CSF proteins of inflammation, proteolysis and lipid transport define preclinical AD and progression to AD dementia in cognitively unimpaired individuals.

This preclinical AD CSF proteome study identified a panel of 12-CSF markers detecting amyloid positivity and clinical progression to AD with high accuracy; some of these CSF proteins related to immune function, neurotrophic processes, energy metabolism and endolysosomal functioning (e.g., ITGB2, CLEC5A, IGFBP-1, CST3) changed before amyloid positivity is established.

MIPPIS: protein-protein interaction site prediction network with multi-information fusion.

Background: The prediction of protein-protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy.

FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins.

The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information.

The Effect of Viscous Drag on the Maximum Residual Stresses Achievable in High-Yield-Strength Materials in Laser Shock Processing.

In this paper, the experimentally observed significant increase in yield stress for strain rates beyond 10 s (viscous regime) is explicitly considered in laser shock processing (LSP) simulations. First, a detailed review of the most common high-strain-rate deformation models is presented, highlighting the expected strain rates in materials subject to LSP for a wide range of treatment conditions. Second, the abrupt yield stress increase presented beyond 10 s is explicitly considered in the material model of a titanium alloy subject to LSP.

Yield, Chemical Composition and Bioactivity of Essential Oils from Common Juniper ( L.) from Different Spanish Origins.

Essential oils (EOs) obtained from L. are frequently used in the production of bioproducts. However, there are no studies regarding industrial crops' production, allowing for better control of the quality and production of juniper EOs.

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings.

Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult.

MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.

Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNA-seq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility.

DNMG: Deep molecular generative model by fusion of 3D information for de novo drug design.

Deep learning is improving and changing the process of de novo molecular design at a rapid pace. In recent years, great progress has been made in drug discovery and development by using deep generative models for de novo molecular design. However, most of the existing methods are string-based or graph-based and are limited by the lack of some very important properties, such as the three-dimensional information of molecules.

PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.

Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed.

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and lack accuracy. Hence, coming up with an effective computational method is necessary.

TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer.

Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge.

Female reproduction and the microbiota in mammals: Where are we?

While it is generally accepted that the mammalian vagina contains a site-specific microbiota that plays relevant roles in genital and reproductive health, the existence of an extra-vaginal microbiota in the female reproductive tract (i.e. follicular fluid, oviduct, endometrium, and placenta) is, at least, a matter of controversy.

Urban Accessibility in World Heritage Cities. Accessibility Considerations in Pedestrian Routes in Historic City Centres.

In Spain, fifteen cities have been declared World Heritage Cities by UNESCO. This implies a responsibility to conserve all the heritage wealth of these places. However, what is the point of heritage if it cannot be known and visited? In order to be able to do this for all people, in equal and inclusive conditions, it is essential to consider Accessibility and Universal Design principles.

The Level of Inclusiveness of Current 15-Minute City Models. A Qualitative Analysis on How Far City of Proximity Strategies and Design for All Are Merging.

This article seeks to analyze different city plans in terms of proximity and accessibility. A total of 6 highly-recognized pedestrian models were chosen to compare their inclusive micro-mobility measures, not only in international contexts (Paris, Melbourne or Portland); but also a closer look will be taken at Spain, as it has managed to present its own referents (Valladolid, Vitoria and Pontevedra). A qualitative approach study was undergone to assess the real extent of inclusive proximity criteria, triggering a more in-depth, critical analysis by recognizing implicit, non-explicit, inclusive micro-mobility measures.

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction.

Background: Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to predict PPI in biology, they are time-consuming and lack accuracy, so it is necessary to build an efficiently and accurately computational model in the field of PPI prediction.

Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction.

Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug-drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects.

Multi-TransDTI: Transformer for Drug-Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy.

Prediction on drug-target interaction has always been a crucial link for drug discovery and repositioning, which have witnessed tremendous progress in recent years. Despite many efforts made, the existing representation learning or feature generation approaches of both drugs and proteins remain complicated as well as in high dimension. In addition, it is difficult for current methods to extract local important residues from sequence information while remaining focused on global structure.

Essential Oils Prime Epigenetic and Metabolomic Changes in Tomato Defense Against .

In this work, we studied the direct and indirect plant protection effects of an essential oil (AEO) on tomato seedlings against sp. (). AEO exhibited a toxic effect against .

DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.

Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. Recently, deep learning methods have achieved relatively significant performance in predicting DTIs. Generally, it needs a large amount of approved data of DTIs to train the model, which is actually tedious to obtain.

IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks.

There is a strong need to eliminate batch-specific differences when integrating single-cell RNA-sequencing (scRNA-seq) datasets generated under different experimental conditions for downstream task analysis. Existing batch correction methods usually transform different batches of cells into one preselected "anchor" batch or a low-dimensional embedding space, and cannot take full advantage of useful information from multiple sources. We present a novel framework, called IMGG, i.

Network-Based Approaches for Drug Repositioning.

With deep learning creeping up into the ranks of big data, new models based on deep learning and massive data have made great leaps forward rapidly in the field of drug repositioning. However, there is no relevant review to summarize the transformations and development process of models and their data in the field of drug repositioning. Among all the computational methods, network-based methods play an extraordinary role.

AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction.

The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction.

CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions' prediction.

SE-OnionNet: A Convolution Neural Network for Protein-Ligand Binding Affinity Prediction.

Deep learning methods, which can predict the binding affinity of a drug-target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein-ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein-drug molecular complex.

Direct Generation of High-Aspect-Ratio Structures of AISI 316L by Laser-Assisted Powder Deposition.

The effect of process parameters and the orientation of the cladding layer on the mechanical properties of 316L stainless steel components manufactured by laser metal deposition (LMD) was investigated. High aspect-ratio walls were manufactured with layers of a 4.5 mm wide single-cladding track to study the microstructure and mechanical properties along the length and the height of the wall.