Large Number Of Samples Research Articles

Semi-supervised few-shot class-incremental learning based on dynamic topology evolution

Incremental learning of new classes is crucial to developing real-world artificial intelligence systems. However, in some cases, we can only use a limited number of images to train the incremental model due to the difficulty of obtaining them. Few-shot class-incremental learning (FSCIL) is a new paradigm of incremental learning, which continually learns new concepts from a few examples. This setup can quickly lead to catastrophic forgetting and overfitting problems, severely affecting model performance. To address these problems, we propose a dynamic topological evolution (DTE) model that unifies knowledge of old and new categories. In this work, we focus on a novel semi-supervised few-shot class-incremental learning (SSFSCIL) to incrementally learn new categories with a limited number of labeled samples and a large number of unlabeled samples. In detail, we use the Voronoi polygon criterion to divide the feature space and construct a Hebbian graph to represent topological relationships between classes. Then, the corresponding k-nearest neighbor subgraph is constructed for the new incremental task, and label propagation of graph nodes is carried out on the unlabeled set to generate the corresponding pseudo labels. Finally, the new category knowledge is evolved into the actual category knowledge by integrating the Hebbian graph and the k-nearest neighbor subgraph obtained in the new incremental task. Extensive experiments on the mini-ImageNet and CIFAR100 datasets show that the accuracy of the DTE outperforms the baseline and improves by 1.41% and 1.13% on the last incremental task. Furthermore, our methodology achieved a 35.6% time improvement compared to full-supervised methods on the CIFAR100 dataset. In addition to the experiments on the two datasets mentioned above, the validation of our proposed DTE model extends to the engineering application domain of the RESICS-45 aircraft image scene classification dataset.

A new method of high-resolution NPP estimation and its implementation in caragana shrublands on the Loess Plateau, China

ABSTRACT Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management.

A new ECT image reconstruction algorithm based on Vision transformer (ViT)

Aiming at the problem of low accuracy of ECT image reconstruction, this paper proposes an ECT image reconstruction algorithm based on Vision Transformer (ViT). ViT is a method that applies the Transformer to the field of image classification, which is characterized by strong long-distance dependency learning ability and strong multi-modal fusion ability compared to CNN. This paper fully utilizes the characteristics of ViT to transform the image reconstruction process of ECT into the classification process of ViT. Here, a method is proposed to use the object field distribution as the image classification label. This method converts a two-dimensional image of the object field distribution into a one-dimensional vector, which is the label vector of the image classification. These vectors and their corresponding reconstructed images obtained by the Landweber algorithm form the sample set of ViT. Extracting a large number of flow pattern samples with various shapes and distributions through COMSOL is used as a training set. After training ViT, this network model is used to infer the labels of the predicted flow patterns. After post-processing this label, the corresponding reconstructed image can be obtained. Finally, simulation experiments are conducted, and the experiments results show that the image errors and correlation coefficients of the reconstructed images obtained through the algorithm in this paper are better than those of Tikhonov algorithm, Landweber algorithm and Long Short-Term Memory Network(LSTM). And this algorithm has better resistance to noise interference than Tikhonov algorithm, Landweber algorithm, and LSTM. This also provides a new approach and means for ECT image reconstruction.

Comparison of a quasi Newton method using Broyden’s update formula and an adjoint method for determining local magnetic material properties of electrical steel sheets

Purpose Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data. Design/methodology/approach The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered. Findings The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed. Originality/value The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

