Development and Usability Assessment of a Connected Resistance Exercise Band Application for Strength-Monitoring

Effect of core stabilization versus rebound therapy on balance in children with cerebral palsy

OBJECTIVE

This study aimed to compare the effect of core stabilization exercises and rebound therapy on balance in children with hemiplegic cerebral palsy (CP).

METHODS

Fifty- two children of spastic hemiplegic CP aged 5 up to 8 years from both genders were assigned randomly into two groups: core stability and rebound therapy groups. Both groups received 3 sessions/week, 1.5-h training per session, for 12 successive weeks. The measurement was performed at baseline and post-treatment. Balance as a primary outcome for this study was measured by a Biodex Balance System (BBS), and knee extensor strength and functional capacity as secondary outcomes were assessed using a hand-held dynamometer, and a six-minute walk test (6MWT), respectively.

RESULTS

All variables showed a significant improvement after intervention in each group (p < 0.0001), with significant improvement in all stability indices (overall, anteroposterior, and mediolateral) in core stability group when compared to rebound therapy group.

CONCLUSION

Core stability exercises and rebound therapy are recommended in the rehabilitation of children with hemiplegic CP. Core stability exercises were more effective than rebound therapy for balance improvement.

TRIAL REGISTRATION NUMBER

NCT05739396.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Acute kidney injury after robot-assisted laparoscopic prostatectomy: A meta-analysis

BACKGROUND

We investigated the rates of acute kidney injury (AKI) post robot-assisted laparoscopic prostatectomy (RALP).

METHODS

A comprehensive search was conducted to identify studies that reported the rates of AKI post-RALP. A random effects model was used, and the pooled rates of AKI were calculated.

RESULTS

We identified 10 studies with 60,937 patients to be included. The mean age was 65.1 years. The mean anaesthesia time was 234.3 min (95% CI: 177.8-290.9). The mean operation time was 212.2 min (95% CI: 188.7-235.6). The mean estimated blood loss was 314.1 mL (95% CI: 153-475.3). The mean intraoperative IV fluids administered were 1985 mL (95% CI: 1516.3-2453.7). The pooled rate of AKI post RALP was 7.2% (95% CI 19-23.9).

CONCLUSIONS

The rates of AKI after RALP are significant. Further studies are needed to detect the risk factors for AKI and to determine the rates of chronic kidney disease post-RALP.

Effect of Pilates exercises on balance and gross motor coordination in children with Down syndrome

OBJECTIVE

To investigate the effect of Pilates exercises on balance and gross motor coordination in children with Down syndrome (DS).

METHODS

Forty children with DS, aged 8 up to 10 years, were randomly divided into two groups; experimental and control groups. A designed physical therapy program was applied for both groups, while the experimental group received an additional Pilates exercise program. Balance and gross motor coordination as primary outcomes and quality of life (QoL) as secondary outcome were assessed using Biodex balance system (BBS), Bruininks Oseretsky of Motor Proficiency (BOT-2), and Pediatric quality of life inventory (PedsQL™) sequentially.

RESULTS

Both experimental (Pilates) and control groups demonstrated significant improvements in dynamic balance, gross motor coordination, and QoL after interventions. However, comparison between groups showed significant improvement in favor of the Pilates group in all measured outcomes (P < 0.0001).

CONCLUSION

Adding Pilates exercises to the designed physical therapy program could provide more significant improvements in balance, gross motor coordination, and QoL in children with DS.

TRIAL REGISTRATION

Clinical Trial gov number Identifier: NCT05928949.

Maturation of cortical input to dorsal raphe nucleus increases behavioral persistence in mice

The ability to persist toward a desired objective is a fundamental aspect of behavioral control whose impairment is implicated in several behavioral disorders. One of the prominent features of behavioral persistence is that its maturation occurs relatively late in development. This is presumed to echo the developmental time course of a corresponding circuit within late-maturing parts of the brain, such as the prefrontal cortex, but the specific identity of the responsible circuits is unknown. Here, we used a genetic approach to describe the maturation of the projection from layer 5 neurons of the neocortex to the dorsal raphe nucleus in mice. Using optogenetic-assisted circuit mapping, we show that this projection undergoes a dramatic increase in synaptic potency between postnatal weeks 3 and 8, corresponding to the transition from juvenile to adult. We then show that this period corresponds to an increase in the behavioral persistence that mice exhibit in a foraging task. Finally, we used a genetic targeting strategy that primarily affected neurons in the medial prefrontal cortex, to selectively ablate this pathway in adulthood and show that mice revert to a behavioral phenotype similar to juveniles. These results suggest that frontal cortical to dorsal raphe input is a critical anatomical and functional substrate of the development and manifestation of behavioral persistence.

ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms

Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.

Rapid assays of SARS-CoV-2 virus and noble biosensors by nanomaterials

The COVID-19 outbreak caused by SARS-CoV-2 in late 2019 has spread rapidly across the world to form a global epidemic of respiratory infectious diseases. Increased investigations on diagnostic tools are currently implemented to assist rapid identification of the virus because mass and rapid diagnosis might be the best way to prevent the outbreak of the virus. This critical review discusses the detection principles, fabrication techniques, and applications on the rapid detection of SARS-CoV-2 with three categories: rapid nuclear acid augmentation test, rapid immunoassay test and biosensors. Special efforts were put on enhancement of nanomaterials on biosensors for rapid, sensitive, and low-cost diagnostics of SARS-CoV-2 virus. Future developments are suggested regarding potential candidates in hospitals, clinics and laboratories for control and prevention of large-scale epidemic.

Understanding the Effects of Health Care Distance Simulation: A Systematic Review

ABSTRACT

The use of distance simulation has rapidly expanded in recent years with the physical distance requirements of the COVID-19 pandemic. With this development, there has been a concurrent increase in research activities and publications on distance simulation. The authors conducted a systematic review of the peer-reviewed distance health care simulation literature. Data extraction and a risk-of-bias assessment were performed on selected articles. Review of the databases and gray literature reference lists identified 10,588 titles for review. Of those, 570 full-text articles were assessed, with 54 articles included in the final analysis. Most of these were published during the COVID-19 pandemic (2020-2022). None of the included studies examined an outcome higher than a Kirkpatrick level of 2. Most studies only examined low-level outcomes such as satisfaction with the simulation session. There was, however, a distinction in studies that were conducted in a learning environment where all participants were in different locations ("distance only") as compared with where some of the participants shared the same location ("mixed distance"). This review exclusively considered studies that focused solely on distance. More comparative studies exploring higher level outcomes are required to move the field forward.

