Hasil untuk "Biology"

C. Basaran

This special issue of the International Journal of Damage Mechanics is dedicated to Electronic Packaging. The issue contains papers by the leading researchers in the field. Using damage mechanics for electronic packaging problems is a new research area with a very promising future. Technical literature is full of papers on constitutive modeling of electronic packaging materials under monotonic loading. However, there are very few papers on damage mechanics of electronic packaging. The state-of-the-art practice of predicting number of cycles to failure of electronic packages is using empirical relations that are obtained from laboratory testing. Testing packages for fatigue life predictions is a very expensive and a time consuming process, especially for new designs. Damage mechanics offers a viable alternate procedure for predicting fatigue life of electronic packaging by means of computer simulations. This method is still unknown to many practicing engineers, and of course as with anything new, engineers who know of it don’t yet trust it. Hence, damage mechanics is rarely used by electronic packaging designers and manufacturers. But once there is enough academic research validating the method, practicing engineers will have confidence in it. It is my personal view that damage mechanics is going to be very popular in the industry in reducing dependence on testing as the sole means of predicting fatigue life. Computer simulations of reliability tests can significantly reduce the cost of developing new packages. In this endeavor, IJDM will be one of the primary publications to introduce the new developments.

H. L. Dean, J. Kuijt

E. Neumann, A. Sowers, C. A. Jordan

Walker Jm, W. Bowen, Walker Fo et al.

L. Vitt, J. Caldwell

R. Burdon

D. Hausman, M. Digirolamo, T. Bartness et al.

V. Cristini, J. Lowengrub, Qing Nie

Laurin Lux, Alexander H. Berger, Maria Romeo Tricas et al.

Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers based on neural networks are not interpretable. As a result, clinicians often resort to known biomarkers for diagnosis, although biomarker-based classification typically performs worse than large neural networks. This work proposes a method that surpasses the performance of established machine learning models while simultaneously improving prediction interpretability for diabetic retinopathy staging from optical coherence tomography angiography (OCTA) images. Our method is based on a novel biology-informed heterogeneous graph representation that models retinal vessel segments, intercapillary areas, and the foveal avascular zone (FAZ) in a human-interpretable way. This graph representation allows us to frame diabetic retinopathy staging as a graph-level classification task, which we solve using an efficient graph neural network. We benchmark our method against well-established baselines, including classical biomarker-based classifiers, convolutional neural networks (CNNs), and vision transformers. Our model outperforms all baselines on two datasets. Crucially, we use our biology-informed graph to provide explanations of unprecedented detail. Our approach surpasses existing methods in precisely localizing and identifying critical vessels or intercapillary areas. In addition, we give informative and human-interpretable attributions to critical characteristics. Our work contributes to the development of clinical decision-support tools in ophthalmology.

Rayyah R. Alkhanjari, Maitha M. Alhajeri, Poorna Manasa Bhamidimarri et al.

Abstract Septins are GTP-binding cytoskeletal proteins primarily known to be involved in cell division, membrane remodeling, and cytoskeletal organization. In the nervous system, septins are suggested as key regulators of neural development, including neurite outgrowth, spine morphology, and axon initial segment formation. Septins are localized to specialized membrane domains, such as dendritic spines, axon initial segments, and synaptic terminals, where they function as scaffolding components and diffusion barriers. They are abundant in neurons, oligodendrocytes, Schwann cells, and astrocytes, regulating processes like myelination and synaptic organization. In neuronal cells, specific septin isoforms such as SEPT3, SEPT5, and SEPT7 contribute to dendritic spine formation, neurotransmitter vesicle trafficking, and axonal integrity. Alterations in septin expression or assembly can disrupt synaptic architecture and neuroplasticity, emphasizing their role in neuronal homeostasis. Dysregulation of septin expression and function has been implicated in a range of neurological disorders, including demyelinating diseases like Multiple Sclerosis and Hereditary Neuralgic Amyotrophy. Abnormal septin aggregation has been observed in neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Moreover, septins can modulate inflammatory responses, where antibodies for septins 5 and 7 were associated with autoimmune encephalitis conditions. This review will provide a comprehensive overview of the role of septins in the nervous system, focusing on their molecular mechanisms, cellular functions, and implications in neurological disorders.

Abasalt Bahrami, Leonardo Y. Tanaka, Ricardo C. Massucatto et al.

For more than fifty years, scientists have been gathering evidence of the biological impacts of weak magnetic fields. However, the lack of systematics in experimental studies has hampered research progress on this subject. To systematically quantify magnetic field effects in cell biology, it is crucial to produce fields that can be automatically adjusted and that are stable throughout an experiment's duration, usually operating inside an incubator. Here, we report on the design of a fully automated 1D Helmholtz coil setup that is internally water cooled, thus eliminating any confounding effects caused by temperature fluctuations. The coils also allow cells to be exposed to magnetic fields from multiple directions through automated controlled rotation. Preliminary data, acquired with the coils placed inside an incubator and on a rat vascular smooth muscle cell line, confirm previous reports that both microtubule and actin polymerization and dynamics are altered by weak magnetic fields.

Mohammad Souri, Sohail Elahi, Farshad Moradi Kashkooli et al.

