bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification and prognostic stratification based on genomic features in myelodysplastic neoplasms, myeloproliferative neoplasms, and their overlapping conditions
Authors: Kim Myungshin , Lee Jong-Mi , Lee Ginkyeng , Ahn Ari , Jung Jin , Kim Yoo-Jin , Park Silvia , Kwag Daehun , Lee Sung-Eun , Park Sung-Soo , Kim Tong Yoon , Cho Bin , Chung Nack-Gyun , Lee Jae Wook , Lee Jae Won , Jo Suejung , Kim Yonggoo , Kim Taeksang 
Published: May 2024

Abstract

In this study, we analyzed clinical and genomic data from 1,585 patients diagnosed with myeloid neoplasms (MNs), including myeloproliferative neoplasms (MPN, n = 715), myelodysplastic neoplasms (MDS, n = 698), MDS/MPN (n = 94), and aplastic anemia (AA, n = 94). We identified ten distinct genomic groups that redefine MN classification using unsupervised genomic clustering through the Dirichlet Process (DP), correlating specific genetic mutations with survival outcomes and disease subtypes. Notably, groups DP1 and DP5, characterized by JAK2 and CALR mutations, respectively, showed a very favorable prognosis among patients with MPN. Groups DP2, DP7, and DP9 demonstrated a very adverse prognosis across MN subtypes. Specifically, DP2 encompasses MDS patients with TP53 mutations and complex karyotypes, DP9 is distinguished by acute myeloid leukemia-related mutations, including NPM1, and DP7 includes patients with SETBP1 mutations, indicating heterogeneous MN phenotypes. DP10 and DP8, linked to SF3B1, DDX41 mutations or chromosome 1q derivatives present a favorable risk profile. Our research emphasizes the critical role of genomic insights in enhancing the classification, prognostic accuracy, and therapeutic stratification of MNs. The survival improvement observed with transplantation in the very adverse risk groups underscores the potential of genomic classifications to inform personalized treatment strategies, signifying a significant step toward the integration of genomics into MN clinical management.
Breast Cancer Detection using Explainable AI and Quantum Neural Network
Authors: Waris Saqqiya , Amin Javaria , sarwar amina , Sharif muhammad , Yasmeen Mussarat 
Published: May 2024

Abstract

The number one cancer type for women happens to be breast cancer. Women of any age are more likely to have this disorder because of where they live, their hormones, and the way they live. Women are more likely to be hurt by this. Many more women will have a better chance of living if breast cancer is found earlier. Computers can detect breast cancer early, improve treatment, and increase survival. Therefore, in this article, three models are proposed for the segmentation and classification of breast cancer. The DeepLabv3 model is trained on the fine-tuned hyperparameters for segmentation. The results are computed on BUSIS and DDSM datasets with the accuracy of 99% and 98% respectively. After that for classification of the breast cancer on different magnification levels. The explainable XAI model is designed on the selected fifteen layers and trained on the fine-tuned hyperparameters for breast cancer classification. This model provides the accuracy of. To analyze the classification outcomes quantum neural network is designed on the selected layers, number of Qubits, and hyperparameters. The classification results are computed on the BreakHis publicly dataset at magnification levels of 40x, 100x, 200x, and 400x. The proposed XAI model provides an accuracy of 96.67% and 100% using a quantum neural network for breast cancer classification.
Singular crack-tip fields for XFEM analysis of cracks propagating through multi-material interfaces with minimal enrichment
Authors: Ebrahimi S. Hamed , Tanha A. K. Ghorbani 
Published: May 2024

Abstract

The order of stress singularity alters based on the situation of the crack-tip in multi-material composites. In this study, singular mode shapes for a crack located in an inclined configuration on a bilaminar interface is extracted using finite element local expansion and then implemented in an extended finite element method with minimal enrichment degrees (Direct-XFEM). Crack-tip enrichment parameters are determined systematically and the reflection of the crack in propagation through the bimaterial interface is captured accurately. Direct-SIFs obtained using complete crack-tip fields in XFEM, properly estimate the local stress state and consequently suggest profound predictions for crack propagation compared with other XFEM alternatives.
Developing Probabilistic Ensemble Machine Learning Models for Home-Based Sleep Apnea Screening using Overnight SpO2 Data at Varying Data Granularity
Authors: Liang Zilu 
Published: May 2024

