Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?

Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device.

Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study.

Stress is an increasingly prevalent mental health condition that can have serious effects on human health. The development of stress prediction tools would greatly benefit public health by allowing policy initiatives and early stress-reducing interventions. The advent of mobile health technologies including smartphones and smartwatches has made it possible to collect objective, real-time, and continuous health data. We sought to pilot the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and apply machine learning techniques to develop a stress prediction tool. Random Forest (RF) and Support Vector Machines (SVM) were used to model stress based on ECG measurements and stress questionnaire data collected from 33 study participants. Data were stratified into socio-demographic classes to further explore our prediction model. Overall, the RF model performed slightly better than SVM, with results having an accuracy within the low end of state-of-the-art. Our models showed specificity in their capacity to assess "no stress" states but were less successful at capturing "stress" states. Overall, the results presented here suggest that, with further development and refinement, Apple Watch ECG sensor data could be used to develop a stress prediction tool. A wearable device capable of continuous, real-time stress monitoring would enable individuals to respond early to changes in their mental health. Furthermore, large-scale data collection from such devices would inform public health initiatives and policies.

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home.

Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients' status. Internet of Things (IoT) technologies have been used for monitoring patients' health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.

A face recognition software framework based on principal component analysis.

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective.

The field of precision medicine explores disease treatments by looking at genetic, socio-environmental, and clinical factors, thus trying to provide a holistic view of a person's health. Public health, on the other hand, is focused on improving the health of populations through preventive strategies and timely interventions. With recent advances in technology, we are able to collect, analyze and store for the first-time large volumes of real-time, diverse and continuous health data. Typically, the field of precision medicine deals with a huge amount of data from few individuals; public health, on the other hand, deals with limited data from a population. With the coming of Big Data, the fields of precision medicine and public health are converging into precision public health, the study of biological and genetic factors supported by large amounts of population data. In this paper, we explore through a comprehensive review the data types and use cases found in precision medicine and public health. We also discuss how these data types and use cases can converge toward precision public health, as well as challenges and opportunities provided by research and analyses of health data.

Knowledge reuse in software projects: Retrieving software development Q&A posts based on project task similarity.

Software developers need to cope with a massive amount of knowledge throughout the typical life cycle of modern projects. This knowledge includes expertise related to the software development phases (e.g., programming, testing) using a wide variety of methods and tools, including development methodologies (e.g., waterfall, agile), software tools (e.g., Eclipse), programming languages (e.g., Java, SQL), and deployment strategies (e.g., Docker, Jenkins). However, there is no explicit integration of these various types of knowledge with software development projects so that developers can avoid having to search over and over for similar and recurrent solutions to tasks and reuse this knowledge. Specifically, Q&A sites such as Stack Overflow are used by developers to share software development knowledge through posts published in several categories, but there is no link between these posts and the tasks developers perform. In this paper, we present an approach that (i) allows developers to associate project tasks with Stack Overflow posts, and (ii) recommends which Stack Overflow posts might be reused based on task similarity. We analyze an industry dataset, which contains project tasks associated with Stack Overflow posts, looking for the similarity of project tasks that reuse a Stack Overflow post. The approach indicates that when a software developer is performing a task, and this task is similar to another task that has been associated with a post, the same post can be recommended to the developer and possibly reused. We believe that this approach can significantly advance the state of the art of software knowledge reuse by supporting novel knowledge-project associations.

Neotropical termite microbiomes as sources of novel plant cell wall degrading enzymes.