Abstract PO5-25-09: rs2242652 POLYMORPHISM OF THE hTERT GENE IN WOMEN WITH BREAST CANCER

Abstract Introduction: Breast cancer is the most common neoplasm in women around the world. There are several risk factors for this cancer: sex, age, body mass index (BMI), hormone therapy, family history, genetic mutations, among several others. The vast majority of breast tumors are characterized as carcinomas and may originate from ducts or lobules. Immunohistochemistry is related to the expressions of the estrogen receptor (ER) and progesterone receptor (PR), as well as the expression of the human epidermal growth factor receptor 2 (HER2), cell proliferation or the absence of any expression from receptors on the tumor membrane. The telomeres protect the chromosomes natural endings from suffering DNA loss. Telomerase is a ribonuclear protein complex (RNA and protein) that neutralizes telomeres' shortening. Recurrent point mutations in the TERT telomerase promoter have been identified in several cancers, and the rs2242652 polymorphism is a single nucleotide change, and has been described as associated with breast cancer risk. Objective: The objective of this study is to analyze the TERT rs2242652 polymorphism in breast cancer women and to test the correlation of such data with the prognosis and various clinical variables. Methods: The study was conducted as an observational clinical study of women with breast cancer. A blood sample was collected from the patient and PCR was performed to extract the DNA and to analyze the telomerase polymorphisms in this population. To ensure a more descriptive analysis, the data was used in the construction of tables and graphs, that were then studied in a qualitative manner. The associations were analyzed through direct correlation tests and were followed by comparisons of categorization groups. Results and discussion: Fifty-four women presented polymorphism of the telomerase hTERT gene rs2242652. They were divided into two groups according to the A or G allele that confer the GA/AA and GG gene variants. It was found that the GA/AA group had a trend towards more aggressive tumors, presenting 33% triple negative and 22% of tumors with HER2 overexpression, different from the GG group: 19% triple negative and 10% HER2 overexpression. More aggressive T4 tumors, were more expressive in the GA/AA gene variant group representing 20% of the patients, whereas in the GG group they represented only 8%. Conclusion: The data found in this study on the telomerase TERT gene polymorphism rs2242652 with breast cancer refers to a tendency found in tumors that have a more severe prognosis, to associate with the gene variant linked to the A allele of this gene, however a larger sample number is required for statistical confirmation. Citation Format: Miriam Alvares, Elisa Mascarenhas, Joao Matos. rs2242652 POLYMORPHISM OF THE hTERT GENE IN WOMEN WITH BREAST CANCER [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-25-09.

Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder

Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results on test set data, making it difficult to accurately diagnose transformer faults. A conditional Wasserstein generative adversarial network with gradient penalty optimization (CWGAN-GP) is adopted in this paper, which based on gradient penalty optimization and expand the oil chromatography fault samples of 500 sets of transformer oil chromatography data with 5 types of faults. The proposed method is used to classify transformer faults using a deep autoencoder, and the sample quality of the neural network model proposed in this paper is compared with several other variants of generative adversarial neural network models. The research results show that after using the method proposed in this paper for sample expansion, the overall accuracy of fault diagnosis can reach 93.2 %, which is 4.98 % higher than the original imbalanced samples. Compared with other sample expansion methods, the accuracy of fault diagnosis of the algorithm in this paper is improved by 1.70 %–3.05 %.

The real-time dynamic liquid level calculation method of the sucker rod well based on multi-view features fusion

In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer. During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion (the method proposed in this paper) performs best with the lowest mean absolute error (MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.

An iterative and subsequent proximation method to map historical crop information with satellite images

Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2… process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history.

Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer

BackgroundThis study aimed to explore the expression, molecular mechanism and its biological function of potassium two pore domain channel subfamily K member 1 (KCNK1) in bladder cancer (BC).MethodsWe integrated large numbers of external samples (n = 1486) to assess KCNK1 mRNA expression levels and collected in-house samples (n = 245) for immunohistochemistry (IHC) experiments to validate at the KCNK1 protein level. Single-cell RNA sequencing (scRNA-seq) analysis was performed to further assess KCNK1 expression and cellular communication. The transcriptional regulatory mechanisms of KCNK1 expression were explored by ChIP-seq, ATAC-seq and ChIA-PET data. Highly expressed co-expressed genes (HECEGs) of KCNK1 were used to explore potential signalling pathways. Furthermore, the immunoassay, clinical significance and molecular docking of KCNK1 were calculated.ResultsKCNK1 mRNA was significantly overexpressed in BC (SMD = 0.58, 95% CI [0.05; 1.11]), validated at the protein level (p < 0.0001). Upregulated KCNK1 mRNA exhibited highly distinguishing ability between BC and control samples (AUC = 0.82 [0.78–0.85]). Further, scRNA-seq analysis revealed that KCNK1 expression was predominantly clustered in BC epithelial cells and tended to increase with cellular differentiation. BC epithelial cells were involved in cellular communication mainly through the MK signalling pathway. Secondly, the KCNK1 transcription start site (TSS) showed promoter-enhancer interactions in three-dimensional space, while being transcriptionally regulated by GRHL2 and FOXA1. Most of the KCNK1 HECEGs were enriched in cell cycle–related signalling pathways. KCNK1 was mainly involved in cellular metabolism–related pathways and regulated cell membrane potassium channel activity. KCNK1 expression was associated with the level of infiltration of various immune cells. Immunotherapy and chemotherapy (docetaxel, paclitaxel and vinblastine) were more effective in BC patients in the high KCNK1 expression group. KCNK1 expression correlated with age, pathology grade and pathologic_M in BC patients.ConclusionsKCNK1 was significantly overexpressed in BC. A complex and sophisticated three-dimensional spatial transcriptional regulatory network existed in the KCNK1 TSS and promoted the upregulated of KCNK1 expression. The high expression of KCNK1 might be involved in the cell cycle, cellular metabolism, and tumour microenvironment through the regulation of potassium channels, and ultimately contributed to the deterioration of BC.

