A Noise-Induced Hearing Loss Prediction Model Based on Asymmetric Convolution for Workers Exposed to Complex Industrial Noise.

OBJECTIVES: Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed. DESIGN: Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments. RESULTS: The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM). CONCLUSIONS: This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing.

Micro-Quartz Crystal Tuning Fork-Based Photodetector Array for Trace Gas Detection.

In this paper, a micro-quartz crystal tuning fork (M-QCTF) was first demonstrated for developing a low-cost, highly sensitive quartz tuning fork photodetector array for spectroscopic applications. A gas sensing system based on the M-QCTF photodetector and highly sensitive wavelength modulation spectroscopy was developed. Typically, an atmospheric greenhouse gas methane (CH4) molecule was selected as the target analyte for evaluating the M-QCTF and standard commercial QCTF detectivity. The results indicate that the M-QCTF photodetector exhibits ∼3.3 times sensitivity enhancement compared to the standard commercial QCTF. The long-term stability was evaluated by using the Allan deviation analysis method; a minimum detection limit of 1.2 ppm was achieved with an optimal integration time of 85 s, and the corresponding normalized noise equivalent absorption coefficient was calculated to be 4.45 × 10-10 cm-1 W/√Hz. Finally, a two-M-QCTF array detection scheme was experimentally demonstrated, and a signal-to-noise ratio enhancement factor of more than 1.7 times compared to that achieved using a single M-QCTF photodetector was realized, which proves a great potential for developing ultra-sensitive quartz tuning fork photodetector arrays for various applications.

Tumor micronecrosis predicts poor prognosis of patients with hepatocellular carcinoma after liver transplantation.

BACKGROUND: Tumor micronecrosis is a histopathological feature predicting poor prognosis in patients with hepatocellular carcinoma (HCC) who underwent liver resection. However, the role of tumor micronecrosis in liver transplantation remains unclear. METHODS: We retrospectively reviewed patients with HCC who underwent liver transplantation between January 2015 and December 2021 at our center. We then classified them into micronecrosis(-) and micronecrosis(+) groups and compared their recurrence-free survival (RFS) and overall survival (OS). We identified independent prognostic factors using Cox regression analysis and calculated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive value of RFS for patients with HCC after liver transplantation. RESULTS: A total of 370 cases with evaluable histological sections were included. Patients of the micronecrosis(+) group had a significantly shorter RFS than those of the micronecrosis(-) group (P = 0.037). Shorter RFS and OS were observed in micronecrosis(+) patients without bridging treatments before liver transplantation (P = 0.002 and P = 0.007), while no differences were detected in those with preoperative antitumor therapies that could cause iatrogenic tumor necrosis. Tumor micronecrosis improved the AUC of Milan criteria (0.77-0.79), the model for end-stage liver disease score (0.70-0.76), and serum alpha-fetoprotein (0.63-0.71) for the prediction of prognosis after liver transplantation. CONCLUSION: Patients with HCC with tumor micronecrosis suffer from a worse prognosis than those without this feature. Tumor micronecrosis can help predict RFS after liver transplantation. Therefore, patients with HCC with tumor micronecrosis should be treated with adjuvant therapy and closely followed after liver transplantation. CLINICAL TRIALS REGISTRATION: Not Applicable.

Causal knowledge graph construction and evaluation for clinical decision support of diabetic nephropathy.

