Automated Whole Slide Imaging for Label-Free Histology Using Photon Absorption Remote Sensing Microscopy.

OBJECTIVE: Pathologists rely on histochemical stains to impart contrast in thin translucent tissue samples, revealing tissue features necessary for identifying pathological conditions. However, the chemical labeling process is destructive and often irreversible or challenging to undo, imposing practical limits on the number of stains that can be applied to the same tissue section. Here we present an automated label-free whole slide scanner using a PARS microscope designed for imaging thin, transmissible samples. METHODS: Peak SNR and in-focus acquisitions are achieved across entire tissue sections using the scattering signal from the PARS detection beam to measure the optimal focal plane. Whole slide images (WSI) are seamlessly stitched together using a custom contrast leveling algorithm. Identical tissue sections are subsequently H&E stained and brightfield imaged. The one-to-one WSIs from both modalities are visually and quantitatively compared. RESULTS: PARS WSIs are presented at standard 40x magnification in malignant human breast and skin samples. We show correspondence of subcellular diagnostic details in both PARS and H&E WSIs and demonstrate virtual H&E staining of an entire PARS WSI. The one-to-one WSI from both modalities show quantitative similarity in nuclear features and structural information. CONCLUSION: PARS WSIs are compatible with existing digital pathology tools, and samples remain suitable for histochemical, immunohistochemical, and other staining techniques. SIGNIFICANCE: This work is a critical advance for integrating label-free optical methods into standard histopathology workflows.

Using Unmanned Aerial Vehicles to Evaluate Revegetation Success on Natural Gas Pipelines.

The Appalachian region of the United States has experienced significant growth in the production of natural gas. Developing the infrastructure required to transport this resource to market creates significant disturbances across the landscape, as both well pads and transportation pipelines must be created in this mountainous terrain. Midstream infrastructure, which includes pipeline rights-of-way and associated infrastructure, can cause significant environmental degradation, especially in the form of sedimentation. The introduction of this non-point source pollutant can be detrimental to freshwater ecosystems found throughout this region. This ecological risk has necessitated the enactment of regulations related to midstream infrastructure development. Weekly, inspectors travel afoot along new pipeline rights-of-way, monitoring the re-establishment of surface vegetation and identifying failing areas for future management. The topographically challenging terrain of West Virginia makes these inspections difficult and dangerous to the hiking inspectors. We evaluated the accuracy at which unmanned aerial vehicles replicated inspector classifications to evaluate their use as a complementary tool in the pipeline inspection process. Both RGB and multispectral sensor collections were performed, and a support vector machine classification model predicting vegetation cover were made for each dataset. Using inspector defined validation plots, our research found comparable high accuracy between the two collection sensors. This technique displays the capability of augmenting the current inspection process, though it is likely that the model can be improved further. The high accuracy thus obtained suggests valuable implementation of this widely available technology in aiding these challenging inspections.

YOLOv5 with ConvMixer Prediction Heads for Precise Object Detection in Drone Imagery.

The potency of object detection techniques using Unmanned Aerial Vehicles (UAVs) is unprecedented due to their mobility. This potency has stimulated the use of UAVs with object detection functionality in numerous crucial real-life applications. Additionally, more efficient and accurate object detection techniques are being researched and developed for usage in UAV applications. However, object detection in UAVs presents challenges that are not common to general object detection. First, as UAVs fly at varying altitudes, the objects imaged via UAVs vary vastly in size, making the task at hand more challenging. Second due to the motion of the UAVs, there could be a presence of blur in the captured images. To deal with these challenges, we present a You Only Look Once v5 (YOLOv5)-like architecture with ConvMixers in its prediction heads and an additional prediction head to deal with minutely-small objects. The proposed architecture has been trained and tested on the VisDrone 2021 dataset, and the acquired results are comparable with the existing state-of-the-art methods.

Pattern recognition analysis of marine oil spills in airborne passive infrared multispectral remote sensing images.