Abstract

Purpose
This study aims to develop sleep apnea screening models using a large clinical sleep dataset of SpO2 data, with the goal of achieving better performance and generalizability compared to existing models.
Methods
We utilized SpO2 recordings from the Sleep Heart Health Study database (N = 5667). Probabilistic ensemble machine learning was employed to predict sleep apnea status at three AHI cutoff points: ≥5, ≥ 15, and ≥ 30 events/hour. To investigate the impact of data granularity, SpO2 data were resampled to 1/30, 1/60, and 1/300 Hz. Model performance was evaluated across various decision boundaries ranging from 0.05 to 0.95.
Results
Our models demonstrated good to excellent performance, with AUC values of 0.82, 0.85, and 0.90 for cutoffs ≥ 5, ≥15, and ≥ 30, respectively. Sensitivity ranged from good to excellent (0.76, 0.84, 0.89), while specificity ranged from good to excellent (0.87, 0.86, 0.90). Positive predictive values (PPV) ranged from fair to excellent (0.97, 0.83, 0.66), and negative predictive values (NPV) ranged from low to excellent (0.43, 0.87, 0.98). Both decision boundaries and data granularity had a significant impact on model performance, with optimal decision boundaries aligning with the prevalence of positive cases in the cohort. Lower data granularity resulted in decreased model performance.
Conclusion
Our models demonstrated superior performance across all three AHI cutoff thresholds compared to existing large sleep apnea screening models, even when considering varying SpO2 data granularity. The use of probabilistic ensemble machine learning shows promises for developing generalizable sleep apnea screening models with overnight SpO2 data.
Biomass-derived 3D hydrogel bioanode for improved EET processes and COD removal efficiency
Authors: He Xiaoyan , Tan Zheping , Hou Miaomiao , Ma Shuo , Ma Penggai , Cao Runze , Lu Xiaoquan 
Published: May 2024

Abstract

Microbial fuel cells (MFCs) have garnered significant attention in power generation and wastewater treatment fields. Current MFCs have relatively low power density due to limited biofilm colonization and sluggish extracellular electron transfer (EET) processes. Here, a hybrid hydrogel (PPy-CMC-MXene) was prepared by doping MXene with an inexpensive and readily available biomass source carboxymethyl cellulose and polypyrrole. The MFC equipped with the PPy-CMC-MXene/CC anode exhibited a 2-, 30-, 59-, and 4.8-fold power density, specific capacitance, electron transfer efficiency, and coulombic efficiency, respectively, relative to the original carbon cloth (CC) anode. More notably, the MFC equipped with the PPy-CMC-MXene/CC anode had an excellent chemical oxygen demand (COD) removal efficiency of 89.2%. It was shown that the PPy-CMC-MXene/CC electrode offered good biocompatibility and was beneficial to the enrichment of Proteobacteria and Acinetobacter. The anode material has some application prospects in water treatment and the adsorption of electricity-producing bacteria.
Assessment of occurrence, source appointment, and ecological risks of pharmaceuticals and personal care products in the water-sediment interface of Qiantang River in the Hangzhou region
Authors: Wan Yang , Wang Ziming , Xu Kaiping , Wang Wei , Yao Pengcheng , You Aiju 
Published: May 2024

