Full Data Research Articles

The mediating effects of motivation on the relations between occupational stress and physical activity among underresourced afterschool program staff

ObjectiveGuided by Self-Determination Theory, this study aimed to examine the potential mediating effects of autonomous and controlled motivations on physical activity (PA) experiences of afterschool program (ASP) staff with occupational stress.MethodA total of 58 ASP staff provided full data. Staff occupational stress and self-determination motivations for PA were assessed. Participants’ daily moderate-to-vigorous physical activity (MVPA) was measured using accelerometer wear. A path analysis was used to address the research purpose.ResultsOccupational stress negatively and indirectly predicted daily MVPA which was mediated by controlled motivation (β = − 4.15, p <.05). Autonomous motivation directly and positively predicted daily MVPA across all types and levels of ASP staff occupational stress (β = 9.93, p =.01).ConclusionsAutonomous motivation is a powerful predictor of staff PA levels despite the degree to which they experience stress. In contrast, controlled motivations are more vulnerable to occupational stress, and can lead to lower MVPA.Trial registrationConnect Through PLAY: A Staff-based Physical Activity Intervention for Middle School Youth (Connect). https://clinicaltrials.gov/ct2/show/NCT03732144. Registered 11/06/2018. Registration number: NCT03732144.

Ground- and ship-based microwave radiometer measurements during EUREC4A

Abstract. During the EUREC4A field study, microwave radiometric measurements were performed at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian in the downstream winter trades of the North Atlantic. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January and 14 February 2020 in which all instruments were operational. Multi-channel radiometric measurements were performed at BCO and aboard RV Meteor between 22 and 31 GHz (K-band) and from 51 to 58 GHz (V-band). Combined radar–radiometer measurements of a W-band Doppler radar with a single-channel radiometer instrument were conducted at 89 GHz aboard RV Meteor and RV Maria S Merian. We present a novel retrieval method to retrieve LWP from single-channel 89 GHz measurements, evaluate retrieved quantities with independent measurements, and analyze retrieval uncertainties by site and instrument intercomparison. Mean IWV conditions of 31.8 kg m−2 match independent radiosoundings at BCO with a root-mean-square difference of 1.1 kg m−2. Mean LWP conditions in confidently liquid cloudy, non-precipitating conditions ranged between 63.1 g m−2 at BCO and 46.8 g m−2 aboard RV Maria S Merian. Aboard the ships, 90 % of LWP was below 120 g m−2 with a 30 % uncertainty for LWP of 50 g m−2. Up to 20 % of confidently liquid cloudy profiles ranged below the LWP detection limit due to optically thin clouds. The data set comprises of processed raw data (Level 1), full quality-controlled post-processed instrument data (Level 2), a unified temporal resolution (Level 3), and a ready-to-use multi-site time series of IWV and LWP (Level 4), available to the public via AERIS (https://doi.org/10.25326/454##v2.0; Schnitt et al., 2023a). The data set complements the airborne LWP measurements conducted during EUREC4A and provides a unique benchmark tool for satellite evaluation and model–observation studies.

Active Learning for Stacking and AdaBoost-Related Models

Ensemble learning (EL) has become an essential technique in machine learning that can significantly enhance the predictive performance of basic models, but it also comes with an increased cost of computation. The primary goal of the proposed approach is to present a general integrative framework that allows for applying active learning (AL) which makes use of only limited budget by selecting optimal instances to achieve comparable predictive performance within the context of ensemble learning. The proposed framework is based on two distinct approaches: (i) AL is implemented following a full scale EL, which we call the ensemble learning on top of active learning (ELTAL), and (ii) apply the AL while using the EL, which we call the active learning during ensemble learning (ALDEL). Various algorithms for ELTAL and ALDEL are presented using Stacking and Boosting with various algorithm-specific query strategies. The proposed active learning algorithms are numerically illustrated with the Support Vector Machine (SVM) model using simulated data and two real-world applications, evaluating their accuracy when only a small number instances are selected as compared to using full data. Our findings demonstrate that: (i) the accuracy of a boosting or stacking model, using the same uncertainty sampling, is higher than that of the SVM model, highlighting the strength of EL; (ii) AL can enable the stacking model to achieve comparable accuracy to the SVM model using the full dataset, with only a small fraction of carefully selected instances, illustrating the strength of active learning.