Pattern recognition methodology was developed for the automated detection of marine oil spills in passive infrared multispectral remote sensing images. The images employed in this work were collected from the Deepwater Horizon oil spill accident in 2010. The imaging instrument for data collection was a downward-looking infrared line scanner equipped with eight optical bandpass filters in the spectral range of 8-12 µm on a fixed-wing aircraft. Oil slicks may show either positive or negative thermal contrast against the surrounding sea water, depending on the sun glint conditions or the oil thickness. Classifiers were developed separately to detect oil with different contrasts by the application of backpropagation neural networks to the preprocessed radiances. Preprocessing strategies included: (1) assembly of training data through k-means clustering analysis; (2) elimination of variation in radiance magnitudes by a customized temperature correction method; (3) removal of sun glint artifacts in images by polynomial correction; and (4) extraction of the most representative features as inputs for the neural networks by a subset selection approach. The classifiers designed to detect oil with positive and negative thermal contrast relative to water achieved overall classification accuracies of 88.7 and 92.2%, respectively. Composite classification images were generated by integrating classification scores produced by the two classifiers. The prediction performance of the classification system was demonstrated through its application to images not involved during the training of the networks.

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things.

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

Requirement on the diurnal observation frequency for satellite remote sensing of photosynthetically available radiation.

Photosynthetically available radiation (PAR) is essential for the photosynthesis processes of land plants and aquatic phytoplankton. Satellite observation with different diurnal frequencies (e.g., high frequency from geostationary satellites and low frequency from polar-orbit satellites) provides a unique technique to monitor PAR variation on large tempo-spatial scales. Owing to different climatic characteristics, different regions may require different observation frequencies to obtain accurate PAR estimation, but such requirements are still poorly known. Here, based on Advanced Himawari Imager (AHI) high-frequency (10-min) observation data from the geostationary satellite Himawari-8, we investigated the influence of diurnal observation frequency on the accuracy of PAR estimation and provided the minimal observing frequency to get high accurate PAR estimation in the AHI coverage area. Our results revealed a remarkable difference in the requirements for the diurnal observation frequency in both spatial and temporal distributions. Overall, high-latitude regions need a higher observing frequency than low-latitude areas, and winter half-years need higher observing frequency than summer half-years. These results provide a basis for designing satellites to accurately remote sensing of PAR in different regions.

Correlation between vegetation indexes generated at Vitis Vinifera L. and soil, plant and production parameters for emergency application in decision making / Correlação entre indices de vegetação gerados em Vitis Vinifera L. e parâmetros de solo, planta e produção para aplicação emergencial na tomada de decisão

Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

Avaliou-se a correlação entre as técnicas de sensoriamento proximal e os resultados laboratoriais de variáveis qualitativas, mais os atributos agronômicos do solo de um vinhedo de 3,0 ha no município de Muitos Capões, região nordeste do estado do Rio Grande do Sul, Brasil, na safra 2017/2018. Objetivou avaliar a substituição das análises laboratoriais convencionais em viticultura por Índices de Vegetação, em situações de indisponibilidade de acesso ao laboratório. Com base em pesquisa bibliográfica, buscaram-se índices vegetativos desenvolvidos ou utilizados para análise de refletância de dossel em videiras e cujas bandas de trabalho estavam dentro do intervalo espectral fornecido pelo equipamento utilizado, obtendo-se um total de 17 candidatos viáveis. Esses índices de vegetação escolhidos foram correlacionados, por meio de Pearson (5%), com atributos agronômicos do solo (condutividade elétrica aparente, argila, pH em H2O, fósforo, potássio, matéria orgânica, alumínio, cálcio, magnésio, CTC efetivo, CTC em pH 7,0, zinco, cobre, enxofre e boro) para profundidades de 0 - 20 cm e 20 - 40 cm, e tecido vegetal (nitrogênio, fósforo, potássio, cálcio, magnésio, enxofre, cobre, zinco, ferro, manganês e boro), além de alguns parâmetros enológicos e fitotécnicos essenciais para a quantificação da produção e qualidade do vinho. Deste cruzamento foram obtidas 139 correlações significativas, resultando 36 coeficientes moderados entre 19 variáveis de parâmetros versus 12 dos índices. Concluímos que nos casos em que o acesso ou disponibilidade de análises laboratoriais é difícil ou impraticável, a utilização de índices de vegetação é possível, desde que os coeficientes de correlação atinjam, pelo menos, a magnitude moderada, servindo como suporte para a tomada de decisão até a falta de estrutura analítica ser remediada.

Guidelines for the use of the Attend Anywhere Platform for Telecommunications within the Pain Service.

