Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method.

OBJECTIVES: To develop a facial growth prediction model incorporating individual skeletal and soft tissue characteristics. MATERIALS AND METHODS: Serial longitudinal lateral cephalograms were collected from 303 children (166 girls and 137 boys), who had never undergone orthodontic treatment. A growth prediction model was devised by applying the multivariate partial least squares (PLS) algorithm, with 161 predictor variables. Response variables comprised 78 lateral cephalogram landmarks. Multiple linear regression analysis was performed to investigate factors influencing growth prediction errors. RESULTS: Using the leave-one-out cross-validation method, a PLS model with 30 components was developed. Younger age at prediction resulted in greater prediction error (0.03 mm/y). Further, prediction error increased in proportion to the growth prediction interval (0.24 mm/y). Girls, subjects with Class II malocclusion, growth in the vertical direction, skeletal landmarks, and landmarks on the maxilla were associated with more accurate prediction results than boys, subjects with Class I or III malocclusion, growth in the anteroposterior direction, soft tissue landmarks, and landmarks on the mandible, respectively. CONCLUSIONS: The prediction error of the prediction model was proportional to the remaining growth potential. PLS growth prediction seems to be a versatile approach that can incorporate large numbers of predictor variables to predict numerous landmarks for an individual subject.

Microvascular reactivity as a predictor of major adverse events in patients with on-pump cardiac surgery.

BACKGROUND: Microcirculatory disturbances are typically most severe during cardiopulmonary bypass (CPB), which occurs during cardiac surgeries. If microvascular reactivity compensates for microcirculatory disturbances during CPB, tissue hypoxemia can be minimized. The primary aim of this study was to assess whether microvascular reactivity during CPB could predict major adverse events (MAE) after cardiac surgery. METHODS: This prospective observational study included 115 patients who underwent elective on-pump cardiac surgeries. A vascular occlusion test (VOT) with near-infrared spectroscopy was performed five times for each patient: before the induction of general anesthesia, 30 min after the induction of general anesthesia, 30 min after applying CPB, 10 min after protamine injection, and post-sternal closure. The postoperative MAE was recorded. The area under the receiver operating characteristic (AUROC) curve analysis was performed for the prediction of MAE using the recovery slope. RESULTS: Of the 109 patients, MAE occurred in 32 (29.4%). The AUROC curve for the recovery slope during CPB was 0.701 (P < 0.001; 95% CI [0.606, 0.785]). If the recovery slope during CPB was < 1.08%/s, MAE were predicted with a sensitivity of 62.5% and specificity of 72.7%. CONCLUSIONS: Our study demonstrated that the recovery slope of the VOT during CPB could predict MAE after cardiac surgery. These results support the idea that disturbances in microcirculation induced by CPB can predict the development of poor clinical outcomes, thereby demonstrating the potential role of microvascular reactivity as an early predictor of MAE after cardiac surgery.

A study on activation mechanism in perspective of lignin structures and applicability of lignin-derived activated carbons for pollutant absorbent and supercapacitor electrode.

In this study activated carbons were produced from the biorefinery waste lignin (Asian lignin (AL) USA & Inbicon lignin (IL) Denmark) to evaluate their potential in waste water treatment and as energy storage devices. These products were studied for their surface characteristics as a function of reaction temperature, time, and catalyst loading accordingly. Under the conditions with a temperature lower than 750 °C and within a reaction time of 1 h, the catalytic reaction of alkali-carbon bonding occurred from the external surface, and a turbostratic disorder structure with a large aromatic ring system was formed. More severe reaction conditions accelerated the volatile release of de-alkylated aromatics such as benzene and naphthalene, along with structure and surface collapse. The maximum BET surface area of 2782 m2/g was obtained at 750 °C, 2 h and catalyst ratio of 4. Lignin-derived activated carbon was more efficient for the removal of organic pollutants (<50% adsorption capacity) rather than heavy metals (adsorption capacity >90%) due to interaction of π-π bonding. Furthermore, the activated carbon has a potential to be used as a supercapacitor electrode with high specific capacitance (214.0 F/g AL lignin) and an excellent cyclic stability (95% of their initial capacity). The results of this study demonstrate that lignin is an attractive precursor to produce activated carbons with diverse applications both as biosorbent and as a carbon electrode material even so with acceptable performance.