In this study, we used shotgun metagenomic sequencing to characterise the microbial metabolic potential for lignocellulose transformation in the gut of two colonies of Argentine higher termite species with different feeding habits, Cortaritermes fulviceps and Nasutitermes aquilinus. Our goal was to assess the microbial community compositions and metabolic capacity, and to identify genes involved in lignocellulose degradation. Individuals from both termite species contained the same five dominant bacterial phyla (Spirochaetes, Firmicutes, Proteobacteria, Fibrobacteres and Bacteroidetes) although with different relative abundances. However, detected functional capacity varied, with C. fulviceps (a grass-wood-feeder) gut microbiome samples containing more genes related to amino acid metabolism, whereas N. aquilinus (a wood-feeder) gut microbiome samples were enriched in genes involved in carbohydrate metabolism and cellulose degradation. The C. fulviceps gut microbiome was enriched specifically in genes coding for debranching- and oligosaccharide-degrading enzymes. These findings suggest an association between the primary food source and the predicted categories of the enzymes present in the gut microbiomes of each species. To further investigate the termite microbiomes as sources of biotechnologically relevant glycosyl hydrolases, a putative GH10 endo-ß-1,4-xylanase, Xyl10E, was cloned and expressed in Escherichia coli. Functional analysis of the recombinant metagenome-derived enzyme showed high specificity towards beechwood xylan (288.1 IU/mg), with the optimum activity at 50 °C and a pH-activity range from 5 to 10. These characteristics suggest that Xy110E may be a promising candidate for further development in lignocellulose deconstruction applications.

Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation.

BACKGROUND: Informed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert overload that causes a health care provider to miss important events that could be harmful or even life-threatening. As health care units become more dependent on monitoring devices for patient care purposes, the alarm fatigue issue has to be addressed as a major concern for the health care team as well as to enhance patient safety. OBJECTIVE: The main goal of this paper was to propose a feasible solution for the alarm fatigue problem by using an automatic reasoning mechanism to decide how to notify members of the health care team. The aim was to reduce the number of notifications sent by determining whether or not to group a set of alarms that occur over a short period of time to deliver them together, without compromising patient safety. METHODS: This paper describes: (1) a model for supporting reasoning algorithms that decide how to notify caregivers to avoid alarm fatigue; (2) an architecture for health systems that support patient monitoring and notification capabilities; and (3) a reasoning algorithm that specifies how to notify caregivers by deciding whether to aggregate a group of alarms to avoid alarm fatigue. RESULTS: Experiments were used to demonstrate that providing a reasoning system can reduce the notifications received by the caregivers by up to 99.3% (582/586) of the total alarms generated. Our experiments were evaluated through the use of a dataset comprising patient monitoring data and vital signs recorded during 32 surgical cases where patients underwent anesthesia at the Royal Adelaide Hospital. We present the results of our algorithm by using graphs we generated using the R language, where we show whether the algorithm decided to deliver an alarm immediately or after a delay. CONCLUSIONS: The experimental results strongly suggest that this reasoning algorithm is a useful strategy for avoiding alarm fatigue. Although we evaluated our algorithm in an experimental environment, we tried to reproduce the context of a clinical environment by using real-world patient data. Our future work is to reproduce the evaluation study based on more realistic clinical conditions by increasing the number of patients, monitoring parameters, and types of alarm.

Three novel bacteriophages isolated from the East African Rift Valley soda lakes.

BACKGROUND: Soda lakes are unique environments in terms of their physical characteristics and the biology they harbour. Although well studied with respect to their microbial composition, their viral compositions have not, and consequently few bacteriophages that infect bacteria from haloalkaline environments have been described. METHODS: Bacteria were isolated from sediment samples of lakes Magadi and Shala. Three phages were isolated on two different Bacillus species and one Paracoccus species using agar overlays. The growth characteristics of each phage in its host was investigated and the genome sequences determined and analysed by comparison with known phages. RESULTS: Phage Shbh1 belongs to the family Myoviridae while Mgbh1 and Shpa belong to the Siphoviridae family. Tetranucleotide usage frequencies and G + C content suggests that Shbh1 and Mgbh1 do not regularly infect, and have therefore not evolved with, the hosts they were isolated on here. Shbh1 was shown capable of infecting two different Bacillus species from the two different lakes demonstrating its potential broad-host range. Comparative analysis of their genome sequence with known phages revealed that, although novel, Shbh1 does share substantial amino acid similarity with previously described Bacillus infecting phages (Grass, phiNIT1 and phiAGATE) and belongs to the Bastille group, while Mgbh1 and Shpa are highly novel. CONCLUSION: The addition of these phages to current databases should help with metagenome/metavirome annotation efforts. We describe a highly novel Paracoccus infecting virus (Shpa) which together with NgoΦ6 and vB_PmaS_IMEP1 is one of only three phages known to infect Paracoccus species but does not show similarity to these phages.