BACKGROUND: Many important clinical decisions require causal knowledge (CK) to take action. Although many causal knowledge bases for medicine have been constructed, a comprehensive evaluation based on real-world data and methods for handling potential knowledge noise are still lacking. OBJECTIVE: The objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data. MATERIAL AND METHODS: We extracted causal triples from three knowledge sources (SemMedDB, UpToDate and Churchill's Pocketbook of Differential Diagnosis) based on rule methods and language models, performed ontological encoding, and then designed semantic modeling between electronic health record (EHR) data and the CKG to complete knowledge instantiation. We proposed two graph pruning strategies (co-occurrence ratio and causality ratio) to reduce the potential noise introduced by SemMedDB. Finally, the evaluation was carried out by taking the diagnostic decision support (DDS) of diabetic nephropathy (DN) as a real-world case. The data originated from a Chinese hospital EHR system from October 2010 to October 2020. The knowledge completeness and accuracy of the CKG were evaluated based on three state-of-the-art embedding methods (R-GCN, MHGRN and MedPath), the annotated clinical text and the expert review, respectively. RESULTS: This graph included 153,289 concepts and 1,719,968 causal triples. A total of 1427 inpatient data were used for evaluation. Better results were achieved by combining three knowledge sources than using only SemMedDB (three models: area under the receiver operating characteristic curve (AUC): p < 0.01, F1: p < 0.01), and the graph covered 93.9 % of the causal relations between diseases and diagnostic evidence recorded in clinical text. Causal relations played a vital role in all relations related to disease progression for DDS of DN (three models: AUC: p > 0.05, F1: p > 0.05), and after pruning, the knowledge accuracy of the CKG was significantly improved (three models: AUC: p < 0.01, F1: p < 0.01; expert review: average accuracy: + 5.5 %). CONCLUSIONS: The results demonstrated that our proposed CKG could completely and accurately capture the abstract CK under the concrete EHR data, and the pruning strategies could improve the knowledge accuracy of our CKG. The CKG has the potential to be applied to the DDS of diseases.

Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study.

BACKGROUND: Serious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice. OBJECTIVE: This study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever. METHODS: Clinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility. RESULTS: In total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application. CONCLUSIONS: The clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.

Comprehensive Clinical Analysis of Gallbladder Neuroendocrine Neoplasms: A Large-Volume Multicenter Study During One Decade.

BACKGROUND: This study aimed to comprehensively investigate the clinicopathologic characteristics and therapeutic situations of gallbladder neuroendocrine neoplasms (GB-NENs) in the real world via a multicenter, large-scale cohort study. METHODS: The study searched for patients in 143 hospitals in China and enrolled 154 patients with GB-NENs diagnosed in 40 hospitals between 2004 and 2021. Clinicopathologic characteristics and therapeutic approaches were analyzed retrospectively. RESULTS: The median age at the initial diagnosis of the patients with GB-NENs was 63 years (range 33-83 years), and 61.7% of the patients were women. Tumor-node-metastasis staging classified 92 patients as stage 3 or above. Based on the 2019 World Health Organization classification, 96 cases (62.3%) were confirmed pathologically as poorly differentiated neuroendocrine carcinomas, 13 cases (8.4%) as well-differentiated neuroendocrine tumors, and 45 cases as mixed neuroendocrine-non-neuroendocrine neoplasms. The liver was the most frequent metastatic site. Immunohistochemistry showed that synaptophysin was most frequently positive (80.4%), followed by chromogranin A (61.7%), and CD56 (58.4%). Computed tomography and magnetic resonance imaging showed more common clear boundaries (25/39 cases) and invasive growth features (27 cases). None of these cases had an accurate diagnosis before surgery, with a misdiagnosis rate of 100%. Surgical resection is the main treatment, and platinum-based chemotherapeutic regimens were preferred as adjuvant therapies for patients with GB-NENs. The available survival data for 74 patients showed an overall survival rate of 59% at 1 year, 33% at 3 years, and 29% at 5 years. No significant difference was found between the patients treated with and those treated without adjuvant chemotherapy. CONCLUSIONS: Gallbladder neuroendocrine neoplasms have high malignancy and a poor prognosis. Importantly, this large-scale cohort study significantly improves our understanding of GB-NENs and will benefit the exploration of its mechanism and treatment modes. Further investigation is necessary to explore the management of this disease.

Estimation of Occupational Noise-Induced Hearing Loss Using Kurtosis-Adjusted Noise Exposure Levels.