Introduction Remote consultation is of growing in importance and gaining popularity in both primary and secondary healthcare settings. Reduced necessity for a physical presence of the patient within the healthcare setting is of particular benefit in the current COVID-19 era. It is also of benefit to a diverse group of patients, for example: those who are geographically distant from the base hospital, those suffering from mobility issues or chronic illness, those who require chaperoning as well as those with limited access to transport. We have developed guidelines for the use of the medical telecommunications platform, Attend Anywhere, which has been utilised across the English and Scottish National Health Services, as well as with the Australian Health service, and is now available in Health Service Executive (HSE) settings. Herein we describe and recommend a process that we have found helpful, and we propose guidelines on how a Health Care Worker (HCW) might consider approaching a virtual consultation when initiating and safely executing a patient encounter on Attend Anywhere, in a secure and efficient manner. The guidelines were created following review of the literature on previous experience by others with this software, as well as recent guidance published by the Irish Medical Council. A proportion of this guidance is transferable to other platforms. Methods We also undertook a short survey of our patients and physicians in Sligo University Hospital, who used Attend Anywhere over a six-week period to gauge their satisfaction levels with the experience., We estimated distance that our patients would have travelled for their appointment had the traditional face-to-face consultation been carried out. We noted whether we considered the medium appropriate for the patient consultations. Results 53 patients took part and satisfaction was rated from satisfied to very satisfied on a 3-point scale for all stakeholders. In addition, we found that remote consultation, when compared to face-to-face consultation, alleviated an average of 144km of unnecessary travel per appointment. Remote consultation was deemed appropriate in all cases and no rescheduled face-to-face appointments were required due to failure of the consultation due to difficulties encountered. Conclusion The authors recommend the implementation of the described guidance, with suggested Checklist, Information leaflet and Consent form, as a means of ensuring the confidentiality of the consultation and to ensure that processes are adhered to that optimise protection for both the patient and the clinician, while reducing the burden of attendance to the healthcare location.

Three-dimensional virtual histology in unprocessed resected tissues with photoacoustic remote sensing (PARS) microscopy and optical coherence tomography (OCT).

Histological images are critical in the diagnosis and treatment of cancers. Unfortunately, current methods for capturing these microscopy images require resource intensive tissue preparation that may delay diagnosis for days or weeks. To streamline this process, clinicians are limited to assessing small macroscopically representative subsets of tissues. Here, a combined photoacoustic remote sensing (PARS) microscope and swept source optical coherence tomography system designed to circumvent these diagnostic limitations is presented. The proposed multimodal microscope provides label-free three-dimensional depth resolved virtual histology visualizations, capturing nuclear and extranuclear tissue morphology directly on thick unprocessed specimens. The capabilities of the proposed method are demonstrated directly in unprocessed formalin fixed resected tissues. The first images of nuclear contrast in resected human tissues, and the first three-dimensional visualization of subsurface nuclear morphology in resected Rattus tissues, captured with a non-contact photoacoustic system are presented here. Moreover, the proposed system captures the first co-registered OCT and PARS images enabling direct histological assessment of unprocessed tissues. This work represents a vital step towards the development of a rapid histological imaging modality to circumvent the limitations of current histopathology techniques.

A Mobile Health Intervention to Increase Physical Activity in Pulmonary Arterial Hypertension.

BACKGROUND: Supervised exercise training improves outcomes in patients with pulmonary arterial hypertension (PAH). The effect of an unsupervised activity intervention has not been tested. RESEARCH QUESTION: Can a text-based mobile health intervention increase step counts in patients with PAH? STUDY DESIGN AND METHODS: We performed a randomized, parallel arm, single-blind clinical trial. We randomized patients to usual care or a text message-based intervention for 12 weeks. The intervention arm received three automated text messages per day with real-time step count updates and encouraging messages rooted in behavioral change theory. Individual step targets increased by 20% every 4 weeks. The primary end point was mean week 12 step counts. Secondary end points included the 6-min walk test, quality of life, right ventricular function, and body composition. RESULTS: Among 42 randomized participants, the change in raw steps between baseline and week 12 was higher in the intervention group (1,409 steps [interquartile range, -32 to 2,220] vs -149 steps [interquartile range, -1,010 to 735]; P = .02), which persisted after adjustment for age, sex, baseline step counts, and functional class (model estimated difference, 1,250 steps; P = .03). The intervention arm took a higher average number of steps on all days between days 9 and 84 (P < .05, all days). There was no difference in week 12 six-minute walk distance. Analysis of secondary end points suggested improvements in the emPHasis-10 score (adjusted change, -4.2; P = .046), a reduction in visceral fat volume (adjusted change, -170 mL; P = .023), and nearly significant improvement in tricuspid annular plane systolic excursion (model estimated difference, 1.2 mm; P = .051). INTERPRETATION: This study demonstrated the feasibility of an automated text message-based intervention to increase physical activity in patients with PAH. Additional studies are warranted to examine the effect of the intervention on clinical outcomes. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No. NCT03069716; URL: www.clinicaltrials.gov.

