bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hot rolled steel surface defect detection and classification using an automatic ensemble approach
Authors: Vasan Vinod , Sridharan Naveen Venkatesh , Sugumaran Vaithiyanathan , Jeyavadhanam Balasundaram Rebecca 
Published: May 2024

Abstract
This study introduces an ensemble-based Deep Neural Network (DNN) model for detecting defects on steel surfaces. The method suggested in this study classifies steel surface conditions into six possible fault categories, namely, crazing, inclusion, rolled in, pitted surface, scratches, and patches. The images undergo preprocessing and extraction of features in spatial and frequency domains using image segmentation techniques such as grey level difference method (GLDM), fast Fourier Transform (FFT), grey level co-occurrence matrix (GLCM), texture analysis and discrete wavelet transform (DWT). The ensembling of image features into a fused feature pool is carried out after the preprocessing of input images that are provided as input to a light-weight neural network model for training and testing. The performance of the model is comprehensively evaluated via an ablation study both before and after ensembling. In addition, the model capability is effectively analyzed using receiver operating characteristics (ROC) curve, confusion matrix from which classification accuracy of the model could be obtained and other parameters including precision and f1-score. It was observed that the proposed deep learning network presents phenomenally high accuracy of 99.72% for detection and classification of steel surface faults. This result was found to be superior when compared with the performance of the same neural network over each feature type individually. This study also compares the classification results of the model built based on the ensembled feature set with the results of various other classification approaches available in literature. The ensemble-based model could potentially be integrated into existing inspection systems for real-time, efficient and robust condition monitoring of steel surfaces.
Unifying O(3) equivariant neural networks design with tensor-network formalism
Authors: Li Zimu , Pengmei Zihan , Zheng Han , Thiede Erik , Liu Junyu , Kondor Risi 
Published: May 2024

Abstract
Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increase, maintaining parsimony and equivariance becomes increasingly challenging. In this paper, we propose using fusion diagrams, a technique widely employed in simulating SU(2)-symmetric quantum many-body problems, to design new spatial equivariant components for neural networks. This results in a diagrammatic approach to constructing novel neural network architectures. When applied to particles within a given local neighborhood, the resulting components, which we term ‘fusion blocks,’ serve as universal approximators of any continuous equivariant function defined on the neighborhood. We incorporate a fusion block into pre-existing equivariant architectures (Cormorant and MACE), leading to improved performance with fewer parameters on a range of challenging chemical problems. Furthermore, we apply group-equivariant neural networks to study non-adiabatic molecular dynamics of stilbene cis-trans isomerization. Our approach, which combines tensor networks with equivariant neural networks, suggests a potentially fruitful direction for designing more expressive equivariant neural networks.
Beyond dynamics: learning to discover conservation principles
Authors: Belyshev Antonii , Kovrigin Alexander , Ustyuzhanin Andrey 
Published: May 2024

Abstract
The discovery of conservation principles is crucial for understanding the fundamental behavior of both classical and quantum physical systems across numerous domains. This paper introduces an innovative method that merges representation learning and topological analysis to explore the topology of conservation law spaces. Notably, the robustness of our approach to noise makes it suitable for complex experimental setups and its aptitude extends to the analysis of quantum systems, as successfully demonstrated in our paper. We exemplify our method’s potential to unearth previously unknown conservation principles and endorse interdisciplinary research through a variety of physical simulations. In conclusion, this work emphasizes the significance of data-driven techniques in deepening our comprehension of the principles governing classical and quantum physical systems.
Quantized non-volatile nanomagnetic domain wall synapse based autoencoder for efficient unsupervised network anomaly detection
Authors: Alam Muhammad Sabbir , Misba Walid Al , Atulasimha Jayasimha 
Published: May 2024

Abstract
Anomaly detection in real-time using autoencoders implemented on edge devices is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We further propose nanoscale ferromagnetic racetracks with engineered notches hosting magnetic domain walls (DW) as exemplary non-volatile memory-based autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses to write different magnetoresistance states. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are typically known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point synaptic weights that are extremely memory intensive. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying significant reduction in operation energy for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data.
Phylogenomic perspectives on speciation and reproductive isolation in a North American biodiversity hotspot: an example using California sages (Salvia subgenus Audibertia: Lamiaceae)
Authors: Rose Jeffrey P , Kriebel Ricardo , Sytsma Kenneth J , Drew Bryan T 
Published: May 2024

Abstract

Background and Aims
The California Floristic Province (CA-FP) is the most species-rich region of North America north of Mexico. One of several proposed hypotheses explaining the exceptional diversity of the region is that the CA-FP harbours myriad recently diverged lineages with nascent reproductive barriers. Salvia subgenus Audibertia is a conspicuous element of the CA-FP, with multiple sympatric and compatible species.

Methods
Using 305 nuclear loci and both organellar genomes, we reconstruct species trees, examine genomic discordance, conduct divergence-time estimation, and analyse contemporaneous patterns of gene flow and mechanical reproductive isolation.