OBJECTIVES: Studies have shown that in addition to energy, kurtosis plays an important role in the assessment of hearing loss caused by complex noise. The objective of this study was to investigate how to use noise recordings and audiometry collected from workers in industrial environments to find an optimal kurtosis-adjusted algorithm to better evaluate hearing loss caused by both continuous noise and complex noise. DESIGN: In this study, the combined effects of energy and kurtosis on noise-induced hearing loss (NIHL) were investigated using data collected from 2601 Chinese workers exposed to various industrial noises. The cohort was divided into three subgroups based on three kurtosis (ß) levels (K 1 : 3 ≤ ß ≤ 10, K 2 : 10 <ß ≤ 50, and K 3 : ß > 50). Noise-induced permanent threshold shift at test frequencies 3, 4, and 6 kHz (NIPTS 346 ) was used as the indicator of NIHL. Predicted NIPTS 346 was calculated using the ISO 1999 model for each participant, and the actual NIPTS was obtained by correcting for age and sex using non-noise-exposed Chinese workers (n = 1297). A kurtosis-adjusted A-weighted sound pressure level normalized to a nominal 8-hour working day (L Aeq,8h ) was developed based on the kurtosis categorized group data sets using multiple linear regression. Using the NIPTS 346 and the L Aeq.8h metric, a dose-response relationship for three kurtosis groups was constructed, and the combined effect of noise level and kurtosis on NIHL was investigated. RESULTS: An optimal kurtosis-adjusted L Aeq,8h formula with a kurtosis adjustment coefficient of 6.5 was established by using the worker data. The kurtosis-adjusted L Aeq,8h better estimated hearing loss caused by various complex noises. The analysis of the dose-response relationships among the three kurtosis groups showed that the NIPTS of K 2 and K 3 groups was significantly higher than that of K 1 group in the range of 70 dBA ≤ L Aeq,8h < 85 dBA. For 85 dBA ≤ L Aeq,8h ≤ 95 dBA, the NIPTS 346 of the three groups showed an obvious K 3 > K 2 > K 1 . For L Aeq,8h >95 dBA, the NIPTS 346 of the K 2 group tended to be consistent with that of the K 1 group, while the NIPTS 346 of the K 3 group was significantly larger than that of the K 1 and K 2 groups. When L Aeq,8h is below 70 dBA, neither continuous noise nor complex noise produced significant NIPTS 346 . CONCLUSIONS: Because non-Gaussian complex noise is ubiquitous in many industries, the temporal characteristics of noise (i.e., kurtosis) must be taken into account in evaluating occupational NIHL. A kurtosis-adjusted L Aeq,8h with an adjustment coefficient of 6.5 allows a more accurate prediction of high-frequency NIHL. Relying on a single value (i.e., 85 dBA) as a recommended exposure limit does not appear to be sufficient to protect the hearing of workers exposed to complex noise.

Accurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images.

BACKGROUND: To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural networks (CNNs) have been successfully applied to object detection tasks, however, when applied to mitotic cell detection, most existing methods generate high false-positive rates due to the complex characteristics that differentiate normal cells from mitotic cells. Cell size and orientation variations in each stage make detecting mitotic cells difficult in 2D approaches. Therefore, effective extraction of the spatial and temporal features from mitotic data is an important and challenging task. The computational time required for detection is another major concern for mitotic detection in 4D microscopic images. RESULTS: In this paper, we propose a backbone feature extraction network named full scale connected recurrent deep layer aggregation (RDLA++) for anchor-free mitotic detection. We utilize a 2.5D method that includes 3D spatial information extracted from several 2D images from neighbouring slices that form a multi-stream input. CONCLUSIONS: Our proposed technique addresses the scale variation problem and can efficiently extract spatial and temporal features from 4D microscopic images, resulting in improved detection accuracy and reduced computation time compared with those of other state-of-the-art methods.

Ultrathin two-dimensional Fe-doped cobaltous oxide as a piezoelectric enhancement mechanism in quartz crystal tuning fork (QCTF) photodetectors.