Differential associations of sex and age with changes in HRQoL during outpatient cardiac rehabilitation.

Cardiovascular diseases (CVD) represent the world's leading cause of death. Health-related quality of life (HRQoL) is a widely applied concept of patients' perceived health and is directly linked to CVD morbidity, mortality, and re-hospitalization rates. Cardiac rehabilitation (CR) improves both cardiovascular outcomes and HRQoL. Regrettably, CR is still underutilized, especially in subgroups like women and elderly patients. The aim of our study was to investigate the predictive potential of sex and age on change of HRQoL throughout outpatient CR. 497 patients of outpatient CR were retrospectively assessed from August 2015 to September 2019 at the University Hospital Zurich. A final sample of 153 individuals with full HRQoL data both at CR entry and discharge was analyzed. HRQoL was measured using the 36-Item Short Form Survey (SF-36) with its physical (PCS) and mental (MCS) component scale. In two-factorial analyses of variance, we analyzed sex- and age-specific changes in HRQoL scores throughout CR, adjusting for psychosocial and clinical characteristics. Age was grouped into participants over and under the age of 65. In both sexes, mean scores of physical HRQoL improved significantly during CR (p <.001), while mean scores of mental HRQoL improved significantly in men only (p =.003). Women under the age of 65 had significantly greater physical HRQoL improvements throughout CR, compared with men under 65 (p =.043) and women over 65 years of age (p =.014). Sex and age did not predict changes in mental HRQoL throughout CR. Younger women in particular benefit from CR with regard to their physical HRQoL. Among older participants, women report equal improvements of physical HRQoL than men. Our results indicate that sex- and age-related aspects of HRQoL outcomes should be considered in CR.

Initial safety assessment of the endoscopically injected oncolytic virus OBP-301 in medically inoperable esophageal cancer: NRG-GI007.

341 Background: Definitive chemoradiation (CRT) is a standard-of-care for patients (Pts) with medically inoperable esophageal cancer (EC). The NRG/RTOG 0436 study of cisplatin/paclitaxel and RT (+/- cetuximab) as definitive therapy showed that about half of Pts have locally persistent disease. OBP-301 is a conditionally restricted, replication-competent adenovirus derived from human adenovirus type 5 that adds a human Telomerase Reverse Transcriptase (hTERT) gene promoter, replicating only in tumor cells to cause lysis. It may cause immunogenic cell death, enhance RT, and improve local control. OBP-301 was previously studied with RT in Japanese esophageal cancer Pts and shown to be safe and has promising activity.1 Methods: In NRG-GI007, a phase 1 study (NCT04391049), OBP-301 is added to weekly carboplatin/paclitaxel and RT (50.4 Gy/28 fractions) for Pts with medically inoperable EC. Pts receive 1-2mL of intratumoral OBP-301 1×1012virus particles/ml via endoscopy 3 days prior to and then at days 12 and 26 of CRT. The primary endpoint is dose-limiting toxicity (DLT), defined as adverse events (AEs, by CTCAE v5) definitely or probably attributed to OBP-301 that meet either of the following: (1) leading to a &gt;14-day cumulative delay in CRT or (2) any grade ≥ 3 AE EXCEPT: grade 3 nausea/vomiting, grade 3 esophagitis or dehydration, first occurrence of grade 3/4 neutropenia, or grade 3/4 lymphopenia. Initially, 6 evaluable Pts (eligible and started protocol treatment) are enrolled. If protocol defined DLT occurs in ≤1 of 6 Pts, the dose will be deemed safe and an expansion cohort of 9 more will be enrolled to further assess safety and obtain a preliminary assessment of clinical complete response. If ≥2 Pts have a DLT then one de-escalated OBP-301 dose will be assessed. Results: From June 2020 to April 2023, 6 evaluable Pts were enrolled. Median age was 73.5 years, all male. Five Pts had adenocarcinoma and 1 had squamous cell carcinoma; 3 Pts had node-positive disease. All Pts received all planned OBP-301 injections, along with 50.4 Gy RT. Four out of 6 Pts received all 5 weekly doses of chemo for &gt; 85% of planned total dose, and 2 got 4 weekly doses for &gt; 70% of planned total dose. No Pt experienced ³7 day treatment delay. The following treatment-related (attributed as definitely, probably, or possibly related to CRT and/or OBP-301) grade 3 AEs were reported across 5 Pts: decreased neutrophil count (3 Pts), decreased lymphocyte count (2 Pts), decreased WBC (2 Pts), and fever and fatigue (1 Pt each). There were 4 Pts with AEs reported as definitely or probably related to OBP-301, all grade ≤2. No DLT occurred. Conclusions: No DLTs in the 6 evaluable Pts were observed and it is concluded that the initial OBP-301 dose level is safe. NRG-GI007 was reopened on August 7, 2023 to the expansion cohort. Full toxicity and treatment data will be presented. 1. Eur J Cancer 2021;153:98. Clinical trial information: NCT04391049 .

