Degree Of Similarity Research Articles

Pantoea: A Versatile bacterial Candidate of Agricultural Importance

Genus Pantoea is a diverse group of the family Enterobacteriaceae which inhabit various ecological niches. Various species of Pantoea are found associated with a diverse host range that include plants, animals, insects and humans. Besides this Pantoea spp. has been widely distributed in nature including water, soil, dairy products, meat and fish. It is commonly associated with plants as epiphyte or pathogen. Pantoea was first recognized as a genus about 25 years ago, but since then, approximately 20 species with a wide range of characteristics have been discovered. Identification of plant-pathogenic Pantoea species is difficult, due to the high degree of phenotypic similarity between species of this genus and related Enterobacteriaceae. Pantoea species are typically characterized based on colony morphology, physiological and biochemical tests, and in some cases, fatty acid analysis or quinone composition. These have a wide range of plant hosts where they live as nonpathogenic endophytes as well as epiphytes that colonize the leaves, stems and roots. In this perspective, some Pantoea strains can be beneficial to the plant host by providing growth promoting substances such as the plant-growth hormone indole-acetic acid (IAA), phosphate solubilization or nitrogen fixation. Number of Pantoea species are also involved in bioremediation of various pesticides and organic chemical compounds. This indicates the potential to degrade some recalcitrant and xenobiotic compounds. Some Pantoea strains also provide effective protection to plants against various bacterial and fungal diseases and post-harvest fruit rots. On the other hand, some of the species are recognized as a human pathogen and has led to controversy as limited documented cases of P. agglomerans bacteremia and infections have been reported. Most of the cases reported among immunocompromised patients or the pediatric population. Thus, Pantoea is a multifaceted bacterium that plays a significant role in agriculture as a plant growth promoter by producing hormone like IAA, inorganic pollutant degradation by producing potent enzymes and also protects some plants against pathogens by producing antibacterial and antifungal compounds. In some cases, these also cause diseases in human beings. Therefore, this genus is very important for studies. In this review, the industrial, agricultural as well as ecological significance of the genus Pantoea is emphasized.

Juvenile feeding of pacific salmon in the lower part of the Pymta River in May–September, 2019–2020

Comparative analysis of feeding in the lower part of the Pymta River (West Kamchatka) is carried out for juvenile Pacific salmon individuals of different age representing four species with a short or long freshwater period of their life cycle (chum salmon Oncorhynchus keta, sockeye salmon O. nerka, coho salmon O. kisutsch and Chinook salmon O. tschawytscha) for the period from May to September in 2019–2020. Spectra of food, stomach fullness indices and degree of food similarity between juveniles of different species and age classes are demonstrated. The feeding in all mentioned species was biotop-dependent. There were frequent changes in the composition of the forage consumed in the course of observations of Pacific salmon juveniles in the Pymta River basin, and the indices of stomach fullness varied extensively. The results of the analysis indicate extensive trophic adaptability in juvenile Pacific salmon in the ecosystem of the Pymta River.

A systematic review and meta-analysis: leadership and interactional justice

AbstractThis study aims to execute a systematic literature review on leadership styles and interactional justice when found together or with other variables and a meta-analysis to examine their direct relationship, besides elaborating on the definitions of leadership styles. The ProQuest website was utilized to explore interactional justice and leadership concepts in all abstract and summary texts in English and articles in peer-reviewed scholarly journals at any time. Thirty-six studies were related to the systematic review, and there were twenty-two valid cases for the meta-analysis. The meta-analysis techniques proved the strength of the relationship between leadership styles and interactional justice, with notable heterogeneity among all investigations and ambiguous proof of publication bias. Fisher’s method found a study to be an outlier, but it was significant in WLS, WAAP, and Cohen's d. The review of definitions showed a high degree of similarity, while the most prominent features of leadership styles were transformational, transactional, and ethical leadership. Yet, the systematic review proved unmistakable evidence of the connection between interactional justice and leadership styles, while those two concepts impacted other variables in a relationship. The relationship between leadership and interactional justice is influenced by many facets, such as their status in the connection, leadership styles, sample number, and study place, pushing the heterogeneity of the results. Although it is possible to mix up these diverse leadership styles, transformational or ethical leadership, both styles treat employees respectfully, avoid concealing information, and make staff feel satisfied. Wise leadership is crucial to shaping a positive interactional environment for workers. Therefore, leaders must pursue integrity and ethical behavior to establish organizational justice, particularly interactional justice.