An innovative ultrathin two-dimensional (2D) Fe-doped cobaltous oxide (Fe-CoO) coated quartz crystal tuning fork (QCTF) was introduced for the purpose of developing a low-cost photoelectric detector with a simple configuration. The enhancement mechanism of the piezoelectric signal in the ultrathin 2D Fe-CoO-coated QCTF detector is assumed to be the synergetic photocarrier transfer and photothermal effect of ultrathin 2D Fe-CoO. The ultrathin 2D nanosheet structure of Fe-CoO with a large specific surface area can efficiently absorb and convert light into heat in the QCTF, and the photocarrier transfer from the Fe-CoO nanosheet to the electrode of the QCTF contributes to the enhancement in electricity given the shortened diffusion distance of carriers to the surfaces of the 2D nanosheet. Finite element modeling was adopted to simulate the thermoelastic expansion and mechanical resonance of the QCTF with 2D Fe-CoO coating to support experimental results and analyses. Moreover, the effects of 2D Fe-CoO on the performance of QCTF-based photoelectric detectors were investigated. This Letter demonstrates that ultrathin 2D materials have great potential in applications such as costly and tiny QCTF detectors, light sensing, biomedical imaging, and spectroscopy.

Sensitivity enhancement in photothermal interferometry by balanced detection of the complex response to moving excitation.

The sensitivity of photothermal detection relies on both the magnitude of the response of a sample to excitation and the way the response is sensed. We propose a highly sensitive photothermal interferometry by addressing the above two issues. One is the use of moving excitation to enable a different manner in sample heating and cooling, which results in a strong thermoelastic response of the sample. The other is the use of a balanced Mach-Zehnder interferometer with a defocused probe beam to sense the complex response induced by the phase delays taking place at the sample surface and in the surrounding air. The method was verified experimentally with a Nd-doped glass to have 68-fold sensitivity enhancement over the classical photothermal common-path interferometry.

A novel deep-learning-based approach for automatic reorientation of 3D cardiac SPECT images.

PURPOSE: Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac SPECT images and evaluated its performance on data from two clinical centers. METHODS: We used a convolutional neural network to predict the 6 rigid-body transformation parameters and a spatial transformation network was then implemented to apply these parameters on the input images for image reorientation. A novel compound loss function which balanced the parametric similarity and penalized discrepancy of the prediction and training dataset was utilized in the training stage. Data from a set of 322 patients underwent data augmentation to 6440 groups of images for the network training, and a dataset of 52 patients from the same center and 23 patients from another center were used for evaluation. Similarity of the 6 parameters was analyzed between the proposed and the manual methods. Polar maps were generated from the output images and the averaged count values of the 17 segments were computed from polar maps to evaluate the quantitative accuracy of the proposed method. RESULTS: All the testing patients achieved automatic reorientation successfully. Linear regression results showed the 6 predicted rigid parameters and the average count value of the 17 segments having good agreement with the reference manual method. No significant difference by paired t-test was noticed between the rigid parameters of our method and the manual method (p > 0.05). Average count values of the 17 segments show a smaller difference of the proposed and manual methods than those between the existing and manual methods. CONCLUSION: The results strongly indicate the feasibility of our method in accurate automatic cardiac SPECT reorientation. This deep-learning-based reorientation method has great promise for clinical application and warrants further investigation.

Soil respiration analysis using a mid-infrared quantum cascade laser and calibration-free WMS-based dual-gas sensor.

