Search Results (6)

The best practice for killing decapod crustaceans lacking a centralized ganglion has been debated for a century. Currently, there is a movement away from live boiling towards electrocution and mechanical splitting or spiking, which are efficient in the large commercial setting but may be unavailable and impractical for small decapods such as shrimp and prawn in the small-scale setting of, e.g., the household. Here, using carcasses of varying sizes of prawn, crayfish, lobster and green and brown crab, we used micro-CT imaging to measure surface area and sphericity in relation to body mass. Then, we measured heating profiles at the anterior ganglion and in the core of carcasses of the same species when exposed to standardized boiling regimes. We found a relationship with positive allometry between surface area and body mass for all species and a decrease in sphericity with mass. Heating times until proposed stunning (26 °C) and killing (44 °C) varied with body size and starting temperature and exceeded minutes for larger species. For a small species like prawn, times to stunning and killing by boiling are comparable to electrocution times and within the acceptable range compared to recreational killing of other sentient beings such as game mammals. Full article

The mechanisms underlying bone-implant integration, or osseointegration, are still incompletely understood, in particular how blood and proteins are recruited to implant surfaces. The objective of this study was to visualize and quantify the flow of blood and the model protein fibrinogen using a computational fluid dynamics (CFD) implant model. Implants with screws were designed with three different surface topographies: (1) amorphous, (2) nano-trabecular, and (3) hybrid meso-spikes and nano-trabeculae. The implant with nano-topography recruited more blood and fibrinogen to the implant interface than the amorphous implant. Implants with hybrid topography further increased recruitment, with particularly efficient recruitment from the thread area to the interface. Blood movement significantly slowed at the implant interface compared with the thread area for all implants. The blood velocity at the interface was 3- and 4-fold lower for the hybrid topography compared with the nano-topography and amorphous surfaces, respectively. Thus, this study for the first time provides insights into how different implant surfaces regulate blood dynamics and the potential advantages of surface texturization in blood and protein recruitment and retention. In particular, co-texturization with a hybrid meso- and nano-topography created the most favorable microenvironment. The established CFD model is simple, low-cost, and expected to be useful for a wide range of studies designing and optimizing implants at the macro and micro levels. Full article

Recently, there has been a huge spike in the number of automobiles in the urban areas of many countries, particularly in India. The number of vehicles are increasing rapidly and with the existing infrastructure, the traffic systems stand still during peak hours. Some of the main challenges for traffic management are the movement of overloaded vehicles beyond their restricted zone and time, reckless driving, and overlooking road safety rules. This paper proposes an Internet of Things (IoT)-based real-time Intelligent Traffic Signal System (ITSS), which consists of inductive loops and a programmable micro-controller to determine traffic density. Inter-communication in the centralized control unit sets the timer of the traffic light and synchronizes with the traffic density in real-time for smooth mobility of vehicles with less delay. Additionally, to prioritize emergency vehicles over other vehicles in the same lane, a pre-emption mechanism has been integrated through infrared sensors. The result of traffic density determines the timer of the light post in real-time, which in result enhances the smooth flow of vehicles with reduced delay for travelers. Using its automatic on-demand traffic signaling system, the presented solution has advantages over fixed systems. Full article

Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining the information of traditional clinical tests. We aim at digitizing traditional tests of cognitive and memory performance to derive motor biometrics of pen-strokes and voice, thereby complementing clinical tests with objective criteria, while enhancing the overall characterization of Parkinson’s disease (PD). 35 participants including patients with PD, healthy young and age-matched controls performed a series of drawing and memory tasks, while their pen movement and voice were digitized. We examined the moment-to-moment variability of time series reflecting the pen speed and voice amplitude. The stochastic signatures of the fluctuations in pen drawing speed and voice amplitude of patients with PD show a higher signal-to-noise ratio compared to those of neurotypical controls. It appears that contact motions of the pen strokes on a tablet evoke sensory feedback for more immediate and predictable control in PD, while voice amplitude loses its neurotypical richness. We offer new standardized data types and analytics to discover the hidden motor aspects within the cognitive and memory clinical assays. Full article

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population. Full article

Wearable biosensors, such as those embedded in smart phones, can provide data to assess neuro-motor control in mobile settings, at homes, schools, workplaces and clinics. However, because most machine learning algorithms currently used to analyze such data require several steps that depend on human heuristics, the analyses become computationally expensive and rather subjective. Further, there is no standardized scale or set of tasks amenable to take advantage of such technology in ways that permit broad dissemination and reproducibility of results. Indeed, there is a critical need for fully objective automated analytical methods that easily handle the deluge of data these sensors output, while providing standardized scales amenable to apply across large sections of the population, to help promote personalized-mobile medicine. Here we use an open-access data set from Kaggle.com to illustrate the use of a new statistical platform and standardized data types applied to smart phone accelerometer and gyroscope data from 30 participants, performing six different activities. We report full distinction without confusion of the activities from the Kaggle set using a single parameter (linear acceleration or angular speed). We further extend the use of our platform to characterize data from commercially available smart shoes, using gait patterns within a set of experiments that probe nervous systems functioning and levels of motor control. Full article