Autism spectrum disorder causes, mechanisms, and treatments: focus on neuronal synapses

Autism spectrum disorder (ASD) is a group of developmental disabilities characterized by impairments in social interaction and communication and restricted and repetitive interests/behaviors. Advances in human genomics have identified a large number of genetic variations associated with ASD. These associations are being rapidly verified by a growing number of studies using a variety of approaches, including mouse genetics. These studies have also identified key mechanisms underlying the pathogenesis of ASD, many of which involve synaptic dysfunctions, and have investigated novel, mechanism-based therapeutic strategies. This review will try to integrate these three key aspects of ASD research: human genetics, animal models, and potential treatments. Continued efforts in this direction should ultimately reveal core mechanisms that account for a larger fraction of ASD cases and identify neural mechanisms associated with specific ASD symptoms, providing important clues to efficient ASD treatment.158631scopu

Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach

Observation of vortex-antivortex pairing in decaying 2D turbulence of a superfluid gas

In a two-dimensional (2D) classical fluid, a large-scale flow structure emerges out of turbulence, which is known as the inverse energy cascade where energy flows from small to large length scales. An interesting question is whether this phenomenon can occur in a superfluid, which is inviscid and irrotational by nature. Atomic Bose-Einstein condensates (BECs) of highly oblate geometry provide an experimental venue for studying 2D superfluid turbulence, but their full investigation has been hindered due to a lack of the circulation sign information of individual quantum vortices in a turbulent sample. Here, we demonstrate a vortex sign detection method by using Bragg scattering, and we investigate decaying turbulence in a highly oblate BEC at low temperatures, with our lowest being $\sim 0.5 T_c$, where $T_c$ is the superfluid critical temperature. We observe that weak spatial pairing between vortices and antivortices develops in the turbulent BEC, which corresponds to the vortex-dipole gas regime predicted for high dissipation. Our results provide a direct quantitative marker for the survey of various 2D turbulence regimes in the BEC system.Comment: 8 pages, 8 figure

Scalable Quantum Convolutional Neural Networks

With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve. Moreover, QCNN is attracting attention as the next generation of QNN because it can process high-dimensional vector input. However, due to the nature of quantum computing, it is difficult for the classical QCNN to extract a sufficient number of features. Motivated by this, we propose a new version of QCNN, named scalable quantum convolutional neural network (sQCNN). In addition, using the fidelity of QC, we propose an sQCNN training algorithm named reverse fidelity training (RF-Train) that maximizes the performance of sQCNN

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).Comment: 2 pages, 3 figure

Quantum Split Neural Network Learning using Cross-Channel Pooling

In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, quantum federated learning (QFL) has gained particular attention due to the integration of quantum neural networks (QNNs) with traditional federated learning (FL) techniques. In this study, a novel approach entitled quantum split learning (QSL) is presented, which represents an advanced extension of classical split learning. Previous research in classical computing has demonstrated numerous advantages of split learning, such as accelerated convergence, reduced communication costs, and enhanced privacy protection. To maximize the potential of QSL, cross-channel pooling is introduced, a technique that capitalizes on the distinctive properties of quantum state tomography facilitated by QNNs. Through rigorous numerical analysis, evidence is provided that QSL not only achieves a 1.64\% higher top-1 accuracy compared to QFL but also demonstrates robust privacy preservation in the context of the MNIST classification task

Finger-triggered portable PDMS suction cup for equipment-free microfluidic pumping

This study presents a finger-triggered portable polydimethylsiloxane suction cup that enables equipment-free microfluidic pumping. The key feature of this method is that its operation only involves a “pressing-and-releasing” action for the cup placed at the outlet of a microfluidic device, which transports the fluid at the inlet toward the outlet through a microchannel. This method is simple, but effective and powerful. The cup is portable and can easily be fabricated from a three-dimensional printed mold, used without any pre-treatment, reversibly bonded to microfluidic devices without leakage, and applied to various material-based microfluidic devices. The effect of the suction cup geometry and fabrication conditions on the pumping performance was investigated. Furthermore, we demonstrated the practical applications of the suction cup by conducting an equipment-free pumping of thermoplastic-based microfluidic devices and water-in-oil droplet generation.11Yscopu

Ultraviolet photodepletion spectroscopy of dibenzo-18-crown-6-ether complexes with alkali metal cations

Ultraviolet photodepletion spectra of dibenzo-18-crown-6-ether complexes with alkali metal cations (M+-DB18C6, M = Cs, Rb, K, Na, and Li) were obtained in the gas phase using electrospray ionization quadrupole ion-trap reflectron time-of-flight mass spectrometry. The spectra exhibited a few distinct absorption bands in the wavenumber region of 35450−37800 cm^(−1). The lowest-energy band was tentatively assigned to be the origin of the S_0-S_1 transition, and the second band to a vibronic transition arising from the “benzene breathing” mode in conjunction with symmetric or asymmetric stretching vibration of the bonds between the metal cation and the oxygen atoms in DB18C6. The red shifts of the origin bands were observed in the spectra as the size of the metal cation in M^+-DB18C6 increased from Li^+ to Cs^+. We suggested that these red shifts arose mainly from the decrease in the binding energies of larger-sized metal cations to DB18C6 at the electronic ground state. These size effects of the metal cations on the geometric and electronic structures, and the binding properties of the complexes at the S_0 and S_1 states were further elucidated by theoretical calculations using density functional and time-dependent density functional theories

Visual Simulation Software Demonstration for Quantum Multi-Drone Reinforcement Learning

Quantum computing (QC) has received a lot of attention according to its light training parameter numbers and computational speeds by qubits. Moreover, various researchers have tried to enable quantum machine learning (QML) using QC, where there are also multifarious efforts to use QC to implement quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) using neural network features non-stationarity and uncertain properties due to its large number of parameters. Therefore, this paper presents a visual simulation software framework for a novel QMARL algorithm to control autonomous multi-drone systems to take advantage of QC. Our proposed QMARL framework accomplishes reasonable reward convergence and service quality performance with fewer trainable parameters than the classical MARL. Furthermore, QMARL shows more stable training results than existing MARL algorithms. Lastly, our proposed visual simulation software allows us to analyze the agents' training process and results.Comment: 5 pages, 4 figure