Shrinkage Of Clusters Research Articles

Abstract 3726: Absence of synergistic effects for PARPi and immunotherapy combinations in ex vivo 3D micro tumor assay for ovarian cancer patients

Abstract Introduction: 50% of ovarian cancers are homologous recombination deficient (HRD), with 15% of the patients carrying a BRCA mutation. PARP inhibitors (PARPi) are designed to improve survival in HRD patients, but clinical responses to PARPi often do not match HRD status. In this study, a 3D ex vivo tumor testing platform was applied to quantify patient-specific drug responses to (combinations of) PARPi, chemo- and immunotherapies. Material and Methods: 81 patients with ovarian cancer eligible for chemotherapy were included in the trial between 2019 and 2022 in the Netherlands (IRB P18.032). Tumor clusters were isolated from ascites and solid tissue, seeded and exposed to three PARPi, standard-of-care chemotherapies and immunotherapies, both single agent and in combination. Ex vivo drug sensitivity was quantified using high-throughput 3D imaging and extraction of morphological features. Bliss scores were calculated for synergy analysis. Results: Results were generated in two weeks after sample collection. Ex vivo patient-specific response profiles were observed for all drug classes, with a small percentage of patients (7%) showing strong PARPi sensitivity reflected by strong tumor cluster shrinkage upon treatment. Significant correlation of drug sensitivity profiles was observed comparing the different PARPi between themselves, but also comparing PARPi and platinum responses. Interestingly, ex vivo sensitivity to olaparib and immunotherapy combinations demonstrated additive, but no synergistic effects (21 patients, 79 tests). In addition, olaparib responses were shown to be independent of immune responses (SEA). Conclusion: This study applied a predictive ex vivo 3D micro tumor testing platform to classify patient-specific sensitivity to (a combination of) PARPi, chemo- and immunotherapies in over 80 patients. The current dataset demonstrates the potential of ex vivo testing to identify patients that are responsive to PARPi. No synergistic effects were observed for these drugs in combination with immunotherapies. Discussion: Evaluation of PARPi responses in a functional 3D micro tumor assay could support identification of responsive patients prior to treatment. Distinct responses were observed for PARPi and immunotherapies. No synergistic but additive effects were measured, suggesting that ovarian cancer patients could benefit from PARPi and immunotherapies. Correlation of clinical and ex vivo drug responses to PARPi in combination with available HRD tests will further validate the platform for predicting patient clinical outcome. Citation Format: Lieke J. Ceton, Esmee Koedoot, Timothy J. Sijsenaar, Dieudonné J. van der Meer, Marta Montero Garcia, Jurrian van der Valk, Kristofer Maad, Cor de Kroon, Anne van Altena, Willemijn Vader, Judith Kroep, Nelleke Ottevanger. Absence of synergistic effects for PARPi and immunotherapy combinations in ex vivo 3D micro tumor assay for ovarian cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3726.

Stepwise disassembly of GABAergic synapses during pathogenic excitotoxicity.

SUMMARYGABAergic synaptic inhibition controls neuronal firing, excitability, and synaptic plasticity to regulate neuronal circuits. Following an acute excitotoxic insult, inhibitory synapses are eliminated, reducing synaptic inhibition, elevating circuit excitability, and contributing to the pathophysiology of brain injuries. However, mechanisms that drive inhibitory synapse disassembly and elimination are undefined. We find that inhibitory synapses are disassembled in a sequential manner following excitotoxicity: GABAARs undergo rapid nanoscale rearrangement and are dispersed from the synapse along with presynaptic active zone components, followed by the gradual removal of the gephyrin scaffold, prior to complete elimination of the presynaptic terminal. GABAAR nanoscale reorganization and synaptic declustering depends on calcineurin signaling, whereas disassembly of gephyrin relies on calpain activation, and blockade of both enzymes preserves inhibitory synapses after excitotoxic insult. Thus, inhibitory synapse disassembly occurs rapidly, with nanoscale precision, in a stepwise manner and most likely represents a critical step in the progression of hyperexcitability following excitotoxicity.