Lean blowout detection using topological data analysis

Modern lean premixed combustors are operated in ultra-lean mode to conform to strict emission norms. However, this causes the combustors to become prone to lean blowout (LBO). Online monitoring of combustion dynamics may help to avoid LBO and help the combustor run more safely and reliably. Previous studies have suggested various techniques to early predict LBO in single-burner combustors. In contrast, early detection of LBO in multi-burner combustors has been little explored to date. Recent studies have discovered significantly different combustion dynamics between multi-burner combustors and single-burner combustors. In the present paper, we show that some well-established early LBO detection techniques suitable for single-burner combustor are less effective in early detecting LBO in multi-burner combustors. To resolve this, we propose a novel tool, topological data analysis (TDA), for real-time LBO prediction in a wide range of combustor configurations. We find that the TDA metrics are computationally cheap and follow monotonic trends during the transition to LBO. This indicates that the TDA metrics can be used to fine-tune the LBO safety margin, which is a desirable feature from practical implementation point of view. Furthermore, we show that the sublevel set TDA metrics show approximately monotonic changes during the transition to LBO even with low sampling-rate signals. Sublevel set TDA is computationally inexpensive and does not require phase-space embedding. Therefore, TDA can potentially be used for real-time monitoring of combustor dynamics with simple, low-cost, and low sampling-rate sensors.

Frontoparietal functional connectivity moderates the link between time spent on social media and subsequent negative affect in daily life

Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.

Eliminating the interference of water for direct sensing of submerged plastics using hyperspectral near-infrared imager

Interference from water in the reflectance spectra of plastics is a major obstacle to optical sensing of plastics in aquatic environments. Here we present evidence of the feasibility of sensing plastics in water using hyperspectral near-infrared to shortwave-infrared imaging techniques. We captured hyperspectral images of nine polymers submerged to four depths (2.5-15 mm) in water using a hyperspectral imaging system that utilizes near-infrared to shortwave-infrared light sources. We also developed algorithms to predict the reflectance spectra of each polymer in water using the spectra of the dry plastics and water as independent variables in a multiple linear regression model after a logarithmic transformation. A narrow 1100-1300 nm wavelength range was advantageous for detection of polyethylene, polystyrene, and polyvinyl chloride in water down to the 160-320 µm size range, while a wider 970-1670 nm wavelength range was beneficial for polypropylene reflectance spectrum prediction in water. Furthermore, we found that the spectra of the other five polymers, comprising polycarbonate, acrylonitrile butadiene styrene, phenol formaldehyde, polyacetal, and polymethyl methacrylate, could also be predicted within their respective optimized wavelength ranges. Our findings provide fundamental information for direct sensing of plastics in water on both benchtop and airborne platforms.

Magnetically Powered Microwheel Thrombolysis of Occlusive Thrombi in Zebrafish

Tissue plasminogen activator (tPA) is the only FDA approved treatment for ischemic stroke but carries significant risks, including major hemorrhage. Additional options are needed, especially in small vessel thrombi which account for ~25% of ischemic strokes. We have previously shown that tPA-functionalized colloidal microparticles can be assembled into microwheels (µwheels) and manipulated under the control of applied magnetic fields to enable rapid thrombolysis of fibrin gels in microfluidic models of thrombosis. Providing a living microfluidic analog, transparent zebrafish larvae have a highly conserved coagulation cascade that enables studies of hemostasis and thrombosis in the context of intact vasculature, clotting factors, and blood cells. Here we show that tPA-functionalized µwheels can perform rapid and targeted recanalization in vivo. This effect requires both tPA and µwheels, as minimal to no recanalization is achieved with tPA alone, µwheels alone, or tPA-functionalized microparticles in the absence of a magnetic field. We evaluated tPA-µwheels in CRISPR-generated plasminogen (plg) heterozygous and homozygous mutants and confirmed that tPA-µwheels are dose-dependent on plasminogen for lysis. We have found that magnetically powered µwheels as a targeted tPA delivery system are dramatically more efficient at plasmin-mediated thrombolysis than systemic delivery in vivo. Further development of this system in fish and mammalian models could enable a less invasive strategy for alleviating ischemia that is safer than directed thrombectomy or systemic infusion of tPA.

Structure and Conformation of Hydroxypropylmethyl Cellulose with a Wide Range of Molar Masses in Aqueous Solution─Effects of Hydroxypropyl Group Addition

The structure of hydroxypropylmethyl cellulose (HpMC) samples with a wide range of weight average molar masses (Mw) from 23 to 5000 kg mol-1, a controlled degree of substitution (DS) of 1.9 by methyl groups, and a molar substitution number (MS) of 0.25 by hydroxypropyl groups dissolved in aqueous solution was examined using static light scattering (SLS), dynamic light scattering (DLS), small-to-wide angle neutron scattering (S-WANS) techniques, and intrinsic viscosity ([η]) measurements. The determined Mw and the radius of gyration (Rg) showed the relationships Rg ∝ Mw1.0 and [η] ∝ Mw1.7 in a range of Mw < 100 kg mol-1, similar to rigid rod molecules in solution. However, exponents in the relationships decreased gradually with increasing Mw and reached ∼0.5 in a high Mw region, which is a typical value of flexible chain molecules for both Rg and [η]. These observations suggest that the HpMC samples behave as semiflexible rods with a certain persistence length (lp). The ratios of the hydrodynamic radius via DLS measurements to Rg also supported semiflexible rod behavior. Particle form factors and the average lengths (L) resulting from SLS and S-WANS experiments are well described with rigid rod particles in the range of Mw < 100 kg mol-1 and semiflexible rods with lp ∼ 100 nm in Mw > 100 kg mol-1. Because the average contour length (lc) calculated from Mw is approximately twice as long as L in the Mw range < 100 kg mol-1, the formed HpMC particles possess a folded hairpin-like elongated rigid rod structure. However, the lc/L value increases gradually in the range Mw > 200 kg mol-1, where the formed HpMC particles behave as semiflexible rods. The formed particle structure was substantially different from that found in methyl cellulose samples with a similar DS value, which showed rod-like behavior over a wide Mw range. The addition of hydroxypropyl groups only at MS = 0.25 effectively changed the formed particle structure.