Addressing Fear of Negative Consequences of Overdose Response: A Qualitative Study of the Perceptions of Service Industry Workers Who Encounter an Opioid Overdose in an Urban Commercial District in Atlanta, Georgia.

The increased contamination of illicit drugs with fentanyl in the United States drug market has contributed to escalating mortality from drug overdose. Leisure and hospitality service industry workers are encountering opioid-triggered overdoses in their workplaces, such as restaurants and bars. Consequently, this increases the need for overdose education and naloxone distribution (OEND) training, which has been limited. We aimed to describe the experiences among service industry workers encountering an overdose in their workplace. We conducted in-depth qualitative interviews with service industry workers in Little Five Points (L5P), Atlanta, between October 2019 and April 2020 and triangulated methods with participant observations and fieldwork. Purposive criterion sampling methods were applied to recruit from different establishments in the L5P commercial district, which comprised restaurants, bars, retail shops, and theaters. After an initial seed sample was identified by engaging key stakeholders during fieldwork (business owners, managers, and the business association), a snowball sample followed for a final sample of N = 15. To contextualize the local population of harm reduction workers, people who use drugs and/or obtain safer drug consumption supplies in L5P (sterile syringes, safer using kits, naloxone), and service industry workers and their customers in L5P, the first author volunteered with an Atlanta syringe services program from October 2019 to April 2020. The first author conducted participant observations during the syringe exchange program and field notes were taken during observation (44 hours). This engagement ensured a rich, thick description. We used a pragmatic approach to thematic data analysis for this study. Data were analyzed iteratively and inductively from interviews and observations. Two independent researchers reviewed transcripts to identify passages in the data related to the question of interest. The passages were contextualized within the full data set independently to understand the relationships in developing a theory of what was commonly occurring across participants' experiences, and these relationships led to emerging salient themes regarding encountering an opioid overdose at work. One salient theme related to overdose response emerged with the service industry workers included fear of negative consequences of overdose response, specifically, fear of disease transmission from artifacts of drug use and overdose response, including the spread of blood-borne disease, violence, and exposure to unintentional overdose. When discussing drug use, participants' beliefs about the potential for personal danger from drug use artifacts (syringes and discarded drugs) and violence were identified as barriers to opioid overdose responses. Our findings provide valuable insights for tailoring OEND training for service industry workers to confront fears associated with opioid overdose response in their places of work to decrease mortality from the opioid epidemic. Harm reduction approaches need to be sensitive to the places in which overdose occurs and who the overdose responder is likely to be, which requires appropriately tailoring OEND training for service industry workers.

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study.