Using BM25 weighting and Cluster Shrinkage for Detecting Duplicate Bug Reports

Using BM25 weighting and Cluster Shrinkage for Detecting Duplicate Bug Reports

Effect of solvents on the microstructure aggregation of a heavy crude oil

We study the asphaltene microstructure in a heavy crude oil in response to the addition of solvents of different qualities by measuring the intrinsic viscosity and the Huggins coefficient. Dilution with good solvents (xylene and cyclohexanone) causes the native asphaltene clusters swelling, while that with poor solvents (a diesel oil and a C11-C14 alkanes mixture) may induce different microstructural rearrangements. The mixture of C11-C14 alkanes induces the aggregation of clusters, while the diesel oil their shrinkage. Moreover, if the diesel is added in small quantities instead of cluster shrinkage it may induce cluster aggregation thus forming larger “macro-clusters”. These structures survive after further dilution in both poor and good solvents and their formation is always accompanied by a reduction of the Huggins coefficient that implies the better solubility of these self-aggregated structures in the used solvent. Finally, an unusual negative Huggins coefficient is measured, when the macro-clusters are formed and the solution is diluted in a good solvent. This might be due to a prevalence of the intra-macro-cluster interactions on the inter-macro-cluster ones.

Regression based performance modeling and provisioning for NoSQL cloud databases

Cloud computing is a successful and emerging paradigm that supports on-demand services with pay-as-you-go model. Because of the exponential growth of data, NoSQL databases have been used to manage data in the cloud. In this scenario, it is fundamental for cloud providers guarantee Quality of Service (QoS) by avoiding violations to Service Level Agreement (SLA) contract while reducing the operational costs related to overprovisioning and underprovisioning. In this regard, elastic provisioning mechanisms are employed to maintain QoS by dynamically adding and removing resources to handle workload fluctuations. These mechanisms can also take more accurate provisioning decisions based on performance predictions of the cluster shrinkage and growth. Performance prediction is a challenging task since concurrent access of distributed data can cause non-linear effects on performance. This paper presents a performance modeling approach for NoSQL databases in terms of SLA-based metrics capable of capturing non-linear effects caused by concurrency and distribution aspects. Moreover we present a elastic provisioning strategy that takes advantage on performance models to deliver a reliable resource provisioning. We carried out experiments in order to evaluate our performance modeling and provisioning approaches. The results confirmed that our performance modeling can accurately predict throughput and SLA violations measurements under a wide range of workload settings and also that our elastic provisioning approach can ensure QoS while using resources efficiently.

Radiation-induced glide motion of interstitial clusters in concentrated alloys

We propose a mechanism for glide motion, i.e. one-dimensional (1D) migration, of interstitial clusters in concentrated alloys driven by high-energy particle irradiation. Interstitial clusters are fundamentally mobile on their respective 1D migration tracks, but in concentrated random alloys they are stationary at the position where the fluctuating formation energy achieves a local minimum. Irradiation changes the microscopic distribution of solute atoms through atomic displacement and recovery of the produced Frenkel pairs, which causes cluster 1D migration into a new stable position. In molecular dynamics simulations of interstitial clusters up to 217i in Fe–Cu alloys, stepwise 1D migration was observed under interatomic mixing or shrinkage of the cluster: a single 1D migration was induced by two exchanges per atom or cluster radius change by two interatomic distances. The 1D migration distance ranged up to several nanometers. We compared the frequency and distance of 1D migration with those for in situ observation using high-voltage electron microscopy, allowing for the extremely large rate of interatomic mixing and cluster shrinkage in the present simulation.

