Art

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health...

Microscopes are essential for research and education in science. Unlike computers and online learning tools, however, microscopes are not currently a fixed element in K-12 classrooms, due to steep cost, needless complexity, and often requiring a prohibitive level of staff training to effectively deploy. In a collaboration with Area 10 Labs, Integrated Science Education Outreach (InSciEd Out) developed a state-of-the-art alternative microscope, the InSciEdRS View, to reduce the financial barrier, prohibitive per-student cost, unnecessary complexity, and extensive staff training...

In order to address the high-power consummation issue of conventional multi-input and multi-output (MIMO) adaptive equalizer (AEQ) for short-reach coherent transmissions, several state-of-the-art low-complexity AEQs have been proposed. In our work, optimized adaptation algorithms for low-complexity real-valued (RV) AEQs with different structures are analyzed. Moreover, an approach to avoid introducing additional computational complexity due to the optimized adaptation process is presented here. The advantages of proposed optimized adaptation algorithms are experimentally demonstrated in a 25 Gbaud dual-polarization 16-quadrature-amplitude-modulation (DP-16QAM) back-to-back (BtB) intradyne system with an overall bandwidth of 14 GHz...

Unsupervised spectral reconstruction (SR) aims to recover the hyperspectral image (HSI) from corresponding RGB images without annotations. Existing SR methods achieve it from a single RGB image, hindered by the significant spectral distortion. Although several deep learning-based methods increase the SR accuracy by adding RGB images, their networks are always designed for other image recovery tasks, leaving huge room for improvement. To overcome this problem, we propose a novel, to our knowledge, approach that reconstructs the HSI from a pair of RGB images captured under two illuminations, significantly improving reconstruction accuracy...

Next-generation display and lighting based on quantum dot light-emitting diodes (QLEDs) require a balanced electron injection of electron transport layers (ETLs). However, classical ZnO nanoparticles (NPs) as ETLs face inherent defects such as excessive electron injection and positive aging effects, urgently requiring the development of new types of ETL materials. Here, we show that high stability SnO2 NPs as ETL can significantly improve the QLED performance to 100567 cd·m-2  luminance, 14...

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts...

Understanding tautomerism and characterizing solvent effects on the dynamic processes pose significant challenges. Using enhanced-sampling molecular dynamics based on state-of-the-art deep learning potentials, we investigated the tautomeric equilibria of glycine in water. We observed that the tautomerism between neutral and zwitterionic glycine can occur through both intramolecular and intermolecular proton transfers. The latter proceeds involving a contact anionic-glycine-hydronium ion pair or separate cationic-glycine-hydroxide ion pair...

Introducción: El riesgo de suicidio en el Trastorno del Espectro Autista (TEA) ha emergido como una problemática poco considerada durante mucho tiempo. Esta revisión tiene como objetivo explorar la prevalencia, la evaluación y los tratamientos disponibles para el riesgo de suicidio en los adultos con autismo. Metodología: Se practicó una revisión narrativa sobre tres aspectos relacionados con el riesgo de suicido en la población adulta con TEA: la prevalencia, la evaluación y las intervenciones disponibles basadas en evidencia...

La esquizofrenia es una patología crónica y grave que se caracteriza por la presencia de delirios, alucinaciones, síntomas negativos, desorganización del pensamiento y del comportamiento. Si  bien su prevalencia en menores de 13 años es muy baja, aumenta sustancialmente durante la adolescencia. Cuando se desarrolla antes de los 18 años se denomina esquizofrenia de inicio precoz y representa del 12 al 33 % del total de los individuos con este trastorno. Existen fármacos como el haloperidol, risperidona, paliperidona, aripiprazol, olanzapina, quetiapina, brexpiprazol y lurasidona aprobados por la Food and Drug Administration (FDA) para el tratamiento de la esquizofrenia en adolescentes...