Evaluation of an automated superimposition method based on multiple landmarks for growing patients.

OBJECTIVES: To determine if an automated superimposition method using six landmarks (Sella, Nasion, Porion, Orbitale, Basion, and Pterygoid) would be more suitable than the traditional Sella-Nasion (SN) method to evaluate growth changes. MATERIALS AND METHODS: Serial lateral cephalograms at an average interval of 2.7 years were taken on 268 growing children who had not undergone orthodontic treatment. The T1 and T2 lateral images were manually traced. Three different superimposition methods: Björk's structural method, conventional SN, and the multiple landmark (ML) superimposition methods were applied. Bjork's structural method was used as the gold standard. Comparisons among the superimposition methods were carried out by measuring the linear distances between Anterior Nasal Spine, point A, point B, and Pogonion using each superimposition method. Multiple linear regression analysis was performed to identify factors that could affect the accuracy of the superimpositions. RESULTS: The ML superimposition method demonstrated smaller differences from Björk's method than the conventional SN method did. Greater differences among the cephalometric landmarks tested resulted when: the designated point was farther from the cranial base, the T1 age was older, and the more time elapsed between T1 and T2. CONCLUSIONS: From the results of this study in growing patients, the ML superimposition method seems to be more similar to Björk's structural method than the SN superimposition method. A major advantage of the ML method is likely to be that it can be applied automatically and may be just as reliable as manual superimposition methods.

Evaluation of pyrochar and hydrochar derived activated carbons for biosorbent and supercapacitor materials.

This study investigated effects of different thermal processes on characteristics of activated carbon to produce efficient biosorbents or supercapacitors using biomass resources. Pyrolysis char and hydrochar obtained from woody biomass were used as precursors for activated carbon under different atmospheric conditions (N2 and air). In order to provide functional groups on the carbon surface, activated carbon under N2 condition was subsequently acidified by HNO3 and the other was simultaneously acidified under air condition. Additionally, potential for application as Pb2+ adsorbent and supercapacitor was evaluated. Thermochemical behaviors such as bonding cleavage and dehydration during activation processes were observed by TG and Py-GCMS analysis. Elemental analysis, FT-IR, Raman spectroscopy, and XPS analysis were carried out to confirm changes in structures of each carbon products. New plausible reaction mechanism for this observation was suggested with respect to the formation of a key intermediate in the presence of excess air. As for performance in applications, air activated carbon using hydrochar exhibited high versatility to function as both Pb2+ adsorbent (~41.1 mg/g) and energy storage material (~185.9 F/g) with high specific surface area, mesopore ratio, surface functional groups.

The Prognostic Value of Sex-Determining Region Y-Box 2 and CD8+ Tumor-Infiltrating Lymphocytes in Limited-Stage Small-Cell Lung Cancer.

BACKGROUND: Sex-determining region Y-box 2 (SOX2) is a transcriptional factor that drives embryonic stem cells to neuroendocrine cells in lung development and is highly expressed in small-cell lung cancer (SCLC). However, the prognostic role of SOX2 and its relationship with tumor-infiltrating lymphocytes (TILs) has not been determined in SCLC. Herein, we assessed the expression of SOX2 and CD8+ TILs to obtain insights into the prognostic role of SOX2 and CD8+ TILs in limited-stage (LS)-SCLC. METHODS: A total of 75 patients with LS-SCLC was enrolled. The SOX2 expression and CD8+ TILs were evaluated by immunohistochemistry. RESULTS: High SOX2 and CD8+ TIL levels were identified in 52 (69.3%) and 40 (53.3%) patients, respectively. High SOX2 expression was correlated with increased density of CD8+ TILs (p = 0.041). Unlike SOX2, high CD8+ TIL numbers were associated with significantly longer progression-free survival (PFS; 13.9 vs. 8.0 months, p = 0.014). Patients with both high SOX2 expression and CD8+ TIL numbers (n = 29, 38.7%) had significantly longer PFS and overall survival (OS) compared to those from the other groups (median PFS 19.3 vs. 8.4 months; p = 0.002 and median OS 35.7 vs. 17.4 months; p = 0.004, respectively). Multivariate Cox regression analysis showed that the combination of high SOX2 expression and CD8+ TIL levels was an independent good prognostic factor for OS (HR = 0.471, 95% CI, 0.250-0.887, p = 0.02) and PFS (HR = 0.447, 95% CI, 0.250-0.801, p = 0.007) in SCLC. CONCLUSIONS: Evaluation of the combination of SOX2 and CD8+ TIL levels may be of a prognostic value in LS-SCLC.

