Using your nose to find your way: Ethological comparisons between human and non-human species

Olfaction is arguably the least valued among our sensory systems, and its significance for human behavior is often neglected. Spatial navigation represents no exception to the rule: humans are often characterized as purely visual navigators, a view that undermines the contribution of olfactory cues. Accordingly, research investigating whether and how humans use olfaction to navigate space is rare. In comparison, research on olfactory navigation in non-human species is abundant, and identifies behavioral strategies along with neural mechanisms characterizing the use of olfactory cues during spatial tasks. Using an ethological approach, our review draws from studies on olfactory navigation across species to describe the adaptation of strategies under the influence of selective pressure. Mammals interact with spatial environments by abstracting multisensory information into cognitive maps. We thus argue that olfactory cues, alongside inputs from other sensory modalities, play a crucial role in spatial navigation for mammalian species, including humans; that is, odors constitute one of the many building blocks in the formation of cognitive maps.

Underwater Hyperspectral Imaging (UHI): A Review of Systems and Applications for Proximal Seafloor Ecosystem Studies

Marine ecosystem monitoring requires observations of its attributes at different spatial and temporal scales that traditional sampling methods (e.g., RGB imaging, sediment cores) struggle to efficiently provide. Proximal optical sensing methods can fill this observational gap by providing observations of, and tracking changes in, the functional features of marine ecosystems non-invasively. Underwater hyperspectral imaging (UHI) employed in proximity to the seafloor has shown a further potential to monitor pigmentation in benthic and sympagic phototrophic organisms at small spatial scales (mm–cm) and for the identification of minerals and taxa through their finely resolved spectral signatures. Despite the increasing number of studies applying UHI, a review of its applications, capabilities, and challenges for seafloor ecosystem research is overdue. In this review, we first detail how the limited band availability inherent to standard underwater cameras has led to a data analysis “bottleneck” in seafloor ecosystem research, in part due to the widespread implementation of underwater imaging platforms (e.g., remotely operated vehicles, time-lapse stations, towed cameras) that can acquire large image datasets. We discuss how hyperspectral technology brings unique opportunities to address the known limitations of RGB cameras for surveying marine environments. The review concludes by comparing how different studies harness the capacities of hyperspectral imaging, the types of methods required to validate observations, and the current challenges for accurate and replicable UHI research.

The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.

Profiling a soft solid layer to passively control the conduit shape in a compliant microchannel during flow

The shape of a microchannel during flow through it is instrumental to understanding the physics that govern various phenomena ranging from rheological measurements of fluids to separation of particles and cells. Two commonly used approaches for obtaining a desired channel shape (for a given application) are (i) fabricating the microchannel in the requisite shape and (ii) actuating the microchannel walls during flow to obtain the requisite shape. However, these approaches are not always viable. We propose an alternative, passive approach to a priori tune the elastohydrodynamics in a microsystem toward achieving a predetermined (but not prefabricated) flow geometry when the microchannel is subjected to flow. That is, we use the interaction between a soft solid layer, the viscous flow beneath it, and the shaped rigid wall above it to tune the fluid domain's shape. Specifically, we study a parallel-wall microchannel whose top wall is a slender soft coating of arbitrary thickness attached to a rigid platform. We derive a nonlinear differential equation for the soft coating's fluid-solid interface, which we use to infer how to achieve specific conduit shapes during flow. Using this theory, we demonstrate the tuning of four categories of microchannel geometries, which establishes, via a proof-of-concept, the viability of our modeling framework. We also explore slip length patterning on the rigid bottom wall of the microchannel, a common technique in microfluidics, as an additional "handle" for microchannel shape control. However, we show that this effect is much weaker in practice.

Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control

In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems are typically implemented over wireless sensor networks, where the sensing devices are battery-powered, and thus they have to manage energy constraints by implementing efficient communication schemas. Self-triggered control is an aperiodic sampling strategy capable of reducing the number of networked messages compared to traditional periodical sampling. In this paper, we propose an energy-efficient communication strategy for closed-loop control irrigation, implemented over a wireless sensor network, where event-driven soil moisture measurements are conducted by the sensing devices only when needed. Thereby, the self-triggered algorithm estimates the occurrence of the next sampling period based on the process dynamics. The proposed strategy was evaluated in a pecan crop field and compared with periodical sampling implementations. The experimental results show that the proposed adaptive sampling rate technique decreased the number of communication messages more than 85% and reduced power consumption up to 20%, while still accomplishing the system control objectives in terms of the irrigation efficiency and water consumption.

A myo-inositol bioassay utilizing an auxotrophic strain of S. cerevisiae

Myo-inositol is a six‑carbon sugar that is essential for the growth of mammalian cells and must be obtained through either extracellular uptake or de novo biosynthesis. The physiological importance of myo-inositol stems from its incorporation into phosphoinositides and inositol phosphates, which serve a variety of signaling, regulatory, and structural roles in cells. To study myo-inositol metabolism and function, it is essential to have a reliable method for assaying myo-inositol levels. However, current approaches to assay myo-inositol levels are time-consuming, expensive, and often unreliable. This article describes a simple new myo-inositol bioassay that utilizes an auxotrophic strain of S. cerevisiae to measure myo-inositol concentration in solutions. The accuracy of this method was confirmed by comparing assay values to those obtained by tandem mass spectrometry (LC-MS/MS). It is easy to perform, inexpensive, does not require sophisticated equipment, and is specific for myo-inositol.

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.

Theoretical investigation of conformational deviation of the human parallel telomeric G-quadruplex DNA in the presence of different salt concentrations and temperatures under confinement

Various experimental reports address the stability of G-quadruplex DNA inside a close confinement such as α-hemolysin, nanocavity water pool and different metal-organic-frameworks (MOFs). To understand the conformational change of G-quadruplex DNA at the atomistic level, we have carried out a total of 40 μs simulation run under both non-polar and polar confinement conditions. To investigate the dynamics, we have considered two different KCl salt concentrations, i.e., 0.47 M (minimal salt concentration) and higher than 2 M (higher salt concentration), at two distinct temperatures, 300 K and 350 K. Here, we have observed that the human telomeric G-quadruplex DNA deviates more from its crystal structure at minimal salt concentration under both non-polar and polar confinement conditions. Besides, the loop regions deviate and fluctuate more compared to the other regions, i.e., sugar-phosphate backbone and tetrad regions. The presence of K+ ions is found to be primarily responsible for this phenomenon. From the spatial density function (SDF) plots, a higher density of K+ ions is observed in the backbone region. Furthermore, from the residue-wise first solvation shell estimation, we have noticed that the K+ ions mainly accumulate in the tetrad region under both non-polar and polar confinement conditions due to which the tetrad regions are more rigid than the loop regions. Higher salt concentration results in increased rigidity of the G-quadruplex DNA. Our study provides valuable insight into the conformational deviation of the G-quadruplex DNA under nanoconfinement conditions.

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.

Dissection of the Origin of π-Holes and the Noncovalent Bonds in Which They Engage

Accompanying the rapidly growing list of σ-hole bonds has come the acknowledgment of parallel sorts of noncovalent bonds which owe their stability in large part to a deficiency of electron density in the area above the molecular plane, known as a π-hole. The origins of these π-holes are probed for a wide series of molecules, comprising halogen, chalcogen, pnicogen, tetrel, aerogen, and spodium bonds. Much like in the case of their σ-hole counterparts, formation of the internal covalent π-bond in the Lewis acid molecule pulls density toward the bond midpoint and away from its extremities. This depletion of density above the central atom is amplified by an electron-withdrawing substituent. At the same time, the amplitude of the π*-orbital is enhanced in the region of the density-depleted π-hole, facilitating a better overlap with the nucleophile's lone pair orbital and a stabilizing n → π* charge transfer. The presence of lone pairs on the central atom acts to attenuate the π-hole and shift its position somewhat, resulting in an overall weakening of the π-hole bond. There is a tendency for π-hole bonds to include a higher fraction of induction energy than σ-bonds with proportionately smaller electrostatic and dispersion components, but this distinction is less a product of the σ- or π-character and more a function of the overall bond strength.

Modelling, prediction and design of COVID-19 lockdowns by stringency and duration

The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical "hybrid" approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What-If Analyses of Localized and Dynamic Effects

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what-if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what-if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what-if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what-if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what-if scenarios several orders of magnitude faster than the conventional methods.

Housing and accessibility after the COVID-19 pandemic: Rebuilding for resilience, equity and sustainable mobility

A more sustainable post COVID-19 world requires urban transport policies aiming for resilience, social equity and decarbonisation. Instead of just focusing on the transport sector, the authors propose an integrated approach to housing and mobility. This approach acknowledges the challenges posed by inadequate housing and dependence on motorised transport during the COVID-19 crisis. In contrast, adequate housing and cycling became paramount resources while confronting the pandemic. Using Santiago de Chile as a case study, this research examines how different relocation scenarios for its current housing deficit cannot only affect the ability to implement stay-at-home measures, but also the potential of cycling as a relevant commuting alternative. The current location of the families suffering this deficit is compared to three scenarios: compact, pericentral and extended. In light of the learnings from the COVID-19 crisis, a housing-cycling policy becomes a tool for resilience; equity is achieved by enforcing the right to housing, by increasing job opportunities among the poor, and by reducing the dependence on expensive motorised transport; decarbonisation is achieved by promoting active transportation and reducing the dependence on motorisation.

