long-term memory

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single synaptic memristive device holds the most potential for realizing next-generation neuromorphic systems, but is still challenging yet achievable. Herein, a simple bi-terminal optoelectronic synaptic memristor is newly proposed based on kesterite Cu2ZnSnS4, exhibiting stable nonvolatile resistive switching with excellent spatial uniformity and unique optoelectronic synaptic behaviors. The device demonstrates not only low switching voltage (-0...

Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3 )-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3 /atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3 /ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state...

Gradually reducing a source of fear during extinction treatments may weaken negative memories in the long term.

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico...

Artificial brain is conceived as advanced intelligence technology, capable to emulate in-memory processes occurring in the human brain by integrating synaptic devices. Within this context, improving the functionality of synaptic transistors to increase information processing density in neuromorphic chips is a major challenge in this field. In this article, we present Li-ion migration promoting long afterglow organic light-emitting transistors, which display exceptional postsynaptic brightness of 7000 cd·m-2 under low operational voltages of 10 V...

BACKGROUND: Long-term information on health-related quality of life (HRQOL) and mental health of non-hospitalized individuals with "post COVID-19 syndrome" (PCS) is scarce. Thus, the objectives of the present study were to compare HRQOL and mental health of individuals with and without PCS in a German sample of non-hospitalized persons after SARS-CoV-2 infection, to characterize the long-term course up to 2 years and to identify predictors for post COVID-19 impairments. METHODS: Individuals with past SARS-CoV-2 infection were examined at the University Hospital of Augsburg from November 2020 to May 2021 and completed a postal questionnaire between June and November 2022...

BACKGROUND: Currently, surgical site infection surveillance relies on labor-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods while being harder to interpret. We propose a deep learning model, a long short-term memory network, for the identification of surgical site infection from the medical record with an attention layer for explainability...

Caffeine and anabolic-androgenic steroids (AAS) are commonly used to improve muscle mass and athletic performance. Nandrolone Decanoate (ND) is one of the most abused AAS worldwide, leading to behavioral changes in both humans and rodents. Caffeine, the most widely consumed psychostimulant globally, is present in various thermogenic and gym supplements. Low and moderate doses of caffeine antagonize adenosine receptors and have been linked to improved memory and pain relief. We have previously demonstrated that consuming caffeine prevents the risk-taking behavior triggered by nandrolone...

Developing an accurate and reliable daily streamflow forecasting model is important for facilitating the efficient resource planning and management of hydrological systems. In this study, an explainable multiscale long short-term memory (XM-LSTM) model is proposed for effective daily streamflow by integrating the à trous wavelet transform (ATWT) for decomposing data, the Boruta algorithm for identifying model inputs, and the layer-wise relevance propagation (LRP) for explaining the prediction results. The proposed XM-LSTM is tested by performing multi-step-ahead forecasting of daily streamflow at four stations in the middle and lower reaches of the Yangtze River basin and compared with the X-LSTM...

AIMS: This study was designed to investigate the role of growth arrest and DNA damage-inducible β (GADD45B) in modulating fear memory acquisition and elucidate its underlying mechanisms. MAIN METHODS: Adeno-associated virus (AAV) that knockdown or overexpression GADD45B were injected into ventral hippocampal CA1 (vCA1) by stereotactic, and verified by fluorescence and Western blot. The contextual fear conditioning paradigm was employed to examine the involvement of GADD45B in modulating aversive memory acquisition...

BACKGROUND: Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU...

Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features...

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input...

Tamoxifen, a selective estrogen receptor modulator (SERM), is commonly used as an adjuvant drug therapy for estrogen-receptor-positive breast cancers. Though effective at reducing the rate of cancer recurrence, patients often report unwanted cognitive and affective side effects. Despite this, the impacts of chronic tamoxifen exposure on the brain are poorly understood, and rodent models of tamoxifen exposure do not replicate the chronic oral administration seen in patients. We, therefore, used long-term ad lib consumption of medicated food pellets to model chronic tamoxifen exposure in a clinically relevant way...

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As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas's oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants...

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources...

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system...

This study utilizes neural networks to detect and locate thermal anomalies in low-pressure steam turbines, some of which experienced a drop in efficiency. Standard approaches relying on expert knowledge or statistical methods struggled to identify the anomalous steam line due to difficulty in capturing nonlinear and weak relations in the presence of linear and strong ones. In this research, some inputs that linearly relate to outputs have been intentionally neglected. The remaining inputs have been used to train shallow feedforward or long short-term memory neural networks using measured data...

The hippocampus is crucial for acquiring and retrieving episodic and contextual memories. In previous studies, the inactivation of dentate gyrus (DG) neurons by chemogenetic- and optogenetic-mediated hyperpolarization led to opposing conclusions about DG's role in memory retrieval. One study used Designer Receptors Exclusively Activated by Designer Drugs (DREADD)-mediated clozapine N-oxide (CNO)-induced hyperpolarization and reported that the previously formed memory was erased, thus concluding that denate gyrus is needed for memory maintenance...