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Marine mammals are integral to global biodiversity and marine health through their roles in coastal, benthic, and pelagic ecosystems. Marine mammals face escalating threats from climate change, pollution, and human activities, which perturb their oceanic environment. The diverse biology and extreme adaptations evolved by marine mammals make them important study subjects for understanding anthropogenic pressures on marine ecosystems. However, ethical and logistical constraints restrict the tractability of experimental research with live marine mammals. Additionally, studies on the effects of changing ocean environments are further complicated by intricate gene-environment interactions across populations and species. These obstacles can be overcome with a comprehensive strategy that involves a systems-level approach integrating genotype to phenotype using rigorously defined experimental conditions in vitro and ex vivo. A thorough analysis of the interactions between the genetics of marine mammals and their exposure to anthropogenic pressures will enable robust predictions about how global environmental changes will affect their health and populations. In this perspective, we discuss four challenges of implementing such non-invasive approaches across scientific fields and international borders: 1) practical and ethical limitations of in vivo experimentation with marine mammals, 2) accessibility to relevant tissue samples and cell cultures; 3) open access to harmonized methods and datasets and 4) ethical and equitable research practices. Successful implementation of the proposed approach has the potential impact to inspire new solutions and strategies for marine conservation.

Sustainability and environmental compliance in ship operations is a prominent research topic as the waterborne sector is obliged to adopt ”green” mitigation strategies towards a low emissions operational blueprint. Fuel-Oil-Consumption (FOC) estimation, constitutes one of the key components in maritime transport information systems for efficiency and environmental compliance. This paper deals with FOC estimation in a more novel way than methods proposed in literature, by utilizing a reduced-sized feature set, which allows predicting vessel’s Main-Engine rotational speed (RPM). Furthermore, this work aims to place the deployment of such models in the broader context of a cutting-edge information system, to improve efficiency and regulatory adherence. Specifically, we integrate B-Splines in the context of two Deep Learning architectures and compare their performance against state-of-the-art regression techniques. Finally, we estimate FOC by combining velocity measurements and the predicted RPM with vessel-specific characteristics and illustrate the performance of our estimators against actual FOC data.

Responsive neuromodulation is increasingly being used to treat patients with neuropsychiatric diseases. Yet, inefficient bridges between traditional and new materials and technological innovations impede advancements in neurostimulation tools. Signaling in the brain is accomplished predominantly by ion flux rather than the movement of electrons. However, the status quo for the acquisition of neural signals is using materials, such as noble metals, that can only interact with electrons. As a result, ions accumulate at the biotic/abiotic interface, creating a double-layer capacitance that increases impedance and negatively impacts the efficiency of neural interrogation. Alternative materials, such as conducting polymers, allow ion penetration in the matrix, creating a volumetric capacitor (two orders of magnitude larger than an area-dependent capacitor) that lowers the impedance and increases the spatiotemporal resolution of the recording/stimulation. On the other hand, the increased development and integration capabilities of CMOS-based back-end electronics have enabled the creation of increasingly powerful and energy-efficient microchips. These include stimulation and recording systems-on-a-chip (SoCs) with up to tens of thousands of channels, fully integrated circuitry for stimulation, signal conditioning, digitation, wireless power and data telemetry, and on-chip signal processing. Here, we aim to compile information on the best component for each building block and try to strengthen the vision that bridges the gap among various materials and technologies in an effort to advance neurostimulation tools and promote a solution-centric way of considering their complex problems.