A Spatially-Aware Data-Driven Approach to Automatically Geocoding Non-Gazetteer Place Names

Human and natural processes such as navigation and natural calamities are intrinsically linked to the geographic space and described using place names. Extraction and subsequent geocoding of place names from text are critical for understanding the onset, progression, and end of these processes. Geocoding place names extracted from text requires using an external knowledge base such as a gazetteer. However, a standard gazetteer is typically incomplete. Additionally, widely used place name geocoding—also known as toponym resolution—approaches generally focus on geocoding ambiguous but known gazetteer place names. Hence, there is a need for an approach to automatically geocode non -gazetteer place names. In this research, we demonstrate that patterns in place names are not spatially random. Places are often named based on people, geography, and history of the area and thus exhibit a degree of similarity. Similarly, places that co-occur in text are likely to be spatially proximate as they provide geographic reference to common events. We propose a novel data-driven spatially-aware algorithm, Bhugol , that leverages the spatial patterns and the spatial context of place names to automatically geocode the non-gazetteer place names. The efficacy of Bhugol is demonstrated using two diverse geographic areas – USA and India. The results show that Bhugol outperforms well-known state-of-the-art geocoders.

Impact of hydrogen metallurgy on the current iron and steel industry: A comprehensive material-exergy-emission flow analysis

The development of hydrogen metallurgy holds enormous significance in facilitating the low-carbon and environmentally sustainable transition of iron and steel industries. However, there is a lack of research on the impact of implementing hydrogen metallurgy on the downstream process and even the whole process. To fill this research gap, a model that considers specific internal reactions of steelmaking process was developed utilizing the bottom-up approach. Subsequently, by integrating the hydrogen metallurgy process with the traditional long process and short process, the energy intensity, exergy intensity and carbon emission intensity were quantitatively analyzed from multiple viewpoints. The results of the basic oxygen furnace (BOF) and electric arc furnace (EAF) exhibit a degree of similarity, with an increase in directly reduced iron (DRI) addition, resulting in an insufficient heat and an increase in process energy consumption, exergy loss, and carbon emissions. But there are also some subtle differences, such as oxygen consumption. Following the integration of hydrogen metallurgy, the energy intensity and exergy intensity of the BF-BOF process increase 11.984 kgce/t-CS and 3.083 × 102 MJ/t-CS, respectively. However, the carbon emission intensity decreased by 46.514 kg/t-CS. The integrated Chinese-style short process has the lowest carbon emission intensity of 879.664 kg/t-CS at 40% DRI and 60% scrap. Nevertheless, the energy intensity and exergy intensity have increased by 27.383 kgce/t-CS and 42.012 × 102 MJ/t-CS respectively compared to their initial values. It is noteworthy that the process of hydrogen production plays a significant role in mitigating the exergy intensity and carbon emission intensity.

Fossilized giant sulfide-oxidizing bacteria from the Devonian Hollard Mound seep deposit, Morocco.

The giant sulfide-oxidizing bacteria are particularly prone to preservation in the rock record, and their fossils have been identified in ancient phosphorites, cherts, and carbonates. This study reports putative spherical fossils preserved in the Devonian Hollard Mound hydrocarbon-seep deposit. Based on petrographical, mineralogical, and geochemical evidence the putative microfossils are interpreted as sulfide-oxidizing bacteria similar to the present-day genus Thiomargarita, which is also found at modern hydrocarbon seeps. The morphology, distribution, size, and occurrence of the fossilized cells show a large degree of similarity to their modern counterparts. Some of the spherical fossils adhere to worm tubes analogous to the occurrence of modern Thiomargarita on the tubes of seep-dwelling siboglinid worms. Fluorapatite crystals were identified within the fossilized cell walls, suggesting the intercellular storage of phosphorus analogous to modern Thiomargarita cells. The preservation of large sulfide-oxidizing bacteria was probably linked to changing biogeochemical processes at the Hollard Mound seep or, alternatively, may have been favored by the sulfide-oxidizing bacteria performing nitrate-dependent sulfide oxidation-a process known to induce carbonate precipitation. The presence of sulfide-oxidizing bacteria at a Devonian hydrocarbon seep highlights the similarities of past and present chemosynthesis-based ecosystems and provides valuable insight into the antiquity of biogeochemical processes and element cycling at Phanerozoic seeps.

