Search

Acoustic tweezers can trap and manipulate a target along a desired path without physical contact. Potential applications of this technology may require the propagation of acoustic waves through non-homogeneous media. It is typically assumed that the acoustic impedance of media is the same. However, this assumption leads to reduced efficiency in both the trapping accuracy and strength of the acoustic tweezers. In this study, we propose a method to derive phases driving an 8x8 array of ultrasonic transducers using generalized Snell's law to account for the variation in the speed of sound between media layers of planar or non-planar interfaces. The results indicate that the tweezers formed with our approach maintain their patterns and trapping capability at selected trapping locations. In addition, our method significantly enhances the trapping accuracy and force, achieving up to ten times greater force and more accurate alignment with the selected trapping points compared to the previous method that assumes a uniform speed of sound. [ABSTRACT FROM AUTHOR]

This study examined classroom performance evaluation of secondary teachers in Vietnam. Specially, it sought to determine the possibility of applying supervision into the evaluation for teaching development. Data were collected from interviews with 34 (n=34) participants: ten evaluators and 24 teachers in different school contexts: rural areas, towns, and cities. Data showed considerable impacts: (a) more favorableness on "evaluation conference" and "post-conference analysis" among stages, (b) the high appreciation on open discussions--being willing to share ideas co-existing differences in favorableness of feedback of strengths; and (c) being ready to give or receive appropriate feedback while keeping own ideas for a win-win strategy due to the barrier of the perceived power differential. Suggestions were on more emphasis on classroom performance--discussions before and after class classroom observation rather than inspecting teaching dossiers and on feasible strategies for teacher development--supervision should be referred--rather than executing the "bureaucratic" procedure. (Contains 1 table.)

Introduction : The correlation between alveolar nitric oxide (CANO) and the severity of interstitial lung disease (ILD) evaluated by high resolution computed tomography (HRCT) has not been well demonstrated. Methods : It was a perspective and observational study, including patients with diagnosed systemic sclerosis (SSc). They performed lung function testing (LFT), exhaled nitric oxide (NO) measurements, exercise testing, chest X-ray, and HRCT. Study patients were divided into SSc with ILD (SSc-ILD+) or without ILD (SSc-ILD-). SSC-ILD+ patients were revisited after 6 months and 12 months to complete the study. Results : Thirty-one control subjects and 74 patients with SSc (33 SSc-ILD- and 41 SSc-ILD+) were included. Forty-one SSc-ILD+ patients were followed-up at 6 months and 12 months. Lung functional parameters of patients with SSc-ILD+ were lower than that of SSc-ILD- patients. The level of CANO was significantly higher in SSc-ILD+ than SSc-ILD- patients (8.6 ± 2.5 vs 4.2 ± 1.3 ppb and P<0.01). Warrick and Goldin scores of patients with SSc-ILD+ were respectively 16.5 ± 5.2 and 12.7 ± 4.3. Warrick scores were reduced after 6 and 12 months of follow-up vs at inclusion (12.4 ± 4.3 and 9.1 ± 3.2 vs 16.5 ± 5.2; P<0.05, P<0.01, and P<0.05; respectively). ΔWarrick and ΔGoldin scores were significantly and inversely correlated with ΔFVC, ΔTLC, ΔTLCO, ΔVO 2 max; that was also correlated with ΔCANO (R= 0.783, P<0.01 and R= 0.719 and P<0.05). Conclusion : CANO is a relevant biomarker for the diagnosis of ILD in patients with SSc, especially in combination with HRCT., (Copyright ©2020, Yale Journal of Biology and Medicine.)

Introduction: Subjects with asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) share common features of patients with asthma and COPD. Our study was planned to describe the clinical and functional features of subjects with ACO compared to asthma and COPD patients., Subjects and Methods: Study subjects who met the inclusion criteria were classified into three different groups: asthma, COPD, and ACO groups. All study subjects underwent clinical examination and biological and functional testing. They were then followed for 6 months to evaluate the response to conventional treatment., Results: From March 2015 to March 2017, 76 asthmatic (mean age: 41 ± 22 years), 74 COPD (59 ± 13 years), and 59 ACO (52 ± 14 years) subjects were included. The percentage of subjects with dyspnea on excretion in the ACO group was higher than that in asthma and COPD groups ( P < 0.001 and P < 0.05, resp.). Subjects with COPD and ACO had significant airflow limitation (FEV 1 ) compared to asthma (64 ± 17% and 54 ± 14% versus 80 ± 22%; P < 0.01 and P < 0.01, resp.). The levels of FENO in subjects with asthma and ACO were significantly higher than those in subjects with COPD (46 ± 28 ppb and 34 ± 12 ppb versus 15 ± 8 ppb; P < 0.001 and P < 0.001, resp.). VO 2 max and 6MWD were improved in study subjects after 6 months of treatment. Increased CANO and AHI > 15/hour had a significant probability of risk for ACO (OR = 33.2, P < 0.001, and OR = 3.4, P < 0.05, resp.)., Conclusion: Subjects with ACO share the common clinical and functional characteristics of asthma and COPD but are more likely to have sleep apnea. The majority of patients with ACO have a favourable response to combined treatment.

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B  was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes  BCL2L1, BBC3, FGF2, FN1,  and  TWIST1   was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A,  and TUBB4B ) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies., Competing Interests: Competing interests: PKR cofounded CytoGnomix. A patent application related to biologically inspired gene signatures is pending. The other authors declare that they have no competing interests.