Patient assist device parts list

A complete parts list for the patient assist device is presented along with the schematic diagrams

Long-term Observations Reveal Environmental Conditions and Food Supply Mechanisms at an Arctic Deep-Sea Sponge Ground

Deep-sea sponge grounds are hotspots of benthic biomass and diversity. To date, very limited data exist on the range of environmental conditions in areas containing deep-sea sponge grounds and which factors are driving their distribution and sustenance. We investigated oceanographic conditions at a deep-sea sponge ground located on an Arctic Mid-Ocean Ridge seamount. Hydrodynamic measurements were performed along Conductivity-Temperature-Depth transects, and a lander was deployed within the sponge ground that recorded near-bottom physical properties as well as vertical fluxes of organic matter over an annual cycle. The data demonstrate that the sponge ground is found at water temperatures of −0.5°C to 1°C and is situated at the interface between two water masses at only 0.7° equatorward of the turning point latitude of semi-diurnal lunar internal tides. Internal waves supported by vertical density stratification interact with the seamount topography and produce turbulent mixing as well as resuspension of organic matter with temporarily very high current speeds up to 0.72 m s−1. The vertical movement of the water column delivers food and nutrients from water layers above and below toward the sponge ground. Highest organic carbon flux was observed during the summer phytoplankton bloom period, providing fresh organic matter from the surface. The flux of fresh organic matter is unlikely to sustain the carbon demand of this ecosystem. Therefore, the availability of bacteria, nutrients, and dissolved and particulate matter, delivered by tidally forced internal wave turbulence and transport by horizontal mean flows, likely plays an important role in meeting ecosystem-level food requirements

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi‐disciplinary approach can promote data‐driven decision‐making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation

Climatic and biogeographical drivers of functional diversity in the flora of the Canary Islands

Aim Functional traits can help us to elucidate biogeographical and ecological processes driving assemblage structure. We analysed the functional diversity of plant species of different evolutionary origins across an island archipelago, along environmental gradients and across geological age, to assess functional aspects of island biogeographical theory. Location Canary Islands, Spain. Major taxa studied Spermatophytes. Time period Present day. Methods We collected data for four traits (plant height, leaf length, flower length and fruit length) associated with resource acquisition, competitive ability, reproduction and dispersal ability of 893 endemic, non-endemic native and alien plant species (c. 43% of the Canary Island flora) from the literature. Linking these traits to species occurrences and composition across a 500 m × 500 m grid, we calculated functional diversity for endemic, non-endemic native and alien assemblages using multidimensional functional hypervolumes and related the resulting patterns to climatic (humidity) and island biogeographical (geographical isolation, topographic complexity and geological age) gradients. Results Trait space of endemic and non-endemic native species overlapped considerably, and alien species added novel trait combinations, expanding the overall functional space of the Canary Islands. We found that functional diversity of endemic plant assemblages was highest in geographically isolated and humid grid cells. Functional diversity of non-endemic native assemblages was highest in less isolated and humid grid cells. In contrast, functional diversity of alien assemblages was highest in arid ecosystems. Topographic complexity and geological age had only a subordinate effect on functional diversity across floristic groups. Main conclusions We found that endemic and non-endemic native island species possess similar traits, whereas alien species tend to expand functional space in ecosystems where they have been introduced. The spatial distribution of the functional diversity of floristic groups is very distinct across environmental gradients, indicating that species assemblages of different evolutionary origins thrive functionally in dissimilar habitats.publishedVersio

Quantification of a subsea CO2 release with lab-on-chip sensors measuring benthic gradients

We present a novel approach to detecting and quantifying a subsea release of CO2 from within North Sea sed�iments, which mimicked a leak from a subsea CO2 reservoir. Autonomous lab-on-chip sensors performed in situ measurements of pH at two heights above the seafloor. During the 11 day experiment the rate of CO2 release was gradually increased. Whenever the currents carried the CO2-enriched water towards the sensors, the sensors measured a decrease in pH, with a strong vertical gradient within a metre of the seafloor. At the highest release rate, a decrease of over 0.6 pH units was observed 17 cm above the seafloor compared to background mea�surements. The sensor data was combined with hydrodynamic measurements to quantify the amount of CO2 escaping the sediments using an advective mass transport model. On average, we directly detected 43 ± 8% of the released CO2 in the water column. Accounting for the incomplete carbonate equilibration process increases this estimate to up to 61 ± 10%. This technique can provide long-term in situ monitoring of offshore CO2 res�ervoirs and hence provides a tool to support climate change mitigation activities. It could also be applied to characterising plumes and quantifying other natural or anthropogenic fluxes of dissolved solutes

The important role of sponges in carbon and nitrogen cycling in a deep-sea biological hotspot