Collisional formation of very massive stars in dense clusters

We investigate the contraction of accreting protoclusters using an extension of n-body techniques that incorporates the accretional growth of stars from the gaseous reservoir in which they are embedded. Following on from Monte Carlo studies by Davis et al., we target our experiments toward populous clusters likely to experience collisions as a result of accretion-driven contraction. We verify that in less extreme star forming environments, similar to Orion, the stellar density is low enough that collisions are unimportant, but that conditions suitable for stellar collisions are much more easily satisfied in large-n clusters, i.e. n ~ 30,000 (we argue, however, that the density of the Arches cluster is insufficient for us to expect stellar collisions to have occurred in the cluster's prior evolution). We find that the character of the collision process is not such that it is a route toward smoothly filling the top end of the mass spectrum. Instead, runaway growth of one or two extreme objects can occur within less than 1 Myr after accretion is shut off, resulting in a few objects with masses several times the maximum reached by accretion. The rapid formation of these objects is due to not just the post-formation dynamical evolution of the clusters, but an interplay of dynamics and the accretional growth of the stars. We find that accretion-driven cluster shrinkage results in a distribution of gas and stars that offsets the disruptive effect of gas expulsion, and we propose that the process can lead to massive binaries and early mass segregation in star clusters.

Learning to Integrate Web Taxonomies

We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects from the source taxonomies into categories of the master taxonomy. However, conventional machine learning algorithms totally ignore the availability of the source taxonomies. In fact, source and master taxonomies often have common categories under different names or other more complex semantic overlaps. We introduce two techniques that exploit the semantic overlap between the source and master taxonomies to build better classifiers for the master taxonomy. The first technique, Cluster Shrinkage, biases the learning algorithm against splitting source categories by making objects in the same category appear more similar to each other. The second technique, Co-Bootstrapping, tries to facilitate the exploitation of inter-taxonomy relationships by providing category indicator functions as additional features for the objects. Our experiments with real-world Web data show that these proposed add-on techniques can enhance various machine learning algorithms to achieve substantial improvements in performance for taxonomy integration.

Learning to integrate web taxonomies

We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects from the source taxonomies into categories of the master taxonomy. However, conventional machine learning algorithms totally ignore the availability of the source taxonomies. In fact, source and master taxonomies often have common categories under different names or other more complex semantic overlaps. We introduce two techniques that exploit the semantic overlap between the source and master taxonomies to build better classifiers for the master taxonomy. The first technique, Cluster Shrinkage, biases the learning algorithm against splitting source categories by making objects in the same category appear more similar to each other. The second technique, Co-Bootstrapping, tries to facilitate the exploitation of inter-taxonomy relationships by providing category indicator functions as additional features for the objects. Our experiments with real-world Web data show that these proposed add-on techniques can enhance various machine learning algorithms to achieve substantial improvements in performance for taxonomy integration.

MD and KMC modeling of the growth and shrinkage mechanisms of helium–vacancy clusters in Fe

A multiscale modeling approach, which is based on atomistic simulations, was applied to investigate the growth and shrinkage mechanisms of helium–vacancy (He–V) clusters in Fe. Firstly, a molecular dynamics technique with empirical interatomic potentials was used to determine energies for the formation and dissociation of clusters as a function of their size and He density. Both the number of He atoms and vacancies in a cluster ranged from 0 to 20. The dissociation energy of clusters showed a strong dependence on the He density, rather than the cluster size, indicating that the growth and shrinkage of clusters strongly depend on the He density. Secondly, these dissociation energies were employed in a kinetic Monte-Carlo (KMC) simulation, to explore long-time cluster behavior. The KMC simulation indicated that He can stabilize He–V clusters by suppressing thermal vacancy emission and by promoting thermal self-interstitial Fe atom emission. A preliminary KMC simulation to investigate the migration behavior of He–V clusters is also presented.