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

Rapid: Zero-shot Domain Adaptation for Code Search with Pre-trained Models

Code search, which refers to the process of identifying the most relevant code snippets for a given natural language query, plays a crucial role in software maintenance. However, current approaches heavily rely on labeled data for training, which results in performance decreases when confronted with cross-domain scenarios including domain-specific or project-specific situations. This decline can be attributed to their limited ability to effectively capture the semantics associated with such scenarios. To tackle the aforementioned problem, we propose a ze R o-shot dom A in ada P tion with pre-tra I ned mo D els framework for code search named RAPID. The framework first generates synthetic data by pseudo labeling, then trains the CodeBERT with sampled synthetic data. To avoid the influence of noisy synthetic data and enhance the model performance, we propose a mixture sampling strategy to obtain hard negative samples during training. Specifically, the mixture sampling strategy considers both relevancy and diversity to select the data that are hard to be distinguished by the models. To validate the effectiveness of our approach in zero-shot settings, we conduct extensive experiments and find that RAPID outperforms the CoCoSoDa and UniXcoder model by an average of 15.7% and 10%, respectively, as measured by the MRR metric. When trained on full data, our approach results in an average improvement of 7.5% under the MRR metric using CodeBERT. We observe that as the model’s performance in zero-shot tasks improves, the impact of hard negatives diminishes. Our observation also indicates that fine-tuning CodeT5 for generating pseudo labels can enhance the performance of the code search model, and using only 100-shot samples can yield comparable results to the supervised baseline. Furthermore, we evaluate the effectiveness of RAPID in real-world code search tasks in three GitHub projects through both human and automated assessments. Our findings reveal RAPID exhibits superior performance, e.g., an average improvement of 18% under the MRR metric over the top-performing model.

A New Screening Tool (BAMSA) for Sleep Apnea in Male Professional Truck Drivers.

Obstructive sleep apnea (OSA) is common in professional truck drivers. It is important that OSA is recognized since undiagnosed and/or untreated sleep apnea is a risk factor for sleepiness-related traffic accidents. In this study, we developed a new simple tool to screen for obstructive sleep apnea (OSA) in this population. Altogether, 2066 professional truck drivers received a structured questionnaire. A total of 175 drivers had a clinical examination and were invited to participate in sleep laboratory studies, including cardiorespiratory polygraphy. We studied associations of different risk factors with the presence of sleep apnea. We established a new simple screening tool for obstructive sleep apnea (OSA) that was compared to other existing screening tools. A total of 1095 drivers completed the questionnaire. Successful cardiorespiratory polygraphy was obtained for 172 drivers. Full data were available for 160 male drivers included in the analyses. The following five risk factors for sleep apnea formed the BAMSA score (0 to 5): BMI > 30 kgm-2, age > 50 years, male gender, snoring at least one night per week, and the presence of apnea at least sometimes. BAMSA showed a sensitivity of 85.7% and a specificity of 78.8% in detecting AHI ≥ 15 when using a cut-off point of 4, and the ROC area was 0.823. BAMSA is a sensitive and easy-to-use tool in predicting obstructive sleep apnea in male professional drivers.

Estimating the anisotropy of the vertical transverse isotropy coal seam by rock physics model–based inversion

AbstractCoal seams exhibiting nearly horizontal bedding, and fractures can be characterized as transversely isotropic media with a vertical axis of symmetry, known as vertical transverse isotropy coal seams. The resulting anisotropy cannot be overlooked in high‐precision seismic velocity analysis, migration imaging and pre‐stack inversion. Therefore, we estimate the anisotropy of the vertical transverse isotropy coal seam (using the anisotropic Thomsen's parameters ε, γ and δ) by inverting the horizontal P‐ and SH‐wave velocities and fracture density based on a rock physics model. The Mori–Tanaka model and Brown–Korringa formula were first used to quantify the anisotropy of the vertical transverse isotropy coal seams impacted by dry and fluid‐saturated fractures. Subsequently, we formulated an equation for the inversion of horizontal P‐ and SH‐wave velocities, considering the measured vertical P‐ and SH‐wave velocities as constrained parameters. This approach is generally applied in vertical drilling scenarios. We tested the inversion method using the ultrasonic test results of coal samples collected from the southern margin of the Qinshui Basin, China and then used it on the full waveform logging data from a vertical coalbed methane well. Both the inversion results of horizontal P‐ and SH‐wave velocities and the estimated anisotropic parameters (ε and γ) were in good agreement with the ultrasonic test results of coal samples, although the accuracy of δ was slightly lower. Therefore, we believe that the proposed method can be extended to estimate the anisotropy of vertical transverse isotropy medium (assuming suitable rock physics models) for the ultrasonic testing of rock samples and full waveform logging along the vertical direction in near‐horizontal formations.