Abstract Background Idiopathic Pulmonary Fibrosis (IPF) is the most common and progressive form of the interstitial lung diseases, leading most patients to require lung transplants to survive. Despite the relatively well-defined role of the fibroblast in the progression of IPF, it is the alveolar type II epithelial cell (AEC2) that is now considered the initiation site of damage, driver of disease, and the most efficacious therapeutic target for long-term resolution. Based on our previous studies, we hypothesize that altered lactate metabolism in AEC2 plays a pivotal role in IPF development and progression, affecting key cellular and molecular interactions within the pulmonary microenvironment. Methods AEC2s isolated from human patient specimens of non-fibrotic and IPF lungs were used for metabolic measurements, lactate dehydrogenase (LDH) analyses and siRNA-mediated knockdown experiments. Results AEC2s isolated from human IPF lung explant tissues had lower rates of oxidative metabolism and were more glycolytic lactate-producing cells than were AEC2 from control, non-fibrotic lung explant tissues. Consistent with this shift in metabolism, patient-derived IPF AEC2s exhibited LDH tetramers that have higher ratios of LDHA:LDHB (i.e., favoring pyruvate to lactate conversion) than control AEC2s. Experimental manipulation of LDHA subunit expression in IPF AEC2s restored the bioenergetic profile characteristic of AEC2 from non-fibrotic lungs. Conclusions These results are consistent with the concept that altered lactate metabolism may be an underlying feature of AEC2 dysfunction in IPF and may be a novel and important target for therapeutic treatment.

Mature neural networks synchronize and integrate spatiotemporal activity patterns to support cognition. Emergence of these activity patterns and functions is believed to be developmentally regulated, but the postnatal time course for neural networks to perform complex computations remains unknown. We investigate the progression of large-scale synaptic and cellular activity patterns across development using high spatiotemporal resolution in vivo electrophysiology in immature mice. We reveal that mature cortical processes emerge rapidly and simultaneously after a discrete but volatile transition period at the beginning of the second postnatal week of rodent development. The transition is characterized by relative neural quiescence, after which spatially distributed, temporally precise, and internally organized activity occurs. We demonstrate a similar developmental trajectory in humans, suggesting an evolutionarily conserved mechanism that could facilitate a transition in network operation. We hypothesize that this transient quiescent period is a requisite for the subsequent emergence of coordinated cortical networks.

[This corrects the article DOI: 10.1371/journal.pgen.1005591.].

Internet of Things (IoT) technologies have evolved rapidly during the last decade, and many architecture types have been proposed for distributed and interconnected systems. However, most systems are implemented following fragmented approaches for specific application domains, introducing difficulties in providing unified solutions. However, the unification of solutions is an important feature from an IoT perspective. In this paper, we present an IoT platform that supports multiple application layer communication protocols (Representational State Transfer (REST)/HyperText Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), Constrained Application Protocol (CoAP), and Websockets) and that is composed of open-source frameworks (RabbitMQ, Ponte, OM2M, and RDF4J). We have explored a back-end system that interoperates with the various frameworks and offers a single approach for user-access control on IoT data streams and micro-services. The proposed platform is evaluated using its containerized version, being easily deployable on the vast majority of modern computing infrastructures. Its design promotes service reusability and follows a marketplace architecture, so that the creation of interoperable IoT ecosystems with active contributors is enabled. All the platform’s features are analyzed, and we discuss the results of experiments, with the multiple communication protocols being tested when used interchangeably for transferring data. Developing unified solutions using such a platform is of interest to users and developers as they can test and evaluate local instances or even complex applications composed of their own IoT resources before releasing a production version to the marketplace.

Dyshomeostasis of both ceramides and sphingosine-1-phosphate (S1P) in the brain has been implicated in aging-associated neurodegenerative disorders in humans. However, mechanisms that maintain the homeostasis of these bioactive sphingolipids in the brain remain unclear. Mouse alkaline ceramidase 3 (Acer3), which preferentially catalyzes the hydrolysis of C18:1-ceramide, a major unsaturated long-chain ceramide species in the brain, is upregulated with age in the mouse brain. Acer3 knockout causes an age-dependent accumulation of various ceramides and C18:1-monohexosylceramide and abolishes the age-related increase in the levels of sphingosine and S1P in the brain; thereby resulting in Purkinje cell degeneration in the cerebellum and deficits in motor coordination and balance. Our results indicate that Acer3 plays critically protective roles in controlling the homeostasis of various sphingolipids, including ceramides, sphingosine, S1P, and certain complex sphingolipids in the brain and protects Purkinje cells from premature degeneration.