Abnormal Silicon Etching Behaviors in Nanometer-Sized Channels.

Modern semiconductor fabrication is challenged by difficulties in overcoming physical and chemical constraints. A major challenge is the wet etching of dummy gate silicon, which involves the removal of materials inside confined spaces of a few nanometers. These chemical processes are significantly different in the nanoscale and bulk. Previously, electrical double-layer formation, bubble entrapment, poor wettability, and insoluble intermediate precipitation have been proposed. However, the exact suppression mechanisms remain unclear due to the lack of direct observation methods. Herein, we investigate limiting factors for the etching kinetics of silicon with tetramethylammonium hydroxide at the nanoscale by using liquid-phase transmission electron microscopy, three-dimensional electron tomography, and first-principles calculations. We reveal suppressed chemical reactions, unstripping phenomena, and stochastic etching behaviors that have never been observed on a macroscopic scale. We expect that solutions can be suggested from this comprehensive insight into the scale-dependent limiting factors of fabrication.

PyRINEX: a new multi-purpose Python package for GNSS RINEX data.

Since the first receiver independent exchange format (RINEX) version was released in 1989, it has gone through several versions, making the existing software, such as TEQC, incompatible with certain later versions. This study proposes a new Python package named PyRINEX, which is developed to batch process the most generally used versions of RINEX files, namely 2.0 and 3.0. The proposed package can be used to manage and edit numerous RINEX files as well as perform a data quality check function. PyRINEX can be easily imported into any Python IDE similar to any other open-source Python package, it also makes secondary development easy for users.

Mobile Assessments of Mood, Cognition, Smartphone-Based Sensor Activity, and Variability in Craving and Substance Use in Patients With Substance Use Disorders in Norway: Prospective Observational Feasibility Study.

BACKGROUND: Patients with substance use disorders (SUDs) are at increased risk for symptom deterioration following treatment, with up to 60% resuming substance use within the first year posttreatment. Substance use craving together with cognitive and mental health variables play important roles in the understanding of the trajectories from abstinence to substance use. OBJECTIVE: This prospective observational feasibility study aims to improve our understanding of specific profiles of variables explaining SUD symptom deterioration, in particular, how individual variability in mental health, cognitive functioning, and smartphone use is associated with craving and substance use in a young adult clinical population. METHODS: In this pilot study, 26 patients with SUDs were included at about 2 weeks prior to discharge from inpatient SUD treatment from 3 different treatment facilities in Norway. Patients underwent baseline neuropsychological and mental health assessments; they were equipped with smartwatches and they downloaded an app for mobile sensor data collection in their smartphones. Every 2 days for up to 8 weeks, the patients were administered mobile ecological momentary assessments (EMAs) to evaluate substance use, craving, mental health, cognition, and a mobile Go/NoGo performance task. Repeated EMAs as well as the smartphone's battery use data were averaged across all days per individual and used as candidate input variables together with the baseline measures in models of craving intensity and the occurrence of any substance use episodes. RESULTS: A total of 455 momentary assessments were completed out of a potential maximum of 728 assessments. Using EMA and baseline data as candidate input variables and craving and substance use as responses, model selection identified mean craving intensity as the most important predictor of having one or more substance use episodes and with variabilities in self-reported impulsivity, mental health, and battery use as significant explanatory variables of craving intensity. CONCLUSIONS: This prospective observational feasibility study adds novelty by collecting high-intensity data for a considerable period of time, including mental health data, mobile cognitive assessments, and mobile sensor data. Our study also contributes to our knowledge about a clinical population with the most severe SUD presentations in a vulnerable period during and after discharge from inpatient treatment. We confirmed the importance of variability in cognitive function and mood in explaining variability in craving and that smartphone usage may possibly add to this understanding. Further, we found that craving intensity is an important explanatory variable in understanding substance use episodes.

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study.

