Showing papers by "Ensenada Center for Scientific Research and Higher Education published in 2020"

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Abstract: Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Green synthesis of silver nanoparticles using Lysiloma acapulcensis exhibit high-antimicrobial activity.

Abstract: The scientific community is exploiting the use of silver nanoparticles (AgNPs) in nanomedicine and other AgNPs combination like with biomaterials to reduce microbial contamination. In the field of nanomedicine and biomaterials, AgNPs are used as an antimicrobial agent. One of the most effective approaches for the production of AgNPs is green synthesis. Lysiloma acapulcensis (L. acapulcensis) is a perennial tree used in traditional medicine in Mexico. This tree contains abundant antimicrobial compounds. In the context of antimicrobial activity, the use of L. acapulcensis extracts can reduce silver to AgNPs and enhance its antimicrobial activity. In this work, we demonstrate such antimicrobial activity effect employing green synthesized AgNPs with L. acapulcensis. The FTIR and LC–MS results showed the presence of chemical groups that could act as either (i) reducing agents stabilizing the AgNPs or (ii) antimicrobial capping agents enhancing antimicrobial properties of AgNPs. The synthesized AgNPs with L. acapulcensis were crystalline with a spherical and quasi-spherical shape with diameters from 1.2 to 62 nm with an average size diameter of 5 nm. The disk diffusion method shows the magnitude of the susceptibility over four pathogenic microorganisms of clinical interest. The antimicrobial potency obtained was as follows: E. coli ≥ S. aureus ≥ P. aeruginosa > C. albicans. The results showed that green synthesized (biogenic) AgNPs possess higher antimicrobial potency than chemically produced AgNPs. The obtained results confirm a more significant antimicrobial effect of the biogenic AgNPs maintaining low-cytotoxicity than the AgNPs produced chemically.

Conventional and high order sliding mode control

Abstract: Term “Conventional” sliding mode control (SMC) was introduced in the book Sliding Mode Control and Observation [1] by the authors, working in the area of high order sliding mode (HOSM) control The term is related to all publications on n-dimensional systems with m- dimensional control and with sliding modes and state trajectories in a manifold of order n-m Most of publications on HOSM control studied a new phenomenon for systems with a scalar control (m = 1), specifically, the existence of sliding modes in manifolds of dimension lower than n-1 with a finite reaching time Along with implementation issues, it was natural to discuss to what extent the main principles of the conventional theory were to be revised (definitions, existence conditions, motion equations), what new properties of systems with HOSM can be expected Partially these questions were discussed in [2] along with several international workshops on SMC and CDC in 2018 In this paper we demonstrate that main concepts are similar for both types of SMC, discuss new developments needed for HOSM control, compare the potential to suppress chattering, complexity of the both methods

Bacillus subtilis, an ideal probiotic bacterium to shrimp and fish aquaculture that increase feed digestibility, prevent microbial diseases, and avoid water pollution.

Abstract: Beneficial microorganisms maintain the ecosystems, plants, animals and humans working in healthy conditions. In nature, around 95% of all microorganisms produce beneficial effects by increasing nutrients digestion and assimilation, preventing pathogens development and by improving environmental parameters. However, increase in human population and indiscriminate uses of antibiotics have been exerting a great pressure on agriculture, livestock, aquaculture, and also to the environment. This pressure has induced the decomposition of environmental parameters and the development of pathogenic strains resistant to most antibiotics. Therefore, all antibiotics have been restricted by corresponding authorities; hence, new and healthy alternatives to prevent or eliminate these pathogens need to be identified. Thus, probiotic bacteria utilization in aquaculture systems has emerged as a solution to prevent pathogens development, to enhance nutrients assimilation and to improve environmental parameters. In this sense, B. subtilis is an ideal multifunctional probiotic bacterium, with the capacity to solve these problems and also to increase aquaculture profitability.

Stabilizing DFB laser injection-locked to an external fiber-optic ring resonator.