Application of Physics-Informed Neural Networks for Estimation of Saturation Functions from Countercurrent Spontaneous Imbibition Tests

Summary In this work, physics-informed neural networks (PINNs) are used for history matching data from core-scale countercurrent spontaneous imbibition (COUCSI) tests. To our knowledge, this is the first work exploring the variation in saturation function solutions from COUCSI tests. 1D flow was considered, in which two phases flow in opposite directions driven by capillary forces with one boundary open to flow. The partial differential equation (PDE) depends only on a saturation-dependent capillary diffusion coefficient (CDC). Static properties such as porosity, permeability, interfacial tension, and fluid viscosities are considered known. In contrast, the CDC or its components [relative permeability (RP) and capillary pressure (PC)], are considered unknown. We investigate the range of functions (CDCs or RP/PC combinations) that explain different (synthetic or real) experimental COUCSI data: recovery from varying extents of early-time and late-time periods, pressure transducers, and in-situ saturation profiles. History matching was performed by training a PINN to minimize a loss function based on observational data and terms related to the PDE, boundary, and initial conditions. The PINN model was generated with feedforward neural networks, Fourier/inverse-Fourier transformation, and an adaptive tanh activation function, and trained using full batching. The trainable parameters of both the neural networks and saturation functions (parameters in RP and PC correlations) were initialized randomly. The PINN method successfully matched the observed data and returned a range of possible saturation function solutions. When a full observed recovery curve was provided (recovery data reaching close to its final value), unique and correct CDC functions and correct spatial saturation profiles were obtained. However, different RP/PC combinations composing the CDC were calculated. For limited amounts of recovery data, different CDCs matched the observations equally well but predicted different recovery behavior beyond the collected data period. With limited recovery data, when all points were still following a square root of time trend, a CDC with a low magnitude and peak shifted to high saturations gave the same match as a CDC with a high magnitude and peak shifted to low saturations. Recovery data with sufficient points not being proportional to the square root of time strongly constrained how future recovery would behave and thus which CDCs could explain the results. Limited recovery data combined with an observed in-situ profile of saturations allowed for accurate determination of CDC and prediction of future recovery, suggesting in-situ data allowed for shortened experiments. With full recovery data, in-situ PC data calibrated the PC toward unique solutions matching the input. The RPs were determined, where their phase had much lower mobility than the others. The CDC is virtually independent of the highest fluid mobility, and RPs could not be matched at their high values. Adding artificial noise in the recovery data increased the variation of the estimated CDCs.

Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method

Surface-enhanced Raman spectroscopy (SERS) can quickly identify molecular fingerprints and has been widely used in the field of rapid detection. However, the non-uniformity inherent in SERS substrate signals, coupled with the finite nature of the detection object, significantly hampers the advancement of SERS. Nowadays, the existing mature immunochromatographic assay (ICA) method is usually combined with SERS technology to address the defects of SERS detection. Nevertheless, the porous structure of the strip will also affect the signal uniformity during detection. Obviously, a method using SERS-ICA is needed to effectively solve signal fluctuations, improve detection accuracy, and has certain versatility. This paper introduces an internal standard method combining deep learning to predict and process Raman data. Based on the signal fluctuation of single-antigen SERS-ICA test strip, the double-antigen SERS-ICA test strip was constructed. The full spectrum Raman data of double-antigen SERS-ICA test strip was normalized by the sum of two characteristic peaks of internal standard molecules, and then processed by deep learning algorithm. The Relative Standard Deviation (RSD) of Raman data of bisphenol A was compared before and after internal standard normalization of double-antigen SERS-ICA test strip. The RSD processed by this method was increased by 3.8 times. After normalization, the prediction accuracy of Root Mean Square Error (RMSE) is improved by 2.66 times, and the prediction accuracy of R-square (R2) is increased from 0.961 to 0.994. The results showed that RMSE and R2 were used to comprehensively predict the collected data of double-antigen SERS-ICA test strip, which could effectively improve the prediction accuracy. The internal standard algorithm can effectively solve the challenges of uneven hot spots and poor signal reproducibility on the test strip to a certain extent, so as to improve the semi-quantitative accuracy.