Ti3C2Tx MXene-Based Three-Dimensional Architecture for Carbon Dioxide Capture.

Dramatic increases in fossil fuel consumption inevitably led to the emission of huge amounts of CO2 gas, causing abnormalities in the climate system. Despite continuous efforts to resolve global atmospheric problems through CO2 capture and separation, success has been limited by poor CO2 selectivity in the CO2/N2 mixture. Herein, we demonstrate the fabrication of a three-dimensional (3D) nanostructure from two-dimensional transition metal carbides (Ti3C2Tx, MXene), and assess its utility as an adsorbent in a CO2 capture system. Through structural and textural analysis, we confirm that the as-prepared MXene possesses high size uniformity with a thickness of ~2.5 nm, and that an MXene aerogel interconnected by MXene nanosheets has a 3D porous architecture with micro/nano porosity (Barrett-Joyner-Halenda (BJH) pore size = 11.4 nm). Moreover, the MXene aerogel exhibits favorable adsorption behavior for CO2, due to the high-quality MXene nanosheets even with a low specific surface area. Our approach could lead to significant advances in CO2 capture by adsorbents and open up new opportunities for mass production.

Evaluation of automated cephalometric analysis based on the latest deep learning method.

OBJECTIVES: To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI). MATERIALS AND METHODS: This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR). RESULTS: SDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures. CONCLUSIONS: This latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.

Recent glycemic control can predict the progressive motor deficits of acute subcortical infarction with diabetes or prediabetes.

BACKGROUND AND PURPOSE: The predictors of progressive motor deficits in acute subcortical infarctions are still controversial. It is not known whether glycemic control influences on stroke progression. METHODS: A total of 268 consecutive patients with diabetes or prediabetes who had acute (< 24 h) subcortical infarction were enrolled. (1) All patients were divided into 4 groups by quartile of glycated hemoglobin (HbA1c). (2) Only the patients with diabetes were divided by effective glycemic control. Progressive motor deficits were prospectively captured and defined as an increase of motor score ≥ 1 on the upper or lower limb items of the National Institute of Health Stroke Scale within 72 h from stroke onset. RESULTS: Progressive motor deficits occur in 8/78 (10.3%) for ≤ 5.9, 15/61 (24.6%) for 6.0-6.4, 16/62 (25.8%) for 6.5-7.4, and 30/67 (44.8%) for ≥ 7.5. In diabetic patients alone, those occur in 5/37 (13.5%) for ≤ 6.5, 10/42 (23.8%) for 6.6-7.0, 12/42 (28.6%) for 7.1-8.0, and 24/50 (48.0%) for ≥ 8.1. An adjusted OR of progressive motor deficits was 2.61 (95% confidence interval [CI] 0.98-7.00, P = .056) for 6.0-6.4, 3.42 (95% CI 1.27-9.18, P = .015) for 6.5-7.4, and 6.65 (95% CI 2.38-18.62, P < .001) for ≥ 7.5. In diabetic patients alone, those were 3.15 (95% CI 0.89-11.15, P = .075) for 6.6-7.0, 2.90 (95% CI 0.79-10.61, P = .107) for 7.1-8.0, and 4.17 (95% CI 1.07-16.25, P = .038) for ≥ 8.1. The optimal cutoff value of HbA1c was 6.65% in discriminating progressive motor deficits. CONCLUSION: Increased HbA1c was associated with higher incidence of progressive motor deficits in acute subcortical infarction with diabetes and prediabetes.

Evaluation of an automated superimposition method for computer-aided cephalometrics.