Fracture identification in shale reservoir using a deep learning method: Chang 7 reservoirs, Triassic Yanchang formation

Natural fractures in shale oil formations play a significant role in fluid flow channels. However, their identification can be difficult due to the problems of subtle log responses of fractures and impact of sedimentary cycles on logging curve. To address these issues, a fracture identification method (FRNN) based on deep learning is proposed. FRNN combines window sliding and bidirectional recurrent neural networks (BiLSTM). A large number of sequence data samples from logs are obtained through window sliding, denoted with fracture labels based on rock core observations or borehole image log interpretation. BiLSTM processes sequences bidirectionally to mitigate the impact of sedimentary cycles on well logging curves and amply log responses caused by fractures. The integration of the sliding process and BiLSTM endeavors to address the challenge of fracture identification, which is often marked by significant uncertainty and subtle log responses. The experiments employ a dataset from Triassic Yanchang Formation's Chang 7 reservoirs in Q Oilfield, China, which contains shales and sandstones formed in braided river delta lacustrine systems that act as oil reservoirs. The statistical analysis and blind well test show that the fracture identification by the proposed FRNN method is consistent with those of core descriptions. Furthermore, examination of wellbore cross-sections reveals a congruence between the anticipated vertical distribution of fractures and the influencing factors thereof, thus validating the efficacy of the proposed methodology. The findings of this study can provide assistance in subsurface fracture modeling and hydraulic fracturing operations, etc.

Prediction of Retinol in Fortified Maize Flour using Fourier Transform - Near Infrared Spectroscopy

Food fortification is one strategy for addressing micronutrient deficiencies among the population groups at risk. Non-compliance with fortification standards hinders the success of fortification programs. This is due to a lack of techniques to rapidly check the amounts of the added fortificants. Fourier transform - near-infrared (FT-NIR) spectroscopy is a fast and reliable technique that would be used to ensure adherence to requirements. This study aimed to investigate the potential of using FT-NIR spectroscopy to predict the amount of retinol in fortified maize flour. 150 fortified maize flour samples were used in this study. Partial least squares regression (PLS-R) was used to build calibration models based on the retinol reference values obtained by high-performance liquid chromatography (HPLC), and fortified maize flour NIR spectra acquired from the FT-NIR spectrophotometer. Two calibration models were developed to predict retinol above and below 1.0 mg/kg. The performance metrics of model one developed to predict retinol &lt; 1.0 mg/kg were: R2c = 0.81, RMSEE = 0.08, RPD = 2.29 and R2v = 0.82, RMSEP = 0.09, RPD = 2.07 for the calibration and validation, respectively. The second model developed to predict retinol ≥ 1.0 mg/kg had the following performance metrics: R2c = 0.93, RMSEE = 0.16, RPD = 3.58 and R2v = 0.81, RMSEP = 0.22, RPD = 2.43 for the calibration and validation, respectively. Overall, the findings demonstrated that FT-NIR spectroscopy can be utilised to reliably predict retinol levels in fortified maize flour samples. FT-NIR spectroscopy, by replacing time-consuming and laborious wet chemistry laboratory procedures, has the potential to be used for rapid regulatory monitoring of fortification compliance for a large number of samples.

Comparison of bladder carcinogenesis biomarkers in the urine of traditional cigarette users and e-cigarette users.

