morphological and transcriptional changes. The interaction between chlorothalonil and V. destructor on the survival of honey bee was additive. V. destructor are the dominant factor in the interaction for survival and transcriptome alternation. The downregulation of the genes related to tissue growth and caste differentiation may directly link to the mortality of honey bees. Either chlorothalonil or V. destructor induces the irregular morphology of trophocytes and oenocytes in the fat body. In addition to irregular shapes, oenocytes in V. destructor alone and double-stressor treatment group showed altered nuclei and vacuoles in the cytoplasm. The interaction of V. destructor and chlorothalonil at the larval stage have potential adverse effects on the subsequent adult bees, with up-regulation of genes involved in lipid metabolism and detoxification/defense in fat body tissue. Our findings provide a comprehensive understanding of combinatorial effects between biotic and abiotic stressors on one of the most important pollinators, honey bees.

Improving domain generalization performance for medical image segmentation via random feature augmentation.

Deep convolutional neural networks (DCNNs) have shown remarkable performance in medical image segmentation tasks. However, medical images frequently exhibit distribution discrepancies due to variations in scanner vendors, operators, and image quality, which pose significant challenges to the robustness of trained models when applied to unseen clinical data. To address this issue, domain generalization methods have been developed to enhance the generalization ability of DCNNs. Feature space-based data augmentation methods have been proven effective in improving domain generalization, but they often rely on prior knowledge or assumptions, which can limit the diversity of source domain data. In this study, we propose a novel random feature augmentation (RFA) method to diversify source domain data at the feature level without prior knowledge. Specifically, our RFA method perturbs domain-specific information while preserving domain-invariant information, thereby adequately diversifying the source domain data. Furthermore, we propose a dual-branches invariant synergistic learning strategy to capture domain-invariant information from the augmented features of RFA, enabling DCNNs to learn a more generalized representation. We evaluate our proposed method on two challenging medical image segmentation tasks, optic cup/disc segmentation on fundus images and prostate segmentation on MRI images. Extensive experimental results demonstrate the superior performance of our method over state-of-the-art domain generalization methods.

Convergent changes in melanocortin receptor 1 gene are associated with black-headed coat color in sheep.

As one of the most obvious phenotypic traits, the coat color of sheep is an ideal model to study the genetic mechanisms underlying coat color varieties of mammals. One distinguishable coat color is the black-headed type, such as the famous black-headed Dorper sheep from Africa and Bayinbuluke sheep from Asia. In this study, we compared the genome sequences of black-headed and all-white sheep to identify causative genes for the black-headed sheep, including black-headed Dorper vs. white-headed Dorper, as well as Bayinbuluke (black-headed) vs. Small-tailed Han (all-white). The most differentiating region between black-headed sheep and all-white sheep was found to harbor a haplotype covering melanocortin receptor 1 (MC1R) gene. The share of this haplotype by the black-headed sheep from Africa and Asia suggested that the convergent change in the MC1R region is likely to determine this unique coat color. Two missense mutations (g. 14251947T > A and g. 14252090G > A) within this haplotype of MC1R gene were found. We further analyzed whole genome sequence data of 460 worldwide sheep with diverse coat colors and confirmed the association between the MC1R haplotype with pigmentation variations. Our study provides novel insights into coat color genetics in sheep and expands our knowledge of the link between MC1R gene and varying pigmentation patterns in sheep.

The diverse colors of sheep not only help to distinguish different breeds but also provide an ideal model to study the genetics underlying mammalian coat color variations. One unique coat color in sheep is the black-headed type, as represented by the famous meat breed Dorper sheep from Africa and Bayinbuluke sheep from Asia. In this study, we compared the genomes of black-headed sheep with all-white sheep in order to identify genes responsible for this distinguishable coat color. By analyzing genomic selection signals and haplotypes, we located MC1R as the most likely causative gene determining the black-headed coat color in sheep. Our study expanded our understanding of the genetic mechanisms of coat color diversities in sheep.

Insertion/deletion (Indel) variant of the goat RORA gene is associated with growth traits.