Utility of Wearable Sensors to Assess Postoperative Recovery in Pediatric Patients After Appendectomy.

BACKGROUND: Despite more than two million pediatric operations performed in the United States annually, normal postoperative recovery remains difficult to define. Wearable sensors that assess physical activity and vital signs in real time represent a tool to assess postoperative recovery. This study examined the use of a wearable, the FitBit Inspire HR, to describe recovery in children after appendectomy and to determine the sensitivity of wearable data to distinguish disease severity. MATERIALS AND METHODS: Children 3-18 y old undergoing appendectomy in a tertiary children's hospital were invited to participate. Participants wore the FitBit Inpire HR after surgery for 21 d. t-tests compared daily step counts, and piecewise linear regression models were fit to examine recovery trajectories for patients with simple and complicated appendicitis. RESULTS: Thirty-two patients were enrolled, and 26 met eligibility criteria. Nine (35%) children had complicated appendicitis, and 14 (54%) were female; the mean age was 9.1 y (standard deviation: 2.9). Four hundred nineteen postoperative days were captured (range: 8-22 d; median: 16 d). Step counts increased after surgery; however, piecewise models showed that patients with simple appendicitis had a more rapid increase (P < 0.01) and reached a plateau (approximately 8000 steps/d) on postoperative day 9, whereas patients with complicated appendicitis did not reach a plateau and had lower step counts during the entire 21-postoperative day period (P < 0.01). CONCLUSIONS: Recovery in children after surgery can be characterized using wearables, which can also distinguish recovery trajectories based on disease severity. Establishing such "normative" recovery patterns may lead to earlier detection of complications.

Remote monitoring of head and neck free flaps using near infrared spectroscopic tissue oximetry.

PURPOSE: Near infrared spectroscopy (NIRS) measures tissue oximetry and perfusion of free tissue transfer with the advantage of remote wireless monitoring for free tissue transfer. It has been widely used in breast and extremity reconstruction but has had limited adoption in the head and neck. MATERIALS AND METHODS: A retrospective review of head and neck microvascular reconstruction by three different surgical services over 15 months at one tertiary care hospital was performed. Demographics, flap type, monitoring technique, complications, and flap outcomes were recorded. Monitoring techniques were (1) implantable/handheld Doppler or (2) NIRS. Flap monitoring outcomes were evaluated using multivariate analysis. RESULTS: 119 flaps were performed by four surgeons with a success rate of 92% (109/119). Flaps were monitored with Doppler (40%) or NIRS (60%). There was no difference in flap success based on monitoring technique. An ROC analysis identified that the optimal cutoff in immediate StO2 for classifying flap success at discharge was 68%. CONCLUSIONS: NIRS was successfully implemented in a high-volume head and neck reconstructive practice. NIRS remote monitoring allowed for flap surveillance without requiring in-hospital presence and was able to identify both arterial and venous compromise.

Habitat suitability maps for juvenile tri-spine horseshoe crabs in Japanese intertidal zones: A model approach using unmanned aerial vehicles and the Structure from Motion technique.

The tri-spine horseshoe crab, Tachypleus tridentatus, is a threatened species that inhabits coastal areas from South to East Asia. A Conservation management system is urgently required for managing its nursery habitats, i.e., intertidal flats, especially in Japan. Habitat suitability maps are useful in drafting conservation plans; however, they have rarely been prepared for juvenile T. tridentatus. In this study, we examined the possibility of constructing robust habitat suitability models (HSMs) for juveniles based on topographical data acquired using unmanned aerial vehicles and the Structure from Motion (UAV-SfM) technique. The distribution data of the juveniles in the Tsuyazaki and Imazu intertidal flats from 2017 to 2019 were determined. The data were divided into a training dataset for HSM construction and three test datasets for model evaluation. High accuracy digital surface models were built for each region using the UAV-SfM technique. Normalized elevation was assessed by converting the topographical models that consider the tidal range in each region, and the slope was calculated based on these models. Using the training data, HSMs of the juveniles were constructed with normalized elevation and slope as the predictor variables. The HSMs were evaluated using the test data. The results showed that HSMs exhibited acceptable discrimination performance for each region. Habitat suitability maps were built for the juveniles in each region, and the suitable areas were estimated to be approximately 6.1 ha of the total 19.5 ha in Tuyazaki, and 3.7 ha of the total 7.9 ha area in Imazu. In conclusion, our findings support the usefulness of the UAV-SfM technique in constructing HSMs for juvenile T. tridentatus. The monitoring of suitable habitat areas for the juveniles using the UAV-SfM technique is expected to reduce survey costs, as it can be conducted with fewer investigators over vast intertidal zones within a short period of time.