Abstract: Self-injection locking to an external fiber cavity is an efficient technique enabling drastic linewidth narrowing and self-stabilization of semiconductor lasers. The main drawback of this technique is its high sensitivity to fluctuations of the configuration parameters and surroundings. In the proposed laser configuration, to the best our knowledge, for the first time the self-injection locking mechanism is used in conjunction with a simple active optoelectronic feedback, ensuring stable mode-hopping free laser operation in a single longitudinal mode. Locking to 4-m length fiber resonator causes a drastic narrowing of the DFB laser linewidth down to 2.8 kHz and a reduction of the laser phase noise by three orders of magnitude. We have explored key features of the laser dynamics with and without active feedback, revealing stability and tunability of the laser linewidth as an additional benefit of the proposed technique.

Climatic trends and regional climate models intercomparison over the CORDEX-CAM (Central America, Caribbean, and Mexico) domain

Abstract: An intercomparison of three regional climate models (RCMs) (PRECIS-HadRM3P, RCA4, and RegCM4) was performed over the Coordinated Regional Dynamical Experiment (CORDEX)-Central America, Caribbean, a ...

Shed light in The daRk lineagES of the fungal tree of life—STRES

Abstract: The polyphyletic group of black fungi within the Ascomycota (Arthoniomycetes, Dothideomycetes, and Eurotiomycetes) is ubiquitous in natural and anthropogenic habitats. Partly because of their dark, melanin-based pigmentation, black fungi are resistant to stresses including UV- and ionizing-radiation, heat and desiccation, toxic metals, and organic pollutants. Consequently, they are amongst the most stunning extremophiles and poly-extreme-tolerant organisms on Earth. Even though ca. 60 black fungal genomes have been sequenced to date, [mostly in the family Herpotrichiellaceae (Eurotiomycetes)], the class Dothideomycetes that hosts the largest majority of extremophiles has only been sparsely sampled. By sequencing up to 92 species that will become reference genomes, the "Shed light in The daRk lineagES of the fungal tree of life" (STRES) project will cover a broad collection of black fungal diversity spread throughout the Fungal Tree of Life. Interestingly, the STRES project will focus on mostly unsampled genera that display different ecologies and life-styles (e.g., ant- and lichen-associated fungi, rock-inhabiting fungi, etc.). With a resequencing strategy of 10- to 15-fold depth coverage of up to ~550 strains, numerous new reference genomes will be established. To identify metabolites and functional processes, these new genomic resources will be enriched with metabolomics analyses coupled with transcriptomics experiments on selected species under various stress conditions (salinity, dryness, UV radiation, oligotrophy). The data acquired will serve as a reference and foundation for establishing an encyclopedic database for fungal metagenomics as well as the biology, evolution, and ecology of the fungi in extreme environments.

A Social Robot as Therapy Facilitator in Interventions to Deal with Dementia-related Behavioral Symptoms

Abstract: Several studies have been reported on the use of social robots for dementia care. These robots have been used for diverse tasks such as for companionship, as an exercise coach, and as daily life assistant. However, most of these studies have assessed impact on participants only at the time when the interaction takes place rather than their medium or long-term effects. In this work, we report on a nine-week study conducted in a nursing home in which a autonomous social robot, called Eva, acts as facilitator of a cognitive stimulation therapy (CST). During the study, eight persons with dementia interacted with the robot in a group session which included elements of music therapy, reminiscence, cognitive games, and relaxation. Using the Neuropsychiatric Inventory - Nursing Home version (NPI-NH), we analyzed the impact of the therapy guided by the robot. The results show a statistically significant decrease in the total score of NPI-NH. Also, three dementia-related symptoms: delusions, agitation/aggression, and euphoria/exaltation, show a statistically significant decrease after the intervention. In addition, a qualitative analysis on interviews conducted with caregivers shows that all participants exhibits positive short-term effects after the session and provides insights on why some changes in behavior prevailed beyond the therapy sessions. Our results provide evidence that a social robot could play a role in improving the quality of life of persons with dementia. CCS CONCEPTS • Computer systems organization → Robotics; • Social and professional topics → Medical technologies; People with disabilities; • Human-centered computing → Interactive systems and tools; User studies. ACM Reference Format: Dagoberto Cruz-Sandoval, Arturo Morales-Tellez, Eduardo Benitez Sandoval, and Jesus Favela. 2020. A Social Robot as Therapy Facilitator in Interventions to Deal with Dementia-related Behavioral Symptoms. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374840

Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability

Abstract: Internet-of-Things (IoT) environment has a dynamic nature with high risks of confidentiality, integrity, and availability violations. The loss of information, denial of access, information leakage, collusion, technical failures, and data security breaches are difficult to predict and anticipate in advance. These types of nonstationarity are one of the main issues in the design of the reliable IoT infrastructure capable of mitigating their consequences. It is not sufficient to propose solutions for a given scenario, but mechanisms to adapt the current solution to changes in the environment. In this article, we present a multicloud storage architecture called WA-MRC-RRNS that combines the weighted access scheme, threshold secret sharing, and redundant residue number system with multiple failure detection/recovery mechanisms and homomorphic ciphers. We provide a theoretical analysis of the probability of information loss, data redundancy, speed of encoding/decoding, and show how to dynamically configure parameters to cope with different objective preferences, workloads, and cloud properties. We propose a multiobjective optimization mechanism to adjust redundancy, encryption–decryption speed, and data loss probability. Comprehensive experimental analysis with real data shows that our approach provides a secure way to mitigate the uncertainty of the use of untrusted and not reliable IoT infrastructure.

Neotethyan Subduction Ignited the Iran Arc and Backarc Differently

Abstract: Most arcs show systematic temporal and spatial variations in magmatism with clear shifts in igneous rock compositions between those of the magmatic front (MF) and those in the backarc (BA). It is unclear if similar magmatic polarity is seen for extensional continental arcs. Herein, we use geochemical and isotopic characteristics coupled with zircon U‐Pb geochronology to identify the different magmatic style of the Iran convergent margin, an extensional system that evolved over 100 Myr. Our new and compiled U‐Pb ages indicate that major magmatic episodes for the NE Iran BA occurred at 110–80, 75–50, 50–35, 35–20, and 15–10 Ma. In contrast to NE Iran BA magmatic episodes, compiled data from MF display two main magmatic episodes at 95–75 and 55–5 Ma, indicating more continuous magmatism for the MF than for the BA. We show that Paleogene Iran serves as a useful example of a continental arc under extension. Our data also suggest that there is not a clear relationship between the subduction velocity of Neotethyan Ocean beneath Iran and magmatic activity in Iran. Our results imply that the isotopic compositions of Iran BA igneous rocks do not directly correspond to the changes in tectonic processes or geodynamics, but other parameters such as the composition of lithosphere and melt source(s) should be considered. In addition, changes in subduction zone dynamics and contractional versus extensional tectonic regimes influenced the composition of MF and BA magmatic rocks. These controls diminished the geochemical and isotopic variations between the magmatic front and backarc. Plain Language Summary Most arcs show systematic temporal and spatial variations in magmatism with a clear shift in the composition of igneous products between those of the magmatic front and those in the backarc (BA). Our U‐Pb ages for the NE Iran BA identify five magmatic episodes for the NE Iran backarc, which occurred at 110–80 million years ago (Ma), 75–50, 50–35, 35–20, and 15–10 Ma. The 110–80 Ma magmatic episode was a time of strong regional extension due to subduction initiation along the Zagros suture zone, while the younger episodes reflect maturation of the magmatic arc. The first magmatic episode is represented by magmas dominated by inputs from the underlying mantle, as does the next episode (75–50 Ma). The third pulse (50–35 Ma) shows increasing contributions from the underlying crust. The fourth magmatic pulse occurred at 35–20 Ma, and the volume of these magmas was less than other magmatic pulses. The final pulse in the NE Iran BA (15–10 Ma) suggests that there was a change in magmatic architecture beneath the BA. Magmatic pulses at 110–80 and 50–35 Ma accompanied extension, while others accompanied compression due to collision with Arabia. Contribution of continental crust components was highest for the 50–35 Ma magmatic episode.