Automatic transparency evaluation for open knowledge extraction systems

BACKGROUND

This paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built from the same corpus by three state-of-the-art OKE systems. The evaluation is automatically performed using a combination of three state-of-the-art FAIRness (Findability, Accessibility, Interoperability, Reusability) assessment tools and a linked data quality evaluation framework, called Luzzu. This evaluation includes six Cyrus data transparency dimensions for which existing assessment tools could be identified. OKE systems extract structured knowledge from unstructured or semi-structured text in the form of linked data. These systems are fundamental components of advanced knowledge services. However, due to the lack of a transparency framework for OKE, most OKE systems are not transparent. This means that their processes and outcomes are not understandable and interpretable. A comprehensive framework sheds light on different aspects of transparency, allows comparison between the transparency of different systems by supporting the development of transparency scores, gives insight into the transparency weaknesses of the system, and ways to improve them. Automatic transparency evaluation helps with scalability and facilitates transparency assessment. The transparency problem has been identified as critical by the European Union Trustworthy Artificial Intelligence (AI) guidelines. In this paper, Cyrus provides the first comprehensive view of transparency dimensions for OKE systems by merging the perspectives of the FAccT (Fairness, Accountability, and Transparency), FAIR, and linked data quality research communities.

RESULTS

In Cyrus, data transparency includes ten dimensions which are grouped in two categories. In this paper, six of these dimensions, i.e., provenance, interpretability, understandability, licensing, availability, interlinking have been evaluated automatically for three state-of-the-art OKE systems, using the state-of-the-art metrics and tools. Covid-on-the-Web is identified to have the highest mean transparency.

CONCLUSIONS

This is the first research to study the transparency of OKE systems that provides a comprehensive set of transparency dimensions spanning ethics, trustworthy AI, and data quality approaches to transparency. It also demonstrates how to perform automated transparency evaluation that combines existing FAIRness and linked data quality assessment tools for the first time. We show that state-of-the-art OKE systems vary in the transparency of the linked data generated and that these differences can be automatically quantified leading to potential applications in trustworthy AI, compliance, data protection, data governance, and future OKE system design and testing.

Underexplored and growing economic costs of invasive alien trees

The high ecological impacts of many invasive alien trees have been well documented. However, to date, we lacked synthesis of their economic impacts, hampering management actions. Here, we summarize the cost records of invasive trees to (I) identify invasive trees with cost information and their geographic locations, (II) investigate the types of costs recorded and sectors impacted by invasive trees and (III) analyze the relationships between categories of uses of invasive trees and the invasion costs attributed to these uses. We found reliable cost records only for 72 invasive trees, accumulating a reported total cost of $19.2 billion between 1960 and 2020. Agriculture was the sector with the highest cost records due to invasive trees. Most costs were incurred as resource damages and losses ($3.5 billion). Close attention to the ornamental sector is important for reducing the economic impact of invasive trees, since most invasive trees with cost records were introduced for that use. Despite massive reported costs of invasive trees, there remain large knowledge gaps on most invasive trees, sectors, and geographic scales, indicating that the real cost is severely underestimated. This highlights the need for further concerted and widely-distributed research efforts regarding the economic impact of invasive trees.

Crystal orientation fabric anisotropy causes directional hardening of the Northeast Greenland Ice Stream

The dynamic mass loss of ice sheets constitutes one of the biggest uncertainties in projections of ice-sheet evolution. One central, understudied aspect of ice flow is how the bulk orientation of the crystal orientation fabric translates to the mechanical anisotropy of ice. Here we show the spatial distribution of the depth-averaged horizontal anisotropy and corresponding directional flow-enhancement factors covering a large area of the Northeast Greenland Ice Stream onset. Our results are based on airborne and ground-based radar surveys, ice-core observations, and numerical ice-flow modelling. They show a strong spatial variability of the horizontal anisotropy and a rapid crystal reorganisation on the order of hundreds of years coinciding with the ice-stream geometry. Compared to isotropic ice, parts of the ice stream are found to be more than one order of magnitude harder for along-flow extension/compression while the shear margins are potentially softened by a factor of two for horizontal-shear deformation.

Abundance trends for river macroinvertebrates vary across taxa, trophic group and river typology

There is mounting evidence that terrestrial arthropods are declining rapidly in many areas of the world. It is unclear whether freshwater invertebrates, which are key providers of ecosystem services, are also declining. We addressed this question by analysing a long-term dataset of macroinvertebrate abundance collected from 2002 to 2019 across 5009 sampling sites in English rivers. Patterns varied markedly across taxonomic groups. Within trophic groups we detected increases in the abundance of carnivores by 19% and herbivores by 14.8%, while we estimated decomposers have declined by 21.7% in abundance since 2002. We also found heterogeneity in trends across rivers belonging to different typologies based on geological dominance and catchment altitude, with organic lowland rivers having generally higher rates of increase in abundance across taxa and trophic groups, with siliceous lowland rivers having the most declines. Our results reveal a complex picture of change in freshwater macroinvertebrate abundance between taxonomic groups, trophic levels and river typologies. Our analysis helps with identifying priority regions for action on potential environmental stressors where we discover macroinvertebrate abundance declines.

Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network

PURPOSE

For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic imaging data. This study provides a method to segment accurate, artifact-free 3D surface models of mandibles from CT data using convolutional neural networks.

METHODS

The presented approach cascades two independently trained 3D-U-Nets to perform accurate segmentations of the mandible bone from full resolution CT images. The networks are trained in different settings using three different loss functions and a data augmentation pipeline. Training and evaluation datasets consist of manually segmented CT images from 307 dentate and edentulous individuals, partly with heavy imaging artifacts. The accuracy of the models is measured using overlap-based, surface-based and anatomical-curvature-based metrics.

RESULTS

Our approach produces high-resolution segmentations of the mandibles, coping with severe imaging artifacts in the CT imaging data. The use of the two-stepped approach yields highly significant improvements to the prediction accuracies. The best models achieve a Dice coefficient of 94.824% and an average surface distance of 0.31 mm on our test dataset.