A review of the personality traits in post-concussion syndrome.

Various manifestations ranging from physical symptoms to cognitive and emotional impairments could often be seen following head concussions that lead to mild traumatic brain injury (mTBI). These symptoms are commonly comprising the post-concussion syndrome (PCS) and their resolution could be influenced by multiple factors. Personality traits have been suggested as potential risk factors for the emergence and persistence of PCS. In this study, we aimed to investigate the possible predisposition to PCS given by certain personality traits. Prospective cohort studies, observational studies, and cross-phenotype polygenic risk score analyses were selected from the main scientific databases (PubMed/Medline, Scopus, EMBASE, Web of Science) based on multiple-step screening, using keywords (such as "personality traits", "post-concussion syndrome", "traumatic brain injury", "anxiety", "depression", "resilience", and "somatization") and inclusion/exclusion criteria (English written studies available in full text presenting relevant data on TBI patients and their personality traits; reviews, animal studies, and studies not written in English, not available in full text, or not presenting full demographical and clinical data were excluded). The investigated personality traits included emotional reserve, somatic trait anxiety, embitterment, mistrust, parental anxiety, state anxiety, trait anxiety, anxiety sensitivity, pain catastrophizing, helplessness, sports-concussion symptom load, and cognitive resilience. The reviewed data from 16 selected studies suggested that personality traits play an essential role in the development and persistence of PCS. Emotional reserve, cognitive resilience, and lower levels of somatic trait anxiety were associated with better outcomes in PCS. However, higher levels of anxiety sensitivity, pain catastrophizing, helplessness, and sports-concussion symptom load were associated with worse outcomes in PCS. Parental anxiety was not associated with persistent symptoms in children following concussion. Despite the statistical analysis regarding the included publications bias was low, further studies should further investigate the correlation between TBI and some personality traits, as some of the selected studies did not included healthy individuals and their psychological profiles for comparison and correlation analysis. Personality traits may help predict the development and persistence of PCS following mTBI. Understanding the personality traits roles in PCS could assist the development of targeted interventions for the prevention and treatment of PCS. Further research is needed to better understand the complex interactions between personality traits, neurobiological factors, and psychosocial factors in PCS.

Accounting of aviation carbon emission in developing countries based on flight-level ADS-B data

The aviation industry is one of the key areas to achieve carbon neutrality due to its difficulty in fuel substitution. In developing countries, aviation industry will be in a period of rapid growth for a long time, which means that it will be more affected by the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Therefore, aviation carbon emission reduction in developing countries needs to be paid more attention. This study utilizes the full sample data of global developing countries' fight-level information of June 2018, including a total of 1,048,655 flight data, to account for the aviation fuel consumption, CO2 emissions, and emission intensity of 109 developing countries based on 70 aircraft types. The impacts of aircraft structure and flight pattern on energy efficiency and emission intensity are explored by combining micro fight-level aviation data with spatial index decomposition method. The results show that the average emission intensity of developing countries is actually better than that of developed countries. Most of them are concentrated in the range of 65–85 gCO2 per seat-kilometers. The emission reduction potential of traditional measures in developing countries can theoretically close to 20%, of which about three-fifths comes from the adjustment of aircraft structure, while the remainder comes from the improvement in flight pattern. There is still a lot of room for structural adjustment of long-haul large-sized aircraft in developing countries by avoiding less efficient four-engine aircrafts and choosing more efficient ones in terms of new fleet deployments. But the potential for restructuring of medium-sized aircraft is limited, thus needing to further improve efficiency by increasing flight distance. Large-sized aircraft should be avoided as much as possible within the range that medium-sized aircraft can reach, which is crucial in route planning in many developing countries.