Digital Twins: From Personalised Medicine to Precision Public Health

A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology's history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.

Artificial Intelligence in Repairing Meniscus Injury in Football Sports with Perovskite Nanobiomaterials

Knee meniscus injuries are more likely to occur in young adults in clinical practice, and their lower age of onset and greater impact on joint function after injury also put forward higher requirements for the treatment and rehabilitation of meniscus injuries. With the rapid development of artificial intelligence technology and arthroscopic minimally invasive technology, arthroscopic meniscus plasty and perovskite nanobiomaterial repair have gradually replaced the previous open meniscus surgery of the knee joint and has become the main method of meniscus injury treatment, and the perovskite nanobiomaterial repair technique that incorporates artificial intelligence technology is also gradually being applied. Therefore, this article studies the role of perovskite nanobiomaterials in the repair of meniscus injuries in football sports and analyzes the biological characteristics of the inner and outer meniscus to provide help to improve the healing rate of meniscus injuries. The study selected six male meniscus-injured patients (meniscus injuries caused by football sports) and obtained six injured menisci. The same cross section of the same part of the meniscus was analyzed inside and outside the meniscus. At the same time, a meniscal injury step was performed on the patient. The biological characteristics of perovskite nano-biomaterials in the repair of meniscus injuries in football sports were compared and analyzed, and the patient's gait before and after surgery was also compared. Experiments have shown that the percentage of the postoperative support phase of the affected limb is significantly higher than that before surgery (P < 0.05), the percentage of the postoperative support phase and flatfoot phase decreased compared with that before surgery, and the gait cycle parameters of both lower extremities improved after surgery, obviously (P < 0.05). It explains that the arthroscopic repair of perovskite nanobiomaterials combined with the artificial intelligence technology to repair the meniscus anterior angle injury is simple and does not require special equipment, has fewer complications, is safe and reliable, and has a high clinical healing rate and a high patient satisfaction rate after surgery. The curative effect is significant; artificial intelligence technology and the application of perovskite nanobiomaterials provide more possibilities for meniscus repair.

Enantioselectivity switch in asymmetric Michael addition reactions using phosphonium salts

Efficient access to two enantiomers of one chiral compound is critical for the discovery of drugs. However, it is still a challenging problem owing to the difficulty in obtaining two enantiomers of one chiral catalyst. Here, we report a general method to obtain both enantiomeric products via fine tuning the hydrogen-bonding interactions of phosphonium salts. Amino acid derived phosphonium salts and dipeptide derived phosphonium salts exhibited different properties for controlling the transition state, which could efficiently promote the Michael addition reaction to give opposite configurations of products with high yields and enantioselectivities. Preliminary investigations on the mechanism of the reaction and applications of the products were also performed.

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics

In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

Real-time holographic quantitative measurement of vapor density distribution of suspended droplets

We applied digital holography (DH) technology in a quantitative measurement of the density distribution of a low refractive index transparent substance (e.g., the vapor of suspended droplets). An optical setup was built based on the Mach-Zehnder interferometer. A measurement performance test showed the mean relative error of the measurement error was about 2.0%; that of the environment disturbance error was about 0.47%. By a quantitative method to assess the precision limit, the temperature measurement precision could achieve 0.01°C, and the vapor density measurement precision could achieve 0.0001kg/m³. We believe that all the benefits above make the setup a good choice for application in the Chinese space station.

Role of Laboratory Medicine in SARS-CoV-2 Diagnostics. Lessons Learned from a Pandemic

Since the 2019 novel coronavirus outbreak began in Wuhan, China, diagnostic methods in the field of molecular biology have been developing faster than ever under the vigilant eye of world's research community. Unfortunately, the medical community was not prepared for testing such large volumes or ranges of biological materials, whether blood samples for antibody immunological testing, or salivary/swab samples for real-time PCR. For this reason, many medical diagnostic laboratories have made the switch to working in the field of molecular biology, and research undertaken to speed up the flow of samples through laboratory. The aim of this narrative review is to evaluate the current literature on laboratory techniques for the diagnosis of SARS-CoV-2 infection available on pubmed.gov, Google Scholar, and according to the writers' knowledge and experience of the laboratory medicine. It assesses the available information in the field of molecular biology by comparing real-time PCR, LAMP technique, RNA sequencing, and immunological diagnostics, and examines the newest techniques along with their limitations for use in SARS-CoV-2 diagnostics.