Abstract

Pharmaceuticals and personal care products (PPCPs) have received global attention owing to their potential risks to human health and the ecological environment. However, limited research has explored the occurrence and ecological risks of PPCPs in the Qiantang River (QTR). QTR, the largest water system in Zhejiang Province, China, is significantly influenced by human activities. This study investigated the occurrence, distribution, and ecological risks of 10 types of PPCPs in both surface water and sediment within QTR. The findings revealed that the concentrations of PPCPs detected in surface water ranged from 81.26 to 149.45 ng/L during the wet season (September) and from 98.66 to 198.55 ng/L during the dry season (April). Moreover, in the sediments, PPCP concentration ranged from 63.24 to 80.66 and 72.54 to 75.06 ng/g dw during both wet and dry seasons, respectively. Among the selected PPCPs, triclosan (TCS) exhibited the highest concentration across, different phases and seasons, followed by benzotriazole in surface water. The analysis of sediment-seawater equilibrium distribution indicated that the diffusion tendency of PPCPs was closely correlated with their molecular weights. Particularly, TCS exhibited dynamic equilibrium between water and sediment. Principal component analysis and positive matrix factorization model results indicated similar pollution sources for the detected PPCPs. The dominant sources of the detected PPCPs were identified as wastewater of electroplating enterprises, discharge from wastewater treatment plants, and domestic sewage. The ecological risk assessment based on the risk quotient method revealed that TCS with the highest detected concentration posed a high risk in surface water and a low risk in sediment across all sampling sites. However, other detected PPCPs showed either no or low risks. Additionally, PPCPs showed a higher ecological risk during the dry season than during the wet season.
Fourier Analysis of Signals on Directed Acyclic Graphs (DAG) Using Graph Zero-Padding
Authors: Stankovic Ljubisa , Daković Miloš , Bardi Ali Bagheri , Brajović Miloš , Stanković Isidora 
Published: May 2024

Abstract

Directed acyclic graphs (DAGs) are used for modeling causal relationships, dependencies, and flows in various systems. However, spectral analysis becomes impractical in this setting because the eigendecomposition of the adjacency matrix yields all eigenvalues equal to zero. This inherent property of DAGs results in an inability to differentiate between frequency components of signals on such graphs. This problem can be addressed by alternating the Fourier basis or adding edges in a DAG. However, these approaches change the physics of the considered problem. To address this limitation, we propose a graph zero-padding approach. This approach involves augmenting the original DAG with additional vertices that are connected to the existing structure. The added vertices are characterized by signal values set to zero. The proposed technique enables the spectral evaluation of system outputs on DAGs (in almost all cases), that is the computation of vertex-domain convolution without the adverse effects of aliasing due to changes in a graph structure, with the ultimate goal of preserving the output of the system on a graph as if the changes in the graph structure were not performed.
Active colonisers of the gastrointestinal tract of Atlantic salmon farmed in a warm water region
Authors: Hall Kate , Reid Chantelle E , Tan Qi Zhi , Taylor Richard S , Bissett Andrew , Ruff Nicole , Nowak Barbara F , Bowman John P. 
Published: May 2024

Abstract

Background. Atlantic salmon (Salmo salar) farmed in seawater in Tasmania (lutruwita) can experience temper-atures close to their thermotolerance limit during summer and Vibrionaceae are a major component of their gut microbiome. This study focuses on understanding the microbiome of the Atlantic salmon gastrointestinal tract via the taxonomy, population dynamics, and traits of the main colonists.
Results. Multiyear survey and co-occurrence network analyses confirmed novel members of Aliivibrio reliably colonise the gut microbiome in Atlantic salmon farmed in marine pens in Tasmania when the confounding influences of DNA and bacteria from feed and water was considered. Aliivibrio and Mycoplasma were the main taxa found to colonise the gut mucosal layers. Warm water temperature(19°C) experiments performed in seawater tanks indicated Aliivibrio and Vibrio rapidly become predominant in the digesta of Atlantic salmon that had reduced feeding and high faecal cast production while Mycoplasma were found to be mostly absent. Salmon-associated Aliivibrio isolates were found to share many genes required for host colonisation with that of the faunal mutualist Aliivibrio fischeri. Though most salmon digesta isolates seem to lack obvious virulence factors, strains of two abundant novel colonising Aliivibriostrain groups possessed the genes for cytolethal distending toxin (CDT).
Conclusions. Multiyear surveys confirm Atlantic salmon farmed in the relatively warm conditions of Tasmania are persistently colonised by the same, largely novel, Aliivibrio species and a restricted range of other Vibrionaceae. Some of these species were found to be particularly adept at colonising salmon in warm water conditions. Though some Aliivibrio coloniser strains possessed CDT-genes it is yet unknown whether CDT-gene-positive Aliivibrio negatively impact Atlantic salmon . Since the strains studies possess a range of different colonization enabling mechanisms, interactions between Vibrionaceae in the gut of Atlantic salmon could be important for overall fish health, especially when they are exposed to stressful conditions, such as elevated sea surface temperatures.
Optimized SVM With BWA for CKD Prediction
Authors: P Sirisha A B N , N Dhanalakshmi , S Siva Priyanka 
Published: May 2024