During the use of electronic cigarettes (e-cigarettes), users are still exposed to carcinogens similar to those found in tobacco products. Since these carcinogens are metabolized and excreted in urine, they may have carcinogenic effects on the bladder urinary tract epithelium. This meta-analysis aimed to compare bladder cancer carcinogens in the urine of tobacco users and e-cigarette users using a large number of samples. A systematic meta-analysis was performed using data obtained from several scientific databases (up to November 2023). This cumulative analysis was performed following the Preferred Reporting Items for Systematic Evaluation and Meta-Analysis (PRISMA) and Assessing the Methodological Quality of Systematic Evaluations (AMSTAR) guidelines, according to a protocol registered with PROSPERO. This study was registered on PROSPERO and obtained the unique number: CRD42023455600. The analysis included 10 high-quality studies that considered polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs) and tobacco-specific nitrosamines (TSNAs). Statistical indicators show that there is a difference between the tobacco user group and the e-cigarette user group in terms of 1-Hydroxynaphthalene (1-NAP) [weighted mean difference (WMD)10.14, 95% confidence interval (CI) (8.41 to 11.88), p < 0.05], 1-Hydroxyphenanthrene (1-PHE) [WMD 0.08, 95% CI (-0.14 to 0.31), p > 0.05], 1-Hydroxypyrene (1-PYR) [WMD 0.16, 95% CI (0.12 to 0.20), p < 0.05], 2-Hydroxyfluorene (2-FLU) [WMD 0.69, 95% CI (0.58 to 0.80), p < 0.05], 2-Hydroxynaphthalene (2-NAP) [WMD 7.48, 95% CI (4.15 to 10.80), p < 0.05], 3-Hydroxyfluorene (3-FLU) [WMD 0.57, 95% CI (0.48 to 0.66), p < 0.05], 2-Carbamoylethylmercapturic acid (AAMA) [WMD 66.47, 95% CI (27.49 to 105.46), p < 0.05], 4-Hydroxy-2-buten-1-yl-mercapturic acid (MHBMA) [WMD 287.79, 95% CI (-54.47 to 630.04), p > 0.05], 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNAL) [WMD 189.37, 95% CI (78.45 to 300.29), p < 0.05], or N0-nitrosonornicotine (NNN) [WMD 11.66, 95% CI (7.32 to 16.00), p < 0.05]. Urinary bladder cancer markers were significantly higher in traditional tobacco users than in e-cigarette users.Systematic review registration: PROSPERO (CRD42023455600: https://www.crd.york.ac.uk/PROSPERO/).

Development of Two Eco-Friendly and High-Throughput Microwell Spectrophotometric Methods for Analysis of an Antibacterial Drug Tulathromycin in Pharmaceutical Bulk Form.

Tulathromycin (TUL) is a triamilide antibacterial drug which has been approved for use in the European Union and the United States for the treatment and prevention of bovine respiratory diseases. The existing methods for determination of TUL in its pharmaceutical bulk form are very limited and suffer from major drawbacks. The aim of this study was the development of two innovative microwell spectrophotometric methods (MW-SPMs) for determination of TUL in its pharmaceutical bulk form. The formation of charge transfer complexes (CTCs) of TUL, as an electron donor, was investigated with 2,5-dihydroxy-3,6-dichlorocyclohexa-2,5-diene-1,4-dione (HCD) and 2,3-dichloro-5,6-dicyano-p-benzoquinone (CBQ), as π-electron acceptors. The CTCs were characterized by using UV-visible spectrophotometry and computational calculations. The reactions were employed for the development of two MW-SPMs with a one-step for the quantitative analysis of TUL. The formation of CTCs was confirmed via the formation of characteristic absorption bands with maximum absorption at 520 and 460 nm for CTCs with HCD and CBQ, respectively. The stoichiometry of both CTCs was found to be 1:1, and the values of different spectroscopic and electronic constants confirmed the stability of the CTCs. The mechanisms of the reactions were postulated. The linear range of both MW-SPMs was 10-500 µg/mL. The limits of quantitation were 13.5 and 26.4 µg/mL for methods involving reactions with HCD and CBQ, respectively. Both methods were successfully applied to the quantitation of TUL in pharmaceutical bulk form with acceptable accuracy and precision. The results of eco-friendliness/greenness assessment proved that both MW-SPMs fulfill the requirements of green analytical approaches. In addition, the one-step reactions and simultaneous handling of a large number of samples with micro-volumes in the proposed methods gave them the advantage of high throughput analysis. This study described two new MW-SPMs as valuable analytical tools for the determination of TUL.