BACKGROUND: Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact. OBJECTIVE: We aimed to determine the feasibility of developing a machine learning (ML) model to accurately predict future, that is, same-day BDEs 1 to 6 hours prior BDEs, using smartphone sensor data and to identify the most informative phone sensor features associated with BDEs on weekends and weekdays to determine the key features that explain prediction model performance. METHODS: We collected phone sensor data from 75 young adults (aged 21 to 25 years; mean 22.4, SD 1.9 years) with risky drinking behavior who reported their drinking behavior over 14 weeks. The participants in this secondary analysis were enrolled in a clinical trial. We developed ML models testing different algorithms (eg, extreme gradient boosting [XGBoost] and decision tree) to predict same-day BDEs (vs low-risk drinking events and non-drinking periods) using smartphone sensor data (eg, accelerometer and GPS). We tested various "prediction distance" time windows (more proximal: 1 hour; distant: 6 hours) from drinking onset. We also tested various analysis time windows (ie, the amount of data to be analyzed), ranging from 1 to 12 hours prior to drinking onset, because this determines the amount of data that needs to be stored on the phone to compute the model. Explainable artificial intelligence was used to explore interactions among the most informative phone sensor features contributing to the prediction of BDEs. RESULTS: The XGBoost model performed the best in predicting imminent same-day BDEs, with 95% accuracy on weekends and 94.3% accuracy on weekdays (F1-score=0.95 and 0.94, respectively). This XGBoost model needed 12 and 9 hours of phone sensor data at 3- and 6-hour prediction distance from the onset of drinking on weekends and weekdays, respectively, prior to predicting same-day BDEs. The most informative phone sensor features for BDE prediction were time (eg, time of day) and GPS-derived features, such as the radius of gyration (an indicator of travel). Interactions among key features (eg, time of day and GPS-derived features) contributed to the prediction of same-day BDEs. CONCLUSIONS: We demonstrated the feasibility and potential use of smartphone sensor data and ML for accurately predicting imminent (same-day) BDEs in young adults. The prediction model provides "windows of opportunity," and with the adoption of explainable artificial intelligence, we identified "key contributing features" to trigger just-in-time adaptive intervention prior to the onset of BDEs, which has the potential to reduce the likelihood of BDEs in young adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT02918565; https://clinicaltrials.gov/ct2/show/NCT02918565.

Exploratory Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior.

Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. Our machine learning analysis extends previous research by showing that both adaptive and maladaptive traits are associated with passively sensed behavior providing initial evidence for the utility of this type of data to study personality and its pathology. The analysis also suggests directions for future confirmatory studies into the underlying behavior patterns that link adaptive and maladaptive variants consistent with contemporary models of personality pathology.

Mobile phone sensor-based detection of subjective cannabis intoxication in young adults: A feasibility study in real-world settings.

BACKGROUND: Given possible impairment in psychomotor functioning related to acute cannabis intoxication, we explored whether smartphone-based sensors (e.g., accelerometer) can detect self-reported episodes of acute cannabis intoxication (subjective "high" state) in the natural environment. METHODS: Young adults (ages 18-25) in Pittsburgh, PA, who reported cannabis use at least twice per week, completed up to 30 days of daily data collection: phone surveys (3 times/day), self-initiated reports of cannabis use (start/stop time, subjective cannabis intoxication rating: 0-10, 10 = very high), and continuous phone sensor data. We tested multiple models with Light Gradient Boosting Machine (LGBM) in distinguishing "not intoxicated" (rating = 0) vs subjective cannabis "low-intoxication" (rating = 1-3) vs "moderate-intensive intoxication" (rating = 4-10). We tested the importance of time features (i.e., day of the week, time of day) relative to smartphone sensor data only on model performance, since time features alone might predict "routines" in cannabis intoxication. RESULTS: Young adults (N = 57; 58 % female) reported 451 cannabis use episodes, mean subjective intoxication rating = 3.77 (SD = 2.64). LGBM, the best performing classifier, had 60 % accuracy using time features to detect subjective "high" (Area Under the Curve [AUC] = 0.82). Combining smartphone sensor data with time features improved model performance: 90 % accuracy (AUC = 0.98). Important smartphone features to detect subjective cannabis intoxication included travel (GPS) and movement (accelerometer). CONCLUSIONS: This proof-of-concept study indicates the feasibility of using phone sensors to detect subjective cannabis intoxication in the natural environment, with potential implications for triggering just-in-time interventions.