In Vivo Imaging of Biodegradable Implants and Related Tissue Biomarkers

Non-invasive longitudinal imaging of osseointegration of bone implants is essential to ensure a comprehensive, physical and biochemical understanding of the processes related to a successful implant integration and its long-term clinical outcome. This study critically reviews the present imaging techniques that may play a role to assess the initial stability, bone quality and quantity, associated tissue remodelling dependent on implanted material, implantation site (surrounding tissues and placement depth), and biomarkers that may be targeted. An updated list of biodegradable implant materials that have been reported in the literature, from metal, polymer and ceramic categories, is provided with reference to the use of specific imaging modalities (computed tomography, positron emission tomography, ultrasound, photoacoustic and magnetic resonance imaging) suitable for longitudinal and non-invasive imaging in humans. The advantages and disadvantages of the single imaging modality are discussed with a special focus on preclinical imaging for biodegradable implant research. Indeed, the investigation of a new implant commonly requires histological examination, which is invasive and does not allow longitudinal studies, thus requiring a large number of animals for preclinical testing. For this reason, an update of the multimodal and multi-parametric imaging capabilities will be here presented with a specific focus on modern biomaterial research.

Membrane Fouling Phenomena in Microfluidic Systems: From Technical Challenges to Scientific Opportunities

The almost ubiquitous, though undesired, deposition and accumulation of suspended/dissolved matter on solid surfaces, known as fouling, represents a crucial issue strongly affecting the efficiency and sustainability of micro-scale reactors. Fouling becomes even more detrimental for all the applications that require the use of membrane separation units. As a matter of fact, membrane technology is a key route towards process intensification, having the potential to replace conventional separation procedures, with significant energy savings and reduced environmental impact, in a broad range of applications, from water purification to food and pharmaceutical industries. Despite all the research efforts so far, fouling still represents an unsolved problem. The complex interplay of physical and chemical mechanisms governing its evolution is indeed yet to be fully unraveled and the role played by foulants' properties or operating conditions is an area of active research where microfluidics can play a fundamental role. The aim of this review is to explore fouling through microfluidic systems, assessing the fundamental interactions involved and how microfluidics enables the comprehension of the mechanisms characterizing the process. The main mathematical models describing the fouling stages will also be reviewed and their limitations discussed. Finally, the principal dynamic investigation techniques in which microfluidics represents a key tool will be discussed, analyzing their employment to study fouling.

The efficiency of PCL/HAp electrospun nanofibers in bone regeneration: a review

Electrospinning is a method which produces various nanofiber scaffolds for different tissues was attractive for researchers. Nanofiber scaffolds could be made from several biomaterials and polymers. Quality and virtues of final scaffolds depend on used biomaterials (even about single substance, the origin is effective), additives (such as some molecules, ions, drugs, and inorganic materials), electrospinning parameter (voltage, injection speed, temperature, …), etc. In addition to its benefits, which makes it more attractive is the possibility of modifications. Common biomaterials in bone tissue engineering such as poly-caprolactone (PCL), hydroxyapatite (HAp), and their important features, electrospinning nanofibers were widely studied. Related investigations indicate the critical role of even small parameters (like the concentration of PCL or HAp) in final product properties. These changes also, cause deference in cell proliferation, adhesion, differentiation, and in vivo repair process. In this review was focussed on PCL/HAp based nanofibers and additives that researchers used for scaffold improvement. Then, reviewing properties of gained nanofibers, their effect on cell behaviour, and finally, their valency in bone tissue engineering studies (in vitro and in vivo).

Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States

Goal: The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US' diversity, geographic spread, and economic inequality, the COVID-19 pandemic in the US acts more as a series of diverse regional outbreaks rather than a synchronized homogeneous one. Method: In order to determine how to assess regional risk related to COVID-19, a two-phase modeling approach is developed while considering demographic and economic criteria. First, an unsupervised clustering technique, specifically [Formula: see text]-means, is employed to group US counties based on demographic and economic similarities. Then, time series forecasting of each cluster of counties is developed to assess the short-run viral transmissibility risk. Results: To this end, we test ARIMA and Seasonal Trend Random Walk forecasts to determine which is more appropriate for modeling the spread and lethality of COVID-19. From our analysis, we then utilize the superior ARIMA models to forecast future COVID-19 trends in the clusters, and present the areas in the US which have the highest COVID-19 related risk heading into the winter of 2020. Conclusion: Including sub-national socioeconomic characteristics to data-driven COVID-19 infection and fatality forecasts may play a key role in assessing the risk associated with changes in infection patterns at the national level.