A high response and sensitive dual-gas sensor based on calibration-free wavelength modulation spectroscopy (CF-WMS) has been developed for the simultaneous detection of carbon monoxide (CO) and nitrous oxide (N2O) to eliminate the detection errors caused by light intensity variations. A multi-pass cell (MPC) was employed to lengthen the optical path to improve the precision of the sensing system by combining with a 4.56 µm mid-infrared quantum cascade laser (MIR-QCL). Meanwhile, a LabVIEW-based bi-molecular iterative fitting algorithm was used to infer the respective abundances of each species. The performance of the completed system was accurately evaluated with precisions of 3.4 ppb for CO and 3.8 ppb for N2O at a 1 s averaging time, which could be improved to 0.48 ppb for CO and 0.53 ppb for N2O at an averaging time of 154 s and 278 s, respectively. The grassland soil respiration analysis of CO and N2O was performed under different moisture conditions, which indicated that dried soil samples appeared to be a significant source of CO, while the sinks of CO and the sources of N2O occurred in the moist soil samples. The maximum exchange rates of the two gases were exhibited in moderate moisture soil samples rather than in the over-wet or arid soil samples. Moreover, a possible positive relationship between the sinks of CO and sources of N2O was established to illustrate the correlation of the two species in soil respiration.

A maintenance hemodialysis mortality prediction model based on anomaly detection using longitudinal hemodialysis data.

BACKGROUND: Most end-stage renal disease patients rely on hemodialysis (HD) to maintain their life, and they face a serious financial burden and high risk of mortality. Due to the current situation of the health care system in China, a large number of patients on HD are lost to follow-up, making the identification of patients with high mortality risks an intractable problem. OBJECTIVE: This paper aims to propose a maintenance HD mortality prediction approach using longitudinal HD data under the situation of data imbalance caused by follow-up losses. METHODS: A long short-term memory autoencoder (LSTM AE) based model is proposed to capture the physical condition changes of HD patients and distinguish between surviving and nonsurviving patients. The approach adopts anomaly detection theory, using only the surviving samples in the model training and identifying dead samples based on autoencoder reconstruction errors. The data are from a Chinese hospital electronic health record system between July 30, 2007, and August 25, 2016, and 36/72/108 continuous HD sessions were used to predict mortality within prediction windows of 90/180/365 days. Furthermore, the model performance is compared to that of logistic regression, support vector machine, random forest, LSTM classifier, isolation forest, and stacked autoencoder models. RESULTS: Data for 1200 patients (survival: 1055, death: 145) were used to predict mortality during the next 90 days using 36 continuous HD sessions. The area under the PR curve for the LSTM AE was 0.57, the Recallmacro was 0.86, and the F1-scoremacro was 0.87, outperforming the other models. Upon varying the observation window or prediction window length, LSTM AE continued to outperform the other models. According to the variable importance analysis, the dialysis session length was the feature that contributed the most to the prediction model. CONCLUSIONS: The proposed approach was able to detect patients on maintenance HD with high mortality risk from an imbalanced dataset using anomaly detection theory and leveraging longitudinal HD data.

New Metrics Needed in the Evaluation of Hearing Hazard Associated With Industrial Noise Exposure.

OBJECTIVES: To evaluate (1) the accuracy of the International Organization for Standardization (ISO) standard ISO 1999 [(2013), International Organization for Standardization, Geneva, Switzerland] predictions of noise-induced permanent threshold shift (NIPTS) in workers exposed to various types of high-intensity noise levels, and (2) the role of the kurtosis metric in assessing noise-induced hearing loss (NIHL). DESIGN: Audiometric and shift-long noise exposure data were acquired from a population (N = 2,333) of screened workers from 34 industries in China. The entire cohort was exclusively divided into subgroups based on four noise exposure levels (85 ≤ LAeq.8h < 88, 88 ≤ LAeq.8h < 91, 91 ≤ LAeq.8h < 94, and 94 ≤ LAeq.8h ≤ 100 dBA), two exposure durations (D ≤ 10 years and D > 10 years), and four kurtosis categories (Gaussian, low-, medium-, and high-kurtosis). Predicted NIPTS was calculated using the ISO 1999 model for each participant and the actual measured NIPTS was corrected for age and sex also using ISO 1999. The prediction accuracy of the ISO 1999 model was evaluated by comparing the NIPTS predicted by ISO 1999 with the actual NIPTS. The relation between kurtosis and NIPTS was also investigated. RESULTS: Overall, using the average NIPTS value across the four audiometric test frequencies (2, 3, 4, and 6 kHz), the ISO 1999 predictions significantly (p < 0.001) underestimated the NIPTS by 7.5 dB on average in participants exposed to Gaussian noise and by 13.6 dB on average in participants exposed to non-Gaussian noise with high kurtosis. The extent of the underestimation of NIPTS by ISO 1999 increased with an increase in noise kurtosis value. For a fixed range of noise exposure level and duration, the actual measured NIPTS increased as the kurtosis of the noise increased. The noise with kurtosis greater than 75 produced the highest NIPTS. CONCLUSIONS: The applicability of the ISO 1999 prediction model to different types of noise exposures needs to be carefully reexamined. A better understanding of the role of the kurtosis metric in NIHL may lead to its incorporation into a new and more accurate model of hearing loss due to noise exposure.