RAR related orphan receptor A (RORA), which encodes the retinoid-acid-related orphan receptor alpha (RORα), is a clock gene found in skeletal muscle. Several studies have shown that RORα plays an important role in bone formation, suggesting that RORA gene may take part in the regulation of growth and development. The purpose of this research is to study the insertion/deletion (indel) variations of the RORA gene and investigate the relationship with the growth traits of Shaanbei white cashmere (SBWC) goats. Herein, the current study identified that the P4-11-bp and P11-28-bp deletion sites are polymorphic among 12 pairs of primers within the RORA gene in the SBWC goats (n = 641). Moreover, the P11-28-bp deletion locus was significantly related to the body height (p = 0.046), height at hip cross (p = 0.012), and body length (p = 0.003). Both of P4-11-bp and P11-28-bp indels showed the moderate genetic diversity (0.25

Deciphering the active origin for urea oxidation reaction over nitrogen penetrated nickel nanoparticles embedded in carbon nanotubes.

Urea electrooxidation with favorable thermodynamic potential is highly anticipated but suffering from sluggish kinetics. Deciphering the activity origin and achieving rational structure design are pivotal for developing highly efficient electrocatalyst for urea oxidation reaction (UOR). Herein, nitrogen penetrated nickel nanoparticles confined in carbon nanotubes (Ni-NCNT) is successfully achieved to drive UOR. Active origin of Ni-NCNT is decoded to be the in-situ generated Ni2+δO(OH)ads according to comprehensive analysis. The electrophilic Ni2+δ and protophilic OHads could targeted capture O and H atoms from urea, respectively, achieving molecule activation and accelerating the subsequent proton coupled electron transfer reactions. Nitrogen penetration is identified to promote prior formation of Ni2+δO(OH)ads and push up the d band center of Ni-NCNT, enhancing urea adsorption and subsequent molecule cleavage reactions. As a result, Ni-NCNT exhibits superior UOR performance. This work supplies valuable insights for the rational design and construction of efficient nickel-based catalyst for driving UOR.

Rapid detection of InDel within the KDM3B gene in five sheep breeds using the mathematical expectation (ME) method.

Lysine demethylase 3B (KDM3B), a candidate gene associated with bone formation and growth, and differentiation of osteoblast, might affect the animal growth traits. Herein, the insertion/deletion (InDel) of the KDM3B gene was quickly detected in 882 sheep from five breeds using the mathematical expectation (ME) method. The results showed that there were two genotypes of 7-bp variation in KDM3B, including II (insertion/insertion) and ID (insertion/deletion), and the frequency of two genotypes varied among the five sheep breeds. Association analysis results demonstrated that the 7-bp indel was significantly associated with chest depth of LFT sheep (P = 0.012), and body weight (P = 0.006), body height (P = 0.030), chest depth (P = 0.043), chest circumference (P = 0.016), abdominal width (P = 0.035) and height at hip cross (P = 0.022) in LXBH sheep. Moreover, II genotype was the predominant genotype with excellent consistency in sheep growth traits (P < 0.05). Collectively, the above results suggest that this locus can be used as an effective molecular marker to improve the sheep growth traits and provide a scientific basis for the development of sheep breeding.

A Novel Encoding and Decoding Calibration Guiding Pathway for Pathological Image Analysis.

Diagnostic pathology is the foundation and gold standard for identifying carcinomas, and the accurate quantification of pathological images can provide objective clues for pathologists to make more convincing diagnosis. Recently, the encoder-decoder architectures (EDAs) of convolutional neural networks (CNNs) are widely used in the analysis of pathological images. Despite the rapid innovation of EDAs, we have conducted extensive experiments based on a variety of commonly used EDAs, and found them cannot handle the interference of complex background in pathological images, making the architectures unable to focus on the regions of interest (RoIs), thus making the quantitative results unreliable. Therefore, we proposed a pathway named GLobal Bank (GLB) to guide the encoder and the decoder to extract more features of RoIs rather than the complex background. Sufficient experiments have proved that the architecture remoulded by GLB can achieve significant performance improvement, and the quantitative results are more accurate.

A 7-nt nucleotide sequence variant within the sheep KDM3B gene affects female reproduction traits.