Preparatory activity links the frontal eye field response with small amplitude motor unit recruitment of neck muscles during gaze planning

A hallmark of intelligent behavior is that we can separate intention from action. To understand the mechanism that gates the flow of information between motor planning and execution, we compared the activity of frontal eye field neurons with motor unit activity from neck muscles in the presence of an intervening delay period in which spatial information regarding the target was available to plan a response. Although spatially specific delay period activity was present in the activity of frontal eye field neurons, it was absent in motor unit activity. Nonetheless, motor unit activity was correlated with the time it took to initiate saccades. Interestingly, we observed a heterogeneity of responses among motor units, such that only units with smaller amplitudes showed a clear modulation during the delay period. These small amplitude motor units also had higher spontaneous activity compared with the units which showed modulation only during the movement epoch. Taken together, our results suggest the activity of smaller motor units convey temporal information and explains how the delay period primes muscle activity leading to faster reaction times.NEW & NOTEWORTHY This study shows that the temporal aspects of a motor plan in the oculomotor circuitry can be accessed by peripheral neck muscles hundreds of milliseconds before the instruction to initiate a saccadic eye movement. The coupling between central and peripheral processes during the delay time is mediated by the recruitment pattern of motor units with smaller amplitude. These findings suggest that information processed in cortical areas could be read from periphery before execution.

Novel Insight into Proximal DNA Domain Interactions from Temperature-Controlled Electrospray Ionization Mass Spectrometry

Quadruplexes are non-canonical nucleic acid structures essential for many cellular processes. Hybrid quadruplex-duplex oligonucleotide assemblies comprised of multiple domains are challenging to study with conventional biophysical methods due to their structural complexity. Here, we introduce a novel method based on native mass spectrometry (MS) coupled with a custom-built temperature-controlled nanoelectrospray ionization (TCnESI) source designed to investigate interactions between proximal DNA domains. Thermal denaturation experiments were aimed to study unfolding of multi-stranded oligonucleotide constructs derived from biologically relevant structures and to identify unfolding intermediates. Using the TCnESI MS, we observed changes in Tm and thermodynamic characteristics of proximal DNA domains depending on the number of domains, their position, and order in a single experiment.

In Situ 3D Printing: Opportunities with Silk Inks

In situ 3D printing is an emerging technique designed for patient-specific needs and performed directly in the patient's tissues in the operating room. While this technology has progressed rapidly, several improvements are needed to push it forward for widespread utility, including ink formulations and optimization for in situ context. Silk fibroin inks emerge as a viable option due to the diverse range of formulations, aqueous processability, robust and tunable mechanical properties, and self-assembly via biophysical adsorption to avoid exogenous chemical or photochemical sensitizer additives, among other features. In this review, we focus on this new frontier of 3D in situ printing for tissue regeneration, where silk is proposed as candidate biomaterial ink due to the unique and useful properties of this protein polymer.

A literature survey of the robotic technologies during the COVID-19 pandemic

Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies' perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies' perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work's technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing^® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms^-1, the device's power requirements (i.e., P¯=5.84&nbsp;mW) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

Recent Developments in Coatings for Orthopedic Metallic Implants

Titanium, stainless steel, and CoCrMo alloys are the most widely used biomaterials for orthopedic applications. The most common causes of orthopedic implant failure after implantation are infections, inflammatory response, least corrosion resistance, mismatch in elastic modulus, stress shielding, and excessive wear. To address the problems associated with implant materials, different modifications related to design, materials, and surface have been developed. Among the different methods, coating is an effective method to improve the performance of implant materials. In this article, a comprehensive review of recent studies has been carried out to summarize the impact of coating materials on metallic implants. The antibacterial characteristics, biodegradability, biocompatibility, corrosion behavior, and mechanical properties for performance evaluation are briefly summarized. Different effective coating techniques, coating materials, and additives have been summarized. The results are useful to produce the coating with optimized properties.

Proof of Concept of Novel Visuo-Spatial-Motor Fall Prevention Training for Old People

Falls in the geriatric population are one of the most important causes of disabilities in this age group. Its consequences impose a great deal of economic burden on health and insurance systems. This study was conducted by a multidisciplinary team with the aim of evaluating the effect of visuo-spatial-motor training for the prevention of falls in older adults. The subjects consisted of 31 volunteers aged 60 to 92 years who were studied in three groups: (1) A group under standard physical training, (2) a group under visuo-spatial-motor interventions, and (3) a control group (without any intervention). The results of the study showed that visual-spatial motor exercises significantly reduced the risk of falls of the subjects.

Applications of 3D Bio-Printing in Tissue Engineering and Biomedicine

In recent years, 3D bio-printing technology has developed rapidly and become an advanced bio-manufacturing technology. At present, 3D bio-printing technology has been explored in the fields of tissue engineering, drug testing and screening, regenerative medicine and clinical disease research and has achieved many research results. Among them, the application of 3D bio-printing technology in tissue engineering has been widely concerned by researchers, and it contributing many breakthroughs in the preparation of tissue engineering scaffolds. In the future, it is possible to print fully functional tissues or organs by using 3D bio-printing technology which exhibiting great potential development prospects in th applications of organ transplantation and human body implants. It is expected to solve thebiomedical problems of organ shortage and repair of damaged tissues and organs. Besides,3Dbio-printing technology will benefit human beings in more fields. Therefore, this paper reviews the current applications, research progresses and limitations of 3D bio-printing technology in biomedical and life sciences, and discusses the main printing strategies of 3D bio-printing technology. And, the research emphases, possible development trends and suggestions of the application of 3D bio-printing are summarized to provide references for the application research of 3D bio-printing.