Salinity induced alterations in photosynthetic and oxidative regulation are ameliorated as a function of salt secretion.

Ion secretion facilitates recretohalophytes to tolerate saline and drought conditions but its relative contribution to the survival of many species remains poorly understood. Tamarix chinensis has high potential for restoration of saline deteriorated lands. The water management and high salt tolerance of the plant have highlighted the need to determine the strategies that govern these mechanisms. Here we report the selectivity of this halophyte to transport, utilize, and secrete different cations and anions under various NaCl (0, 100, 200 and 400 mM) concentrations. Plant growth, photosynthesis and antioxidant defense responses were also determined to relate them with the function of ion secretion. Results reflected two different sets of strategies adopted by plants to survive low and high salinities. Exposure to highly saline conditions caused reduction in photosynthesis due to stomatal and biochemical limitations. The decreased content of photosynthetic pigments exposed plants to excessive light energy that accelerated production of ROS (i.e., hydrogen peroxide H2O2) and caused damage to cellular membranes. The increased activities of anti-oxidative enzymes (superoxide-dismutase, catalase, ascorbate-peroxidase, and glutathione-reductase) were insufficient to detoxify H2O2. In contrast, plants treated with low salinity did not face stomatal limitations while the photosynthetic pigments increased. As no damage to membranes was detected, the increased content of H2O2 was postulated for its messenger role. The assimilation of essential nutrients was affected due to increased content of toxic ions (Na+ and Cl-) in the growing medium and within the plants. However, the ability to regulate K+ facilitated plants to improve water use efficiency under hyper-osmotic environment. The removal of toxic ions from the photosynthesizing tissues demands high energy, which was evident in the compromised growth of plants. This study offers a window to physiological mechanisms, e.g., potassium retention that ensure salt secretion as a beneficial strategy for prolonged survival of T. chinensis.

Analysis of correlation between window duration for kurtosis computation and accuracy of noise-induced hearing loss prediction.

Kurtosis is considered an important metric for evaluating noise-induced hearing loss (NIHL). However, how to select window duration to calculate kurtosis remains unsolved. In this study, two algorithms were designed to investigate the correlation between window duration for kurtosis computation and the accuracy of NIHL prediction using a Chinese industrial database. Pure-tone hearing threshold levels (HTLs) and full-shift noise were recorded from each subject. In the statistical comparison, subjects were divided into high- and low-kurtosis groups based on kurtosis values computed over different window durations. Mann-Whitney U test was used to compare the difference in group HTLs to find the optimal window duration to best distinguish these two groups. In the support vector machine NIHL prediction model, kurtosis obtained from different window durations was used as a feature of the model for NIHL evaluation. The area under the curve was used to evaluate the performances of models. Fourteen window durations were tested for each algorithm. Results showed that 60 s was an optimal window duration that allows for both efficient computation and high accuracy for NIHL evaluation at test frequencies of 3, 4 and 6 kHz, and the geometric mean of kurtosis sequence was the best metric in NIHL evaluation.

MicroRNA-125a attenuates the chemoresistance against ubenimex in non-small cell lung carcinoma via targeting the aminopeptidase N signaling pathway.