Development of a program for in silico optimized selection of oligonucleotide-based molecular barcodes.

Short DNA oligonucleotides (~4 mer) have been used to index samples from different sources, such as in multiplex sequencing. Presently, longer oligonucleotides (8-12 mer) are being used as molecular barcodes with which to distinguish among raw DNA molecules in many high-tech sequence analyses, including low-frequent mutation detection, quantitative transcriptome analysis, and single-cell sequencing. Despite some advantages of using molecular barcodes with random sequences, such an approach, however, makes it impossible to know the exact sequences used in an experiment and can lead to inaccurate interpretation due to misclustering of barcodes arising from the occurrence of unexpected mutations in the barcodes. The present study introduces a tool developed for selecting an optimal barcode subset during molecular barcoding. The program considers five barcode factors: GC content, homopolymers, simple sequence repeats with repeated units of dinucleotides, Hamming distance, and complementarity between barcodes. To evaluate a selected barcode set, penalty scores for the factors are defined based on their distributions observed in random barcodes. The algorithm employed in the program comprises two steps: i) random generation of an initial set and ii) optimal barcode selection via iterative replacement. Users can execute the program by inputting barcode length and the number of barcodes to be generated. Furthermore, the program accepts a user's own values for other parameters, including penalty scores, for advanced use, allowing it to be applied in various conditions. In many test runs to obtain 100000 barcodes with lengths of 12 nucleotides, the program showed fast performance, efficient enough to generate optimal barcode sequences with merely the use of a desktop PC. We also showed that VFOS has comparable performance, flexibility in program running, consideration of simple sequence repeats, and fast computation time in comparison with other two tools (DNABarcodes and FreeBarcodes). Owing to the versatility and fast performance of the program, we expect that many researchers will opt to apply it for selecting optimal barcode sets during their experiments, including next-generation sequencing.

Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study.

BACKGROUND: Mobile assessment of the effects of acute marijuana on cognitive functioning in the natural environment would provide an ecologically valid measure of the impacts of marijuana use on daily functioning. OBJECTIVE: This study aimed to examine the association of reported acute subjective marijuana high (rated 0-10) with performance on 3 mobile cognitive tasks measuring visuospatial working memory (Flowers task), attentional bias to marijuana-related cues (marijuana Stroop), and information processing and psychomotor speed (digit symbol substitution task [DSST]). The effect of distraction as a moderator of the association between the rating of subjective marijuana high and task performance (ie, reaction time and number of correct responses) was explored. METHODS: Young adults (aged 18-25 years; 37/60, 62% female) who reported marijuana use at least twice per week were recruited through advertisements and a participant registry in Pittsburgh, Pennsylvania. Phone surveys and mobile cognitive tasks were delivered 3 times per day and were self-initiated when starting marijuana use. Completion of phone surveys triggered the delivery of cognitive tasks. Participants completed up to 30 days of daily data collection. Multilevel models examined associations between ratings of subjective marijuana high (rated 0-10) and performance on each cognitive task (reaction time and number of correct responses) and tested the number of distractions (rated 0-4) during the mobile task session as a moderator of the association between ratings of subjective marijuana high and task performance. RESULTS: Participants provided 2703 data points, representing 451 reports (451/2703, 16.7%) of marijuana use. Consistent with slight impairing effects of acute marijuana use, an increase in the average rating of subjective marijuana high was associated with slower average reaction time on all 3 tasks-Flowers (B=2.29; SE 0.86; P=.008), marijuana Stroop (B=2.74; SE 1.09; P=.01), and DSST (B=3.08; SE 1.41; P=.03)-and with fewer correct responses for Flowers (B=-0.03; SE 0.01; P=.01) and DSST (B=-0.18; SE 0.07; P=.01), but not marijuana Stroop (P=.45). Results for distraction as a moderator were statistically significant only for certain cognitive tasks and outcomes. Specifically, as hypothesized, a person's average number of reported distractions moderated the association of the average rating of subjective marijuana high (over and above a session's rating) with the reaction time for marijuana Stroop (B=-52.93; SE 19.38; P=.006) and DSST (B=-109.72; SE 42.50; P=.01) and the number of correct responses for marijuana Stroop (B=-0.22; SE 0.10; P=.02) and DSST (B=4.62; SE 1.81; P=.01). CONCLUSIONS: Young adults' performance on mobile cognitive tasks in the natural environment was associated with ratings of acute subjective marijuana high, consistent with slight decreases in cognitive functioning. Monitoring cognitive functioning in real time in the natural environment holds promise for providing immediate feedback to guide personal decision making.