CONCLUSION

The use of two cascaded U-Net allows high-resolution predictions for small regions of interest in the imaging data. The proposed method is fast and allows a user-independent image segmentation, producing objective and repeatable results that can be used in automated surgical planning procedures.

Significant changes in lower limb alignment due to flexion and rotation-a systematic 3D simulation of radiographic measurements

BACKGROUND

Many radiographic lower limb alignment  measurements are dependent on patients' position, which makes a standardised image acquisition of long-leg radiographs (LLRs) essential for valid measurements. The purpose of this study was to investigate the influence of rotation and flexion of the lower limb on common radiological alignment parameters using three-dimensional (3D) simulation.

METHODS

Joint angles and alignment parameters of 3D lower limb bone models (n = 60), generated from computed tomography (CT) scans, were assessed and projected into the coronal plane to mimic radiographic imaging. Bone models were subsequently rotated around the longitudinal mechanical axis up to 15° inward/outward and additionally flexed along the femoral intercondylar axis up to 30°. This resulted in 28 combinations of rotation and flexion for each leg. The results were statistically analysed on a descriptive level and using a linear mixed effects model.

RESULTS

A total of 1680 simulations were performed. Mechanical axis deviation (MAD) revealed a medial deviation with increasing internal rotation and a lateral deviation with increasing external rotation. This effect increased significantly (p < 0.05) with combined flexion up to 30° flexion (- 25.4 mm to 25.2 mm). With the knee extended, the mean deviation of hip-knee-ankle angle (HKA) was small over all rotational steps but increased toward more varus/valgus when combined with flexion (8.4° to - 8.5°). Rotation alone changed the medial proximal tibial angle (MPTA) and the mechanical lateral distal femoral angle (mLDFA) in opposite directions, and the effects increased significantly (p < 0.05) when flexion was present.

CONCLUSIONS

Axial rotation and flexion of the 3D lower limb has a huge impact on the projected two-dimensional alignment measurements in the coronal plane. The observed effects were small for isolated rotation or flexion, but became pronounced and clinically relevant when there was a combination of both. This must be considered when evaluating X-ray images. Extension deficits of the knee make LLR prone to error and this calls into question direct postoperative alignment controls.

LEVEL OF EVIDENCE

III (retrospective cohort study).

Text-based automatic personality prediction using KGrAt-Net: a knowledge graph attention network classifier

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.

Installation of a solid state neutral particle analyzer array on mega ampere spherical tokamak upgrade

A compact solid state neutral particle analyzer (SSNPA) diagnostic, previously installed at NSTX-U, has been moved to MAST-U and successfully operated in the first physics campaign (MU01). The SSNPA operates by detecting the flux of fast neutral particles produced by charge exchange (CX) reactions to diagnose the fast ion distribution. The diagnostic consists of three 16-channel sensors, which provide a radial view of the plasma and have a sightline intersection with the South-South neutral beam line. From this radial geometry, active CX signals from mostly trapped particles are observed. Each channel has a spatial resolution of 3-4 cm, a temporal resolution of 200 kHz, and an average pitch angle resolution of a few degrees. The three-sensor configuration allows for coarse energy resolution of the CX signals; each sensor sees similar sightlines but different filter thicknesses alter the energy cutoffs by known amounts. Experimental data show that all channels are collecting data as intended. The signal to noise ratio is typically around 15. Preliminary data analysis shows a correlation between the SSNPA signal and magnetohydrodynamic activity in the plasma as expected.

IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy

In this paper, we propose a smartphone-based biosensor for detecting human total hemoglobin concentration in vivo with high accuracy. Compared to the existing biosensors used to measure hemoglobin concentration, the smartphone-based sensor utilizes the camera, memory, and computing power of the phone. Thus, the cost is largely reduced. Compared to existing smartphone-based sensors, we developed a highly integrated multi-wavelength LED module and a specially designed phone fixture to reduce spatial errors and motion artifacts, respectively. In addition, we embedded a new algorithm into our smartphone-based sensor to improve the measurement accuracy; an L*a*b* color space transformation and the “a” parameter were used to perform the final quantification. We collected 24 blood samples from normal and anemic populations. The adjusted R² of the prediction results obtained from the multiple linear regression method reached 0.880, and the RMSE reached 9.04, which met the accuracy requirements of non-invasive detection of hemoglobin concentration.

Evidence of Long Two-Dimensional Folding Chain Structure Formation of Poly(vinylidene fluoride) in N-Methylpyrrolidone Solution: Total Form Factor Determination by Combining Multiscattering Data

Very recently, we proposed that poly(vinylidene fluoride) (PVDF) dissolves in a long rectangular column conformation induced by the formation of a two-dimensional folding chain structure in an N-methylpyrrolidone (NMP) solution based on the results obtained from static light scattering (SLS), small- to wide-angle X-ray scattering (S-WAXS), and viscometric experiments. Small- to wide-angle neutron scattering (S-WANS) experiments were able to provide clear decisive evidence for the presence of such a two-dimensional folding chain structure in a deuterated (d)NMP solution of PVDF samples with two different weight average molar masses (M w) of 100 and 1200 kg mol-1, even under dilute conditions at concentrations less than the overlapping concentrations due to the quite high neutron scattering contrast obtained by using (d)NMP as a solvent. An increase in M w from 100 to 1200 kg mol-1 substantially increases the particle length and width from L = 80 to 350 nm and w 2 = 5.0 to 15 nm, respectively, while the thickness, w 1, is maintained at 0.3 nm. Since L is much longer than w 2 irrespective of M w, the particles formed by PVDF molecules in NMP simply behave as long rigid rods in a scattering vector (q) range covered by SLS measurements. A combination study of S-WANS and S-WAXS experiments covering a higher q range with the SLS techniques could clearly reveal increases in both the L and w 2 values with increasing M w.