Mechanism Controlling Permeability Change in Clays due to Changes in Pore Fluid

When a water-saturated clayey soil is leached with an organic fluid such as heptane, it has been found that under some conditions the hydraulic conductivity (or the permeability) increases manyfold. While it is generally agreed that physicochemical changes (e.g., compression of double-layer thickness) and the consequent alterations to the internal fabric (e.g., shrinkage of clusters) are responsible in most cases for such an increase, the underlying mechanism is not clearly understood. Two possible mechanisms are (1) formation of a few macrocracks due to shrinkage of clusters and (2) uniform increase in intercluster porosity throughout the volume of the soil due to shrinkage of clusters. The objective of the study presented here is to examine the most plausible mechanism of permeability increase caused by leaching. With the aid of Olsen’s permeability equation based on the cluster model, Poiseuille’s law for laminar fluid flow between two parallel plates, physicochemical theories, and experimental permeability data, it is shown that the formation of macrocracks is the most plausible mechanism of permeability increase.

Influence of Xe +-ion irradiation on the microstructure of diamond-like carbon films

The optical and electrical properties of diamond-like carbon films irradiated with Xe + ions (energy: 80 keV, fluence range: 6×10 12–1.2×10 16 cm −2) have been investigated. It was shown that high-density cascades developing under irradiation with heavy Xe ions lead to changes of the subsystem of graphite-like nanoclusters in diamond-like carbon films, which essentially differ from changes due to irradiation with light ions. The changes can be divided into five subsequent steps: (1) destruction of some nanoclusters, initially existing in diamond-like carbon films; (2) ballistic shrinkage of clusters; (3) the sharp increase of nanocluster sizes because of hydrogen desorption; (4) strong reduction of the cluster growth; (5) accumulation of defects in clusters. The steps are clearly distinguished in the optical and electrical characteristics of the investigated films.

Analytic model for the development of bamboo microstructures in thin film strips undergoing normal grain growth

The kinetics of the transformation of polygranular thin film strips to bamboo structures through two-dimensional (2D) normal grain growth are studied. A differential model for the evolution of the polygranular cluster length distribution is developed. It is observed, and demonstrated using a dimensional analysis, that the rate of bamboo-segment nucleation per unit time and unit of untransformed length is proportional to $\ensuremath{\mu}{/w}^{3},$ and is negligible in the growth-dominated steady state. It is also demonstrated that the cluster shrinkage velocity reaches a constant steady-state value proportional to $\ensuremath{\mu}/w$ (assuming constant and uniform \ensuremath{\mu}). This is shown to lead to a time-invariant, steady-state exponential cluster length distribution with an average cluster length proportional to the strip width, and a cluster length fraction decaying exponentially with $\ensuremath{\tau}=\ensuremath{\mu}{t/w}^{2}.$ The analytic model is validated through comparison with data generated using a 2D computer simulation of grain growth. The distribution of grain lengths in the resulting final bamboo grain structure is well fit by a log-normal distribution, with a median grain length scaling with the linewidth, and a linewidth-independent normalized deviation in the grain length.

Quantitative visualization of entrapped phase dissolution within a horizontal flowing fracture

An experiment was conducted to demonstrate the utility of quantitative fracture flow visualization techniques in the study of entrapped fluid phase (air) dissolution into a flowing phase (water) within a horizontal, transparent, analog rough‐walled fracture. The fracture aperture field and phase occupancy were measured using light transmission techniques and then combined to calculate bulk water‐phase saturation within the fracture as a function of time. Fracture relative permeability as a function of water‐phase saturation showed a smooth power law behavior during dissolution. Periodic step pulses of clear water within the dyed water inflow yielded dye concentration fields that demonstrate channeling induced by the entrapped air phase. Clusters of the entrapped air‐phase exhibited three types of dissolution behavior: general shrinkage, interfacial recession along cluster appendages, and cluster splitting. Locations for the advance of the wetting phase (water) into a nonwetting entrapped air cluster on its dissolution are not always correlated with either zones of high mass transfer rate (as inferred from gradients in the pulsed dye concentration fields) or with narrow apertures where the wetting phase has been thought to most easily invade. These results suggest that within an individual cluster of the entrapped phase, fluid pressure is at equilibrium and that the path of cluster shrinkage may be controlled primarily by capillary forces resulting from the full three‐dimensional curvature that minimizes surface energy of the phase interface.