Future changes in the wintertime cyclonic activity over the CORDEX-CORE southern hemisphere domains in a multi-model approach

Abstract: Changes in the characteristics of austral winter (June–July–August) synoptic activity in three domains (Africa, Australia and South America) of the extratropical Southern Hemisphere projected with the Regional Climate Model version 4 (RegCM4) are presented. The model is nested in three global climate models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5) under the Representative Concentration Pathway 8.5. The model grid spacing is 25 km and the simulations cover the period 1970–2100. Synoptic activity is analyzed using both Eulerian and Lagrangian approaches. The Eulerian analysis shows an increase of the synoptic activity south of 40° S in the RegCM4 and GCMs ensembles for the future (2080–2099) compared to the present (1995–2014), but this signal does not necessarily indicate an increase in the cyclone frequency since it includes cyclonic and anticyclonic features. The Lagrangian analysis, however, indicates a decrease in the frequency of cyclones, with a positive tendency towards stronger systems, although the latter is not statistically significant at 95% confidence level. Lifetime, traveled distance and mean speed of the cyclones do not present statistically significant changes in the future climate. On the other hand, a significant increase in both intensity and extension of areas affected by precipitation associated with cyclones is found. As a consequence, there is a statistically significant trend of individual cyclones to produce more rainfall in the future.

Dual-Band Microstrip Resonant Sensor for Dielectric Measurement of Liquid Materials

Abstract: A novel compact microstrip resonant dielectric permittivity sensor with dual-band operation is presented in this paper The circuit makes use of a single stepped-impedance-resonator as the sensing element, working at the 245-GHz and 58-GHz ISM bands, under differential and common modes, respectively Sensitivity of the circuit and resonator coupling at each band can be configured independently Moreover, testing of the sample is done by using a single sensing region without the need of duplicating the sample location Experimental measurements are carried out using a very small amount of liquids and the results are presented validating the theory This work delivers a compact, reusable, label-free and non-destructive microwave device and paves a way for sensing dielectric properties of chemicals with accuracy due to the dual-band performance

On the Continuous Processing of Health Data in Edge-Fog-Cloud Computing by Using Micro/Nanoservice Composition

Abstract: The edge, the fog, the cloud, and even the end-user’s devices play a key role in the management of the health sensitive content/data lifecycle However, the creation and management of solutions including multiple applications executed by multiple users in multiple environments (edge, the fog, and the cloud) to process multiple health repositories that, at the same time, fulfilling non-functional requirements (NFRs) represents a complex challenge for health care organizations This paper presents the design, development, and implementation of an architectural model to create, on-demand, edge-fog-cloud processing structures to continuously handle big health data and, at the same time, to execute services for fulfilling NFRs In this model, constructive and modular $blocks$ , implemented as microservices and nanoservices, are recursively interconnected to create edge-fog-cloud processing structures as infrastructure-agnostic services Continuity schemes create dataflows through the blocks of edge-fog-cloud structures and enforce, in an implicit manner, the fulfillment of NFRs for data arriving and departing to/from each block of each edge-fog-cloud structure To show the feasibility of this model, a prototype was built using this model, which was evaluated in a case study based on the processing of health data for supporting critical decision-making procedures in remote patient monitoring This study considered scenarios where end-users and medical staff received insights discovered when processing electrocardiograms (ECGs) produced by sensors in wireless IoT devices as well as where physicians received patient records (spirometry studies, ECGs and tomography images) and warnings raised when online analyzing and identifying anomalies in the analyzed ECG data A scenario where organizations manage multiple simultaneous each edge-fog-cloud structure for processing of health data and contents delivered to internal and external staff was also studied The evaluation of these scenarios showed the feasibility of applying this model to the building of solutions interconnecting multiple services/applications managing big health data through different environments