Middle Pleistocene Hippopotamuses from the Italian Peninsula: An Overview

Our work presents an updated overview of the Italian Middle Pleistocene records of hippopotamuses, including the two species Hippopotamus antiquus and Hippopotamus amphibius. In addition to reviewing several well-known fossils in the literature, a large number of samples are described herein for the first time. Following the recent results published in the literature, where the skull from the Middle Pleistocene of Cava Montanari (ca. MIS 13) was confirmed to belong to H. amphibius, one of the aims of this work was to investigate the H. antiquus–H. amphibius transition. A morphological analysis applied to a large sample confirmed the validity of the arrangement of the enamel ridges of the external surfaces of the lower canines as a diagnostic character for specific identifications. Finally, biometric analyses allowed us to test the size variability during the Middle Pleistocene, which confirmed that H. antiquus was generally larger than H. amphibius. Nevertheless, the remains of H. antiquus dated to ca. 600 ka show a reduced size when compared to older fossils of the same taxon, probably as a response to severe glacial conditions that occurred during MIS 16.

Potential Diagnostic Value of Abnormal Pyroptosis Genes Expression in Myelodysplastic Syndromes (MDS): A Primary Observational Cohort Study

Background: Myelodysplastic syndromes (MDS) are determined by ineffective hematopoiesis and bone marrow cytological dysplasia with somatic gene mutations and chromosomal abnormalities. Accumulating evidence has revealed the pivotal role of NLRP3 inflammasome activation and pyroptotic cell death in the pathogenesis of MDS. Although MDS can be diagnosed with a variety of morphologic and cytogenetic tests, most of these tests have limitations or problems in practice. Materials and Methods: In the present study, we evaluated the expression of genes that form the inflammasome (NLRP3, ASC, and CASP1) in bone marrow specimens of MDS patients and compared the results with those of other leukemias to evaluate their diagnostic value for MDS. Primary samples of this observational cohort study were collected from aspiration samples of patients with myelodysplastic syndromes (27 cases) and patients with non-myelodysplastic syndrome hematological cancers (45 cases). After RNA extraction and c.DNA synthesis, candidate transcripts and housekeeping transcripts were measured by real-time PCR method (SYBER Green assay). Using Kruskal-Wallis the relative gene expressions were compared and differences with p value less than 0.05 were considered as significant. Discrimination capability, cut-off, and area under curve (AUC) of all markers were analyzed with recessive operation curve (ROC) analysis. Results:  We found that Caspase-1 and ASC genes expressed at more levels in MDS specimens compared to non-MDS hematological malignancies. A relative average expression of 10.22 with a p-value of 0.001 and 1.86 with p=0.019 was detected for Caspase-1 and ASC, respectively. ROC curve analysis shows an AUC of 0.739 with p=0.0001 for Caspase-1 and an AUC of 0.665 with p=0.0139 for ASC to MDS discrimination. Conclusion: Our results show that Caspase-1 and ASC gene expression levels can be used as potential biomarkers for MDS diagnosis. Prospective studies with large sample numbers are suggested.  

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. More specifically, both of the proposed frameworks utilize AlpaCare as base LLM which employs both few-shot in-context learning and instruction tuning techniques to extract PICO-related terms from the clinical trial reports. We applied these approaches to the widely used coarse-grained datasets such as EBM-NLP, EBM-COMET and fine-grained datasets such as EBM-NLPrev and EBM-NLPh. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at https://github.com/shrimonmuke0202/AlpaPICO.git.

Phenolic profiles differentiate wild bilberry and cultivated blueberry fruit

The dark fruit of European wild-grown blueberry known as common bilberry (Vaccinium myrtillus L.) is associated with many health-promoting properties and has a steadily growing global market, especially in the food supplement sector. The adulteration of bilberry products with some less expensive fruits and berries with an intense color is a well-known problem. Phytochemical profiling is a viable option to discern different plant species from each other. In this study, the chemical composition of the fruit of wild bilberry and its close relative, cultivated blueberry (Vaccinium sp.), were analyzed and compared. A large number of samples of different geographical origin was collected. The major phenolics, including anthocyanins, chlorogenic acid, and condensed tannins, were determined by liquid chromatography using various detection methods. Significant differences were found in the phenolic profiles between wild-grown bilberry and cultivated blueberry. Principal component analysis indicated that it was possible to distinguish bilberry from cultivated blueberry based solely on their anthocyanin or tannin profiles. However, the geographical origin of the berries could not be identified according to their phenolic compound composition.