Topic-based influential user detection: a survey

Online Social networks have become an easy means of communication for users to share their opinion on various topics, including breaking news, public events, and products. The content posted by a user can influence or affect other users, and the users who could influence or affect a high number of users are called influential users. Identifying such influential users has a wide range of applications in the field of marketing, including product advertisement, recommendation, and brand evaluation. However, the users’ influence varies in different topics, and hence a tremendous interest has been shown towards identifying topic-based influential users over the past few years. Topic-level information in the content posted by the users can be used in various stages of the topic-based influential user detection (IUD) problem, including data gathering, construction of influence network, quantifying the influence between two users, and analyzing the impact of the detected influential user. This has opened up a wide range of opportunities to utilize the existing techniques to model and analyze the topic-level influence in online social networks. In this paper, we perform a comprehensive study of existing techniques used to infer the topic-based influential users in online social networks. We present a detailed review of these approaches in a taxonomy while highlighting the challenges and limitations associated with each technique. Moreover, we perform a detailed study of different evaluation techniques used in the literature to overcome the challenges that arise in evaluating topic-based IUD approaches. Furthermore, closely related research topics and open research questions in topic-based IUD are discussed to provide a deep understanding of the literature and future directions.

Prospects of telomere-to-telomere assembly in barley: Analysis of sequence gaps in the MorexV3 reference genome

The first gapless, telomere-to-telomere (T2T) sequence assemblies of plant chromosomes were reported recently. However, sequence assemblies of most plant genomes remain fragmented. Only recent breakthroughs in accurate long-read sequencing have made it possible to achieve highly contiguous sequence assemblies with a few tens of contigs per chromosome, that is a number small enough to allow for a systematic inquiry into the causes of the remaining sequence gaps and the approaches and resources needed to close them. Here, we analyse sequence gaps in the current reference genome sequence of barley cv. Morex (MorexV3). Optical map and sequence raw data, complemented by ChIP-seq data for centromeric histone variant CENH3, were used to estimate the abundance of centromeric, ribosomal DNA, and subtelomeric repeats in the barley genome. These estimates were compared with copy numbers in the MorexV3 pseudomolecule sequence. We found that almost all centromeric sequences and 45S ribosomal DNA repeat arrays were absent from the MorexV3 pseudomolecules and that the majority of sequence gaps can be attributed to assembly breakdown in long stretches of satellite repeats. However, missing sequences cannot fully account for the difference between assembly size and flow cytometric genome size estimates. We discuss the prospects of gap closure with ultra-long sequence reads.

Chinese English as Foreign Language Teachers' Self-Efficacy and Motivation as Predictors of Burnout

In contemporary education, educators are a central and focal component of academic structures accountable for education growth. Educators have an effective role in presenting and planning an effective and important academic program. However, they usually experience burnout due to their stressful job that affects the procedure of their teaching. So, considering the variables that help them in mitigating their burnout should be taken into account. The concept such as educator efficacy has recently attracted significant attention in education fields which by itself emphasizes educators and gets them into the focus of attention in education. Also, the concept of motivation has a strong relationship with the expert aspect of the educators and its significance for language educators and their students are increasingly gaining respect in various worldwide settings, in which lower educators' motivation is generally a reason for distress. To focus on the predictability role of these constructs on teachers' burnout, 428 female and male Chinese English as Foreign Language (EFL) teachers from more than 17 cities in nine provinces of China were asked to fill out the three scales, namely, teacher motivation, efficacy, and burnout. The main results of the study, achieved through Pearson Correlation, postulate that the relationships exist among the variables of the study, and by running multiple regression analysis, it is revealed that both variables, namely, self-efficacy and motivation, were the predictors of teachers' burnout, while self-efficacy was a better predictor. Regarding the applications, educator training plans can focus more on educator self-efficacy and motivation, because of their proven important role in decreasing educator burnout.

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images

Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images.

Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model.

Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81–0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92–0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging.

Conclusion: The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.

DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks

Determining and minimizing risk exposure pose one of the biggest challenges in the financial industry as an environment with multiple factors that affect (non-)identified risks and the corresponding decisions. Various estimation metrics are utilized towards robust and efficient risk management frameworks, with the most prevalent among them being the Value at Risk (VaR). VaR is a valuable risk-assessment approach, which offers traders, investors, and financial institutions information regarding risk estimations and potential investment insights. VaR has been adopted by the financial industry for decades, but the generated predictions lack efficiency in times of economic turmoil such as the 2008 global financial crisis and the COVID-19 pandemic, which in turn affects the respective decisions. To address this challenge, a variety of well-established variations of VaR models are exploited by the financial community, including data-driven and data analytics models. In this context, this paper introduces a probabilistic deep learning approach, leveraging time-series forecasting techniques with high potential of monitoring the risk of a given portfolio in a quite efficient way. The proposed approach has been evaluated and compared to the most prominent methods of VaR calculation, yielding promising results for VaR 99% for forex-based portfolios.

SciKit-SurgeryGlenoid, an Open Source Toolkit for Glenoid Version Measurement

Correct understanding of the geometry of the glenoid (the socket of the shoulder joint) is key to successful planning of shoulder replacement surgery. This surgery typically involves placing an implant in the shoulder joint to restore joint function. The most relevant geometry is the glenoid version, which is the angular orientation of the glenoid surface relative to the long axis of the scapula in the axial plane. However, measuring the glenoid version is not straightforward and there are multiple measurement methods in the literature and used in commercial planning software. In this paper we introduce SciKit-SurgeryGlenoid, an open source toolkit for the measurement of glenoid version. SciKit-SurgeryGlenoid contains implementations of the 4 most frequently used glenoid version measurement algorithms enabling easy and unbiased comparison of the different techniques. We present the results of using the software on 10 sets of pre-operative CT scans taken from patients who have subsequently undergone shoulder replacement surgery. We further compare these results with those obtained from a commercial implant planning software. SciKit-SurgeryGlenoid currently requires manual segmentation of the relevant anatomical features for each method. Future work will look at automating the segmentation process to build an automatic and repeatable pipeline from CT or radiograph to quantitative glenoid version measurement.

Are There Sex Differences in Balance Performance after a Short-Term Physical Intervention in Seniors 65+? A Randomized Controlled Trial

A systematic review and meta-analysis of studies demonstrated a relationship between sex and balance determinants in seniors; however, no study has focused on sex-related differences in static and dynamic balance performance after a physical intervention as primary interest. The aim was to investigate sex differences in the static and dynamic balance performance after a 4-week physical intervention based on yoga in seniors 65+ years of age. Five-hundred participants (234 males, 266 females) were assessed with the Tinetti Balance Assessment Tool in pre-and post-testing. The experimental group (122 males, 140 females) underwent the intervention, while the control group (112 males, 126 females) ran their usual daily program. ANCOVA model was used for the statistical evaluation of the results. No sex differences were found in balance performance after a short-term physical intervention in seniors 65+ years of age. At the same time, a significant positive shift was demonstrated in performance in both static and dynamic balance.