Key Results
Despite strong genomic discordance, an underlying bifurcating tree is supported. Organellar genomes capture additional introgression events not detected in the nuclear genome. Most interfertility is found within clades, indicating that reproductive barriers arise with increasing genetic divergence. Species are generally not mechanically isolated, suggesting that it is unlikely to be the primary factor leading to reproductive isolation.

Conclusions
Rapid, recent speciation with some interspecific gene flow in conjunction with the onset of a Mediterranean-like climate is the underlying cause of extant diversity in Salvia subgenus Audibertia. Speciation has largely not been facilitated by gene flow. Its signal in the nuclear genome seems to mostly be erased by backcrossing, but organellar genomes each capture different instances of historical gene flow, probably characteristic of many CA-FP lineages. Mechanical reproductive isolation appears to be only part of a mosaic of factors limiting gene flow.
ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events
Authors: Hyung Daejin , Cho Soo Young , Lee Kyubin , Yu Namhee , Hong Sehwa , Park Charny 
Published: May 2024

Abstract

Motivation
Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor-binding sites and protein domains. These elements involve in exon usage and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes.

Results
With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein–protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing.

Availability and implementation
The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R.
Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data
Authors: Liu Xiaofan , Tao Yuhuan , Cai Zilin , Bao Pengfei , Ma Hongli , Li Kexing , Li Mengtao , Zhu Yunping , Lu Zhi John 
Published: May 2024

Abstract

Motivation
Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability.

Results
To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%–14.7% in F1 score for cancer survival prediction, 5.1%–12% for cancer stage prediction, and 8.1%–13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer’s potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients’ blood, providing potential candidate targets for cancer microenvironment study.

Availability and implementation
Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.
Targeted nanoparticles for imaging and therapy of endometriosis
Authors: Slayden Ov , Luo Fangzhou , Park Youngrong , Moses Abraham S , Demessie Ananiya A , Singh Prem , Korzun Tetiana , Taratula Olena , Taratula Oleh 
Published: May 2024

Abstract
In this brief review, we discuss our efforts to validate nanoplatforms for imaging and treatment of endometriosis. We specifically highlight our use of nonhuman primates and primate tissues in this effort. Endometriosis is a painful disorder of women and nonhuman primates where endometrium-like tissue exists outside of the uterus. There are no reliable, specific, and noninvasive diagnostic tests for endometriosis. Laparoscopic imaging remains the gold standard for identifying small endometriotic lesions in both women and monkeys. Visualizing and surgically removing microscopic lesions remains a clinical challenge. To address this challenge, we have created nanoparticle reagents that, when administered intravenously, enter endometriotic lesions both passively and by targeting endometriotic cells. The particles can carry payloads, including near-infrared fluorescent dyes and magnetic nanoparticles. These agents can be used for imaging and thermal ablation of diseased tissues. We evaluated this approach on macaque endometriotic cells, human and macaque endometrium engrafted into immunodeficient mice, in endometrium subcutaneously autografted in macaques, and in rhesus monkeys with spontaneous endometriosis. Employing these models, we report that nanoplatform-based reagents can improve imaging and provide thermal ablation of endometriotic tissues.
Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis
Authors: He Jia , Wang Shang-xian , Liu Peng 
Published: May 2024

Abstract

Objectives
To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.

Methods
We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before March 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity.

Results
A total of 1699 patients from 24 studies were included. For machine learning models in predicting pCR to nCRT, the meta-analysis calculated a pooled area under the curve (AUC) of 0.91 (95% CI, 0.88-0.93), pooled sensitivity of 0.83 (95% CI, 0.74-0.89), and pooled specificity of 0.86 (95% CI, 0.80-0.91). We investigated 6 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 6 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep-learning model was 0.93 and 0.89 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90 and 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.93, and was 0.83 in studies conducted in other countries.

Conclusions
This systematic study showed that machine learning has promising potential in predicting pCR to nCRT in patients with locally advanced rectal cancer. Compared to traditional machine learning models, although deep-learning-based studies are less predominant and more heterogeneous, they are able to obtain higher AUC.

Advances in knowledge
Compared to traditional machine learning models, deep-learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine learning-based models may bring us closer towards precision medicine.
Clinical application of digital technology in the use of anterolateral thigh lobulated perforator flaps to repair complex soft tissue defects of the limbs
Authors: Dong Kai-xuan , Zhou Ya , Cheng Yao-yu , Luo Hao-tian , Duan Jia-zhang , Yang Xi , Xu Yong-qing , Lu Sheng , He Xiao-qing 
Published: May 2024

Abstract

Background
It is challenging to repair wide or irregular defects with traditional skin flaps, and anterolateral thigh (ALT) lobulated perforator flaps are an ideal choice for such defects. However, there are many variations in perforators, so good preoperative planning is very important. This study attempted to explore the feasibility and clinical effect of digital technology in the use of ALT lobulated perforator flaps for repairing complex soft tissue defects in limbs.