Maximising the value of transmitted data from PSATs tracking marine fish: a case study on Atlantic bluefin tuna

BackgroundThe use of biologging tags to answer questions in animal movement ecology has increased in recent decades. Pop-up satellite archival tags (PSATs) are often used for migratory studies on large fish taxa. For PSATs, movements are normally reconstructed from variable amounts of transmitted data (unless tags are recovered, and full data archives accessed) by coupling geolocation methods with a state-space modelling (SSM) approach. Between 2018 and 2019, we deployed Wildlife Computers PSATs (MiniPATs) from which data recovery varied considerably. This led us to examine the effect of PSAT data volume on SSM performance (i.e., variation in reconstructed locations and their uncertainty). We did this by comparing movements reconstructed using partial (< 100%) and complete (100%) geolocation data sets from PSATs and investigated the variation in Global Position Estimator 3 (GPE3; Wildlife Computers’ proprietary light-based geolocation SSM) reconstructed locations and their certainty in relation to data volume and movement type (maximum dispersal distance).ResultsIn this analysis, PSATs (n = 29) deployed on Atlantic bluefin tuna (Thunnusthynnus) transmitted data after detaching from study animals for between 0.3 and 10.8 days (mean 4.2 ± 3 days), yielding between 2 and 82% (mean 27% ± 22%) of total geolocation data. The volume of geolocation data received was positively related to the amount of time a tag transmitted for and showed a weak negative relationship to the length of the tag deployment. For 12 recovered PSATs (i.e., 100% of geolocation data; mean ± 1 S.D. = 301 ± 90 days of data per fish), (i) if ABT travelled short-distances (< 1000 km), movements reconstructed from partial data sets were more similar to their complete data set counterpart than fish that travelled over longer distances (> 1000 km); (ii) for fish that travelled long distances, mean distance of locations from corresponding complete data set locations were inversely correlated with the volume of data received; (iii) if only 5% of data was used for geolocation, reconstructed locations for long-distance fish differed by 2213 ± 647 km from the locations derived from complete data sets; and, (iv) track reconstructions omitted migrations into the Mediterranean Sea if less than 30% of data was used for geolocation.ConclusionsFor Wildlife Computers MiniPATs in our specific application, movements reconstructed with as little as 30% of the total geolocation data results in plausible outputs from the GPE3. Below this data volume, however, significant differences of more than 2000 km can occur. Whilst for a single species and manufacturer, this highlights the importance of careful study planning and the value of conducting study-specific sensitivity analysis prior to inclusion of modelled locations in research outputs. Based on our findings, we suggest general steps and refinements to maximise the value of light geolocation data from PSATs deployed on aquatic animals and highlight the importance of conducting data sensitivity analyses.

Optimizing milling parameters based on full factorial experiment and backpropagation artificial neural network of lamina milling temperature prediction model.

Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature Tc and bone surface temperature Tb for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R= 0.99661, validation set R= 0.85003, testing set R= 0.90421, all temperature data set R= 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety.

Evaluation of Inlet Temperature with Three Sprayer Designs for Desorption Electrospray Ionization Mass Spectrometry Tissue Analysis.