Clustering Balinese Language Documents using the Balinese Stemmer Method and Mini Batch K-Means with K-Means++

Clustering aims to categorize data into n groups, where data within each group exhibits maximum similarity, while the similarity between groups is minimized. Among various clustering methods, k-means is widely employed due to its simplicity and ability to yield optimal clustering results. However, the k-means method is susceptible to slow processing in high-dimensional datasets and the clustering outcomes are sensitive to the initial selection of cluster center values. In addressing these limitations, this study employs the k-means mini-batch method to enhance processing speed for high-dimensional data and utilizes the k-means++ method to optimize the selection of initial cluster center values. The dataset for this research comprises 300 news articles in Balinese sourced from the https://balitv.tv/ website. Prior to the clustering process, a stemming procedure is applied using the Balinese stemmer method to enhance recall. The obtained results reveal that a majority of the 300 data instances exhibit a high degree of similarity, as indicated by the clustering results. If the number of clusters (n) exceeds two, the data fails to be distinctly separated due to the high structural similarity among the data instances. This can be attributed to the relatively small number of words or attributes produced. In future research, feature reduction will be implemented, and a clustering method capable of addressing data overlap will be explored.

Pseudomonas aestuarii sp. nov., isolated from tidal flat sediment.

Two novel Pseudomonas strains, SA3-5T and SA3-6, were isolated from a tidal flat (getbol) in the Republic of Korea. Strains SA3-5T and SA3-6 were subjected to polyphasic characterization to determine their taxonomic affiliations. Cells were Gram-stain-negative, aerobic, rod-shaped and motile by using peritrichous flagella. Based on their 16S rRNA gene sequences, strains SA3-5T and SA3-6 exhibited a high degree of similarity (100 %) and were classified within the genus Pseudomonas. Furthermore, the closest related species to SA3-5T and SA3-6 were Pseudomonas taeanensis MS-3T (98.3 %). The ranges of average nucleotide identity and digital DNA-DNA hybridization values between SA3-5T and closely related species were 75.9-89.1% and 21.3-38.7%, respectively, both of which being below the thresholds for delineating novel strains. Strain SA3-5T and SA3-6 contained C16 : 1 ω6с and/or C16 : 1 ω7с (summed feature 3), C16 : 0 and C18 : 1 ω6с and/or C18 : 1 ω7с (summed feature 8) as the major fatty acids. The predominant respiratory quinone was Q-9. The DNA G+C content of strain SA3-5T was 62.5 mol%. Based on their combined phenotypic, chemotaxonomic and phylogenetic characterisitics, strains SA3-5T and SA3-6 represent a novel species of the genus Pseudomonas for which the name Pseudomonas aestuarii sp. nov. is proposed. The type strain is SA3-5T (=KCTC 92395T=JCM 35697T).

Distribution-based limited fuzzy bisimulations for nondeterministic fuzzy transition systems

Bisimulation techniques have been adopted and developed in fuzzy transition systems (FTSs) to compare their behavior. Recently, distribution-based bisimulation has been introduced to nondeterministic fuzzy transition systems (NFTSs) to measure the similarity between two distributions which can be regarded as generalized states. However, the new bisimulation does not consider the similarity of states. In terms of limited approximate bisimilarity, in this article, a notion of distribution-based k-limited bisimulation is proposed for NFTSs, which can measure the similarity between two distributions in the neighboring subgraphs (subsystems) and take the similarity of states into account, where k is a natural number. This article discusses some properties of limited bisimulation and the nonexpansiveness of typical parallel composition operators with respect to the limited bisimulation which makes compositional verification possible. Then a fixed point characterization of the limited bisimulation is investigated and an algorithm is given to compute the degree of k-limited similarity between two distributions. Moreover, a fuzzy modal logic interpreted on distributions is adopted to characterize the limited bisimulation.

