Registration of brain tumor images using hyper-elastic regularization

In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean strain energy density model as a regularizer in deformable registration framework. Implementations on synthetic and patient data provide promising results, which might have relevant use in clinical problems

Cellular automata segmentation of brain tumors on post contrast MR images

In this paper, we re-examine the cellular automata(CA) al- gorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmenta- tion method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Val- idation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type

Multiparameter Persistent Homology for Molecular Property Prediction

In this study, we present a novel molecular fingerprint generation method based on multiparameter persistent homology. This approach reveals the latent structures and relationships within molecular geometry, and detects topological features that exhibit persistence across multiple scales along multiple parameters, such as atomic mass, partial charge, and bond type, and can be further enhanced by incorporating additional parameters like ionization energy, electron affinity, chirality and orbital hybridization. The proposed fingerprinting method provides fresh perspectives on molecular structure that are not easily discernible from single-parameter or single-scale analysis. Besides, in comparison with traditional graph neural networks, multiparameter persistent homology has the advantage of providing a more comprehensive and interpretable characterization of the topology of the molecular data. We have established theoretical stability guarantees for multiparameter persistent homology, and have conducted extensive experiments on the Lipophilicity, FreeSolv, and ESOL datasets to demonstrate its effectiveness in predicting molecular properties.Comment: ICLR 2023-Machine Learning for Drug Discovery. arXiv admin note: text overlap with arXiv:2211.0380

Topology-Aware Focal Loss for 3D Image Segmentation

The efficacy of segmentation algorithms is frequently compromised by topological errors like overlapping regions, disrupted connections, and voids. To tackle this problem, we introduce a novel loss function, namely Topology-Aware Focal Loss (TAFL), that incorporates the conventional Focal Loss with a topological constraint term based on the Wasserstein distance between the ground truth and predicted segmentation masks' persistence diagrams. By enforcing identical topology as the ground truth, the topological constraint can effectively resolve topological errors, while Focal Loss tackles class imbalance. We begin by constructing persistence diagrams from filtered cubical complexes of the ground truth and predicted segmentation masks. We subsequently utilize the Sinkhorn-Knopp algorithm to determine the optimal transport plan between the two persistence diagrams. The resultant transport plan minimizes the cost of transporting mass from one distribution to the other and provides a mapping between the points in the two persistence diagrams. We then compute the Wasserstein distance based on this travel plan to measure the topological dissimilarity between the ground truth and predicted masks. We evaluate our approach by training a 3D U-Net with the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, which requires accurate segmentation of 3D MRI scans that integrate various modalities for the precise identification and tracking of malignant brain tumors. Then, we demonstrate that the quality of segmentation performance is enhanced by regularizing the focal loss through the addition of a topological constraint as a penalty term

Malnutrition Incidence Among Inpatients: A Cross-Sectional Retrospective Study

Malnutrition in hospital is a significant problem which involves clinical complication risks, lengthened hospital stay and worsening prognosis. In order to overcome this problem, it is important to identify malnutrition in patients. The aim of this study was to retrospectively research the malnutrition risk of patients at time of admission and to create awareness about the importance of malnutrition screening. This research is a cross-sectional descriptive retrospective study which screened inpatients in İstanbul Sultan Abdülhamid Han Education and Research hospital from August 2018 to January 2019. Research data and NRS-2002 scores were retrospectively gathered from patient files. Between the dates of the study, a total of 10,060 patients stayed in the hospital and of these 490 (4.9%) were identified to be malnourished. Of these patients, 0.87% developed malnutrition after admission. The clinic with highest malnutrition was the anesthesia intensive care (25.9%). There was a significant increase identified in NRS-2002 scores with age (p=0.001); as NRS-2002 scores increased the mortality rates were found to significantly increase (p=0.015). The mortality in the patient group with NRS-2002 scores of 7 was 50%. Malnutrition screening will contribute to monitoring malnourished patients from time of admission and reducing mortality rates. According to the results of our study, the malnutrition risk is higher among elderly patients and as NRS-2002 score increases mortality increases. Due to this correlation between inpatient NRS-2002 scores and mortality, it was concluded that rapid screening and close surveillance of nutritional interventions for patients with high scores are important in terms of mortality.. Keywords: malnutrition, NRS, length of stay, nutrition DOI: 10.7176/JHMN/76-10 Publication date:June 30th 202

EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG

One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a modernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cognitive activity classification along with better generalizability across cohorts

SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction

In this study, we present a novel computational method for generating molecular fingerprints using multiparameter persistent homology (MPPH). This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital. By integrating SE(3)-invariance with Vietoris-Rips persistent homology, we effectively capture the three-dimensional representations of molecular chirality. This non-superimposable mirror image property directly influences the molecular interactions, serving as an essential factor in molecular property prediction. We explore the underlying topologies and patterns in molecular structures by applying Vietoris-Rips persistent homology across varying scales and parameters such as atomic weight, partial charge, bond type, and chirality. Our method's efficacy can be improved by incorporating additional parameters such as aromaticity, orbital hybridization, bond polarity, conjugated systems, as well as bond and torsion angles. Additionally, we leverage Stochastic Gradient Langevin Boosting in a Bayesian ensemble of GBDTs to obtain aleatoric and epistemic uncertainty estimates for gradient boosting models. With these uncertainty estimates, we prioritize high-uncertainty samples for active learning and model fine-tuning, benefiting scenarios where data labeling is costly or time consuming. Compared to conventional GNNs which usually suffer from oversmoothing and oversquashing, MPPH provides a more comprehensive and interpretable characterization of molecular data topology. We substantiate our approach with theoretical stability guarantees and demonstrate its superior performance over existing state-of-the-art methods in predicting molecular properties through extensive evaluations on the MoleculeNet benchmark datasets.Comment: NeurIPS 2023 AI for Science Worksho

High Temperature Tensile Properties of TiN-Reinforced 17-4 PH Stainless Steel Produced by Laser Powder-Bed Fusion

In this study, the effect of TiN particle reinforcements on high temperature tensile behavior of laser powder-bed fusion processed 17-4 PH stainless steel has been investigated. Tensile tests have been conducted at room temperature (RT), 400 °C, and 600 °C. It was observed that the TiN incorporation increased the strength at RT and 400 °C with a slight tradeoff in elongation. However, at 600 °C, yield and tensile strength values were lower for the composite than those of the unreinforced steel matrix, yet with a threefold increase in elongation. Further investigation on the ruptured specimens revealed that dynamic recovery, recrystallization, and phase transformation were responsible for this behavior. It has been concluded that dispersion hardening offers advantages both at room and elevated temperatures, especially in the ranges where the original alloy is normally used. However, the physical characteristics of the matrix become dominant at high temperatures (> 0.5 Tm), setting an upper bound to the possible contribution of the reinforcement to a selected matrix, yet, enabling for higher ductility

Synthesis and Application of Dye-Ligand Affinity Adsorbents

Dye-ligand affinity chromatography is a widely used technique in protein purification. The utility of the reactive dyes as affinity ligands results from their unique chemistry, which confers wide specificity towards a large number of proteins. They are commercially available, are inexpensive, and can easily be immobilized. Important factors that contribute to the successful operation of a dye-ligand chromatography include adsorbent properties, such as matrix type and ligand concentration, the buffer conditions used in the adsorption and elution stages, and contacting parameters like flow rate and column geometry. In general, with dye-ligand affinity chromatography, the specificity is provided by the adsorption and elution conditions employed in a particular purification, and these must often be worked out by trial and error. The present chapter provides protocols for the synthesis of dye-ligand affinity adsorbents as well as protocols for screening, selection, and optimization of a dye-ligand purification step. The purification of the glutathione transferases from Phaseolus vulgaris crude extract on Cibacron Blue 3GA-Sepharose is given as an example

The Impact of Sustainable Supply Chain Management and Supply Chain Collaboration on Turkish Firms Performance: Moderator Effect of Uncertainty

Supply Chain-Related Sustainability Cases offer organizations the challenge of enriching environmental, social, and economic performance within supply networks. Firms are increasingly implementing environmental and social dimensions of sustainability. During the implementation, they check the collaboration efforts for getting information outside of the organizations to develop and improve both firm and supply chain performance. Due to drastic changes in the business environment, firms face uncertainty. This study aims to analyze the impact of sustainable supply chain management and collaboration under the supply chain uncertainty on firms' performance. Based on the literature review the conceptual framework was developed. To test the research hypotheses, multi-item scales and survey questionnaires were adopted from prior research. The research is based on a quantitative approach using a questionnaire survey. We obtained 240 usable questionnaires from 112 companies. The Partial Least Square method was used to test the proposed conceptual model. The results show that sustainable supply chain management is positively associated with supply chain performance and supply chain collaboration. Also, we found that supply chain collaboration has a positive effect on supply chain performance. Supply chain performance is positively associated with firm performance. Furthermore, supply chain uncertainty moderates the relationship between collaboration, sustainable supply chain management, and supply chain performance