The mechanism of the amidases: mutating the glutamate adjacent to the catalytic triad inactivates the enzyme due to substrate mispositioning.

All known nitrilase superfamily amidase and carbamoylase structures have an additional glutamate that is hydrogen bonded to the catalytic lysine in addition to the Glu, Lys, Cys "catalytic triad." In the amidase from Geobacillus pallidus, mutating this glutamate (Glu-142) to a leucine or aspartate renders the enzyme inactive. X-ray crystal structure determination shows that the structural integrity of the enzyme is maintained despite the mutation with the catalytic cysteine (Cys-166), lysine (Lys-134), and glutamate (Glu-59) in positions similar to those of the wild-type enzyme. In the case of the E142L mutant, a chloride ion is located in the position occupied by Glu-142 O(ε1) in the wild-type enzyme and interacts with the active site lysine. In the case of the E142D mutant, this site is occupied by Asp-142 O(δ1.) In neither case is an atom located at the position of Glu-142 O(ε2) in the wild-type enzyme. The active site cysteine of the E142L mutant was found to form a Michael adduct with acrylamide, which is a substrate of the wild-type enzyme, due to an interaction that places the double bond of the acrylamide rather than the amide carbonyl carbon adjacent to the active site cysteine. Our results demonstrate that in the wild-type active site a crucial role is played by the hydrogen bond between Glu-142 O(ε2) and the substrate amino group in positioning the substrate with the correct stereoelectronic alignment to enable the nucleophilic attack on the carbonyl carbon by the catalytic cysteine.

Hypolithic microbial communities: between a rock and a hard place.

Drylands are the largest terrestrial biome on Earth and a ubiquitous feature is desert pavement terrain, comprising rocks embedded in the mineral soil surface. Quartz and other translucent rocks are common and microbial communities termed hypoliths develop as biofilms on their ventral surfaces. In extreme deserts these represent major concentrations of biomass, and are emerging as key to geobiological processes and soil stabilization. These highly specialized communities are dominated by cyanobacteria that support diverse heterotrophic assemblages. Here we identify global-scale trends in the ecology of hypoliths that are strongly related to climate, particularly with regard to shifts in cyanobacterial assemblages. A synthesis of available data revealed a linear trend for colonization with regard to climate, and we suggest potential application for hypoliths as 'biomarkers' of aridity on a landscape scale. The potential to exploit the soil-stabilizing properties of hypolithic colonization in environmental engineering on dryland soils is also discussed.

Rapid microbial response to the presence of an ancient relic in the Antarctic Dry Valleys.

The extreme cold and aridity of the Antarctic McMurdo Dry Valleys have led to the longstanding belief that metabolic rates of soil microbiota are negligible, and that ecosystem changes take place over millennia. Here we report the first direct experimental evidence that soil microbial communities undergo rapid and lasting changes in response to contemporary environmental conditions. Mummified seals, curious natural artifacts found scattered throughout Dry Valleys, alter their underlying soil environment by stabilizing temperatures, elevating relative humidity and reducing ultraviolet exposure. In a unique, multi-year mummified seal transplantation experiment, we found that endemic Dry Valley microbial communities responded to these changes within 3 years, resulting in a sevenfold increase in CO(2) flux and a significant reduction in biodiversity. These findings challenge prevailing ideas about Antarctic Dry Valley ecosystems and indicate that current and future environmental conditions may strongly influence the ecology of the dominant biota in the Dry Valleys.