A Study of the Characteristics of Micro-Seismic (ME) and Electromagnetic Radiation (EMR) Signals under the Static Load Conditions of Rocks

Geological hazards, such as the frequent occurrence of rock bursts in deep mining, emphasize the critical necessity for the early warning and prediction of dynamic fractures in coal and rock masses, as well as the destabilization of the surrounding rock. This study delves into the mechanisms of electromagnetic radiation (EMR) signals and their synchronous coupling with micro-seismic (ME) signals. EMR and ME signals from rock specimens were systematically collected during the uniaxial compression fracture process using a dedicated monitoring and acquisition system. Employing the wavelet analysis method, the original data underwent reconstruction and denoising, while the EMR and ME spectra, derived through fast Fourier transform, were subjected to detailed scrutiny. The comprehensive analysis unveiled that EMR signals arising from rock fractures exhibited precise timing synchronization with ME signals. Moreover, the dominant frequencies of both signals are closely aligned within the low-frequency band, indicating a remarkable degree of similarity and homology. These findings establish an experimental basis for the development of monitoring and early warning systems geared toward assessing damage to coal and rock masses using EMR and ME signals.

Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals

Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights—together with neuroscience, biology and data science—closer.

Automated lexical analysis of story recall in healthy controls

AbstractBackgroundImpaired episodic memory is one of the earliest and most prominent symptoms of Alzheimer’s disease (AD). Examining how patients produce words and phrases in story recall tasks is useful for tracking and understanding progression of their disease. Traditional approaches rely on manual assessments of recalled stories which can be limited, time‐consuming, and not applicable in large‐scale studies. Here, we implement an automated, computational approach to measure verbal episodic memory in a digitally recorded story recall task performed by young healthy speakers using natural language processing models.MethodWe analyzed digitized speech samples of pre‐recorded immediate and delayed Craft Story recall tasks performed by healthy speakers (n = 67, mean age = 20.31 years, SD = 2.17, 45 (67%) females). All transcripts were transcribed by trained annotators and automatically scored for number of verbatim and paraphrase recall, total recall score ( = verbatim+paraphrase), and semantic distance from the original story (degree of semantic similarity between the original craft story and the recalled story based on the Word2vec module in python). We also calculated total word count and number of unique words, and rated all recalled words for word familiarity, concreteness, and semantic ambiguity based on published norms. Number of pauses and total durations of speech and silent pauses were also measured.ResultWe found larger semantic distance between successive words in the delayed recall task compared with immediate recall within speaker (p<.001). Delayed recall also elicited lower total recall scores than immediate recall (p = .03). Comparing the effect sizes of semantic distance vs. total recall scores, our model comparison showed a smaller prediction error for semantic distance (AIC = 95.45) than total recall score (AIC = 736.18). In addition, speakers in the delayed recall task performed more paraphrase recall, had higher total word count, number of unique words, more ambiguous words, and produced more speech (all p‐values<.001).ConclusionOur findings suggest that automated speech analysis can provide informative and cost‐effective measures from a recorded story recall task. The study provides an automated and quantitative way to better measure memory abilities by including semantic distance. Future work will test procedures in patients with AD or other neurodegenerative diseases.