Deep-sea sponge grounds are hotspots of biodiversity, harbouring thriving ecosystems in the otherwise barren deep sea. It remains unknown how these sponge grounds survive in this food-limited environment. Here, we unravel how sponges and their associated fauna sustain themselves by identifying their food sources and food-web interactions using bulk and compound-specific stable isotope analysis of amino and fatty acids. We found that sponges with a high microbial abundance had an isotopic composition resembling organisms at the base of the food web, suggesting that they are able to use dissolved resources that are generally inaccessible to animals. In contrast, low microbial abundance sponges had a bulk isotopic composition that resembles a predator at the top of a food web, which appears to be the result of very efficient recycling pathways that are so far unknown. The compound-specific-isotope analysis, however, positioned low-microbial abundance sponges with other filter-feeding fauna. Furthermore, fatty-acid analysis confirmed transfer of sponge-derived organic material to the otherwise food-limited associated fauna. Through this subsidy, sponges are key to the sustenance of thriving deep-sea ecosystems and might have, due to their ubiquitous abundance, a global impact on biogeochemical cycles.publishedVersio

Realistic measurement uncertainties for marine macronutrient measurements conducted using gas segmented flow and Lab-on-Chip techniques

Highlights • Accounting for systematic bias is required for a realistic analytical uncertainty • Gas segmented flow techniques achieved a combined uncertainties of 1-4 % • Lab-on-Chip nitrate + nitrite analysers achieved a combined uncertainties < 5% Abstract Accurate and precise measurements of marine macronutrient concentrations are fundamental to our understanding of biogeochemical cycles in the ocean. Quantifying the measurement uncertainty associated with macronutrient measurements remains a challenge. Large systematic biases (up to 10 %) have been identified between datasets, restricting the ability of marine biogeochemists to distinguish between the effects of environmental processes and analytical uncertainty. In this study we combine the routine analyses of certified reference materials (CRMs) with the application of a simple statistical technique to quantify the combined (random + systematic) measurement uncertainty associated with marine macronutrient measurements using gas segmented flow techniques. We demonstrate that it is realistic to achieve combined uncertainties of ~1-4 % for nitrate + nitrite (ΣNOx), phosphate (PO43-) and silicic acid (Si(OH)4) measurements. This approach requires only the routine analyses of CRMs (i.e. it does not require inter-comparison exercises). As CRMs for marine macronutrients are now commercially available, it is advocated that this simple approach can improve the comparability of marine macronutrient datasets and therefore should be adopted as ‘best practice’. Novel autonomous Lab-on-Chip (LoC) technology is currently maturing to a point where it will soon become part of the marine chemist’s standard analytical toolkit used to determine marine macronutrient concentrations. Therefore, it is critical that a complete understanding of the measurement uncertainty of data produced by LoC analysers is achieved. In this study we analysed CRMs using 7 different LoC ΣNOx analysers to estimate a combined measurement uncertainty of < 5%. This demonstrates that with high quality manufacturing and laboratory practices, LoC analysers routinely produce high quality measurements of marine macronutrient concentrations

Career Choice: A Case Study of College Students Shifting Career Paths

This study aimed to determine the causes, impacts, and varying perspectives of how two years of education can alter a person's ambitions. A qualitative research design, specifically the case study approach was used in this study. It focused on why the participants enrolled in a college program that was not aligned with their SHS Strand. The responses are subsequently transcribed and themes are identified. Students whose college courses do not correspond to their Senior High School strands have questioned their previous strand selection. This is due to difficulties they encountered as a result of the misalignment. According to the general findings, one of the most important factors to consider when choosing a career is one's interest. It was also evident within the data presented that the finances and practicality of the predetermined college courses that the students chose were weighed into their decision-making. It also shows that the majority of the participants agreed that their families have an impact on their career choices. The study also discovered that doing things that are not one's passion in the first place can be difficult, but it can help people challenge their abilities. The study concludes that indeed, pre-coaching strategies and guidance provided by schools play a large factor in students' awareness of their strengths and weaknesses. Also, setting goals may make the desired changes more difficult; however, the participants rediscovered their inclination and satisfaction with what they have pursued now, which motivated them to continue

Can Molecular Motors Drive Distance Measurements in Injured Neurons?

Injury to nerve axons induces diverse responses in neuronal cell bodies, some of which are influenced by the distance from the site of injury. This suggests that neurons have the capacity to estimate the distance of the injury site from their cell body. Recent work has shown that the molecular motor dynein transports importin-mediated retrograde signaling complexes from axonal lesion sites to cell bodies, raising the question whether dynein-based mechanisms enable axonal distance estimations in injured neurons? We used computer simulations to examine mechanisms that may provide nerve cells with dynein-dependent distance assessment capabilities. A multiple-signals model was postulated based on the time delay between the arrival of two or more signals produced at the site of injury–a rapid signal carried by action potentials or similar mechanisms and slower signals carried by dynein. The time delay between the arrivals of these two types of signals should reflect the distance traversed, and simulations of this model show that it can indeed provide a basis for distance measurements in the context of nerve injuries. The analyses indicate that the suggested mechanism can allow nerve cells to discriminate between distances differing by 10% or more of their total axon length, and suggest that dynein-based retrograde signaling in neurons can be utilized for this purpose over different scales of nerves and organisms. Moreover, such a mechanism might also function in synapse to nucleus signaling in uninjured neurons. This could potentially allow a neuron to dynamically sense the relative lengths of its processes on an ongoing basis, enabling appropriate metabolic output from cell body to processes