Methods
Computed tomography angiography (CTA) was performed on 28 patients with complex soft tissue defects of the limbs, and the CTA data were imported into Mimics 20.0 software in DICOM format. According to the perforation condition of the lateral circumflex femoral artery and the size of the limb defect, one thigh that had two or more perforators from the same source vessel was selected for 3D reconstruction of the ALT lobulated perforator flap model. Mimics 20.0 software was used to visualize the vascular anatomy, virtual design and harvest of the flap before surgery. The intraoperative design and excision of the ALT lobulated perforator flap were guided by the preoperative digital design, and the actual anatomical observations and measurements were recorded.

Results
Digital reconstruction was successfully performed in all patients before surgery; this reconstruction dynamically displayed the anatomical structure of the flap vasculature and accurately guided the design and harvest of the flap during surgery. The parameters of the harvested flaps were consistent with the preoperative parameters. Postoperative complications occurred in 7 patients, but all flaps survived uneventfully. All of the donor sites were closed directly. All patients were followed up for 13–27 months (mean, 19.75 months). The color and texture of each flap were satisfactory and each donor site exhibited a linear scar.

Conclusions
Digital technology can effectively and precisely assist in the design and harvest of ALT lobulated perforator flaps, provide an effective approach for individualized evaluation and flap design and reduce the risk and difficulty of surgery.
Exploring the burden of actinic keratoses through development of a patient decision aid: a mixed-methods study
Authors: Drumm Ciara , Moloney Aoife , Fleming Louise , Morgan Imelda , Keating Ciara , Hughes Clodagh , Dolan Roisin , Moriarty Bláithín , Lally Aoife 
Published: May 2024

Abstract

Background
Actinic keratoses (AKs) are present on sun-exposed sites and are considered precursors of cutaneous squamous cell carcinoma. A better understanding of the experiences of patients with this condition may improve patient–provider relationships and guide the introduction of shared decision-making (SDM) to treatment decisions.

Objectives
To develop a patient decision aid (PDA) for field treatment of multiple AKs in line with the International Patient Decision Aid Standards (IPDAS) by (i) characterizing the burden and lived experiences of patients with multiple AKs, (ii) understanding the decisional needs of patients requiring field treatment, and (iii) exploring clinician preferences regarding field treatment for multiple AKs.

Methods
This mixed-methods study followed the most up-to-date guidelines set out by the IPDAS collaboration; a voluntary body that aims to enhance the quality of PDAs by developing an evidence-based systematic process for the development of unbiased and effective PDAs.

Results
Multiple AKs have a psychosocial impact on patients. Patients feel supported through the integration of evidence-based information to guide SDM.

Conclusions
We propose that the use of a PDA for multiple AKs provides a key role in supporting informed shared patient–provider decision-making and empowers patient involvement in their prospective treatment strategy.
Does online high-volume hemodiafiltration offer greater efficiency and sustainability compared with high-flux hemodialysis? A detailed simulation analysis anchored in real-world data
Authors: Canaud Bernard , Gagel Alfred , Peters Arne , Maierhofer Andreas , Stuard Stefano 
Published: May 2024

ABSTRACT
Recent findings, including the CONVINCE (comparison of high-dose HDF with high-flux HD) study report, suggest the superiority of high-volume hemodiafiltration (HDF) over high-flux hemodialysis (HD) in improving patients’ outcomes. Despite positive patient outcomes, concerns have arisen about the potential negative environmental impact of high-volume HDF, as it may lead to increased water and dialysis fluid consumption and higher waste production. In this manuscript, we address the environmental impact of high-volume HDF, focusing on three key factors: water treatment consumption, dialysis fluid consumption, and solute efficiency markers of HD and HDF.
By optimizing HDF prescription through adjustments in operational capabilities, while keeping a high blood flow (i.e., &gt;350 ml/min) such as reducing the QD/QB ratio to 1.2 rather than 1.4 or 1.5 and incorporating automated ultrafiltration and substitution control, we demonstrate that HDF delivers a higher dialysis dose for small- and middle-molecule uremic compounds with the same dialysis fluid consumption, and at equal dialysis doses dialysis fluid consumption is reduced. This finding is supported by real-world data from 26 031 patients who underwent high-volume postdilution HDF at a reduced dialysis flow (430 mL/min) and achieved an effective OCMKt/V of 1.70 (where “OCM” stands for online clearance measurement, “K” represents effective dialysis clearance and “V” denotes total body water measured by multifrequency bioimpedance). In addition, simulation modeling calculations, using blood extraction coefficient, dialysate saturation coefficient and solute clearances with urea (small molecular weight) and β2-microglobulin (middle molecular weight), consistently show the superiority of postdilution HDF to HD. This holds true even with a significant reduction in dialysis flow down to 430 mL/min, reflecting QD/QB ratio of 1.2. Postdilution HDF generates high ultrafiltrate flow (up to 35% of blood flow), delivering saturated ultrafiltrate to the lower solute concentration containing effluent dialysate, thus enhancing solute clearance which opens the way to reduce the dialysis flow. In conclusion, our analysis, combining simulation and real-world data, suggests that postdilution HDF could be a more environmentally friendly treatment option compared with conventional HD. Additionally, automated user-friendly functions that minimize dialysis fluid use can further strengthen this environmental benefit while enhancing efficiency.