Evaluation of Calf Muscular Function During the Recovery Phase After the Repair of an Achilles Tendon Rupture.

Background: During the recovery phase after the repair of an Achilles tendon rupture, measuring calf muscular function is important for predicting prognosis. Tensiomyography (TMG) is a selective and non-invasive diagnostic method for skeletal muscular contractile properties based on the displacement of the muscle belly. Case Presentation: Tensiomyography gives information about maximal displacement of the muscle belly (Dm), delay time, contraction time (Tc), sustain time, and relaxation time. Using Tensiomyography we evaluated a patient that had Achilles tendon rupture surgery. The contralateral normal side measurements were also performed for evaluation and comparison of the site of injury. Findings: In this study, the maximal displacement of the muscle belly changed significantly compared to other parameters. The maximal displacement of the muscle belly decreased after cast removal day and increased gradually during the early recovery phase and then slightly decreased again during the late recovery phase. Conclusions: These responses of the maximal displacement of muscle belly show a correlation with the recovery of muscular function.

Evaluation of an antimicrobial silver foam dressing.

UNLABELLED: CuraVAC Ag, a product that delivers negative pressure wound therapy through a polyurethane foam dressing, contains silver nanoparticles, which, when moistened with water, release silver ions onto a wound surface. The in vitro antimicrobial action of silver can destroy both gram-positive and gram-negative bacteria, as well as methicillin-resistant Staphylococcus aureus (MRSA). The purpose of this study was to assess the efficacy and in vivo outcomes of using the product. METHODS: Thirty-six female Sprague-Dawley white rats, 8-weeks old and 250 g - 300 g in weight, were used. The experimental product was prepared using a vacuum-assisted closure (VAC) kit and coating it using the silver nanoparticles. For the control group, a 10% povidone-iodine solution was applied. RESULTS: All groups showed decreases in wound area over time, in the order CuraVAC Ag (group A) > CuraVAC (group B) > control (group C). On the third, fifth, and seventh days, wound healing efficacy scores increased in both group A and group C. Groups A and B showed more rapid decreases than group C in bacterial culture from wounds. CONCLUSION: CuraVAC Ag may be useful for treatment of wounds infected with bacteria..

Quantification of the physiological loading of one week of "pre-season" and one week of "in-season" training in professional soccer players.

The aim of this study was to quantify the physiological loads of programmed "pre-season" and "in-season" training in professional soccer players. Data for players during each period were included for analysis (pre-season, n = 12; in-season, n = 10). We monitored physiological loading of training by measuring heart rate and rating of perceived exertion (RPE). Training loads were calculated by multiplying RPE score by the duration of training sessions. Each session was sub-categorized as physical, technical/tactical, physical and technical/tactical training. Average physiological loads in pre-season (heart rate 124 ± 7 beats · min(-1); training load 4343 ± 329 Borg scale · min) were higher compared with in-season (heart rate 112 ± 7 beats · min(-1); training load 1703 ± 173 Borg scale · min) (P < 0.05) and there was a greater proportion of time spent in 80-100% maximum heart rate zones (18 ± 2 vs. 5 ± 2%; P < 0.05). Such differences appear attributable to the higher intensities in technical/tactical sessions during pre-season (pre-season: heart rate 137 ± 8 beats · min(-1); training load 321 ± 23 Borg scale · min; in-season: heart rate 114 ± 9 beats · min(-1); training load 174 ± 27 Borg scale · min; P < 0.05). These findings demonstrate that pre-season training is more intense than in-season training. Such data indicate that these adjustments in load are a direct attempt to deliver training to promote specific training adaptations.

3D surface profiling and high resolution imaging for refining the florin rings and epicuticular wax crystals of Pinus koraiensis needles.