Abstract

Chronic Kidney Disease (CKD) is a significant health concern affecting millions worldwide. It is a prevalent health condition which results in gradual loss of Kidney function. About 10% of global population is affected by it which accounts to more than 800 million people. There is a need for early diagnosis and intervention to mitigate its risk. By Utilizing the AI/ML algorithms prediction of CKD can be done accurately. In this paper the efficiency of Black widow optimization algorithm with SVM is proposed in identifying whether it is prone to CKD or not. CKD dataset from standard UCI machine learning repository which consists of wide range of clinical features and biomarkers which aids in CKD Classification has been used. The accuracy and execution time of BWO-SVM achieved is 98.75% and 0.0023s in classifying CKD. It is observed that the BWO SVM algorithm outperforms other algorithms in terms of both accuracy and execution time metrics. The intention of this project is to contribute in CKD diagnosis and help to provide early detection and timely intervention.
Convergent Synthesis, Kinetics and Computational Comprehensions of Indole- (Phenyl)triazole Bi-heterocycles Amalgamated with Propanamides as Elastase Inhibitors
Authors: Shakila Shakila , Abbasi Muhammad Athar , Rehman Aziz-ur- , Siddiqui Sabahat Z. , Nazir Majid , Muhammad Shabbir , Raza Hussain , Shah Syed A. Ali , Shahid Muhammad , Chaudhry Aijaz Rasool , Kim Song Ja 
Published: May 2024

Abstract

In the study presented here, 4-(1H-indol-3-yl)butanoic acid was sequentially converted into ethyl 4-(1H-indol-3-yl)butanoate, 4-(1H-indol-3-yl)butanohydrazide and 5-[3-(1H-indol-3-yl)propyl]-1,2,4-triazole-2-thiol as a nucleophile. In a parallel series of reactions, various electrophiles were synthesized by reacting aryl amines with 3-bromopropanoyl chloride to afford N-(aryl)-3-bromopropanamides. Then the nucleophilic substitution reaction of 5 was carried out with different electrophiles, to achieve final bi-heterocyclic derivative. The structural confirmation of all the synthesized compounds was done by IR, 1H-NMR, 13C-NMR and CHN analysis data. The inhibitory effects of these bi-heterocyclic propanamides were evaluated against elastase, and all these molecules were identified as potent inhibitors relative to the standard used. The kinetics mechanism was ascribed by evaluating the Lineweaver–Burk plots, which revealed that compound 9d inhibited elastase competitively to form an enzyme–inhibitor complex. The inhibition constant Ki calculated from Dixon plots for this compound was 0.51 µM. These molecules also exhibited mild cytotoxicity toward red blood cell membranes, when analyzed through hemolysis. So, these molecules might be deliberated as nontoxic medicinal scaffolds for dealing with the elastase related ailments such as lungs diseases, cyclic neutropenia, pruritic skin disease and liver infection.
Enhancement of laser noise image based on photorefractive nonlinearity of lithium niobate crystals
Authors: Gao Yuzhen , Gao Chengyong , Han Guangbing , Bao Fangxun 
Published: May 2024

Abstract

Enhancement of laser noise image based on stochastic resonance process in lithium niobate crystals is studied theoretically and experimentally in this paper. A noise function based on the refractive change in crystal is proposed for the numerical simulation. The simulation results show that the presence of noise can induce the optical nonlinearity in lithium niobate crystals to change from self-defocusing to self-focusing. Under an appropriate noise intensity, the strongest stochastic resonance process can occur, resulting in restored the image signal submerged by noise eventually. There is a good agreement between the experimental and numerical results. When a single noise image is transmitting in a nonlinear crystal system, the stochastic resonance process is considered to be the result of the self-coupling between the scattered noise and the transmitted signal when the noise and signal are scattered simultaneously in the crystal. The research results in this paper can provide theoretical and experimental guidance for the early acquisition and post-processing of images containing inherent noise, and will promote the industrialization of noise information sensing technology largely.
Comprehensive analysis of RNA sequencing and integrated machine learning reveals a metabolism related prognostic signature for improving outcomes in ccRCC
Authors: Liu Xiuheng , Liu Yunxun , Yan Zhiwei , Yang Rui , Wang Minghui , Wang Lei , Weng Xiaodong , Chen Zhiyuan 
Published: May 2024

