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Advancing spatio-temporal processing through adaptation in spiking neural networks
Spontaneously formed phonon frequency combs in van der Waals solid CrGeTe3 and CrSiTe3
Abstract Optical phonon engineering through nonlinear effects has been utilized in ultrafast control of material properties. However, nonlinear optical phonons typically exhibit rapid decay due to strong mode-mode couplings, limiting their effectiveness in temperature or frequency sensitive applications. Here we report the observation of long-lived nonlinear optical phonons through the spontaneous formation of phonon frequency combs in the van der Waals material CrXTe3 (X=Ge, Si) using high-resolution Raman scattering. Unlike conventional optical phonons, the highest A g mode in CrGeTe3 splits into equidistant, sharp peaks forming a frequency comb that persists for hundreds of oscillations and survives up to 200K. These modes correspond to localized oscillations of Ge2Te6 clusters, isolated from Cr hexagons, behaving as independent quantum oscillators. Introducing a cubic nonlinear term to the harmonic oscillator model, we simulate the phonon time evolution and successfully replicate the observed comb structure. Similar frequency comb behavior is observed in CrSiTe3, demonstrating the generalizability of this phenomenon. Our findings demonstrate that Raman scattering effectively probes high-frequency nonlinear phonon modes, offering insight into the generation of long-lived, tunable phonon frequency combs with potential applications in ultrafast material control and phonon-based technologies.
Stable ultrafast graphene hot-electron source on optical fiber
Dual therapeutic potential of Scoparia dulcis in combating hyperglycemia and inflammation in diabetes through network pharmacology and in silico analysis
Global estimate of mesopelagic mesozooplankton biomass
U-shaped relationship between the non-HDL to HDL cholesterol ratio and mortality in hypertensive individuals in the study from NHANES database
Enhanced YOLOv8 with lightweight and efficient detection head for for detecting rice leaf diseases
A qualitative study of facilitators and barriers to healthy eating among older adults in China based on nutritional literacy and the capability opportunity motivation behaviour model
Regulatory roles of long non-coding RNAs in minipigs revealed by cross-breed and cross-tissue transcriptomic analyses
Depressive symptoms as a mediator between sedentary behavior and social frailty among Chinese older adults in nursing home
Optimizing lightweight neural networks for efficient mobile edge computing
Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images
Abstract In this work, we developed an automated system for the detection and classification of soil-transmitted helminths (STH) and Schistosoma (S.) mansoni eggs in microscopic images of fecal smears. We assembled an STH and S. mansoni dataset comprising over 3,000 field-of-view (FOV) images containing parasite eggs, extracted from more than 300 fecal smear prepared using the Kato-Katz technique. These images were acquired using Schistoscope—a cost-effective automated digital microscope. After annotating the STH and S. mansoni eggs, we employed a transfer learning approach to train an EfficientDet deep learning model, using 70% of the dataset for training, 20% for validation, and 10% for testing. The developed model successfully identified STH and S. mansoni eggs in the FOV images, achieving weighted average scores of $$95.9\%(\pm 1.1\%)$$ Precision, $$92.1\%(\pm 3.5\%)$$ Sensitivity, $$98.0\%(\pm 0.76\%)$$ Specificity, and $$94.0\%(\pm 1.98\%)$$ F-Score across four classes of helminths (A. lumbricoides, T. trichiura, hookworm, and S. mansoni). Our system highlights the potential of the Schistoscope, enhanced with artificial intelligence, for detecting STH and S. mansoni infections in remote, resource-limited settings and for supporting the monitoring and evaluation of neglected tropical disease (NTD) control programs.
Proteomic analysis of Down syndrome cerebrospinal fluid compared to late-onset and autosomal dominant Alzheimer´s disease
Cryo-EM of wild-type and mutant PMEL amyloid cores reveals structural mechanism of pigment dispersion syndrome
Accelerating cell culture media development using Bayesian optimization-based iterative experimental design
Abstract Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show that this approach yields new compositions of media with cytokine supplementation to maintain the viability and distribution of human peripheral blood mononuclear cells in the culture. Second, we apply this framework to optimize the production of three recombinant proteins in cultivations of K.phaffii . We identified conditions with improved outcomes for both applications compared to the initial standard media using 3–30 times fewer experiments than that estimated for other methods such as the standard Design of Experiments. Subsequently, we also demonstrated the extensibility of our approach to efficiently account for additional design factors through transfer learning. These examples demonstrate how coupling data collection, modeling, and optimization in this iterative paradigm, while using an exploration-exploitation trade-off in each iteration, can reduce the time and resources for complex optimization tasks such as the one demonstrated here.
Redefining text-to-SQL metrics by incorporating semantic and structural similarity
Abstract The rapid advancements in text-to-SQL systems have driven the scientific community to create increasingly complex benchmarks for this task. However, evaluation metrics often rely on simplistic or binary approaches that fail to capture the similarities and differences between equivalent SQL queries. Current metrics overlook critical aspects such as partial correctness, structural differences, and semantic equivalence. To address these limitations, we propose a novel metric for SQL query comparison, designed to offer a more precise assessment of the similarity between SQL queries at both the semantic (string) and execution result (resultant table) levels. This new metric allows for a granular evaluation of SQL query similarity, supporting a more accurate assessment and ranking of text-to-SQL tools and models. The proposed approach could have a meaningful impact on text-to-SQL research and development. It might improve evaluation by distinguishing between models that handle simple queries and those capable of tackling more complex ones. The metric could also help to identify where the differences between two queries lie. Additionally, it may support the development of more accurate language models by offering precise training signals to help the model recognize query similarities. The experimental results highlight the metric’s effectiveness over existing evaluation methodologies, allowing us to identify the current best text-to-SQL models through distribution analysis. In some cases, the metric allows the detection of missing aggregation operators or variations in query ordering operators.