Literature Review on Transfer Learning for Human Activity Recognition Using Mobile and Wearable Devices with Environmental Technology

Abstract: Activity recognition systems utilise data from sensors in mobile, environmental and wearable devices, ubiquitously available to individuals. It is a growing research area within intelligent systems that aims to model and identify human physical, cognitive and social actions, patterns and skills. They typically rely on supervised machine-learning approaches, in which the cost of gathering and labelling data is high due to the diverse, interleaved and dynamic nature of human behaviour. Transfer learning is an approach in which previously learned knowledge is utilised to model a new but related setting. For instance, it can reuse existing knowledge to recognise activities performed by different types of users, using different sensor technologies and in different environmental conditions. As the adoption of Internet of Thing devices increases, mobile and wearable sensing is becoming pervasive, and more challenging behaviour recognition activities are being tackled. Yet, the availability of more data does not necessarily translate to better recognition models, if these data are not properly labelled. Thus, the importance of taking advantage of transfer learning to advance the field of activity recognition. This literature review summarises the transfer learning techniques and explores the benefits of combining mobile and wearable devices with environmental sensors in support of transfer learning. We also discuss the maturity of transfer learning by analysing the validation method used in the papers reviewed. Overall, 170 selected articles published between 2014 and 2019 were reviewed following the Okali and Schabram methodology. Findings show an increase of 41% of publications when comparing the output of 2019 against the average number of papers published in the previous 5 years (2014–2018). Inertial sensors such as accelerometers and gyroscopes, are the most frequently used. Feature and instance representation are mature techniques for transfer knowledge. Unsupervised learning across users is a typical application, and shallow techniques and active learning are areas of opportunity in transfer learning methodologies.

Brome mosaic virus-like particles as siRNA nanocarriers for biomedical purposes.

Abstract: There is an increasing interest in the use of plant viruses as vehicles for anti-cancer therapy. In particular, the plant virus brome mosaic virus (BMV) and cowpea chlorotic mottle virus (CCMV) are novel potential nanocarriers for different therapies in nanomedicine. In this work, BMV and CCMV were loaded with a fluorophore and assayed on breast tumor cells. The viruses BMV and CCMV were internalized into breast tumor cells. Both viruses, BMV and CCMV, did not show cytotoxic effects on tumor cells in vitro. However, only BMV did not activate macrophages in vitro. This suggests that BMV is less immunogenic and may be a potential carrier for therapy delivery in tumor cells. Furthermore, BMV virus-like particles (VLPs) were efficiently loaded with small interfering RNA (siRNA) without packaging signal. The gene silencing was demonstrated by VLPs loaded with siGFP and tested on breast tumor cells that constitutively express the green fluorescent protein (GPF). After VLP-siGFP treatment, GFP expression was efficiently inhibited corroborating the cargo release inside tumor cells and the gene silencing. In addition, BMV VLP carring siAkt1 inhibited the tumor growth in mice. These results show the attractive potential of plant virus VLPs to deliver molecular therapy to tumor cells with low immunogenic response.

Assessing the thermal performance of a rooftop solar chimney attached to a single room

Abstract: The passive design approach can help to maintain comfortable thermal conditions in buildings and reduce energy consumption by minimizing the use of auxiliary mechanical or electromechanical air-conditioning systems. In particular, the implementation of solar chimneys for cooling increases energy efficiency in buildings located in warm climates by decreasing electricity bills and CO2 emissions. This paper presents a pseudo-transient thermal performance assessment of a ventilated room with and without the integration of a rooftop solar chimney for summer and winter conditions in Merida (Mexico). A global in-house numerical code based on a novel integration of both computational fluid dynamics and global energy balances was developed to solve the conjugate turbulent heat transfer in the ventilated room with the integrated solar chimney system. Numerical results were verified and validated, and they showed a good agreement with the benchmark solutions reported in the literature. The room with the integrated solar chimney improved the ventilation rates by 8–45% in summer and by 1.16–24.89% in winter. Both configurations under analysis fulfilled the requirements of 0.5–2.5 air changes per hour by the ASHRAE Standard 62.1. In general, we concluded that the integration of the solar chimney system showed higher ventilation rates, demonstrating its higher potential as a passive ventilation system in warm climates.