Bio-Wastes as Aggregates for Eco-Efficient Boards and Panels: Screening Tests of Physical Properties and Bio-Susceptibility

Screening tests were developed or adapted from RILEM recommendations, standards and past studies, and carried out to characterize some agro-industrial wastes and to assess their feasibility as aggregates for eco-efficient building composites. Spent coffee grounds, grape and olive press waste and hazelnut shells were used, as well as maritime pine chips as control material. Particle size distribution, loose bulk density, thermal conductivity and hygroscopicity properties were analysed. The selected bio-wastes did not show good thermal insulation properties if compared with some bio-wastes already studied and used for thermal insulation composites. Values of loose bulk density and thermal conductivity were between 325.6–550.5 kg/m3 and 0.078–0.107 W/(m·K); moisture buffering values higher than 2.0 g/(m2·%RH). Biological susceptibility to mould and termites were also tested, using not yet standardized methods. The low resistance to biological attack confirms one of the greatest drawbacks of using bio-wastes for building products. However, final products properties may be changed by adding other materials, pre-treatments of the wastes and the production process.

Paradigmatic Uniformity: Evidence from Heritage Speakers of Spanish

Subject-verb agreement mismatches have been reported in the L2 and heritage literature, usually involving infinitives, analyzed as default morphological forms for fully specified T-heads. This article explores the mechanisms behind these mismatches, testing two hypotheses: the default form and the surface-similarity hypotheses. It compares non-finite and finite S-V mismatches with subjects with different persons, testing whether similarity with other paradigmatic forms makes them more acceptable, controlling for the role of verb frequency. Participants were asked to rate sentences on a Likert scale that included (a) infinitive forms with first, second and third person subjects, and (b) third person verbal forms with first, second and third person subjects. Two stem-stressed verbs (e.g., tra.j-o ‘brought.3p.past’) and two affix-stressed verbs (e.g., me.ti-o ‘introduced.3p.past’), varying in frequency were tested. Inflectional affixes of stem-stressed verbs are similar to other forms of the paradigm both phonologically and in being unstressed (tra.j-o ‘brought.3p.past’ vs. trai.g-o ‘bring.1 p.pres’), whereas affixes of affix-stressed verbs have dissimilar stress patterns (me.ti-o ´introduced.3p.past’ vs. me.t-o ‘introduce.1p.pres’). Results show significantly higher acceptability for finite vs. non-finite non-matching, and for 1st vs. 2nd person subjects. Stem-stressed verbs showed higher acceptability ratings than affix-stressed ones, suggesting a role for surface-form correspondence, partially confirming previous findings.

Effects of UV Radiation on the Chlorophyte Micromonas polaris Host-Virus Interactions and MpoV-45T Virus Infectivity

Polar seas are under threat of enhanced UV-radiation as well as increasing shipping activities. Considering the ecological importance of marine viruses, it is timely to study the impact of UV-AB on Arctic phytoplankton host-virus interactions and also test the efficacy of ballast water (BW) UV-C treatment on virus infectivity. This study examined the effects of: (i) ecologically relevant doses of UV-AB radiation on Micromonas polaris RCC2258 and its virus MpoV-45T, and (ii) UV-C radiation (doses 25-800 mJ cm-2) on MpoV-45T and other temperate algal viruses. Total UV-AB exposure was 6, 12, 28 and 48 h (during the light periods, over 72 h total). Strongest reduction in algal growth and photosynthetic efficiency occurred for 28 and 48 h UV-AB treatments, and consequently the virus production rates and burst sizes were reduced by more than half (compared with PAR-only controls). For the shorter UV-AB exposed cultures, negative effects by UV (especially Fv/Fm) were overcome without impacting virus proliferation. To obtain the BW desired log-4 reduction in virus infectivity, a UV-C dose of at least 400 mJ cm-2 was needed for MpoV-45T and the temperate algal viruses. This is higher than the commonly used dose of 300 mJ cm-2 in BW treatment.

Neuroptera Diversity from Tacaná Volcano, Mexico: Species Composition, Altitudinal and Biogeographic Pattern of the Fauna

Approximately 340 species of ten families of Neuroptera have been recorded from Mexico. The Tacaná volcano, reaching an elevation of 4092 m a.s.l., is the northernmost representative of the Central American Nucleus volcanic range. Recent survey efforts of the Neuroptera diversity of the Tacaná volcano, Chiapas, along an altitudinal gradient, increased the known fauna of this order in Mexico by 31 species and two genera: Biramus Oswald, 1993 (Hemerobiidae), and Titanochrysa Sosa & Freitas, 2012 (Chrysopidae), with extension of the known distributional range of 25 species of five families. Most of the new country records are from species previously known only from Central and South America. The lacewing fauna of Chiapas is updated from 91 to 147 species. The Neuroptera of the Tacaná volcano is mostly Neotropical with some taxa of Nearctic affinity restricted to medium and high elevations. More than 80% of the Tacaná volcano lacewing species also occur in the Brazilian subregion, especially the Mesoamerican and Pacific domains. Neuropteran species were recorded from 650 to more than 3500 m a.s.l. A higher species richness was present between 600 and 1700 m, with a few species occurring at altitudes above 3000 m. A species checklist and an identification key to the genera of Neuroptera of the Tacaná volcano are provided.

Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

Intracellular nanovesicles mediate α5β1 integrin trafficking during cell migration

Membrane traffic is an important regulator of cell migration through the endocytosis and recycling of cell surface receptors such as integrin heterodimers. Intracellular nanovesicles (INVs) are transport vesicles that are involved in multiple membrane trafficking steps, including the recycling pathway. The only known marker for INVs is tumor protein D54 (TPD54/TPD52L2), a member of the TPD52-like protein family. Overexpression of TPD52-like family proteins in cancer has been linked to poor prognosis and an aggressive metastatic phenotype, which suggests cell migration may be altered under these conditions. Here, we show that TPD54 directly binds membrane and associates with INVs via a conserved positively charged motif in its C terminus. We describe how other TPD52-like proteins are also associated with INVs, and we document the Rab GTPase complement of all INVs. Depletion of TPD52-like proteins inhibits cell migration and invasion, while their overexpression boosts motility. We show that inhibition of migration is likely due to altered recycling of α5β1 integrins in INVs.