Model human behavior: don't constrain it!

Healthcare processes are complex and highly variable from day to day. Healthcare process execution can be affected by any participant in a process, including clinicians, the patient, and the patient's family, as well as environmental factors such as clinician, staff, facility and equipment availability, and patient clinical status. However, only a few solutions exist that enable computer support for a process to address the full complexity and variability of healthcare processes. We have re-conceptualized workflow and developed an innovative process representation and execution framework based on concepts from software engineering, machine learning, complexity, and database management. This new framework frees processes to track human behavior, thereby releasing us from the constraints of past methods. Our approach is also serving as a new architecture for software systems.

Retrieval of full-length functional genes using subtractive hybridization magnetic bead capture.

Numerous gene-specific PCR methods have been developed for the cultivation-independent discovery of novel genes from complex environmental DNA samples. The recovery of full-length genes is, however, technically challenging. Here, we present an efficient and relatively simple approach that combines magnetic bead capture with subtractive hybridization for the rapid and direct recovery of full-length target ORFs. When compared with other PCR-based techniques, a higher degree of specificity is achieved through the use of larger gene fragments during hybridization followed by several high-stringency washes. Together with the recent advances in environmental nucleic acid extraction techniques, this approach should allow for the further exploration of the metagenomic sequence space.

Extremophiles in biofuel synthesis.

The current global energy situation has demonstrated an urgent need for the development of alternative fuel sources to the continually diminishing fossil fuel reserves. Much research to address this issue focuses on the development of financially viable technologies for the production of biofuels. The current market for biofuels, defined as fuel products obtained from organic substrates, is dominated by bioethanol, biodiesel, biobutanol and biogas, relying on the use of substrates such as sugars, starch and oil crops, agricultural and animal wastes, and lignocellulosic biomass. This conversion from biomass to biofuel through microbial catalysis has gained much momentum as biotechnology has evolved to its current status. Extremophiles are a robust group of organisms producing stable enzymes, which are often capable of tolerating changes in environmental conditions such as pH and temperature. The potential application of such organisms and their enzymes in biotechnology is enormous, and a particular application is in biofuel production. In this review an overview of the different biofuels is given, covering those already produced commercially as well as those under development. The past and present trends in biofuel production are discussed, and future prospects for the industry are highlighted. The focus is on the current and future application of extremophilic organisms and enzymes in technologies to develop and improve the biotechnological production of biofuels.

Structural and biochemical characterization of a nitrilase from the thermophilic bacterium, Geobacillus pallidus RAPc8.

Geobacillus pallidus RAPc8 (NRRL: B-59396) is a moderately thermophilic gram-positive bacterium, originally isolated from Australian lake sediment. The G. pallidus RAPc8 gene encoding an inducible nitrilase was located and cloned using degenerate primers coding for well-conserved nitrilase sequences, coupled with inverse PCR. The nitrilase open reading frame was cloned into an expression plasmid and the expressed recombinant enzyme purified and characterized. The protein had a monomer molecular weight of 35,790 Da, and the purified functional enzyme had an apparent molecular weight of approximately 600 kDa by size exclusion chromatography. Similar to several plant nitrilases and some bacterial nitrilases, the recombinant G. pallidus RAPc8 enzyme produced both acid and amide products from nitrile substrates. The ratios of acid to amide produced from the substrates we tested are significantly different to those reported for other enzymes, and this has implications for our understanding of the mechanism of the nitrilases which may assist with rational design of these enzymes. Electron microscopy and image classification showed complexes having crescent-like, "c-shaped", circular and "figure-8" shapes. Protein models suggested that the various complexes were composed of 6, 8, 10 and 20 subunits, respectively.

Acrolein in wine: importance of 3-hydroxypropionaldehyde and derivatives in production and detection.