Pooled testing for SARS-CoV-2 infection in an automated high-throughput platform

BACKGROUND

Active detection of SARS-CoV-2 infection through testing is elementary for the control of COVID-19 pandemic. The implementation of large-scale RT-PCR testing has led to a rise in the demand for testing kits whose availability is always a concern.

OBJECTIVE

To find out the feasibility of pooled testing in a high-throughput platform.

METHODOLOGY

Pooled testing was conducted in Roche cobas 6800 in 2 methods. Firstly, the simple two-stage testing algorithm was conducted for 1410 samples individually and then as pooled samples. Secondly, we evaluated the sensitivity of cobas 6800 for the detection of a single positive sample within a pool of negative samples.

RESULTS

Implementing the five-sample Dorfman pooling to test 1410 samples, we identified 42 (2.9%) individual SARS-CoV-2-positive samples and 27 (9.5%) positive pool samples. The pooling strategy precisely identified all the positive samples. All individually negative samples were also accurately determined by pooling. There was 100% sensitivity of detecting positive samples in a pool of negative samples even up to 1:64 dilution. There was a threefold increase in total throughput in one-third of the cost per day.

CONCLUSION

A high-throughput platform such as Cobas 6800 can effectively increase the testing capacity by twofold to threefold by adopting the pooled testing strategy for successful management of SARS-CoV-2 and helping in the containment of community transmission.

Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement

The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.

The DNA binding domain of the Vibrio vulnificus SmcR transcription factor is flexible and binds diverse DNA sequences

Quorum sensing gene expression in vibrios is regulated by the LuxR/HapR family of transcriptional factors, which includes Vibrio vulnificus SmcR. The consensus binding site of Vibrio LuxR/HapR/SmcR proteins is palindromic but highly degenerate with sequence variations at each promoter. To examine the mechanism by which SmcR recognizes diverse DNA sites, we generated SmcR separation-of-function mutants that either repress or activate transcription but not both. SmcR N55I is restricted in recognition of single base-pair variations in DNA binding site sequences and thus is defective at transcription activation but retains interaction with RNA polymerase (RNAP) alpha. SmcR S76A, L139R and N142D substitutions disrupt the interaction with RNAP alpha but retain functional DNA binding activity. X-ray crystallography and small angle X-ray scattering data show that the SmcR DNA binding domain exists in two conformations (wide and narrow), and the protein complex forms a mixture of dimers and tetramers in solution. The three RNAP interaction-deficient variants also have two DNA binding domain conformations, whereas SmcR N55I exhibits only the wide conformation. These data support a model in which two mechanisms drive SmcR transcriptional activation: interaction with RNAP and a multi-conformational DNA binding domain that permits recognition of variable DNA sites.

A Novel One-Pot Synthesis and Characterization of Silk Fibroin/α-Calcium Sulfate Hemihydrate for Bone Regeneration

This study aims to fabricate silk fibroin/calcium sulfate (SF/CS) composites by one-pot synthesis for bone regeneration applications. The SF was harvested from degummed silkworm cocoons, dissolved in a solvent system comprising of calcium chloride:ethanol:water (1:2:8), and then mixed with a stoichiometric amount of sodium sulfate to prepare various SF/CS composites. The crystal pattern, glass transition temperature, and chemical composition of SF/CS samples were analyzed by XRD, DSC, and FTIR, respectively. These characterizations revealed the successful synthesis of pure calcium sulfate dihydrate (CSD) and calcium sulfate hemihydrate (CSH) when it was combined with SF. The thermal analysis through DSC indicated molecular-level interaction between the SF and CS. The FTIR deconvolution spectra demonstrated an increment in the β-sheet content by increasing CS content in the composites. The investigation into the morphology of the composites using SEM revealed the formation of plate-like dihydrate in the pure CS sample, while rod-like structures of α-CSH surrounded by SF in the composites were observed. The compressive strength of the hydrated 10 and 20% SF-incorporated CSH composites portrayed more than a twofold enhancement (statistically significant) in comparison to that of the pure CS samples. Reduced compressive strength was observed upon further increasing the SF content, possibly due to SF agglomeration that restricted its uniform distribution. Therefore, the one-pot synthesized SF/CS composites demonstrated suitable chemical, thermal, and morphological properties. However, additional biological analysis of its potential use as bone substitutes is required.