Dynamic Hilbert clustering based on convex set for web services aggregation

<div align=""><span lang="EN-US">In recent years, web services run by big corporations and different application-specific data centers have all been embraced by several companies worldwide. Web services provide several benefits when compared to other communication technologies. However, it still suffers from congestion and bottlenecks as well as a significant delay due to the tremendous load caused by a large number of web service requests from end users. Clustering and then aggregating similar web services as one compressed message can potentially achieve network traffic reduction. This paper proposes a dynamic Hilbert clustering as a new model for clustering web services based on convex set similarity. Mathematically, the suggested models compute the degree of similarity between simple object access protocol (SOAP) messages and then cluster them into groups with high similarity. Next, each cluster is aggregated as a compact message that is finally encoded by fixed-length or Huffman. The experiment results have shown the suggested model performs better than the conventional clustering techniques in terms of compression ratio. The suggested model has produced the best results, reaching up to 15 with fixed-length and up to 20 with Huffman</span></div>

Amyloid‐Beta PET Synthesis from Structural MRI: A Potential Alternative Method for Alzheimer’s Disease Screening

AbstractBackgroundAmyloid‐beta and structural brain atrophy are known to be hallmarks of Alzheimer’s Disease (AD), and can be quantified with Positron Emission Tomography (PET) and structural Magnetic Resonance (MRI), respectively. PET scans use radiotracers that binds to amyloid‐beta molecules, whereas MRI measures changes in structural morphology. PET scans are difficult to perform due to cost (∼$5000/scan), invasiveness, and ionizing radiation exposure, making them inaccessible for screening early‐onset AD. Conversely, MRI is a cheaper (∼$500/scan), non‐invasive, and free from ionizing radiation technique, however, it cannot provide molecular information obtained from PET. There is a known relationship between amyloid‐beta and brain atrophy, hence, amyloid‐beta PET images might be synthesized from structural MRI using image translation, which is an advanced form of machine learning.MethodAn image translation algorithm was developed using the Open Access Series of Imaging Studies (OASIS‐3) dataset that provides 929 subjects with pairs of T1‐weighted MRI and amyloid‐beta PET images, where 609 are cognitively normal (CN) and 489 at different stages of cognitive decline based on Clinical Dementia Rating scores. The image translation algorithm implemented with Conditional Generative Adversarial Networks, shown in Figure 1, was trained with 550 pairs with 156 axial slices each of amyloid‐beta and MRI images. Eight models where generated with different parameters to assess their impact in the generalization quality, then compared in a sample of 334 subjects using Structural Similarity Index (SSIM) and Peak Signal‐to‐Noise Ratio (PSNR).ResultFigure 2, compares the models performance based on SSIM and PSNR, where the best performing model generates synthetic PET images with a high degree of similarity with the real ones in Figure 3.ConclusionAn image translation pipeline was implemented and explored by changing parameters that can affect the model performance, the developed models are robust against the different parameters that were changed and it demonstrates that amyloid‐beta PET images can be synthesized from structural MRI, reducing cost, invasiveness and increasing availability. Such a method could enable non‐invasive screening of early onset AD.

Industry 4.0 and its impact on reshoring trends in the global economy

Methods. The results of the study were obtained through the application of scientific research methods, namely: abstraction – to define the reshoring trends on the influence of sustainable development goals based on Industry 4.0; analysis and synthesis – to determine the likelihood of reshoring of companies located abroad according to the degree of similarity between its technology situation; comparative studies – to establish the intensity of reshoring based on different technical solutions and type of manufacturers. Results. Industry 4.0 represents the fourth industrial revolution, emphasizing the crucial role of intelligent machines and smart automation in business activities. It has been demonstrated that these technologies provide the necessary flexibility for companies to overcome the difficulties of reshoring and at the same time maintain their competitiveness in the global value chain. Industry 4.0 solutions bring valuable insights into the supply chain during reshoring initiatives. Armed with Industry 4.0 technologies, managers are better equipped to achieve successful operations by controlling material flows before, during and after relocation of the company divisions. These technologies will offer valuable insights into various aspects, such as switching costs, supply chain governance, manufacturing planning, financial incentives and workforce planning. This enables companies to make informed decisions and optimize their reshoring strategies for long-term success. The main reasons for the spread of reshoring have been identified and its most widespread forms in the developed countries of the world have been singled out. The influence of Industry 4.0 technologies on the reshoring process is demonstrated and the current trends in its development are determined. Novelty. The scientific novelty of the research results lies in identifying the current trends of reshoring in the countries of the European Union and the USA and the influence of sustainable development goals on reshoring initiatives based on Industry 4.0. Practical value. The results of the study actualize the processes inherent in the modern economy, which is becoming more and more globalized. The identified modern trends of the reshoring initiative allow companies to build and implement more effective reshored business functions based on Industry 4.0 technologies.