Abstract

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, Single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis. Simultaneously, we created a machine learning-based approach for creating metabolism related prognostic signature (MRPS). Following the administration of immune checkpoint inhibitors (ICIs), MRPS shows consistent and strong performance and is an independent risk factor for overall survival. Furthermore, compared to 51 published signatures and conventional clinical features, MRPS showed substantially higher accuracy. GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. In conclusion, MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC.
Construction of a competing endogenous RNA network and identification of ITGA2 as a potential target in papillary thyroid carcinoma
Authors: Wu Guoliang , Wang Xinyu , Zhu Yiming , Liu Shaoyan , Ni Song 
Published: May 2024

Abstract

Background
Papillary thyroid carcinoma (PTC) stands as the prevalent malignancy within the endocrine system. This study's primary aim is to probe the domain of potential biomarkers associated with PTC
Methods
Datasets from GEO and TCGA databases were used to analyze the differentially expressed mRNAs (DE-mRNAs), miRNA (DE-miRNAs), and methylated DNAs, which were further integrated to establish a mRNAs-miRNAs-mRNAs competing endogenous RNA (ceRNA) network by the integrative bioinformatics analyses. Additionally, pathway enrichment analysis was performed to reveal the functions of the ceRNAs by means of Metascape. qRT-PCR and western blot were used to evaluate the expression level of several genes. Methylation-specific PCR was used to assess the methylation levels of Integrin Subunit Alpha 2 (ITGA2) promoter. CCK-8 and transwell assays were used to investigate the biological function of ITGA2.
Results
160 potential ceRNA pairs were identified from the intersection of mRNA-miRNA-mRNA regulatory network. Simultaneously, 970 methylated genes including 127 hypermethylated and 843 hypomethylated were recognized by overlapping the methylation datasets. Then, we retained 51 methylation-related ceRNA pairs. KEGG pathway enrichment analysis revealed that the 51 genes were primarily involved in ECM-receptor interaction and proteoglycans in cancer. Finally, we demonstrated that ITGA2 acted as an oncogene in thyroid cancer.
Conclusion
Our study constructed an intricate mRNA-miRNA-mRNA regulatory network as well as pinpointed numerous prospective candidates within the domain of thyroid cancer. Furthermore, our findings suggest that ITGA2 could potentially serve as a viable target in the treatment of thyroid cancer.
Agromorphological genetic identification of almond genetic resources regarding physicochemical properties in Turkey
Authors: ÇOLAK Ayşen Melda , GUNDOGDU Muttalip , BERK Selma KURU , SAHİN Turhan , CANAZ Mustafa 
Published: May 2024

Abstract

Almond is one of the important fruit types for the nuts and cosmetics industry. At this stage, the selection of cultivars to be grown according to their intended use is important and new cultivars are breding. In this study, the physical and chemical properties of fruits of Ferragnes, Ferraduel and Nonpareil cultivars and Sıra, Ak and Nurlu genotypes were examined. Nurlu genotype was found to have higher values than other cultivars and genotypes in terms of fruit weight (1.52 g) and dimensions (28.28 mm length, 14.25 mm width, 7.97 mm thickness). While Sıra genotype stood out in terms of micro elements (Fe, Mn), Nurlu genotype stood out in terms of macro elements (N, P, K). The highest amounts of lineloeic and oleic acids (10.07 and 68.71%, respectively) were obtained in the Ferragnes cultivar, and the highest oil content was obtained in the Sıra genotype (60.09%). In terms of phenolic compounds, the Nonpareil cultivar, which has the highest content of catechin (45.51 mg kg− 1) and o-coumaric acid (2.52 mg kg− 1), gained value. When the relationships of all traits were examined, it was determined that Nurlu genotype and Ferragnes cultivar were independent from others. While a linear relationship was found between oleic, stearic, palmitic and lionelenic acids, an opposite relationship was found these acids to linoleic acid. In this study, it was determined that genotypes have superior characteristics as well as cultivars, and it was observed that they can guide growers in the cultivar selection stage in garden establishment.