Review of Low-Cost 3D Bioprinters: State of the Market and Observed Future Trends

Three-dimensional (3D) bioprinting has become mainstream for precise and repeatable high-throughput fabrication of complex cell cultures and tissue constructs in drug testing and regenerative medicine, food products, dental and medical implants, biosensors, and so forth. Due to this tremendous growth in demand, an overwhelming amount of hardware manufacturers have recently flooded the market with different types of low-cost bioprinter models-a price segment that is most affordable to typical-sized laboratories. These machines range in sophistication, type of the underlying printing technology, and possible add-ons/features, which makes the selection process rather daunting (especially for a nonexpert customer). Yet, the review articles available in the literature mostly focus on the technical aspects of the printer technologies under development, as opposed to explaining the differences in what is already on the market. In contrast, this paper provides a snapshot of the fast-evolving low-cost bioprinter niche, as well as reputation profiles (relevant to delivery time, part quality, adherence to specifications, warranty, maintenance, etc.) of the companies selling these machines. Specifically, models spanning three dominant technologies-microextrusion, droplet-based/inkjet, and light-based/crosslinking-are reviewed. Additionally, representative examples of high-end competitors (including up-and-coming microfluidics-based bioprinters) are discussed to highlight their major differences and advantages relative to the low-cost models. Finally, forecasts are made based on the trends observed during this survey, as to the anticipated trickling down of the high-end technologies to the low-cost printers. Overall, this paper provides insight for guiding buyers on a limited budget toward making informed purchasing decisions in this fast-paced market.

Can Counter-Intuitive Halogen Bonding Be Coulombic?

We use the term "counter-intuitive" to describe an intermolecular interaction in which the electrostatic potentials of the interacting regions of the ground-state molecules have the same sign, both positive or both negative. In the present work, we consider counter-intuitive halogen bonding with nitrogen bases, in which both the halogen σ-hole and the nitrogen lone pair have negative potentials on their molecular surfaces. We show that these interactions can be treated as Coulombic despite the apparent repulsion between the ground-state molecules, provided that both electrostatics and polarization are explicitly taken into account. We demonstrate first that the energies of 20 counter-intuitive interactions with four nitrogen bases can be expressed very well in terms of just two molecular properties: the electrostatic potential of the halogen σ-hole and the average polarizability of the nitrogen base. Then we show that the same two properties can also represent the energies of an expanded data base that includes the 20 counter-intuitive plus an additional 20 weak and moderately-strong intuitive halogen bonding interactions (in which the σ-hole potentials are now positive).

Novel Substrates for Kinases Involved in the Biosynthesis of Inositol Pyrophosphates and Their Enhancement of ATPase Activity of a Kinase

IP6K and PPIP5K are two kinases involved in the synthesis of inositol pyrophosphates. Synthetic analogs or mimics are necessary to understand the substrate specificity of these enzymes and to find molecules that can alter inositol pyrophosphate synthesis. In this context, we synthesized four scyllo-inositol polyphosphates-scyllo-IP₅, scyllo-IP₆, scyllo-IP₇ and Bz-scyllo-IP₅-from myo-inositol and studied their activity as substrates for mouse IP6K1 and the catalytic domain of VIP1, the budding yeast variant of PPIP5K. We incubated these scyllo-inositol polyphosphates with these kinases and ATP as the phosphate donor. We tracked enzyme activity by measuring the amount of radiolabeled scyllo-inositol pyrophosphate product formed and the amount of ATP consumed. All scyllo-inositol polyphosphates are substrates for both the kinases but they are weaker than the corresponding myo-inositol phosphate. Our study reveals the importance of axial-hydroxyl/phosphate for IP6K1 substrate recognition. We found that all these derivatives enhance the ATPase activity of VIP1. We found very weak ligand-induced ATPase activity for IP6K1. Benzoyl-scyllo-IP₅ was the most potent ligand to induce IP6K1 ATPase activity despite being a weak substrate. This compound could have potential as a competitive inhibitor.

Evaluating the utility of high-resolution proximity metrics in predicting the spread of COVID-19

High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such a mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ODE based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We subsequently evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and 87% F1-score.

Sulfuroxygen interaction-controlled (Z)-selective anti-Markovnikov vinyl sulfides

The sulfur oxygen (SO) interaction was used herein to obtain (Z)-selective anti-Markovnikov vinyl sulfides from the addition of thiyl radicals to terminal alkynes. DFT calculations predicted that SO interaction originated from the delocalization of the lone-pair of the carbonyl oxygen to the adjacent σ* orbital of the S atom of C-S.