Lysine demethylase 3B (KDM3B) gene is a histone demethylase, demonstrating specific demethylation of the histone H3 lysine 9. It was detected as a sheep reproductive candidate gene by genome-wide scans, and related studies also showed its significance in female reproductive process. However, rare study researched its polymorphism. Herein, we hypothesized that the polymorphisms of KDM3B gene were associated with sheep reproduction traits. A 7-nt nucleotide sequence variant (rs1088697156) within KDM3B gene was identified in a total of 888 individuals, including the Australian White (AUW) sheep and Lanzhou Fat-tailed (LFT) sheep. II (insertion/insertion) and ID (insertion/deletion) genotypes of 7-nt variant were detected, which were at Hardy-Weinberg equilibrium (HWE) in detected breeds. Association analysis illustrated the 7-nt variant was significantly associated with the litter size, duration of pregnancy, live lamb number, live lamb rate, stillbirth number, stillbirth rate of average and different parity (P < 0.05) in AUW sheep. Moreover, 'ID' was the dominant genotype with excellent consistency in reproductive traits. It is instrumental to select individuals with ID genotype for improving the sheep reproduction traits. These findings suggest that the 7-nt variant within KDM3B gene can be used as a candidate marker of reproduction traits for sheep breeding improvement by marker-assisted selection.

Effect of indel variants within the sorting nexin 29 (SNX29) gene on growth traits of goats.

The sorting nexin 29 gene (SNX29) is a well-known regulator of myocyte differentiation and proliferation. In this work, two indels (17-bp and 21-bp) were identified in the goat SNX29 gene, and their effects on the growth traits of 1,759 Shaanbei white cashmere (SBWC) goats were analyzed. Both indels had three genotypes [homozygote wild type (II), heterozygote (ID), and homozygote mutation (DD)] and displayed medium genetic diversity (0.25 < polymorphism information content (PIC) < 0.50) in the population. The 17-bp indel was significantly associated with chest width (p = 0.009), body weight (p = 0.021), and chest depth (p = 0.032), with the II genotype dominant. The 21-bp indel was significantly associated with chest width (p = 0.001), chest depth (p = 4.8E-5), heart girth (p = 0.007), and hip width (p = 0.002). Because the two indels were in the upstream (17-bp) and intron (21-bp) regions of the SNX29 gene, transcription factor binding sites were predicted. The IRF5 and MYC could bind with the 17-bp indel and 21-bp indel sequences, respectively. This study indicates that SNX29 is a promising candidate gene that can be used to improve meat production in goat breeding.

A Robust Training Method for Pathological Cellular Detector via Spatial Loss Calibration.

Computer-aided diagnosis of pathological images usually requires detecting and examining all positive cells for accurate diagnosis. However, cellular datasets tend to be sparsely annotated due to the challenge of annotating all the cells. However, training detectors on sparse annotations may be misled by miscalculated losses, limiting the detection performance. Thus, efficient and reliable methods for training cellular detectors on sparse annotations are in higher demand than ever. In this study, we propose a training method that utilizes regression boxes' spatial information to conduct loss calibration to reduce the miscalculated loss. Extensive experimental results show that our method can significantly boost detectors' performance trained on datasets with varying degrees of sparse annotations. Even if 90% of the annotations are missing, the performance of our method is barely affected. Furthermore, we find that the middle layers of the detector are closely related to the generalization performance. More generally, this study could elucidate the link between layers and generalization performance, provide enlightenment for future research, such as designing and applying constraint rules to specific layers according to gradient analysis to achieve "scalpel-level" model training.

Detection of mRNA Expression and Copy Number Variations Within the Goat Fec B Gene Associated With Litter Size.

The Booroola fecundity (Fec B ) gene, as the first major fecundity gene identified in Booroola sheep, has attracted careful attention. So far, previous research have uncovered the FecB mutation (Q249R) as the main mutation by virtue of which sheep exhibits multiple lambing phenomena. This mutation is now being intensively studied and widely used. However, such effect of the FecB mutation has not been applied to goats, and similar types of the Fec B gene in goats still need to be studied. Thus, the current study attempted to verify potential mutations in the goat Fec B gene as well as investigate their functions related to fecundity. First, Fec B expression was investigated in six different goat tissues, and we found that Fec B expression was highest in the mammary gland, followed by the ovary. Next, the influence of the Fec B gene was analyzed from a new perspective, where five potential copy number variations (CNVs) (CNV1-5) within the Fec B gene were identified for the first time, and then their effects on litter size were measured. Our results point out that CNV3 (P = 3.44E-4) and CNV5 (P = 0.034) could significantly influence the litter size of goats. Identically, the combination genotype of CNV3 and CNV5 which consisted of their dominant genotypes was also significantly associated with goat litter size (P = 7.80E-5). Hence, CNV3 and CNV5 could serve as potential DNA molecular markers applied to DNA editing and DNA microarray. Additionally, the abovementioned study has laid a theoretical foundation for the detection of potential fertility-related quantitative trait loci within the goat Fec B gene.