3D surface profiling and high resolution imaging were performed to refine the Florin rings and epicuticular wax crystals of Pinus koraiensis needles. Needles were collected from four-year-old seedlings and air-dried for surface observations. Field emission scanning electron microscopy revealed that stomata were found on the abaxial (lower) surface of needles. Measured as ca. 40 µm long, they were largely elliptical or oval-shaped. Epicuticular wax crystals were present in the epistomatal chambers as well as on the surrounding epidermis. Rodlets were prevalently found on the stomatal bands and furrows as well as within the epistomatal chambers. The presence of wax tubules was ascertained by the distinct terminal openings at their ends. The occurrence of wax ridges was evident on the epidermis near the saw-tooth margins (nonstomatal areas). No distinct wax ridges were detected on the dewaxed needles. Raised Florin rings were distinct on the stomata. White light scanning interferometry showed that the diameter and width of stomata were ca. 44.02 ± 3.33 µm and 32.10 ± 3.30 µm, respectively. Measured from the neighboring epidermis to the stomatal aperture, the mean height of the stoma reached ca. 6.23 ± 1.28 µm. Focus variation metrology allowed measuring the mean elevation angle of the stoma, reaching ca. 41.41 ± 11.25°. This is the first report on a novel approach to the establishment of quantitative criteria of Florin ring classification by nontactile 3D surface profiling beyond the previous qualitative descriptions of Florin rings of coniferous species.

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data.

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

Influence of stand density on soil CO2 efflux for a Pinus densiflora forest in Korea.

We investigated the influence of stand density [938 tree ha(-1) for high stand density (HD), 600 tree ha(-1) for medium stand density (MD), and 375 tree ha(-1) for low stand density (LD)] on soil CO(2) efflux (R (S)) in a 70-year-old natural Pinus densiflora S. et Z. forest in central Korea. Concurrent with R (S) measurements, we measured litterfall, total belowground carbon allocation (TBCA), leaf area index (LAI), soil temperature (ST), soil water content (SWC), and soil nitrogen (N) concentration over a 2-year period. The R (S) (t C ha(-1) year(-1)) and leaf litterfall (t C ha(-1) year(-1)) values varied with stand density: 6.21 and 2.03 for HD, 7.45 and 2.37 for MD, and 6.96 and 2.23 for LD, respectively. In addition, R (S) was correlated with ST (R (2) = 0.77-0.80, P < 0.001) and SWC (R (2) = 0.31-0.35, P < 0.001). It appeared that stand density influenced R (S) via changes in leaf litterfall, LAI and SWC. Leaf litterfall (R (2) = 0.71), TBCA (R (2) = 0.64-0.87), and total soil N contents in 2007 (R (2) = 0.94) explained a significant amount of the variance in R (S) (P < 0.01). The current study showed that stand density is one of the key factors influencing R (S) due to the changing biophysical and environmental factors in P. densiflora.

Photocatalytic hydrogen production from water-methanol mixtures using N-doped Sr2Nb2O7 under visible light irradiation: effects of catalyst structure.

Nitrogen-doped perovskite type materials, Sr2Nb2O7-xNx (0, 1.5 < x < 2.8), have been studied as visible light-active photocatalysts for hydrogen production from methanol-water mixtures. Nitrogen doping in Sr2Nb2O7 red-shifted the light absorption edge into the visible light range and induced visible light photocatalytic activity. There existed an optimum amount of nitrogen doping that showed the maximum rate of hydrogen production. Among the potential variables that might cause this activity variation, the crystal structure appeared to be the most important. Thus, as the extent of N-doping increased, the original orthorhombic structure of the layered perovskite was transformed into an unlayered cubic oxynitride structure. The most active catalytic phase was an intermediate phase still maintaining the original layered perovskite structure, but with a part of its oxygen replaced by nitrogen and oxygen vacancy to adjust the charge difference between oxygen and doped nitrogen. These experimental observations were explained by density functional theory calculations. Thus, in Sr2Nb2O7-xNx, N2p orbital was the main contributor to the top of the valence band, causing band gap narrowing while the bottom of conduction band due to Nb 4d orbital remained almost unchanged.