Influence of Environmental Factors on the Aboveground Biomass of Mature and Postmining Forests in Chocó.

Environmental factors control the accumulation of aboveground biomass (AB) in tropical forests, along with the role of AB in climate change mitigation. As such, the objective of this study was to evaluate the influence of factors such as forest type, succession, abundance of individuals, species richness, height, diameter, texture, and soil nutrient levels on the AB in mature and postmining forests in Chocó, Colombia. Five plots each were set up in primary and postmining forests with 15 and 30 years of regeneration, in which the amount of AB was measured and related to the environmental factors.

Biotechnological applications of purine and pyrimidine deaminases.

Deaminases, ubiquitous enzymes found in all living organisms from bacteria to humans, serve diverse and crucial functions. Notably, purine and pyrimidine deaminases, while biologically essential for regulating nucleotide pools, exhibit exceptional versatility in biotechnology. This review systematically consolidates current knowledge on deaminases, showcasing their potential uses and relevance in the field of biotechnology.

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%.

Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD).

Reconstruction of the Maërl habitat to better understand its ecological integrity.

Maërl habitats are composed of coralline red algae species that can live freely rolling on the seabed and forming nodules, the so-called rhodoliths, or incrusted forming coralligenous habitats. Maërl habitats are generally distributed in the Mediterranean at a depth of between 30 m and 70 m and are considered one of the most emblematic Mediterranean seabeds. In the present study, the complex structure of maërl habitats was investigated to i) characterise the relief features and classify the different sediments, ii) to estimate the abundance of the coralline red algae (both rhodoliths and encrusting ones) and iii) to analyse the biodiversity of the species inhabiting the habitat.

Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.

The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve into the transformative impact of ML and DL in this domain.

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.

Background: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome.

Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability.

A Novel, Highly Potent NADPH-Dependent Cytochrome P450 Reductase from Waste Liver.

The use of marine enzymes as catalysts for biotechnological applications is a topical subject. Marine enzymes usually display better operational properties than their animal, plant or bacterial counterparts, enlarging the range of possible biotechnological applications. Due to the fact that cytochrome P450 enzymes can degrade many different toxic environmental compounds, these enzymes have emerged as valuable tools in bioremediation processes.

Enhancing Multimodal Patterns in Neuroimaging by Siamese Neural Networks with Self-Attention Mechanism.

The combination of different sources of information is currently one of the most relevant aspects in the diagnostic process of several diseases. In the field of neurological disorders, different imaging modalities providing structural and functional information are frequently available. Those modalities are usually analyzed separately, although a joint of the features extracted from both sources can improve the classification performance of Computer-Aided Diagnosis (CAD) tools.

Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern.

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress.

Rational Design of a Thermostable 2'-Deoxyribosyltransferase for Nelarabine Production by Prediction of Disulfide Bond Engineering Sites.

One of the major drawbacks of the industrial implementation of enzymatic processes is the low operational stability of the enzymes under tough industrial conditions. In this respect, the use of thermostable enzymes in the industry is gaining ground during the last decades. Herein, we report a structure-guided approach for the development of novel and thermostable 2′-deoxyribosyltransferases (NDTs) based on the computational design of disulfide bonds on hot spot positions.

Tiled Sparse Coding in Eigenspaces for Image Classification.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches.

Identification of a novel tailor-made chitinase from white shrimp Fenneropenaeus merguiensis.

Fenneropenaeus merguiensis (commonly named banana shrimp) is one of the most important farmed crustacean worldwide species for the fisheries and aquaculture industry. Besides its nutritional value, it is a good source of chitinase, an enzyme with excellent biological and catalytic properties for many industrial applications. In the present study, a putative chitinase-encoding cDNA was synthesized from mRNA from F.

New trends in the biocatalytic production of nucleosidic active pharmaceutical ingredients using 2'-deoxyribosyltransferases.

Nowadays, pharmaceutical industry demands competitive and eco-friendly processes for active pharmaceutical ingredients (APIs) manufacturing. In this context, enzyme and whole-cell mediated processes offer an efficient, sustainable and cost-effective alternative to the traditional multi-step and environmentally-harmful chemical processes. Particularly, 2'-deoxyribosyltransferases (NDTs) have emerged as a novel synthetic alternative, not only to chemical but also to other enzyme-mediated synthetic processes.

Magnetic micro-macro biocatalysts applied to industrial bioprocesses.

The use of magnetic biocatalysts is highly beneficial in bioprocesses technology, as it allows their easy recovering and enhances biocatalyst lifetime. Thus, it simplifies operational processing and increases efficiency, leading to more cost-effective processes. The use of small-size matrices as carriers for enzyme immobilization enables to maximize surface area and catalysts loading, also reducing diffusion limitations.

A Bump-Hole Strategy for Increased Stringency of Cell-Specific Metabolic Labeling of RNA.

Profiling RNA expression in a cell-specific manner continues to be a grand challenge in biochemical research. Bioorthogonal nucleosides can be utilized to track RNA expression; however, these methods currently have limitations due to background and incorporation of analogs into undesired cells. Herein, we design and demonstrate that uracil phosphoribosyltransferase can be engineered to match 5-vinyluracil for cell-specific metabolic labeling of RNA with exceptional specificity and stringency.

Neural representations of social valence bias economic interpersonal choices.

Prior personal information is highly relevant during social interactions. Such knowledge aids in the prediction of others, and it affects choices even when it is unrelated to actual behaviour. In this investigation, we aimed to study the neural representation of positive and negative personal expectations, how these impact subsequent choices, and the effect of mismatches between expectations and encountered behaviour.

Hypoxanthine-Guanine Phosphoribosyltransferase/adenylate Kinase From : A Bifunctional Catalyst for the Synthesis of Nucleoside-5'-Mono-, Di- and Triphosphates.

In our search for novel biocatalysts for the synthesis of nucleic acid derivatives, we found a good candidate in a putative dual-domain hypoxanthine-guanine phosphoribosyltransferase (HGPRT)/adenylate kinase (AMPK) from (HGPRT/AMPK). In this respect, we report for the first time the recombinant expression, production, and characterization of a bifunctional HGPRT/AMPK. Biochemical characterization of the recombinant protein indicates that the enzyme is a homodimer, with high activity in the pH range 6-7 and in a temperature interval from 30 to 80°C.

-Ribosyltransferase From : A Novel Halotolerant and Thermostable Biocatalyst for the Synthesis of Purine Ribonucleoside Analogs.

Nucleoside-2'-deoxyribosyl-transferases (NDTs) catalyze a transglycosylation reaction consisting of the exchange of the 2'-deoxyribose moiety between a purine and/or pyrimidine nucleoside and a purine and/or pyrimidine base. Because NDTs are highly specific for 2'-deoxyribonucleosides they generally display poor activity on modified C2' and C3' nucleosides and this limitation hampers their applicability as biocatalysts for the synthesis of modified nucleosides. We now report the production and purification of a novel NDT from that is endowed with native ribosyltransferase activity and hence it is more properly classified as an -ribosyltransferase (NRT).

Neural representation of current and intended task sets during sequential judgements on human faces.

Engaging in a demanding activity while holding in mind another task to be performed in the near future requires the maintenance of information about both the currently-active task set and the intended one. However, little is known about how the human brain implements such action plans. While some previous studies have examined the neural representation of current task sets and others have investigated delayed intentions, to date none has examined the representation of current and intended task sets within a single experimental paradigm.

Representational Organization of Novel Task Sets during Proactive Encoding.

Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations.

Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data.

Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employed.