OBJECTIVES: To evaluate a new superimposition method compatible with computer-aided cephalometrics and to compare superimposition error to that of the conventional Sella-Nasion (SN) superimposition method. MATERIALS AND METHODS: A total of 283 lateral cephalometric radiographs were collected and cephalometric landmark identification was performed twice by the same examiner at a 3-month interval. The second tracing was superimposed on the first tracing by both the SN superimposition method and the new, proposed method. The proposed method not only relied on SN landmarks but also minimized the differences between four additional landmarks: Porion, Orbitale, Basion, and Pterygoid. The errors between the landmarks of the duplicate tracings oriented by the two superimposition methods were calculated at Anterior Nasal Spine, Point A, Point B, Pogonion, and Gonion. The paired t-test was used to find any statistical difference in the superimposition errors by the two superimposition methods and to investigate whether there existed clinically significant differences between the two methods. RESULTS: The proposed method demonstrated smaller superimposition errors than did the conventional SN superimposition method. When comparisons between the two superimposition methods were made with a 1-mm error range, there were clinically significant differences between them. CONCLUSIONS: The proposed method that was compatible with computer-aided cephalometrics might be a reliable superimposition method for superimposing serial cephalometric images.

How much deep learning is enough for automatic identification to be reliable?

OBJECTIVES: To determine the optimal quantity of learning data needed to develop artificial intelligence (AI) that can automatically identify cephalometric landmarks. MATERIALS AND METHODS: A total of 2400 cephalograms were collected, and 80 landmarks were manually identified by a human examiner. Of these, 2200 images were chosen as the learning data to train AI. The remaining 200 images were used as the test data. A total of 24 combinations of the quantity of learning data (50, 100, 200, 400, 800, 1600, and 2000) were selected by the random sampling method without replacement, and the number of detecting targets per image (19, 40, and 80) were used in the AI training procedures. The training procedures were repeated four times. A total of 96 different AIs were produced. The accuracy of each AI was evaluated in terms of radial error. RESULTS: The accuracy of AI increased linearly with the increasing number of learning data sets on a logarithmic scale. It decreased with increasing numbers of detection targets. To estimate the optimal quantity of learning data, a prediction model was built. At least 2300 sets of learning data appeared to be necessary to develop AI as accurate as human examiners. CONCLUSIONS: A considerably large quantity of learning data was necessary to develop accurate AI. The present study might provide a basis to determine how much learning data would be necessary in developing AI.

Applicability of lignin polymers for automobile brake pads as binder and filler materials and their performance characteristics.

We present environmentally friendly brake pads produced with three different types of lignin, soda lignin (SL), sulphuric acid lignin (SAL) and heat-treated SAL (HL), as frictional materials to replace phenol formaldehyde resin (PFR, binder) and cashew nut shell liquid (CNSL, filler) in commercial automobile brake pad. Then the performance characteristics of the lignin-added brake pads were tested and compared using several fundamental tests. The results showed that lignin-added brake pads adhered to the SAE standard (0.25) for friction coefficient, which is the primary contributor to the performance of a braking system. In particular, the replacement of PFR with SL demonstrated a better friction coefficient than did replacement with SAL or HL, reaching up to 0.6. On the other hand, when lignin was substituted for CNSL as filler, HL-added brake pads showed a significant improvement in wear resistance of 0.12 g (dust generation) compared to SL and SAL, which had a resistance of approximately 0.25 g.

Automated identification of cephalometric landmarks: Part 2-Might it be better than human?

OBJECTIVES: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. MATERIALS AND METHODS: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. RESULTS: Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. CONCLUSIONS: AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.

Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD.

OBJECTIVE: To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks. MATERIALS AND METHODS: A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep-learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point-to-point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded. RESULTS: The YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature. CONCLUSIONS: Between the two latest deep-learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.

Comprehensive characterization of hydrothermal liquefaction products obtained from woody biomass under various alkali catalyst concentrations.

Hydrothermal liquefaction (HTL) of lignocellulosic biomass has been widely investigated for the production of renewable and alternative bio-crude oil. In this study, catalytic hydrothermal processing of two biomasses (larch and Mongolian oak) was performed using different K2CO3 concentrations (0, 0.1, 0.5, 1.0 wt% of solvent) to improve fuel yield and properties. HTL oil, hydrochar, water-soluble fraction (WSF) and gas were characterized, and carbon balance was investigated. As a result, the maximum yield of HTL oil, 27.7 wt% (Mongolian oak) and 25.7 wt% (larch), and the highest carbon conversion ratio was obtained with 0.5 wt% of catalyst. The high catalyst concentration also resulted in an increase in higher heating values up to 31.9 MJ/kg. In addition, the amount of organic compounds in HTL oil also increased, specifically for lignin-derived compounds including catechol and hydroquinone which can be derived from secondary hydrolysis of lignin. On the other hand, formation of hydrochar was suppressed with the addition of alkali catalyst and the yield dramatically decreased from 30.7-40.8 wt.% to 20.0-21.8 wt.%. Furthermore, it was revealed that WSF had low organic carbon content less than 3.4% and high potassium content mostly derived from alkali catalyst, indicating that it may be reusable with simple purification. This work suggests that the addition of the proper amount of alkali catalyst can improve the production efficiency and quality of bio-crude oil, and another potential of WSF to be recyclable in further work.