BACKGROUND: Since several long noncoding RNAs (lncRNAs) have been implicated in the development of chemoresistance in non-small cell lung carcinoma (NSCLC), the aim of this study was to investigate whether antisense noncoding RNA in the INK4 locus (ANRIL) was associated with the chemoresistance of NSCLC. METHOD: Real-time polymerase chain reaction was performed to identify potential lncRNAs involved in the chemoresistance of NSCLC, while in-silicon analyses and luciferase assays were carried out to explore the regulatory relationship among ANRIL, miR-125a, and aminopeptidase N (APN). RESULTS: Ubenimex resistant cells were associated with a high expression of ANRIL, which directly binds to miR-125a. MiR-125a directly targeted APN expression. In addition, miR-125a and ANRIL small interfering RNA inhibited the expression of APN but promoted the expression of beclin-1 and LC3, whereas ANRIL, by competing with miR-125a, promoted cell proliferation and inhibited cell apoptosis. CONCLUSION: The data of this study suggested that, by targeting ANRIL and the APN signaling pathway, miR-125a inhibited the proliferation of NSCLC cells and promoted their apoptosis, thus attenuating the chemoresistance of NSCLC against Ubenimex.

Multigas Sensing Technique Based on Quartz Crystal Tuning Fork-Enhanced Laser Spectroscopy.

A compact multigas sensor system based on a single quartz crystal tuning fork (QCTF) and multifrequency synchronous modulation strategy is proposed for trace gas detection. To demonstrate the novel detection technique, three near-infrared continuous-wave (CW) distributed feedback (DFB) diode lasers with center wavelengths of near 1391, 1574, and 1653 nm and a standard 32 kHz QCTF were integrated for simultaneous detection of H2O, CO2, and CH4, respectively. Wavelength modulation spectroscopy with second harmonic detection (WMS-2f) was selected for enhancing sensitivity. Design of the sensor configuration and primary performance between the traditional single-frequency modulation and the proposed tri-frequency modulation were experimentally investigated and compared in detail. The results indicate that the proposed sensing technique has significant advantages of cost effectiveness, portability, and ease-of-use, and detection limits of 1.4, 353, and 3.1 ppm for simultaneously measuring H2O, CO2, and CH4, respectively, are obtained, corresponding to the normalized noise equivalent absorption (NNEA) coefficients of 2.65 × 10-10, 8.09 × 10-10, and 8.28 × 10-10 cm-1 W/√Hz, respectively. Moreover, the use of an erbium-doped fiber amplifier (EDFA) has been demonstrated as an effective method for sensitivity enhancement.

Observation of light rays on absolute geodesic lenses.

Absolute optical instruments with rotation symmetry and corresponding absolute geodesic lenses have drawn considerable attention for their property of perfect imaging of light rays. In this paper, we systematically explore a series of absolute geodesic lenses which is mapped from generalized Maxwell's fish-eye lenses with a rational number index {p}. Moreover, we construct new types of duplex absolute geodesic lenses by splicing two different half absolute geodesic lenses, which is inspired by the work [Huiyan Peng, et al Phys. Rev. Applied13, 034050 (2020)]. Also, we fabricate some samples of absolute geodesic lenses based on the 3D printing technique and observe light rays on them. Our findings enlarge the family of absolute geodesic lenses and might find an application on classical imaging systems.

Enhanced calibration for freeform surface misalignments in non-null interferometers by convolutional neural network.

Most tested surface calibration methods in interferometers, such as the direct coefficients removing method, the sensitive matrix (SM) method, and deep neural network (DNN) calibration method, rely on Zernike coefficients. However, due to the inherent rotationally non-symmetric aberrations in a non-null freeform surface interferometer, the interferograms are usually non-circular even if the surface apertures are circular. The Zernike coefficients based methods are inaccurate due to the non-orthogonality of Zernike polynomials in the non-circular area. A convolutional neural network (CNN)-based misalignment calibration method is proposed. Instead of Zernike coefficients, the well-trained CNN treats the interferogram directly to estimate the specific misalignments. Simulations and experiments are carried out to validate the high accuracy.