Reinforcement learning control of a biomechanical model of the upper extremity

Among the infinite number of possible movements that can be produced, humans are commonly assumed to choose those that optimize criteria such as minimizing movement time, subject to certain movement constraints like signal-dependent and constant motor noise. While so far these assumptions have only been evaluated for simplified point-mass or planar models, we address the question of whether they can predict reaching movements in a full skeletal model of the human upper extremity. We learn a control policy using a motor babbling approach as implemented in reinforcement learning, using aimed movements of the tip of the right index finger towards randomly placed 3D targets of varying size. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal with the curse of dimensionality, we use a simplified second-order muscle model, acting at each degree of freedom instead of individual muscles. The results confirm that the assumptions of signal-dependent and constant motor noise, together with the objective of movement time minimization, are sufficient for a state-of-the-art skeletal model of the human upper extremity to reproduce complex phenomena of human movement, in particular Fitts' Law and the [Formula: see text] Power Law. This result supports the notion that control of the complex human biomechanical system can plausibly be determined by a set of simple assumptions and can easily be learned.

Interoception Primes Emotional Processing: Multimodal Evidence from Neurodegeneration

Recent frameworks in cognitive neuroscience and behavioral neurology underscore interoceptive priors as core modulators of negative emotions. However, the field lacks experimental designs manipulating the priming of emotions via interoception and exploring their multimodal signatures in neurodegenerative models. Here, we designed a novel task that involves interoceptive and control-exteroceptive priming conditions followed by post-interoception and post-exteroception facial emotion recognition (FER). We recruited 114 participants, including healthy controls (HCs) as well as patients with behavioral variant frontotemporal dementia (bvFTD), Parkinson's disease (PD), and Alzheimer's disease (AD). We measured online EEG modulations of the heart-evoked potential (HEP), and associations with both brain structural and resting-state functional connectivity patterns. Behaviorally, post-interoception negative FER was enhanced in HCs but selectively disrupted in bvFTD and PD, with AD presenting generalized disruptions across emotion types. Only bvFTD presented impaired interoceptive accuracy. Increased HEP modulations during post-interoception negative FER was observed in HCs and AD, but not in bvFTD or PD patients. Across all groups, post-interoception negative FER correlated with the volume of the insula and the ACC. Also, negative FER was associated with functional connectivity along the (a) salience network in the post-interoception condition, and along the (b) executive network in the post-exteroception condition. These patterns were selectively disrupted in bvFTD (a) and PD (b), respectively. Our approach underscores the multidimensional impact of interoception on emotion, while revealing a specific pathophysiological marker of bvFTD. These findings inform a promising theoretical and clinical agenda in the fields of nteroception, emotion, allostasis, and neurodegeneration.SIGNIFICANCE STATEMENT We examined whether and how emotions are primed by interoceptive states combining multimodal measures in healthy controls and neurodegenerative models. In controls, negative emotion recognition and ongoing HEP modulations were increased after interoception. These patterns were selectively disrupted in patients with atrophy across key interoceptive-emotional regions (e.g., the insula and the cingulate in frontotemporal dementia, frontostriatal networks in Parkinson's disease), whereas persons with Alzheimer's disease presented generalized emotional processing abnormalities with preserved interoceptive mechanisms. The integration of both domains was associated with the volume and connectivity (salience network) of canonical interoceptive-emotional hubs, critically involving the insula and the anterior cingulate. Our study reveals multimodal markers of interoceptive-emotional priming, laying the groundwork for new agendas in cognitive neuroscience and behavioral neurology.

Usability Assessment of a Bluetooth-Enabled Resistance Exercise Band Among Young Adults

Background

Resistance-based exercises effectively enhance muscle strength, which is especially important in older populations as it reduces the risk of disability. Our group developed a Bluetooth-enabled handle for resistance exercise bands that wirelessly transmits relative force data through low-energy Bluetooth to a local smartphone or similar device. We present a usability assessment that evaluates an exercise system featuring a novel Bluetooth-enabled resistance exercise band, ultimately intended to expand the accessibility of resistance training through technology-enhanced home-based exercise programs for older adults. Although our target population is older adults, we assess the user experience among younger adults as a convenient and meaningful starting point in the testing and development of our device.

Methods

There were 32 young adults participating in three exercise sessions with the exercise band, after which each completed an adapted version of the Usefulness, Satisfaction, and Ease (USE) questionnaire to characterize the exercise system's strengths and weaknesses in usability.

Results

Questionnaire data reflected a positive and consistent user experience, with all 20 items receiving mean scores greater than 5.0 on a seven-point Likert scale. There were no specific areas of significant weakness in the device's user experience.

Conclusions

The positive reception among young adults is a promising indication that the device can be successfully incorporated into exercise interventions and that the system can be further developed and tested for the target population of older adults.

Binding Costs in Processing Efficiency as Determinants of Cognitive Ability

Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a person's ability to establish and maintain temporary bindings (e.g., relations between items or relations between items and their context), we manipulated binding requirements (i.e., 2, 4, and 6 relations) in three choice reaction time paradigms (i.e., two comparison tasks, two change detection tasks, and two substitution tasks) measuring mental speed. Response time distributions of 115 participants were analyzed with the diffusion model. Higher binding requirements resulted in generally reduced efficiency of information processing, as indicated by lower drift rates. Additionally, we fitted bi-factor confirmatory factor analysis to the elementary cognitive tasks to separate basal speed and binding requirements of the employed tasks to quantify their specific contributions to working memory capacity, as measured by Recall-1-Back tasks. A latent factor capturing individual differences in binding was incrementally predictive of working memory capacity, over and above a general factor capturing speed. These results indicate that the relation between reaction time tasks and working memory capacity hinges on the complexity of the reaction time tasks. We conclude that binding requirements and, therefore, demands on working memory capacity offer a satisfactory account of task complexity that accounts for a large portion of individual differences in ability.