Certain lactic acid bacteria strains belonging to the genus Lactobacillus have been implicated in the accumulation of 3-hydroxypropionaldehyde (3-HPA) during anaerobic glycerol fermentation. In aqueous solution 3-HPA undergoes reversible dimerization and hydration, resulting in an equilibrium state between different derivatives. Wine quality may be compromised by the presence of 3-HPA due to the potential for spontaneous conversion into acrolein under winemaking conditions. Acrolein is highly toxic and has been implicated in the development of bitterness in wine. Interconversion between 3-HPA derivatives and acrolein is a complex and highly dynamic process driven by hydration and dehydration reactions. Acrolein is furthermore highly reactive and its steady-state concentration in complex systems very low. As a result, analytical detection and quantification in solution is problematic. This paper reviews the biochemical and environmental conditions leading to accumulation of its precursor, 3-HPA. Recent advances in analytical detection are summarized, and the roles played by natural chemical derivatives are highlighted.

Hypolithic microbial community of quartz pavement in the high-altitude tundra of central Tibet.

The hypolithic microbial community associated with quartz pavement at a high-altitude tundra location in central Tibet is described. A small-scale ecological survey indicated that 36% of quartz rocks were colonized. Community profiling using terminal restriction fragment length polymorphism revealed no significant difference in community structure among a number of colonized rocks. Real-time quantitative PCR and phylogenetic analysis of environmental phylotypes obtained from clone libraries were used to elucidate community structure across all domains. The hypolithon was dominated by cyanobacterial phylotypes (73%) with relatively low frequencies of other bacterial phylotypes, largely represented by the chloroflexi, actinobacteria, and bacteriodetes. Unidentified crenarchaeal phylotypes accounted for 4% of recoverable phylotypes, while algae, fungi, and mosses were indicated by a small fraction of recoverable phylotypes.

Endolithic microbial colonization of limestone in a high-altitude arid environment.

The morphology of endolithic colonization in a limestone escarpment and surrounding rocky debris (termed float) at a high-altitude arid site in central Tibet was documented using scanning electron microscopy. Putative lichenized structures and extensive coccoid bacterial colonization were observed. Absolute and relative abundance of rRNA gene signatures using real-time quantitative polymerase chain reaction and phylogenetic analysis of environmental phylotypes were used to characterize community structure across all domains. Escarpment endoliths were dominated by eukaryotic phylotypes suggestive of lichenised associations (a Trebouxia lichen phycobiont and Leptodontidium lichen mycobiont), whereas float endoliths were dominated by bacterial phylotypes, including the cyanobacterium Chroococcidiopsis plus several unidentified beta proteobacteria and crenarchaea. Among a range of abiotic variables tested, ultraviolet (UV) transmittance by rock substrates was the factor best able to explain differences in community structure, with eukaryotic lichen phylotypes more abundant under conditions of greater UV-exposure compared to prokaryotes. Variously pigmented float rocks did not support significantly different communities. Estimates of in situ carbon fixation based upon (14)C radio-labelled bicarbonate uptake indicated endolithic productivity of approximately 2.01 g C/m(2)/year(-1), intermediate between estimates for Antarctic and temperate communities.

Thermophilic ethanologenesis: future prospects for second-generation bioethanol production.

Strategies for improving fermentative ethanol production have focused almost exclusively on the development of processes based on the utilization of the carbohydrate fraction of lignocellulosic material. These so-called 'second-generation' technologies require metabolically engineered production strains that possess a high degree of catabolic versatility and are homoethanologenic. It has been suggested that the production of ethanol at higher temperatures would facilitate process design, and as a result the engineered progeny of Geobacillus thermoglucosidasius, Thermoanerobacterium saccharolyticum and Thermoanerobacter mathranii now form the platform technology of several new biotechnology companies. This review highlights the milestones in the development of these production strains, with particular focus on the development of reliable methods for cell competency, gene deletion or upregulation.