The mid-summer drought over Mexico and Central America in the 21st century

Abstract: The southern Mexico and Central America (SMCA) region shows a dominant well-defined precipitation annual cycle. The rainy season usually begins in May and ends in October, with a relatively dry per ...

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

Abstract: The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the “ocean” of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

Dual-frequency laser comprising a single fiber ring cavity for self-injection locking of DFB laser diode and Brillouin lasing.

Abstract: Low-noise lasers are a powerful tool in precision spectroscopy, displacement measurements, and development of advanced optical atomic clocks. While all applications benefit from lower frequency noise and robust design, some of them also require lasing at two frequencies. Here, we introduce a simple dual-frequency laser leveraging a ring fiber cavity exploited both for self-injection locking of a standard semiconductor distributed feedback (DFB) laser and for generation of Stokes light via stimulated Brillouin scattering. In contrast to the previous laser configurations, the system is supplied by a low-bandwidth active optoelectronic feedback. Importantly, continuous operation of two mutually locked frequencies is provided by self-injection locking, while the active feedback loop is used just to support this regime. The fiber configuration reduces the natural Lorentzian linewidth of light emitted by the laser at pump and Stokes frequencies down to 270 Hz and 110 Hz, respectively, and features a stable 300-Hz-width RF spectrum recorded with beating of two laser outputs. Translating the proposed laser design to integrated photonics will dramatically reduce cost and footprint for many laser applications such as ultra-high capacity fiber and data center networks, atomic clocks, and microwave photonics.

Engineering nanoparticles with pure high-order multipole scattering

Abstract: The ability to control scattering directionality of nanoparticles is in high demand for many nanophotonic applications. One of the challenges is to design nanoparticles producing pure high-order multipole scattering (e.g., octopole, hexadecapole), whose contribution is usually negligible compared with strong low-order multipole scattering (i.e., dipole or quadrupole). Here we present an intuitive way to design such nanoparticles by introducing a void inside them. We show that both shell and ring nanostructures allow regimes with nearly pure high-order multipole scattering. Experimentally measured scattering diagrams from properly designed silicon rings at near-infrared wavelengths (∼800 nm) reproduce well scattering patterns of an electric octopole and magnetic hexadecapole. Our findings advance significantly inverse engineering of nanoparticles from given complex scattering characteristics, with possible applications in biosensing, optical metasurfaces, and quantum communications.

Smart Nanoformulation Based on Stimuli-Responsive Nanogels and Curcumin: Promising Therapy against Colon Cancer.

Abstract: Curcumin (CUR) has gained much attention for its widely reported anticancer effect; however, its clinical use is restricted due to its low water solubility and, consequently, its poor bioavailabili...

The Mycobiota of the Deep Sea: What Omics Can Offer

Abstract: The deep sea (>1000 m below sea level) represents one of the most extreme environments of the ocean. Despite exhibiting harsh abiotic conditions such as low temperatures, high hydrostatic pressure, high salinity concentrations, a low input of organic matter, and absence of light, the deep sea encompasses a great fungal diversity. For decades, most knowledge on the fungal diversity of the deep sea was obtained through culture-dependent techniques. More recently, with the latest advances of high-throughput next generation sequencing platforms, there has been a rapid increment in the number of studies using culture-independent techniques. This review brings into the spotlight the progress of the techniques used to assess the diversity and ecological role of the deep-sea mycobiota and provides an overview on how the omics technologies have contributed to gaining knowledge about fungi and their activity in poorly explored marine environments. Finally, current challenges and suggested coordinated efforts to overcome them are discussed.

Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches.

Abstract: Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatory concern. Thus, many in silico tools have been proposed to improve the hepatotoxicity predictio...