Evaluation of OMI NO2 Vertical Columns Using MAX-DOAS Observations over Mexico City

Nitrogen dioxide (NO2) is a gas pollutant that can be measured from space and several operational products are now available from instruments on-board of satellite-based platforms. There are still, however, many unknowns about the accuracy of these products under different viewing and surface conditions since ground-based observations are generally scarce. This is particularly the case of high-altitude sub-tropical megacities such as the Mexico City Metropolitan Area (MCMA). In this study, we use more than five years of data from four ground-based MAX-DOAS instruments distributed within the MCMA in order to evaluate the DOMINO product from the Ozone Monitoring Instrument (OMI) on board the Aura satellite. We compare OMI against each MAX-DOAS site independently using the vertical column densities (VCDs) reported by each instrument. The VCDs are also compared after smoothing the MAX-DOAS profiles with the a priori and the Averaging Kernels of the satellite product. We obtain an overall correlation coefficient (R) of 0.6 that does not improve significantly after the smoothing is applied. However, the slopes in the linear regressions for the individual sites improve when applying the smoothing from 0.36 to 0.62 at UNAM, from 0.26 to 0.49 at Acatlán, from 0.78 to 1.23 at Vallejo, and from 0.50 to 0.97 at the Cuautitlán station. The large differences observed between the OMI and MAX-DOAS VCDs are attributed to a reduced sensitivity of the satellite product near the surface and the large aerosol loading typically present within the mixed layer of the MCMA. This may also contribute to a slight overestimation of the VCDs from the MAX-DOAS measurements that presents a total error (random + systematic) of about 20%. As a result of this comparison, we find that OMI retrievals are on average 56% lower than the MAX-DOAS without any correction. The near-surface concentrations are estimated from the lowest layers of the MAX-DOAS retrievals and these compare well with surface measurements from in situ analyzers operated at the co-located air quality monitoring stations. The diurnal variability for each station is analyzed and discussed in relation to their location within the city.

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error

The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson's R >0.97 versus CWV from AERONET sun photometers), it has not yet been assessed whether machine-learning approaches can further improve CWV. Using a novel spatiotemporal cross-validation approach to avoid overfitting, our XGBoost model, with nine features derived from land use terms, date, and ancillary variables from the MAIAC retrieval, quantifies and can correct a substantial portion of measurement error relative to collocated measurements at AERONET sites (26.9% and 16.5% decrease in root mean square error (RMSE) for Terra and Aqua datasets, respectively) in the Northeastern USA, 2000-2015. We use machine-learning interpretation tools to illustrate complex patterns of measurement error and describe a positive bias in MAIAC Terra CWV worsening in recent summertime conditions. We validate our predictive model on MAIAC CWV estimates at independent stations from the SuomiNet GPS network where our corrections decrease the RMSE by 19.7% and 9.5% for Terra and Aqua MAIAC CWV. Empirically correcting for measurement error with machine-learning algorithms is a postprocessing opportunity to improve satellite-derived CWV data for Earth science and remote sensing applications.

Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

Background

Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process.

Objective

This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis

Methods

Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes.

Results

The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics.

Conclusions

Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

Complete chloroplast genomes of E. umbellata Thunb., E. multiflora Thunb., E. macrophylla Thunb., and E. glabra Thunb. (Elaeagnaceae)

Elaeagnus is a genus which consists about 70 species of flowering plants in the family Elaeagnaceae, and its edible fruit is a natural product used as food and in traditional medicine. In this study, we sequenced the complete chloroplast (cp) genome of four species, namely Elaeagnus umbellate Thunb., E. multiflora Thunb., E. macrophylla Thunb., and E. glabra Thunb., to study their phylogenetic relationships within the Elaeagnaceae. Total lengths of the chloroplast genome were 152,261 bp, 152,267 bp, 152,224 bp, and 152,227 bp, respectively. The four genomes had representative quadripartite structures, with an LSC region (82,207 bp, 82,191 bp, 82,136 bp, and 82,139 bp) and an SSC region (18,262 bp, 18,282 bp,and 18,278 bp for both species) separated by a pair of IRs (25,896 bp, 25,897 bp, and 25,905 bp for the latter two species), respectively. Moreover, they were composed of 136–137 genes, including 88 protein-coding genes, 40–41 tRNA genes, and 8 rRNA genes. A maximum likelihood phylogenetic analysis indicated that E. umbellata was most closely related to E. multiflora, whereas E. macrophylla was close to E. glabra.

CytoCensus, mapping cell identity and division in tissues and organs using machine learning

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D 'point-and-click' user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.

Stability and nuclear localization of yeast telomerase depend on protein components of RNase P/MRP

RNase P and MRP are highly conserved, multi-protein/RNA complexes with essential roles in processing ribosomal and tRNAs. Three proteins found in both complexes, Pop1, Pop6, and Pop7 are also telomerase-associated. Here, we determine how temperature sensitive POP1 and POP6 alleles affect yeast telomerase. At permissive temperatures, mutant Pop1/6 have little or no effect on cell growth, global protein levels, the abundance of Est1 and Est2 (telomerase proteins), and the processing of TLC1 (telomerase RNA). However, in pop mutants, TLC1 is more abundant, telomeres are short, and TLC1 accumulates in the cytoplasm. Although Est1/2 binding to TLC1 occurs at normal levels, Est1 (and hence Est3) binding is highly unstable. We propose that Pop-mediated stabilization of Est1 binding to TLC1 is a pre-requisite for formation and nuclear localization of the telomerase holoenzyme. Furthermore, Pop proteins affect TLC1 and the RNA subunits of RNase P/MRP in very different ways.

Sparse estimation of mutual information landscapes quantifies information transmission through cellular biochemical reaction networks

Measuring information transmission from stimulus to response is useful for evaluating the signaling fidelity of biochemical reaction networks (BRNs) in cells. Quantification of information transmission can reveal the optimal input stimuli environment for a BRN and the rate at which the signaling fidelity decreases for non-optimal input probability distributions. Here we present sparse estimation of mutual information landscapes (SEMIL), a method to quantify information transmission through cellular BRNs using commonly available data for single-cell gene expression output, across a design space of possible input distributions. We validate SEMIL and use it to analyze several engineered cellular sensing systems to demonstrate the impact of reaction pathways and rate constants on mutual information landscapes.

Sarkar et al. present a method called sparse estimation of mutual information landscapes (SEMIL) that quantifies information transmission landscapes through cellular biochemical reaction networks across a space of input distributions using single-cell gene expression data. This study suggests that mutual information landscapes can be used as a performance metric for biochemical reaction networks.

Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.