El presente artículo se propone llevar a cabo una revisión exhaustiva de la literatura científica existente sobre esta modalidad conductual humana particular, impulsiva y transitoria, que escapa al control consciente del sujeto, desde un enfoque psicopatológico y forense. Para ello, se realizarán puntualizaciones históricas de los diferentes matices de la temática, proporcionando una visión panorámica y crítica de la misma. Se puede decir que se trata de memorias implícitas acerca de situaciones traumáticas pasadas que, a raíz de una reedición presente del evento displacentero mediante un mecanismo disociativo, generan en el individuo una reacción impulsiva agresiva, sobre la cual la persona no dispone de control ni conciencia...

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial-temporal attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intraclass feature distribution...

Unsupervised feature selection is an important tool in data mining, machine learning, and pattern recognition. Although data labels are often missing, the number of data classes can be known and exploited in many scenarios. Therefore, a structured graph, whose number of connected components is identical to the number of data classes, has been proposed and is frequently applied in unsupervised feature selection. However, methods based on the structured graph learning face two problems. First, their structured graphs are not always guaranteed to maintain the same number of connected components as the data classes with existing optimization algorithms...

Natural language understanding (NLU) is integral to various social media applications. However, the existing NLU models rely heavily on context for semantic learning, resulting in compromised performance when faced with short and noisy social media content. To address this issue, we leverage in-context learning (ICL), wherein language models learn to make inferences by conditioning on a handful of demonstrations to enrich the context and propose a novel hashtag-driven ICL (HICL) framework. Concretely, we pretrain a model, which employs #hashtags (user-annotated topic labels) to drive BERT-based pretraining through contrastive learning...

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum graph convolutional layer, the quantum graph pooling layer, and the network optimization. In particular, the trainability of the QSGCN model is analyzed through discussions on the barren plateau phenomenon...

Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks...

Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete descriptions of the scene than perspective images. However, fully-convolutional networks that most current solutions rely on fail to capture rich global contexts from the panorama...

Isothiazolinones represent a class of heterocyclic compounds widely used in various applications, including as biocides in cosmetics, detergents, and paints, as well as in industrial wastewater treatment. Indeed, the presence of isothiazolinones in the environment and their associated potential health hazards have raised significant concerns. In this study, a non-adiabatic dynamics investigation was conducted using state-of-the-art methodologies to explore the photochemistry of isothiazolinones. A simplified model, isothiazol-3( 2H )-one (ISO), was employed to represent this compound class...

Lithium-ion batteries, essential for electronics and electric vehicles, predominantly use cathodes made from critical materials like cobalt. Sulfur-based cathodes, offering a high theoretical capacity of 1675 mAh g-1 and environmental advantages due to sulfur's abundance and lower toxicity, present a more sustainable alternative. However, state-of-the-art sulfur-based electrodes do not reach the theoretical capacities, mainly because conventional electrode production relies on mixing of components into weakly coordinated slurries...

EndoBridge 2023 took place on October 20-22, 2023, in Antalya, Turkey. Accredited by the European Council, the 3-day scientific program of the 11th Annual Meeting of EndoBridge included state-of-the-art lectures and interactive small group discussion sessions incorporating interesting and challenging clinical cases led by globally recognized leaders in the field and was well attended by a highly diverse audience. Following its established format over the years, the program provided a comprehensive update across all aspects of endocrinology and metabolism, including topics in pituitary, thyroid, bone, and adrenal disorders, neuroendocrine tumors, diabetes mellitus, obesity, nutrition, and lipid disorders...

We describe an emerging hard X-ray spectroscopy technique, stimulated X-ray emission spectroscopy (S-XES). S-XES has the potential to characterize the electronic structure of 3d transition metal complexes with spectral information currently not reachable and might lead to the development of new ultrafast X-ray sources with properties beyond the state of the art. S-XES has become possible with the emergence of X-ray free-electron lasers (XFELs) that provide intense femtosecond X-ray pulses that can be employed to generate a population inversion of core-hole excited states resulting in stimulated X-ray emission...