Quantification of total human DNA using qPCR in mixture samples of Crimes Against Sexual Freedom to estimate the success of analysis with autosomal STRs and Y-STRs

ABSTRACT In this study, 241 mixed autosomal STR genetic profiles (swabs − 172, slides − 69) from processed casework samples were analysed. From these samples, DNA was extracted and quantified by qPCR; the results were analysed with the PowerQuant Analysis Software. The extracted DNA samples were amplified with the Investigator 24Plex QS and VeriFiler kits and separated and detected in an ABI 3500 Genetic Analyzer. A quantification correlation was observed between the total autosomal DNA targets [Auto] and the Y chromosome target [Y] in mixed samples in which the success of amplification of the male autosomal STRs markers was associated with high values of the quantification of the target of [Y] and low DNA ratio values [Auto]/[Y] (p = 0.00 < 0.05). Samples with [Auto]/[Y] ratio values much higher than the estimated value required analysis with Y-STR markers. The study herein, which assessed a relatively large number of sexual assault samples, demonstrates that quantitation ratio values of DNA mixtures effectively contribute to the decision-making process on whether and what genetic marker system should be selected for analysis of a male component in unknown casework samples.

Applicability of enzymatic and phenol–sulfuric acid methods for determination of cellulose nanofibers in ecotoxicity testing

In parallel with the development of novel materials, such as cellulose nanofibers (CNFs), their effects on aquatic organisms should be evaluated. In ecotoxicity testing, concentration measurements are important to obtain accurate exposure concentrations; however, few studies have measured the concentration and dispersion state of CNFs in test solutions. Therefore, we evaluated the applicability of enzymatic and phenol–sulfuric acid methods for CNF quantification in ecotoxicity testing. In the enzymatic method, CNF content was determined by quantifying the d-glucose produced from the CNFs using cellulase with a glucose assay kit. Three types of cellulases were purchased, and impurities such as d-glucose were identified and removed. The differences in CNF degradation efficiency among the cellulases were evaluated, and a cellulase suitable for CNF degradation was selected. In the phenol–sulfuric acid method, a microplate-based method was employed to handle a large number of samples. We demonstrated that both the enzymatic and phenol–sulfuric acid methods could quantify CNFs in media for ecotoxicity testing. The enzymatic method was able to measure lower CNF concentrations (~ 1 mg/L) than the phenol–sulfuric acid method (~ 10 mg/L) and had less interference with CNF quantification by algae and non-cellulosic polysaccharides.

Pyrrolizidine alkaloids and tropane alkaloids in milk samples from individual dairy farms of the German federal states of Bavaria and Schleswig-Holstein

1,2-Dehydro-pyrrolizidine alkaloids (PA), their corresponding N-oxides (PANO) and tropane alkaloids (TA), are toxic plant metabolites. If plant material, containing these toxins, is present in the feed of dairy cows these toxins can be transferred into milk. Here, milk was sampled directly from dairy farms in the German federal states of Bavaria and Schleswig-Holstein in 2020–2022 in order to investigate a possible contamination of milk at the production stage. In total, 228 milk samples were analysed for 54 PA/PANO and two TA by a sensitive LC–ESI-MS/MS method. In addition, a subset of milk samples (n = 85) was independently analysed for TA by a cooperating laboratory for verification. PA/PANO were found in 26 samples (11%) with a low median sum content of the contaminated samples of 0.024 µg/L. The highest level of contamination was 5.6 µg/L. Senecionine-, lycopsamine- and heliotrine-type PA/PANO were detected. In four samples (1.8%), atropine was determined up to 0.066 µg/L. The toxin levels in the milk samples hardly contributed to the total daily exposure. These data are first-time results on contamination rates and levels occurring in milk from individual dairy farms, based on a large sample number.