Zn-substituted Mg2SiO4 nanoparticles-incorporated PCL-silk fibroin composite scaffold: A multifunctional platform towards bone tissue regeneration

Electrospun porous bone scaffolds are known to imitate the extracellular matrix very well and provide an environment through which the tissue formation is enhanced. Although polymeric scaffolds have a great potential in bone tissue regeneration, their weak bioactivity (bone bonding ability) and mechanical properties have left room for improvement. Therefore, the present study focused on the developing a ternary multifunctional platform composed of polycaprolactone (PCL)/silk fibroin (SF)/Zn-substituted Mg₂SiO₄ nanoparticles for bone tissue regeneration. This study is composed of two connected sections including synthesis and characterization of Mg_(2-x)Zn_xSiO₄, x = 0, 0.5, 1, 1.5, 2 through surfactant-assisted sol-gel technique followed by incorporation of the nanoparticles into PCL/SF hybrid scaffold via electrospinning technique. The weight ratios of polymers and ceramic nanoparticles were optimized to reach desirable textural-porosity, pore size, and fiber diameter-and mechanical properties. Having optimized the ternary scaffold, it was then undergone different physical, chemical, and biological tests in vitro. A precise comparison study between the ternary (PCL/SF/ceramic nanoparticles), binary (PCL/SF), and pure PCL was made to shed light on the effect of each composition on the applicability of ternary scaffold. The overall results confirmed that the Mg₁Zn₁SiO₄ nanoparticles-incorporated PCL/SF scaffold with fluorescence property was the one yielding the highest Young's modulus and desirable textural properties. The ternary scaffold showed improved biological properties making it a promising candidate for further studies towards bone tissue regeneration.

Role of Intermediate Filaments in Blood-Brain Barrier in Health and Disease

The blood–brain barrier (BBB) is a highly selective cellular monolayer unique to the microvasculature of the central nervous system (CNS), and it mediates the communication of the CNS with the rest of the body by regulating the passage of molecules into the CNS microenvironment. Limitation of passage of substances through the BBB is mainly due to tight junctions (TJ) and adherens junctions (AJ) between brain microvascular endothelial cells. The importance of actin filaments and microtubules in establishing and maintaining TJs and AJs has been indicated; however, recent studies have shown that intermediate filaments are also important in the formation and function of cell–cell junctions. The most common intermediate filament protein in endothelial cells is vimentin. Vimentin plays a role in blood–brain barrier permeability in both cell–cell and cell–matrix interactions by affecting the actin and microtubule reorganization and by binding directly to VE-cadherin or integrin proteins. The BBB permeability increases due to the formation of stress fibers and the disruption of VE–cadherin interactions between two neighboring cells in various diseases, disrupting the fiber network of intermediate filament vimentin in different ways. Intermediate filaments may be long ignored key targets in regulation of BBB permeability in health and disease.

A Global Analysis of Delayed SARS-CoV-2/Cancer Model with Immune Response

Coronavirus disease 2019 (COVID-19) is a respiratory disease caused by SARS-CoV-2. It appeared in China in late 2019 and rapidly spread to most countries of the world. Cancer patients infected with SARS-CoV-2 are at higher risk of developing severe infection and death. This risk increases further in the presence of lymphopenia affecting the lymphocytes count. Here, we develop a delayed within-host SARS-CoV-2/cancer model. The model describes the occurrence of SARS-CoV-2 infection in cancer patients and its effect on the functionality of immune responses. The model considers the time delays that affect the growth rates of healthy epithelial cells and cancer cells. We provide a detailed analysis of the model by proving the nonnegativity and boundedness of the solutions, finding steady states, and showing the global stability of the different steady states. We perform numerical simulations to highlight some important observations. The results indicate that increasing the time delay in the growth rate of cancer cells reduced the size of tumors and decreased the likelihood of deterioration in the condition of SARS-CoV-2/cancer patients. On the other hand, lymphopenia increased the concentrations of SARS-CoV-2 particles and cancer cells, which worsened the condition of the patient.

A new SEIAR model on small-world networks to assess the intervention measures in the COVID-19 pandemics

A new susceptible-exposed-infected-asymptomatically infected-removed (SEIAR) model is developed to depict the COVID-19 transmission process, considering the latent period and asymptomatically infected. We verify the suppression effect of typical measures, cultivating human awareness, and reducing social contacts. As for cutting off social connections, the feasible measures encompass social distancing policy, isolating infected communities, and isolating hub nodes. Furthermore, it is found that implementing corresponding anti-epidemic measures at different pandemic stages can achieve significant results at a low cost. In the beginning, global lockdown policy is necessary, but isolating infected wards and hub nodes could be more beneficial as the situation eases. The proposed SEIAR model emphasizes the latent period and asymptomatically infected, thus providing theoretical support for subsequent research.