Mass spectrometry imaging (MSI) allows for the spatially resolved detection of endogenous and exogenous molecules and atoms in biological samples, typically prepared as thin tissue sections. Desorption electrospray ionization (DESI) is one of the most commonly utilized MSI modalities in preclinical research. DESI ion source technology is still rapidly evolving, with new sprayer designs and heated inlet capillaries having recently been incorporated in commercially available systems. In this study, three iterations of DESI sprayer designs are evaluated: (1) the first, and until recently only, commercially available Waters sprayer; (2) a developmental desorption electro-flow focusing ionization (DEFFI)-type sprayer; and (3) a prototype of the newly released Waters commercial sprayer. A heated inlet capillary is also employed, allowing for controlled inlet temperatures up to 500 °C. These three sprayers are evaluated by comparative tissue imaging analyses of murine testes across this temperature range. Single ion intensity versus temperature trends are evaluated as exemplar cases for putatively identified species of interest, such as lactate and glutamine. A range of trends are observed, where intensities follow either increasing, decreasing, bell-shaped, or other trends with temperature. Data for all sprayers show approximately similar trends for the ions studied, with the commercial prototype sprayer (sprayer version 3) matching or outperforming the other sprayers for the ions investigated. Finally, the mass spectra acquired using sprayer version 3 are evaluated by uniform manifold approximation and projection (UMAP) and k-means clustering. This approach is shown to provide valuable insight that is complementary to the presented univariate evaluation for reviewing the parameter space in this study. Full spectral temperature optimization data are provided as supporting data to enable other researchers to design experiments that are optimal for specific ions.

Open Data governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum.

Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access.

Estimated energy and nutrient intake in complementary feeding methods in Brazilian infants: randomized clinical trial

Inadequate nutrient intake during complementary feeding (CF) can affect healthy infant growth and development. A randomized clinical trial was conducted to examine the energy and nutrient intake in Brazilian children randomly assigned to three distinct CF methods. Mother-infant pairs participated in the study, with mothers receiving interventions in one of three CF approaches: (A) strict Parent-Led Weaning (PLW); (B) strict Baby-Led Introduction to Solids (BLISS); and (C) a mixed method. Assessments were made at 5.5 months, nine months, and 12 months of the child's age. Food consumption was measured through 24-h dietary recalls at nine and 12 months, with intake estimates calculated using the Brazilian Food Composition Table. Means or medians of energy and nutrients were compared between groups using ANOVA with Tukey's post hoc test or the Kruskal–Wallis test. A total of 115 infants were evaluated at nine months, and 102 at 12 months. Children in the PLW, BLISS, and mixed method groups exhibited comparable dietary intakes of energy, macronutrients, and micronutrients at both nine and 12 months. Infants following PLW, BLISS, and mixed methods demonstrated similar levels of energy and nutrient intake, underscoring the effectiveness of these strategies in ensuring comparable nutrient intake during the critical phase of CF.Trial registration The trial was registered in the Brazilian Registry of Clinical Trials (ReBEC) with identifier [RBR-229scm U1111-1226-9516], [https://ensaiosclinicos.gov.br/rg/RBR-229scm]. The full data of the first registration was on 24/09/2019.

Variational Bayes Ensemble Learning Neural Networks With Compressed Feature Space.

We consider the problem of nonparametric classification from a high-dimensional input vector (small n large p problem). To handle the high-dimensional feature space, we propose a random projection (RP) of the feature space followed by training of a neural network (NN) on the compressed feature space. Unlike regularization techniques (lasso, ridge, etc.), which train on the full data, NNs based on compressed feature space have significantly lower computation complexity and memory storage requirements. Nonetheless, a random compression-based method is often sensitive to the choice of compression. To address this issue, we adopt a Bayesian model averaging (BMA) approach and leverage the posterior model weights to determine: 1) uncertainty under each compression and 2) intrinsic dimensionality of the feature space (the effective dimension of feature space useful for prediction). The final prediction is improved by averaging models with projected dimensions close to the intrinsic dimensionality. Furthermore, we propose a variational approach to the afore-mentioned BMA to allow for simultaneous estimation of both model weights and model-specific parameters. Since the proposed variational solution is parallelizable across compressions, it preserves the computational gain of frequentist ensemble techniques while providing the full uncertainty quantification of a Bayesian approach. We establish the asymptotic consistency of the proposed algorithm under the suitable characterization of the RPs and the prior parameters. Finally, we provide extensive numerical examples for empirical validation of the proposed method.