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling.

Mobile sensing data has become a popular data source for geo-spatial analysis, however, mapping it accurately to other sources of information such as statistical data remains a challenge. Popular mapping approaches such as point allocation or voronoi tessellation provide only crude approximations of the mobile network coverage as they do not consider holes, overlaps and within-cell heterogeneity. More elaborate mapping schemes often require additional proprietary data operators are highly reluctant to share. In this paper, I use human settlement information extracted from publicly available satellite imagery in combination with stochastic radio propagation modelling techniques to account for that. I show in a simulation study and a real-world application on unemployment estimates in Senegal that better coverage approximations do not necessarily lead to better outcome predictions.

Improving maximal safe brain tumor resection with photoacoustic remote sensing microscopy.

Malignant brain tumors are among the deadliest neoplasms with the lowest survival rates of any cancer type. In considering surgical tumor resection, suboptimal extent of resection is linked to poor clinical outcomes and lower overall survival rates. Currently available tools for intraoperative histopathological assessment require an average of 20 min processing and are of limited diagnostic quality for guiding surgeries. Consequently, there is an unaddressed need for a rapid imaging technique to guide maximal resection of brain tumors. Working towards this goal, presented here is an all optical non-contact label-free reflection mode photoacoustic remote sensing (PARS) microscope. By using a tunable excitation laser, PARS takes advantage of the endogenous optical absorption peaks of DNA and cytoplasm to achieve virtual contrast analogous to standard hematoxylin and eosin (H&E) staining. In conjunction, a fast 266 nm excitation is used to generate large grossing scans and rapidly assess small fields in real-time with hematoxylin-like contrast. Images obtained using this technique show comparable quality and contrast to the current standard for histopathological assessment of brain tissues. Using the proposed method, rapid, high-throughput, histological-like imaging was achieved in unstained brain tissues, indicating PARS' utility for intraoperative guidance to improve extent of surgical resection.

Application of Ground-Based LiDAR for Analysing Oil Palm Canopy Properties on the Occurrence of Basal Stem Rot (BSR) Disease.

Ground-based LiDAR also known as Terrestrial Laser Scanning (TLS) technology is an active remote sensing imaging method said to be one of the latest advances and innovations for plant phenotyping. Basal Stem Rot (BSR) is the most destructive disease of oil palm in Malaysia that is caused by white-rot fungus Ganoderma boninense, the symptoms of which include flattening and hanging-down of the canopy, shorter leaves, wilting green fronds and smaller crown size. Therefore, until now there is no critical investigation on the characterisation of canopy architecture related to this disease using TLS method was carried out. This study proposed a novel technique of BSR classification at the oil palm canopy analysis using the point clouds data taken from the TLS. A total of 40 samples of oil palm trees at the age of nine-years-old were selected and 10 trees for each health level were randomly taken from the same plot. The trees were categorised into four health levels - T0, T1, T2 and T3, which represents the healthy, mildly infected, moderately infected and severely infected, respectively. The TLS scanner was mounted at a height of 1 m and each palm was scanned at four scan positions around the tree to get a full 3D image. Five parameters were analysed: S200 (canopy strata at 200 cm from the top), S850 (canopy strata at 850 cm from the top), crown pixel (number of pixels inside the crown), frond angle (degree of angle between fronds) and frond number. The results taken from statistical analysis revealed that frond number was the best single parameter to detect BSR disease as early as T1. In classification models, a linear model with a combination of parameters, ABD - A (frond number), B (frond angle) and D (S200), delivered the highest average accuracy for classification of healthy-unhealthy trees with an accuracy of 86.67 per cent. It also can classify the four severity levels of infection with an accuracy of 80 per cent. This model performed better when compared to the severity classification using frond number. The novelty of this research is therefore on the development of new approach to detect and classify BSR using point clouds data of TLS.

Radio Signal Sensing and Signal Processing to Monitor Behavioral Symptoms in Dementia: A Case Study.