Unveiling the close relationship between Betta burdigala and Betta uberis through DNA barcoding based on COI Gene

A Wild Betta burdigala is only known from Bangka Island, while a Wild Betta uberis resides in Kalimantan Tengah and Kalimantan Barat in Indonesia. Both species share numerous physical characteristics, particularly in the meristic count, body color, and specific habitat. In this study, we estimate the degree of similarity between Betta burdigala and Betta uberis using DNA barcoding based on the Cytochrome Oxidase Sub Unit I (COI) gene. The COI gene is a unique and small gene which found in mitochondrial DNA and is used in the DNA barcoding approach to identify species. This approach allows for the analysis of species similarities and the learning of the evolutionary histories of those species. Based on the COI gene, Betta burdigala and Betta uberis have a close genetic distance and a DNA similarity of 96.66%, much higher than other bettas. They differ by eight nucleotide bases, and their genetic distance is 0.04, while a genetic distance between 0.010 and 0.099 is considered to be low and indicative of high similarity. According to the phylogenetic tree, these species are descended from a single, closely related ancestor on the same branch. Based on the COI gene, we assume that they are identical. Additionally, we advise conducting additional research using the mitochondrial DNA complex and in-depth morphological examination to confirm the accuracy of the study’s findings.

Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules

The task of fine-grained named entity recognition is to locate entities in text and classify them into predefined fine-grained categories. At present, Chinese fine-grained NER only uses the pretrained language model to encode the characters in the sentence and lacks the ability to extract the deep semantic, sequence, and position information. The sequence annotation method is character-based and lacks the processing of entity boundaries. Fine-grained entity categories have a high degree of similarity, which makes it difficult to distinguish similar categories. To solve the above problems, this paper constructs the BILTAR deep semantic extraction module and adds the GlobalPointer module to improve the accuracy of Chinese fine-grained named entity recognition. The BILTAR module is used to extract deep semantic features from the coding information of pretrained language models and use higher-quality features to improve the model performance. In the GlobalPointer module, the model first adds the rotation position encoding information to the feature vector, using the position information to achieve data enhancement. Finally, the model considers all possible entity boundaries through the GlobalPointer module and calculates the scores for all possible entity boundaries in each category. In this paper, all possible entity boundaries in the text are considered by the above method, and the accuracy of entity recognition is improved. In this paper, the corresponding experiments were carried out on CLUENER 2020 and the micro Chinese fine-grained NER dataset, and the F1 scores of the model in this paper reached 80.848% and 75.751%, respectively. In ablation experiments, the proposed method outperforms the most advanced baseline model and improves the performance of the basic model.

Evaluation of digital soil mapping projection in soil organic carbon change modeling

There is increasing interest in the application of digital soil mapping (DSM) projections to infer changes in soil carbon across both space and time. This approach relies on the assumption that the spatially modeled soil carbon-environment relationship can be transferred over time. However, this assumption is rarely tested due to a lack of temporally independent validation data. This paper assesses this assumption by developing models of topsoil organic carbon stocks (SOCS) with a deep learning algorithm and data covering mainland China pertaining to the 1980s and 2010s. The temporal prediction performance of models capturing a specific period was assessed by evaluating their performance in the prediction of data during another period. The results revealed that the prediction accuracy of temporal modeling decreased, as indicated by the coefficient of determination, and was lower than that of spatial modeling. The lower prediction accuracy obtained with the DSM-projection approach may result from differences in the magnitudes of influential variables across periods. We found that different levels of environmental similarity and model projection sensitivity to dynamic variables may cause discrepancies in forecast and hindcast accuracy. Our results demonstrate that the prediction error in temporal modeling is related to the degree of environmental similarity between periods. Our findings generally support the implementation of the DSM-projection approach in soil carbon change modeling. However, caution should be exercised, as there exists much uncertainty regarding the projection of spatial models over time.

