Results 51 to 60 of about 73,328 (265)
Loss of the miR‐214/199a cluster is associated with recurrence in ovarian cancer. Engineered small extracellular vesicles (m214‐sEVs) elevate miR‐214‐3p/miR‐199a‐5p in tumor cells, suppress β‐catenin, TLR4, and YKT6 signaling, reprogram tumor‐derived sEV cargo, reduce chemoresistance and migration, and enhance carboplatin efficacy and survival in ...
Weida Wang +12 more
wiley +1 more source
Prompt Optimization in Large Language Models
Prompt optimization is a crucial task for improving the performance of large language models for downstream tasks. In this paper, a prompt is a sequence of n-grams selected from a vocabulary.
Antonio Sabbatella +4 more
doaj +1 more source
KDM7A and KDM1A inhibition suppresses tumour promoting pathways in prostate cancer
Treatment resistance is a major challenge for patients with advanced prostate cancer. This study examined an alternative approach to target the major prostate cancer‐promoting pathway by targeting epigenetic factors, whose levels are higher in tumours.
Jennie N Jeyapalan +16 more
wiley +1 more source
Intratumour heterogeneity complicates precision management of advanced endometrial cancer. Circulating tumor DNA (ctDNA) offers a minimally invasive strategy to capture tumor evolution and therapeutic resistance. Here, we compare tumor‐agnostic NGS with tumor‐informed ddPCR, outlining their relative sensitivity, concordance, and clinical implications ...
Carlos Casas‐Arozamena +15 more
wiley +1 more source
Raman‐based label‐free microscopic analysis of the pancreas in living zebrafish larvae
Forward stimulated Raman scattering (F‐SRS) and epi coherent anti‐Stokes Raman scattering (E‐CARS) allow label‐free discrimination of distinct subcellular structures in the pancreas of living zebrafish larvae. Given the straightforward applicability, we anticipate broad implementation of Raman microscopy in other organs and across various biomedical ...
Noura Faraj +3 more
wiley +1 more source
Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization
In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization.
Mario Andrés Muñoz, Michael Kirley
doaj +1 more source
Optimal Black-Box Reductions Between Optimization Objectives
The diverse world of machine learning applications has given rise to a plethora of algorithms and optimization methods, finely tuned to the specific regression or classification task at hand. We reduce the complexity of algorithm design for machine learning by reductions: we develop reductions that take a method developed for one setting and apply it ...
Zeyuan Allen Zhu, Elad Hazan
openaire +3 more sources
Distributed Evolution Strategies for Black-Box Stochastic Optimization
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms. We propose a distributed evolution strategy (DES) algorithm grounded on a proper modification to evolution strategies, a family of classic ...
Xiaoyu He 0001 +5 more
openaire +2 more sources
CRISPRI‐mediated gene silencing and phenotypic exploration in nontuberculous mycobacteria. In this Research Protocol, we describe approaches to control, monitor, and quantitatively assess CRISPRI‐mediated gene silencing in M. smegmatis and M. abscessus model organisms.
Vanessa Point +7 more
wiley +1 more source
Machine–Learning in Optimization of Expensive Black–Box Functions
Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn may hamper the optimization process.
Tenne Yoel
doaj +1 more source