OBJECTIVES: Alzheimer's Disease (AD)-related behavioral symptoms (i.e. agitation and/or pacing) develop in nearly 90% of AD patients. In this N = 1 study, we provide proof-of-concept of detecting changes in movement patterns that may reflect underlying behavioral symptoms using a highly novel radio sensor and identifying environmental triggers. METHODS: The Emerald device is a Wi-Fi-like box without on-body sensors, which emits and processes radio-waves to infer patient movement, spatial location and activity. It was installed for 70 days in the room of patient 'E', exhibiting agitated behaviors. RESULTS: Daily motion episode aggregation revealed motor activity fluctuation throughout the data collection period which was associated with potential socio-environmental triggers. We did not detect any adverse events attributable to the use of the device. CONCLUSION: This N-of-1 study suggests the Emerald device is feasible to use and can potentially yield actionable data regarding behavioral symptom management. No active or potential device risks were encountered.

Effects of prophylactic antibiotic-treatment on post-surgical recovery following intraperitoneal bio-logger implantation in rainbow trout.

Bio-logging devices can provide unique insights on the life of freely moving animals. However, implanting these devices often requires invasive surgery that causes stress and physiological side-effects. While certain medications in connection to surgeries have therapeutic capacity, others may have aversive effects. Here, we hypothesized that the commonly prescribed prophylactic treatment with enrofloxacin would increase the physiological recovery rate and reduce the presence of systemic inflammation following the intraperitoneal implantation of a heart rate bio-logger in rainbow trout (Oncorhynchus mykiss). To assess post-surgical recovery, heart rate was recorded for 21 days in trout with or without enrofloxacin treatment. Contrary to our hypothesis, treated trout exhibited a prolonged recovery time and elevated resting heart rates during the first week of post-surgical recovery compared to untreated trout. In addition, an upregulated mRNA expression of TNFα in treated trout indicate a possible inflammatory response 21 days post-surgery. Interestingly, the experience level of the surgeon was observed to have a long-lasting impact on heart rate. In conclusion, our study showed no favorable effects of enrofloxacin treatment. Our findings highlight the importance of adequate post-surgical recovery times and surgical training with regards to improving the welfare of experimental animals and reliability of research outcomes.

Surgical implantation of radio tags in three eel species (Anguilla spp.) in South Africa.

Studies have reported poor survival of surgically tagged freshwater fishes in warm African waters. This study aimed to assess the applicability of using radio telemetry (and surgical implantation of tags) for Anguilla spp. Nineteen yellow eels (Anguilla bengalensis, A. marmorata and A. mossambica) were surgically implanted with radio tags between October 2018 and January 2019 in the Thukela River, South Africa. Most eels were alive 6 months after tagging, and recaptured eels displayed advanced or complete healing at the incision site. Therefore, this method appears suitable for African freshwater eels.

Self-Reported Head Trauma Predicts Poor Dual Task Gait in Retired National Football League Players.

OBJECTIVE: Symptomatic head trauma associated with American-style football (ASF) has been linked to brain pathology, along with physical and mental distress in later life. However, the longer-term effects of such trauma on objective metrics of cognitive-motor function remain poorly understood. We hypothesized that ASF-related symptomatic head trauma would predict worse gait performance, particularly during dual task conditions (ie, walking while performing an additional cognitive task), in later life. METHODS: Sixty-six retired professional ASF players aged 29 to 75 years completed a health and wellness questionnaire. They also completed a validated smartphone-based assessment in their own homes, during which gait was monitored while they walked normally and while they performed a verbalized serial-subtraction cognitive task. RESULTS: Participants who reported more symptomatic head trauma, defined as the total number of impacts to the head or neck followed by concussion-related symptoms, exhibited greater dual task cost (ie, percentage increase) to stride time variability (ie, the coefficient of variation of mean stride time). Those who reported ≥1 hit followed by loss of consciousness, compared to those who did not, also exhibited greater dual task costs to this metric. Relationships between reported trauma and dual task costs were independent of age, body mass index, National Football League career duration, and history of musculoskeletal surgery. Symptomatic head trauma was not correlated with average stride times in either walking condition. INTERPRETATION: Remote, smartphone-based assessments of dual task walking may be utilized to capture meaningful data sensitive to the long-term impact of symptomatic head trauma in former professional ASF players and other contact sport athletes. ANN NEUROL 2020;87:75-83.