Conservation laws for electron vortices in strong-field ionisation.

ABSTRACT: We investigate twisted electrons with a well-defined orbital angular momentum, which have been ionised via a strong laser field. By formulating a new variant of the well-known strong field approximation, we are able to derive conservation laws for the angular momenta of twisted electrons in the cases of linear and circularly polarised fields. In the case of linear fields, we demonstrate that the orbital angular momentum of the twisted electron is determined by the magnetic quantum number of the initial bound state. The condition for the circular field can be related to the famous ATI peaks, and provides a new interpretation for this fundamental feature of photoelectron spectra. We find the length of the circular pulse to be a vital factor in this selection rule and, employing an effective frequency, we show that the photoelectron OAM emission spectra are sensitive to the parity of the number of laser cycles. This work provides the basic theoretical framework with which to understand the OAM of a photoelectron undergoing strong field ionisation.

Insertion/deletion variants within the IGF2BP2 gene identified in reported genome-wide selective sweep analysis reveal a correlation with goat litter size.

Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2, also called IMP2) plays an essential role in the development and maturation of germ cells and embryos and is a candidate gene for goat litter size, based on a previous genome-wide selective sweep analysis. In this study, the mRNA expression level of IGF2BP2 was found to be significantly higher in a single-lamb group than in a multi-lamb group. Insertions/deletions (indels) within the goat IGF2BP2 gene, including P4-Ins-13bp and P5-Del-12bp, were verified in 918 Shaanbei White Cashmere (SBWC) female goats. The minor allelic frequencies (MAFs) of P4-Ins-13bp and P5-Del-12bp loci were 0.349 and 0.295, respectively. Analysis using the Chi-square (χ2) test showed that the genotype (χ2=14.479, P=0.006) distribution of P4-Ins-13bp was significantly different between the single-lamb and multi-lamb groups. Correlation analysis demonstrated that P4-Ins-13bp was significantly associated with goat litter size (P=0.022), and individual goats with the homozygous deletion/deletion (DD) genotype produced more litters than other goats. Therefore, considered as a potential molecular marker significantly related to lambing traits, the P4-Ins-13bp mutation of the goat IGF2BP2 gene can be used in goat breeding with practical molecular marker-assisted selection (MAS) to optimize female reproduction and improve economic efficiency in the goat industry.

Automated tumor proportion scoring for PD-L1 expression based on multistage ensemble strategy in non-small cell lung cancer.

INTRODUCTION: Programmed cell death ligand-1 (PD-L1) expression is a promising biomarker for identifying treatment related to non-small cell lung cancer (NSCLC). Automated image analysis served as an aided PD-L1 scoring tool for pathologists to reduce inter- and intrareader variability. We developed a novel automated tumor proportion scoring (TPS) algorithm, and evaluated the concordance of this image analysis algorithm with pathologist scores. METHODS: We included 230 NSCLC samples prepared and stained using the PD-L1(SP263) and PD-L1(22C3) antibodies separately. The scoring algorithm was based on regional segmentation and cellular detection. We used 30 PD-L1(SP263) slides for algorithm training and validation. RESULTS: Overall, 192 SP263 samples and 117 22C3 samples were amenable to image analysis scoring. Automated image analysis and pathologist scores were highly concordant [intraclass correlation coefficient (ICC) = 0.873 and 0.737]. Concordances at moderate and high cutoff values were better than at low cutoff values significantly. For SP263 and 22C3, the concordances in squamous cell carcinomas were better than adenocarcinomas (SP263 ICC = 0.884 vs 0.783; 22C3 ICC = 0.782 vs 0.500). In addition, our automated immune cell proportion scoring (IPS) scores achieved high positive correlation with the pathologists TPS scores. CONCLUSIONS: The novel automated image analysis scoring algorithm permitted quantitative comparison with existing PD-L1 diagnostic assays and demonstrated effectiveness by combining cellular and regional information for image algorithm training. Meanwhile, the fact that concordances vary in different subtypes of NSCLC samples, which should be considered in algorithm development.