Enhanced compatibility between a copper nanowire-based transparent electrode and a hybrid perovskite absorber by poly(ethylenimine).

Copper nanowires (CuNWs) have been applied to hybrid perovskite solar cells (PSCs) as a window electrode. By sandwiching the CuNW network between aluminum-doped zinc oxide and adopting a poly(ethylenimine) buffer layer, the compatibility between the CuNWs and the perovskite layer could be dramatically improved. PSCs containing the CuNW-based composite electrode exhibited an average power conversion efficiency of 8.65%.

A pre-strain strategy for developing a highly stretchable and foldable one-dimensional conductive cord based on a Ag nanowire network.

A highly stretchable and foldable conductive cord was developed by depositing silver nanowires (AgNWs) on a polyurethane cord. The application of pre-strain to the cord during the deposition of AgNWs resulted in the formation of a wrinkled AgNW layer, thereby inducing structural stability towards stretching and bending. This conductive cord exhibited stable electric conduction under strain values of up to 167% and at a bending radius of 0.3 mm. Also, it could withstand a stretching test of 500 cycles at a strain of 50%.

Evaluation of Stress Response During Mesiodens Extraction Under General Anesthesia Using Heart Rate Variability.

PURPOSE: Stress related to dental treatment can be associated with negative outcomes. Heart rate variability (HRV) is an objective measurement of autonomic nervous system activity. Therefore, HRV was used to identify autonomic nervous system reactions during mesiodens extraction under general anesthesia in children. MATERIALS AND METHODS: Electrocardiography was performed with customized software during treatment. HRV parameters were analyzed according to time and frequency domains during each dental procedure (local anesthesia, incision, flap, bone removal, extraction of mesiodens, and suturing). The relations of HRV parameters to age also were determined. RESULTS: Total autonomic nervous system activity decreased markedly after local anesthesia injection. Depending on the responses of sympathetic nerve activity, patients were categorized in a stress group and a nonstress group. The ratio of low-frequency power (LF) to high-frequency power (HF), an indication of sympathetic and parasympathetic balance, increased in the stress group after incision and flap formation. Conversely, the LF/HF ratio decreased during treatment in the nonstress group. However, HR, widely used to evaluate stress responses, did not change statistically during mesiodens extraction in either group. HRV parameters did not differ statistically according to age. CONCLUSIONS: The internal stress related to mesiodens extraction can be evaluated more objectively with HRV parameters than with conventional methods. Sympathetic nerve activity in the stress group differed from that in the nonstress group during the treatment procedures.

Roll-to-roll-compatible, flexible, transparent electrodes based on self-nanoembedded Cu nanowires using intense pulsed light irradiation.

Copper nanowire (Cu NW)-based flexible transparent conductive electrodes (FTCEs) have been investigated in detail for use in various applications such as flexible touch screens, organic photovoltaics and organic light-emitting diodes. In this study, hexadecylamine (HDA) adsorbed onto the surface of NWs is changed into polyvinylpyrrolidone (PVP) via a ligand exchange process; the high-molecular-weight PVP enables high dispersion stability. Intense pulsed light (IPL) irradiation is used to remove organic species present on the surface of the NWs and to form direct connections between the NWs rapidly without any atmospheric control. NWs are self-nanoembedded into a plastic substrate after IPL irradiation, which results in a smooth surface, strong NW/substrate adhesion, excellent mechanical flexibility and enhanced oxidation stability. Moreover, Cu NW FTCEs with high uniformities are successfully fabricated on a large area (150 mm × 200 mm) via successive IPL irradiation that is synchronized with the motion of the sample stage. This study demonstrates the possibility of roll-to-roll-based, large-scale production of low-cost, high-performance Cu NW-based FTCEs.