Abstract Intratumoral delivery and localized chemotherapy have demonstrated promise in tumor treatment; however, the rapid drainage of therapeutic agents from well-vascularized tumors limits their ability to achieve maximum therapeutic efficacy. Therefore, innovative approaches are needed to enhance treatment efficacy in such tumors. This study utilizes a mathematical modeling platform to assess the efficacy of combination therapy using anti-angiogenic drugs and drug-loaded nanoparticles. Anti-angiogenic drugs are included to reduce blood microvascular density and facilitate drug retention in the extracellular space. In addition, incorporating negatively charged nanoparticles aims to enhance diffusion and distribution of therapeutic agents within well-vascularized tumors. The findings indicate that, in the case of direct injection of free drugs, using compounds with lower drainage rates and higher diffusion coefficients is beneficial for achieving broader diffusion. Otherwise, drugs tend to accumulate primarily around the injection site. For instance, the drug doxorubicin, known for its rapid drainage, requires the prior direct injection of an anti-angiogenic drug with a high diffusion rate to reduce microvascular density and facilitate broader distribution, enhancing penetration depth by 200%. Moreover, the results demonstrate that negatively charged nanoparticles effectively disperse throughout the tissue due to their high diffusion coefficient. In addition, a faster drug release rate from nanoparticles further enhance treatment efficacy, achieving the necessary concentration for complete eradication of tumor compared to slower drug release rates. This study demonstrates the potential of utilizing negatively charged nanoparticles loaded with chemotherapy drugs exhibiting high release rates for localized chemotherapy through intratumoral injection in well-vascularized tumors.

Nils Wetzstein, Margo Diricks, Thomas B. Anton et al.

Abstract Background The Mycobacterium avium complex (MAC) comprises the most frequent non-tuberculous mycobacteria (NTM) in Central Europe and currently includes twelve species. M. avium (MAV), M. intracellulare subsp. intracellulare (MINT), and M. intracellulare subsp. chimaera (MCH) are clinically most relevant. However, the population structure and genomic landscape of MAC linked with potential pathobiological differences remain little investigated. Methods Whole genome sequencing (WGS) was performed on a multi-national set of MAC isolates from Germany, France, and Switzerland. Phylogenetic analysis was conducted, as well as plasmids, resistance, and virulence genes predicted from WGS data. Data was set into a global context with publicly available sequences. Finally, detailed clinical characteristics were associated with genomic data in a subset of the cohort. Results Overall, 610 isolates from 465 patients were included. The majority could be assigned to MAV (n = 386), MCH (n = 111), and MINT (n = 77). We demonstrate clustering with less than 12 SNPs distance of isolates obtained from different patients in all major MAC species and the identification of trans-European or even trans-continental clusters when set into relation with 1307 public sequences. However, none of our MCH isolates clustered closely with the heater-cooler unit outbreak strain Zuerich-1. Known plasmids were detected in MAV (325/1076, 30.2%), MINT (62/327, 19.0%), and almost all MCH-isolates (457/463, 98.7%). Predicted resistance to aminoglycosides or macrolides was rare. Overall, there was no direct link between phylogenomic grouping and clinical manifestations, but MCH and MINT were rarely found in patients with extra-pulmonary disease (OR 0.12 95% CI 0.04–0.28, p < 0.001 and OR 0.11 95% CI 0.02–0.4, p = 0.004, respectively) and MCH was negatively associated with fulfillment of the ATS criteria when isolated from respiratory samples (OR 0.28 95% CI 0.09-0.7, p = 0.011). With 14 out of 43 patients with available serial isolates, co-infections or co-colonizations with different strains or even species of the MAC were frequent (32.6%). Conclusions This study demonstrates clustering and the presence of plasmids in a large proportion of MAC isolates in Europe and in a global context. Future studies need to urgently define potential ways of transmission of MAC isolates and the potential involvement of plasmids in virulence.

Xhulio Dhori, Silvia Gioiosa, Stefania Gonfloni

Abstract Granulosa cells (GCs) play crucial roles in oocyte maturation. Through gap junctions and extracellular vesicles, they mediate the exchange of molecules such as microRNAs and messenger RNAs. Different ovarian cell types exhibit unique gene expression profiles, reflecting their specialized functions and stages. By combining RNA-seq data from various cell types forming the follicle, we aimed at capturing a wide range of expression patterns, offering insights into the functional diversity and complexity of the transcriptome regulation across GCs. Herein, we performed an integrated bioinformatics analysis of RNA sequencing datasets present in public databases, with a unique and standardized workflow., By combining the data from different studies, we successfully increased the robustness and reliability of our findings and discovered novel genes, miRNAs, and signaling pathways associated with GCs function and oocyte maturation. Moreover, our results provide a valuable resource for further wet-lab research on GCs biology and their impact on oocyte development and competence.

D. Leckband, J. Israelachvili

F. Bruggeman, H. Westerhoff

M. Chaplin

M. Gladwin, A. Schechter, D. Kim-Shapiro et al.

J. Fitzgerald, B. Schoeberl, U. Nielsen et al.

Pengfei Liu, Yiming Ren, Jun Tao et al.

Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules. However, most existing language models cannot capture the rich information with complex molecular structures or images. In this paper, we introduce GIT-Mol, a multi-modal large language model that integrates the Graph, Image, and Text information. To facilitate the integration of multi-modal molecular data, we propose GIT-Former, a novel architecture that is capable of aligning all modalities into a unified latent space. We achieve a 5%-10% accuracy increase in properties prediction and a 20.2% boost in molecule generation validity compared to the baselines. With the any-to-language molecular translation strategy, our model has the potential to perform more downstream tasks, such as compound name recognition and chemical reaction prediction.