Single Cell Transcriptome Comparisons of Diagnosis-Relapse Pairs Uncover Programs Associated with Chemoresistance in Pediatric AML

Background: Relapse rates for pediatric AML remain unacceptably high at nearly 50% after frontline therapy. Once relapse occurs, the disease is often refractory to further chemotherapy. Prior studies have shown that acquired mutations conferring chemoresistance are uncommon, and most relapses are thought to arise from the selection of clones already present at diagnosis. Therefore, characterizing chemoresistant disease at the gene expression level could lead to new insights and novel therapy targets. Profiling cases by bulk RNA sequencing has revealed important disease features such as novel gene fusions. However, identifying resistant subpopulations by bulk sequencing is challenging, particularly if they constitute small fractions of the leukemia cells at diagnosis. We hypothesize that by applying single-cell transcriptome profiling (scRNA-seq) to diagnosis-relapse pairs we can identify and characterize the subclones selected by treatment. Additionally, we will discover pathways and gene-expression programs that are associated with chemoresistance. Methods: We profiled thirteen diagnosis-relapse pairs from pediatric AML patients who were treated on or similarly to the Children's Oncology Group (COG) study AAML1031 and consented to banking of cells for future research. Six pairs were banked at Texas Children's Cancer Center and 7 were obtained from the COG biobank. The COG cases were flow-sorted to enrich for blasts before undergoing scRNA-seq with 10X-Chromium and Illumina NovaSeq systems. In addition, two local normal bone marrow cases were profiled, one with prior sorting and one with and without prior sorting. We included scRNA-seq profiles of 8 pediatric AML patients and 4 non-cancer bone marrow samples from Bailur et al. (JCI insight, 2020) as additional controls. Sequencing data were analyzed using CellRanger and Seurat. Four of the COG cases were profiled at the protein level by mass cytometry (CyTOF) and analyzed in Cytobank. Results: In total, for the 13 diagnosis-relapse pairs, 17,233 to 34,246 cells were profiled per patient, and 22,800 transcripts were detected. Unsupervised clustering of our samples and controls yielded 43 clusters (Figure 1) that could be confidently identified as either malignant or non-malignant (normal). We further classified all clusters into inferred hematopoietic developmental stages based on comparisons to publicly available expression reference data (Figure 2). We distinguished between patients with cell types at relapse that were also identified at diagnosis (Group 1), albeit often at a lower proportion, and patients whose cell types at relapse were not detected at diagnosis (Group 2). We also characterized 4 of the diagnosis-relapse pairs by CyTOF with an antibody panel that quantified surface lineage markers and intracellular signaling proteins. The protein expression data validated the RNA data in both the degree of differentiation of AML cells and the degree of similarity between diagnosis and relapse. We identified a Relapse-Up signature composed of 139 differentially expressed genes that were upregulated in AML clusters that expanded between diagnosis and relapse, compared to clusters that diminished between diagnosis and relapse. The Relapse-Up gene signature is enriched in genes that are co-expressed with FLT3, CDK6, and ILK kinase genes. With publicly available TARGET gene expression data, we confirmed that this gene signature is upregulated in relapsed AML samples when compared to diagnosis samples. We further used the profiles of the cell types that expanded between diagnosis and relapse to construct a risk-predictive function that predicted patient outcomes based on bulk RNA-Seq profiles of their diagnostic samples with significant accuracy. Conclusion: Single-cell RNA-seq of diagnosis-relapse pairs from pediatric patients with AML revealed distinct patterns of relapse that were also apparent at the protein level. The Relapse-Up signature was enriched in pediatric AML relapse samples in a large bulk RNA-seq data set, confirming its broader applicability. The kinase co-expression programs present in the signature are likely to be functionally associated with chemotherapy resistance and represent targets for future preclinical therapeutic studies.

Single Cell RNA Sequencing Enables Identification of Mantle Cell Lymphoma Subclones with Drug-Resistant Profiles Already at Diagnosis and May be Used for Therapy Prognostication and Personalization

Mantle cell lymphoma (MCL) is a chronically relapsing B cell lymphoproliferative malignancy characterized by translocation t(11;14)(q13;q32), cyclin D1 overexpression and significant chromosomal instability. One of the main hypotheses of MCL relapses is a clonal evolution driven by therapy selection of subpopulations carrying adverse genetic and epigenetic aberrations. Our previous whole exome sequencing analysis of 25 MCL tumors showed that the majority of the detected single-nucleotide variants and copy number variants (CNV) were already detectable at diagnosis. To further analyze the subclonal structure of MCL at diagnosis and clonal evolution at relapse, we implemented single cell RNA sequencing analysis of five patients with MCL at diagnosis and at the first clinical relapse after failure of standard immunochemotherapy. All analyzed patients achieved at least partial remission. Fresh-frozen tumor cells were sorted to obtain Hoechst − CD45 + population which was processed using single cell 3′ protocol (10X Genomics). The libraries were paired-end sequenced on Novaseq 6000 (Illumina). The reads were aligned to GRCh38 human genome. The MCL cells were distinguished from the normal B cells based on of CCND1 expression and unsupervised clustering. CNVs were predicted using inferCNV package. These CNVs shared a high degree of similarity with the CNVs predicted using available exome sequencing data. The t-SNE representation of the data clearly showed unique positions for the malignant lymphocytes obtained from each of the five analyzed patients ( Figure 1a). In contrast, non-malignant cells (T cells, B cells and monocytes) have grouped together irrespective of the patient or compartment of origin ( Figure 1b). These data suggest two conclusions. First, each patient's malignant cells have unique genetic background. Second, the impact of the microenvironment does not override the impact of the genetic makeup of the non-malignant cells. The data thus suggest comparability of the malignant cells obtained from different compartments. Hierarchical clustering of the per-cell CNV profiles revealed subclonal heterogeneity of the MCL population in all analyzed compartments both at diagnosis and at relapse. In all analyzed patients the clustering identified several large subclones with significant differences (DG L) and at least one minor subclone (DG S) with significant similarities compared to the major clone detectable at relapse (R L). These data suggest that the majority of the diagnostic MCL cells (i.e., DG L clones) were indeed eliminated by therapy, which on clinical level corresponded to achieving remission. On the other hand, the data also suggest that MCL cells with CNV profiles highly similar to the major relapse clone (i.e., R L) were detectable already at diagnosis (i.e., DG S, Figure 1c). To gain a better insight into the biology of the DG S clones with potential drug-resistant phenotype, we analyzed transcriptome differences between DG S and DG L clones. For each patient we identified different sets of significantly upregulated genes in DG S compared to DG L. These included genes coding for important cell surface molecules ( MARCKS, CD52, CD79A, LTB), regulators of cell motility and cytoskeleton dynamics ( STMN1, SWAP70, CFL1, TUBB, TUBA1B, TMSB4X), transcription factor PRDM2, energy metabolism proteins ( MT-ND4, MT-CO1, ATP5MG), protein degradation proteins ( UBA52, PSMC4, UFD1, UBE2L3) and others. Among these genes, significant upregulation of TMSB4X, SWAP70, HLA-A, TUBB4B, RPS26, UBA52, PSMB2, SNRPG, ARPC1B, ATP5MG was detected in three out of five patients. Many of these genes code for oncogenes involved in cell motility and aggressiveness. To further characterize subclonal clusters in MCL population we used marker gene detection and subsequent gene set enrichment analysis, which identified cell adhesion molecules as major category upregulated in four out of five patients in clusters associated with relapse. Our data shows the applicability of the single cell genomic approaches not only to analyze subclonal heterogeneity for the study of disease biology, but also to identify drug-resistant subclones at diagnosis, predict response to various types of therapies thereby enabling therapy personalization. Acknowledgement: Supported by Grant Agency of the Czech Republic GA23-05474S and National Institute for Cancer Research (EXCELES) LX22NPO5102.