progenyClust: an R package for Progeny Clustering

Identifying the optimal number of clusters is a common problem faced by data scientists in various research fields and industry applications. Though many clustering evaluation techniques have been developed to solve this problem, the recently developed algorithm Progeny Clustering is a much faster alternative and one that is relevant to biomedical applications. In this paper, we introduce an R package progenyClust that implements and extends the original Progeny Clustering algorithm for evaluating clustering stability and identifying the optimal cluster number. We illustrate its applicability using two examples: a simulated test dataset for proof-of-concept, and a cell imaging dataset for demonstrating its application potential in biomedical research. The progenyClust package is versatile in that it offers great flexibility for picking methods and tuning parameters. In addition, the default parameter setting as well as the plot and summary methods offered in the package make the application of Progeny Clustering straightforward and coherent

Proteomics in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an extremely heterogeneous and deadly hematological cancer. Cytogenetic abnormalities and genetic mutations, though well recognized and highly prognostic, do not fully capture the degree of heterogeneities manifested in AML clinically. Additionally, current treatment of AML still largely depends on chemotherapy and allogeneic stem cell transplantation, with few options for personalized and molecularly targeted therapies. Proteomics holds promise for unraveling biological heterogeneities in AML beyond the scope of cytogenetics and genomics. In recent years, proteomics has emerged as an important tool for discovering new diagnostic biomarkers, enabling more prognostic patient classifications, and identifying novel therapeutic targets. In this chapter, we review recent advances in proteomic studies of AML, including an overview of AML pathology, popular proteomic techniques, various applications of proteomics in AML from biomarker discovery to target identification, challenges and future directions in this field

Shrinkage Clustering: A Fast and Size-Constrained Algorithm for Biomedical Applications

Motivation: Many common clustering algorithms require a two-step process that limits their efficiency. The algorithms need to be performed repetitively and need to be implemented together with a model selection criterion, in order to determine both the number of clusters present in the data and the corresponding cluster memberships. As biomedical datasets increase in size and prevalence, there is a growing need for new methods that are more convenient to implement and are more computationally efficient. In addition, it is often essential to obtain clusters of sufficient sample size to make the clustering result meaningful and interpretable for subsequent analysis. Results: We introduce Shrinkage Clustering, a novel clustering algorithm based on matrix factorization that simultaneously finds the optimal number of clusters while partitioning the data. We report its performances across multiple simulated and actual datasets, and demonstrate its strength in accuracy and speed in application to subtyping cancer and brain tissues. In addition, the algorithm offers a straightforward solution to clustering with cluster size constraints. Given its ease of implementation, computing efficiency and extensible structure, we believe Shrinkage Clustering can be applied broadly to solve biomedical clustering tasks especially when dealing with large datasets

Quantitative-Morphological and Cytological Analyses in Leukemia

Leukemia, a blood cancer originating in the bone marrow, presents as a heterogeneous disease with highly variable survival rates. Leukemia is classified into major types based on the rate of cancerous cell growth and cell lineage: chronic or acute and myeloid or lymphoid leukemia. Histological and cytological analysis of the peripheral blood and the bone marrow can classify these major leukemia categories. However, histological analyses of patient biopsies and cytological microscopic assessment of blood and bone marrow smears are insufficient to diagnose leukemia subtypes and to direct therapy. Hence, more expensive and time-consuming diagnostic tools routinely complement histological-cytological analysis during a patient’s diagnosis. To extract more accurate and detailed information from patient tissue samples, digital pathology is emerging as a powerful tool to enhance biopsy- and smear-based decisions. Furthermore, digital pathology methods integrated with advances in machine learning enable new diagnostic features from leukemia patients’ histological and cytological slides and optimize patient classification, thus providing a cheaper, more robust, and faster diagnostic tool than current standards. This review summarizes emerging approaches to automatically diagnose leukemia from morphological and cytological-histological analyses

Rapid RBE-Weighted Proton Radiation Dosimetry Risk Assessment

Proton therapy dose is affected by relative biological effectiveness differently than X-ray therapies. The current clinically accepted weighting factor is 1.1 at all positions along the depth–dose profile. However, the relative biological effectiveness correlates with the linear energy transfer, cell or tissue type, and the dose per fraction causing variation of relative biological effectiveness along the depth–dose profile. In this article, we present a simple relative biological effectiveness-weighted treatment planning risk assessment algorithm in 2-dimensions and compare the results with those derived using the standard relative biological effectiveness of 1.1. The isodose distribution profiles for beams were accomplished using matrices that represent coplanar intersecting beams. These matrices were combined and contoured using MATLAB to achieve the distribution of dose. There are some important differences in dose distribution between the dose profiles resulting from the use of relative biological effectiveness = 1.1 and the empirically derived depth-dependent values of relative biological effectiveness. Significant hot spots of up to twice the intended dose are indicated in some beam configurations. This simple and rapid risk analysis could quickly evaluate the safety of various dose delivery schema

Unlocking Potential: Exploring the Impact of Selective Attention Training on Enhancing Communication in Children with Autism

This study investigates the concept of selective attention and its significance in fostering desired behavioral changes, both verbal and nonverbal, in autistic children. The research involved implementing a specialized behavioral program as part of the daily routine for a group of male children aged 3–6 years. The program was conducted over a period of 35 days, with one-hour sessions each day. For the outcome assessment, several tools were utilized, including diagnostic criteria for autism, a social adaptation scale tailored to the Jordanian environment, and the Schiller Behavior Assessment Scale. The CARS, adapted for the Saudi environment, was also employed. Prior to implementation, no statistically significant differences were observed in the average scores for verbal and nonverbal communication, responses to selective attention (specifically involving objects), and the development in interpersonal communication. However, after the implementation, significant differences were found in these areas. In addition to the aforementioned results, the study recommendations emphasized the importance of employing visual communication strategies and organized environments in autism programs. Furthermore, the inclusion of autistic children in training programs to enhance fundamental learning skills, image concept training with non-distracting backgrounds, and the establishment of support rooms within autism care centers to address communication weaknesses were also highlighted

Experimental Investigation of the Effect of Radial Gap and Impeller Blade Exit on Flow-Induced Vibration at the Blade-Passing Frequency in a Centrifugal Pump

It has been recognized that the pressure pulsation excited by rotor-stator interaction in large pumps is strongly influenced by the radial gap between impeller and volute diffusers/tongues and the geometry of impeller blade at exit. This fluid-structure interaction phenomenon, as manifested by the pressure pulsation, is the main cause of flow-induced vibrations at the blade-passing frequency. In the present investigation, the effects of the radial gap and flow rate on pressure fluctuations, vibration, and pump performance are investigated experimentally for two different impeller designs. One impeller has a V-shaped cut at the blade's exit, while the second has a straight exit (without the V-cut). The experimental findings showed that the high vibrations at the blade-passing frequency are primarily raised by high pressure pulsation due to improper gap design. The existence of V-cut at blades exit produces lower pressure fluctuations inside the pump while maintaining nearly the same performance. The selection of proper radial gap for a given impeller-volute combination results in an appreciable reduction in vibration levels

Identifying Cancer Specific Metabolic Signatures Using Constraint-Based Models

Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers