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Objectives: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. Methods: A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. Results: DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation. Conclusions: DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. Clinical relevance statement: We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. Key Points: • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction. [ABSTRACT FROM AUTHOR]

A shift of El Niño onset location from eastern Pacific (EP) to western Pacific (WP) occurred around 1970. It was accompanied by a faster mean sea surface temperature (SST) warming in WP and a change of precursory SST and wind anomaly patterns. The eigenvalue analysis of a simple coupled model shows that an SST anomaly (SSTA) in WP may grow under the post-1970 mean condition but cannot under the pre-1970 mean condition. As a result, a warm SSTA appeared in WP accompanying to a preceding La Niña condition in EP after 1970, whereas such a warming was rarely seen before 1970. The preceding SSTA patterns led to distinctive zonal wind responses in EP, favoring El Niño onset in EP prior to 1970. For the post-1970 El Niño onset, an initial warming in WP was induced by anomalous downward solar radiation in association with atmospheric meridional overturning circulation or anomalous horizontal advection associated with thermocline induced eastward geostrophic currents. [ABSTRACT FROM AUTHOR]

Objectives: As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. Methods: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). Results: Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942–0.958), sensitivity of 0.833 (95% CI: 0.780–0.848), specificity of 0.929 (95% CI: 0.914–0.936), positive predictive value of 0.833 (95% CI: 0.811–0.860), and negative predictive value of 0.929 (95% CI: 0.914–0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. Conclusions: A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. Clinical relevance statement: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. Key Points: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95. [ABSTRACT FROM AUTHOR]

This study employs the regional Climate-Weather Research and Forecasting model (CWRF) to first investigate the primary physical mechanisms causing biases in simulating summer precipitation over the Yangtze River Basin (YRB), and then enhance its predictive ability through an optimal multi-physics ensemble approach. The CWRF 30-km simulations in China are compared among 28 combinations of varying physics parameterizations during 1980–2015. Long-term average summer biases in YRB precipitation are remotely correlated with those of large-scale circulations. These teleconnections of biases are highly consistent with the observed correlation patterns between interannual variations of precipitation and circulations, despite minor shifts in their primary action centers. Increased YRB precipitation aligns with a southward shifted East Asian westerly jet, an intensified low-level southerly flow south of YRB, and a south-eastward shifted South Asian high, alongside higher moisture availability over YRB. Conversely, decreased YRB precipitation corresponds to an opposite circulation pattern. The CWRF control configuration using the ensemble cumulus parameterization (ECP), compared to other cumulus schemes, best captures the observed YRB precipitation characteristics and associated circulation patterns. Coupling ECP with the Morrison or Morrison-aerosol microphysics and the CCCMA or CAML radiation schemes enhances the overall CWRF skills. Compared to the control CWRF, the ensemble average of these skill-enhanced physics configurations more accurately reproduces YRB summer precipitation's spatial distributions, interannual anomalies, and associated circulation patterns. The Bayesian Joint Probability calibration to these configurations improves the ensemble's spatial distributions but compromises its interannual anomalies and teleconnection patterns. Our findings highlight substantial potential for refining the representation of climate system physics to improve YRB precipitation prediction. This is notably achieved by realistically coupling cumulus, microphysics, and radiation processes to accurately capture circulation teleconnections. Further enhancements can be achieved by optimizing the multi-physics ensemble among skill-enhanced configurations. [ABSTRACT FROM AUTHOR]

Purpose: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. Methods: OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. Results: Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. Conclusion: OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well. [ABSTRACT FROM AUTHOR]

Model simulations show that drought may become more severe and widespread in the 21st century due to human-induced global warming. However, the contributions from the key factors to the model-projected drought changes in China have not yet been examined in detail. We used the self-calibrated Palmer Drought Severity Index with Penman–Monteith potential evapotranspiration (scPDSIpm) based on 10 model simulations selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We investigated the contributions from precipitation (P), near-surface air temperature and specific humidity [Δ(T + q)], net surface longwave radiation (LW), net surface shortwave radiation (SW), and wind speed (WS) to the future changes in scPDSIpm, including the long-term mean, drying area, probability distribution function (PDF), drought frequency, and drought duration based on the scPDSIpm over China. Our results show that model-projected drying mainly occurs over southern China, whereas the dry areas under drought conditions increase from 20% to about 23%/30% under the two scenarios of the shared socioeconomic pathway (SSP2-4.5/SSP5-8.5) from 1985 to 2100, despite large uncertainties in individual projections partly due to internal variability. Drought frequency is projected to increases by about 10%–54% (15%–88%) under the SSP2-4.5 (SSP5-8.5) scenario by the late 21st century, along with increases in drought duration. These changes are accompanied by a decrease in the mean scPDSIpm and flattening of the PDFs. The changes in drying over southern China are mainly attributed to surface warming and the increased surface vapor pressure deficit (VPD), with small contributions from changes in the surface net radiation. The changes in wetting over northern China mostly result from increased precipitation along with a small wetting effect from the changes in wind speed. [ABSTRACT FROM AUTHOR]

As a common but critical dynamic crossover in glass-forming liquids (GFLs), the discovery of fragile-to-strong (F-S) transition promises a novel route for understanding supercooled liquid and glass transition. The present work, for the first time, successfully realizes the quantitative prediction of the F-S transition in nine metallic glass-forming liquids, by a counter-intuitive approach that focuses on local atomic activation events, rather than relaxation, upon cooling. The dynamic crossover originates from a disorder-to-order transition by self-regulating behavior of atomic position within a cage controlled by finite atomic activation events, due to the appearance of local cooperative motion of nearest neighborhood atoms. Moreover, the dominant role of entropy in this anomaly has been discovered, and the correspondence between the crossover of configuration entropy involved in activation events and the occurrence of F-S transition has been found. Our work implies that the feature of atomic energy fluctuations reflected by atomic activation events has a close linkage to complex dynamic behaviors of disordered systems. [ABSTRACT FROM AUTHOR]

In the presence of an MMC-HVDC system, current differential protection (CDP) has the risk of failure in operation under an internal fault. In addition, CDP may also incur security issues in the presence of current transformer (CT) saturation and outliers. In this paper, a current trajectory image-based protection algorithm is proposed for AC lines connected to MMC-HVDC stations using a convolution neural network improved by a channel attention mechanism (CA-CNN). Taking the dual differential currents as two-dimensional coordinates of the moving point, the moving-point trajectories formed by differential currents have significant differences under internal and external faults. Therefore, internal faults can be identified using image recognition based on CA-CNN. This is improved by a channel attention mechanism, data augmentation, and adaptive learning rate. In comparison with other machine learning algorithms, the feature extraction ability and accuracy of CA-CNN are greatly improved. Various fault conditions like different network structures, operation modes, fault resistances, outliers, and current transformer saturation, are fully considered to verify the superiority of the proposed protection algorithm. The results confirm that the proposed current trajectory image-based protection algorithm has strong learning and generalizability, and can identify internal faults reliably. [ABSTRACT FROM AUTHOR]

Understanding the impact of anthropogenic climate change on drought is of great significance to the prevention of its adverse effects. Two drought indices, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), are used here for the detection and attribution of autumn droughts in China, and for the exploration of the role played by the anthropogenic climate change. SPI is only related to precipitation, but SPEI involves both precipitation and potential evapotranspiration. For their trend's spatial patterns, the historical simulations (including all forcings, noted as ALL) from 11 models of the Coupled Model Intercomparison Project phase 6, as an ensemble, are able to reproduce their observational counterpart. SPI shows wetting trend in the north of 35°N and drying trend in the south. SPEI shows drying trend in almost whole China. The drying trend in historical simulations ALL is significantly stronger, compared with the counterpart from the accompanying simulations (called NAT) with only natural forcings implemented. This result clearly indicates that anthropogenic climate change plays a dominant role in the enhancement of autumn drought in China. A more rigorous detection work is also performed with the signal's fingerprint of ALL (and NAT) projected onto the observation and assessed with the background noise from no external-forcing control simulations. The trend pattern signal in ALL is significantly detected in observation for both SPI and SPEI, with a more pronounced signal in SPEI than in SPI, while the signal of NAT is not detected for neither SPI nor SPEI. Finally, extreme droughts (with indices beyond −2) are assessed in terms of probability ratio between ALL and NAT. It is shown that the anthropogenic precipitation change plays a leading role in the south of 35°N, while the anthropogenic temperature change leads in the north. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury is a common and devastating injury that is the leading cause of neurological disability and death worldwide. Patients with cerebral lobe contusion received conservative treatment because of their mild manifestations, but delayed intracranial hematoma may increase and even become life-threatening. We explored the noninvasive method to predict the prognosis of progression and Glasgow Outcome Scale (GOS) by using a quantitative radiomics approach and statistical analysis. Methods: Eighty-eight patients who were pathologically diagnosed were retrospectively studied. The radiomics method developed in this work included image segmentation, feature extraction, and feature selection. The nomograms were established based on statistical analysis and a radiomics method. We conducted a comparative study of hematoma progression and GOS between the clinical factor alone and fusion radiomics features. Results: Nineteen clinical factors, 513 radiomics features, and 116 locational features were considered. Among clinical factors, international normalized ratio, prothrombin time, and fibrinogen were enrolled for hematoma progression. As for GOS, treatment strategy, age, Glasgow Coma Scale score, and blood platelet were associated factors. Eight features for GOS and five features for hematoma progression were filtered by using sparse representation and locality preserving projection-combined method. Four nomograms were constructed. After fusion radiomics features, area under the curve of hematoma progression prediction increased from 0.832 to 0.899, whereas GOS prediction went from 0.794 to 0.844. Conclusions: A radiomic-based model that merges radiomics and clinical features is a noninvasive approach to predict hematoma progression and clinical outcomes of cerebral contusions in traumatic brain injury. [ABSTRACT FROM AUTHOR]

Climate change has and will continue to affect food security in different dimensions. This study aims at comparing current and future maize and sorghum climate suitability and yield using five state-of-art climate models in the Representative Concentration Pathways (RCP8.5) in Couple Intercomparison Project Phase 5 (CMIP5). The study considered 1970–2000 as the baseline and 2040–2060 (the 2050s) and 2061–2080 (2070s) as the projection periods. The mechanist FAO Eco-crop model and statistical crop model were used. The results show that rainfall and temperature are projected to increase by 2050s and 2070s with certain (uncertainty) in temperature (rainfall) projection. The central and western highlands are the optimum suitable zones for maize. Gradual reduction of maize suitable zone is anticipated leading to a yield loss of 10.8% (23.7%) by 2050s (2070s). The sorghum climate suitable areas are expected to increase in the future leading to a yield gain of 114% (80.7%) by 2050s (2070s) from the baseline yield. Currently, temperature creates more uncertainties in crop yield than rainfall. The findings of this research enlighten the policymakers and farmers on the probable susceptibilities of the agricultural system in future climate and the viable strategic measures to curb food insecurity over the country. [ABSTRACT FROM AUTHOR]

Based on the reforecast data (1999–2010) of three operational models [the China Meteorological Administration (CMA), the National Centers for Environmental Prediction of the U.S. (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF)] that participated in the Subseasonal to Seasonal Prediction (S2S) project, we identified the major sources of subseasonal prediction skill for heatwaves over the Yangtze River basin (YRB). The three models show limited prediction skills in terms of the fraction of correct predictions for heatwave days in summer; the Heidke Skill Score drops quickly after a 5-day forecast lead and falls down close to zero beyond the lead time of 15 days. The superior skill of the ECMWF model in predicting the intensity and duration of the YRB heatwave is attributable to its fidelity in capturing the phase evolution and amplitude of high-pressure anomalies associated with the intraseasonal oscillation and the dryness of soil moisture induced by less precipitation via the land-atmosphere coupling. The effects of 10–30-day and 30–90-day circulation prediction skills on heatwave predictions are comparable at shorter forecast leads (10 days), while the biases in 30–90-day circulation amplitude prediction show close connection with the degradation of heatwave prediction skill at longer forecast leads (> 15–20 days). The biases of intraseasonal circulation anomalies further affect precipitation anomalies and thus land conditions, causing difficulty in capturing extremely hot days and their persistence in the S2S models. [ABSTRACT FROM AUTHOR]

Purpose: The evaluation of fetal lung maturity is critical for clinical practice since the lung immaturity is an important cause of neonatal morbidity and mortality. For the evaluation of the development of fetal lung maturation degree, our study established a deep model from ultrasound images of four-cardiac-chamber view plane. Methods: A two-stage transfer learning approach is proposed for the purpose of the study. A specific U-net structure is designed for the applied deep model. In the first stage, the model is to first learn the recognition of fetal lung region in the ultrasound images. It is hypothesized in our study that the development of fetal lung maturation degree is generally proportional to the gestational age. Then, in the second stage, the pretrained deep model is trained to accurately estimate the gestational age from the fetal lung region of ultrasound images. Results: Totally 332 patients were included in our study, while the first 206 patients were used for training and the subsequent 126 patients were used for the independent testing. The testing results of the established deep model have the imprecision as 1.56 ± 2.17 weeks on the gestational age estimation. Its correlation coefficient with the ground truth of gestational age achieves 0.7624 (95% CI 0.6779 to 0.8270, P value < 0.00001). Conclusion: The hypothesis that the development of fetal lung maturation degree can be represented by the texture information from ultrasound images has been preliminarily validated. The fetal lung maturation degree can be considered as being represented by the deep model's output denoted by the estimated gestational age. [ABSTRACT FROM AUTHOR]

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer. Breast cancer is frequently diagnosed using ultrasound. Here, the authors show that, in addition to ultrasound, shear wave elastography can be used to diagnose breast cancer and, in conjunction with deep learning and radiomics, can predict whether the disease has spread to axillary lymph nodes. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients.Method: A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis.Result: For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001).Conclusion: Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy.Key Points: • The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies. • Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks. • The ovarian cancer patients with high-risk scores had poor prognosis. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors' accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians' reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images. [ABSTRACT FROM AUTHOR]

Maize (Zea Mays) is the major food crop in Kenya. Its production variation has devastating consequences on people’s basic food availability. This study will investigate the relationships between climate variability and maize yield using observed weather data from Kenya Meteorological Department and national annual maize yield data from the Ministry of Agriculture for the period 1979-2012. Mann-Kendall test was used to detect a trend in precipitation, minimum, and maximum temperature. Location-wise correlation method was performed between each climate variable and maize yield in every station. Stations which had significant correlations were aggregated to form climate indices which were used to build multiple linear regression model. The results revealed that maize yield in Kenya was significantly decreasing at a rate of 0.07 tons/ha/decade at the 95% confidence level accompanied by high inter-annual variation, while world average was increasing at a rate of 0.6 tons/ha/decade. This reduction was accredited to a significant increasing temperature and reduction in seasonal rainfall. Empirical relationship derived from multiple regression models indicates that 67.53% of yield variance was attributed to varying seasonal climate indices. Precipitation is the dominant predictor accounting to 49.73% of yield variance. There is a significant correlation of 0.78 between the modeled and observed yield hence high credibility of the statistical model. A Continuous decrease of maize yield is expected under the influence of climate change which threatens national food security if effective measures to raise maize production are not endorsed. These findings form a framework for designing policies geared towards the reduction of climate-related vulnerability in many parts of the world. [ABSTRACT FROM AUTHOR]

Purpose: B-mode ultrasound (B-US) and strain elastography ultrasound (SE-US) images have a potential to distinguish thyroid tumor with different lymph node (LN) status. The purpose of our study is to investigate whether the application of multi-modality images including B-US and SE-US can improve the discriminability of thyroid tumor with LN metastasis based on a radiomics approach.Methods: Ultrasound (US) images including B-US and SE-US images of 75 papillary thyroid carcinoma (PTC) cases were retrospectively collected. A radiomics approach was developed in this study to estimate LNs status of PTC patients. The approach included image segmentation, quantitative feature extraction, feature selection and classification. Three feature sets were extracted from B-US, SE-US, and multi-modality containing B-US and SE-US. They were used to evaluate the contribution of different modalities. A total of 684 radiomics features have been extracted in our study. We used sparse representation coefficient-based feature selection method with 10-bootstrap to reduce the dimension of feature sets. Support vector machine with leave-one-out cross-validation was used to build the model for estimating LN status.Results: Using features extracted from both B-US and SE-US, the radiomics-based model produced an area under the receiver operating characteristic curve (AUC) = 0.90, accuracy (ACC) = 0.85, sensitivity (SENS) = 0.77 and specificity (SPEC) = 0.88, which was better than using features extracted from B-US or SE-US separately.Conclusions: Multi-modality images provided more information in radiomics study. Combining use of B-US and SE-US could improve the LN metastasis estimation accuracy for PTC patients. [ABSTRACT FROM AUTHOR]

Objective: The status of isocitrate dehydrogenase 1 (IDH1) is highly correlated with the development, treatment and prognosis of glioma. We explored a noninvasive method to reveal IDH1 status by using a quantitative radiomics approach for grade II glioma.Methods: A primary cohort consisting of 110 patients pathologically diagnosed with grade II glioma was retrospectively studied. The radiomics method developed in this paper includes image segmentation, high-throughput feature extraction, radiomics sequencing, feature selection and classification. Using the leave-one-out cross-validation (LOOCV) method, the classification result was compared with the real IDH1 situation from Sanger sequencing. Another independent validation cohort containing 30 patients was utilised to further test the method.Results: A total of 671 high-throughput features were extracted and quantized. 110 features were selected by improved genetic algorithm. In LOOCV, the noninvasive IDH1 status estimation based on the proposed approach presented an estimation accuracy of 0.80, sensitivity of 0.83 and specificity of 0.74. Area under the receiver operating characteristic curve reached 0.86. Further validation on the independent cohort of 30 patients produced similar results.Conclusions: Radiomics is a potentially useful approach for estimating IDH1 mutation status noninvasively using conventional T2-FLAIR MRI images. The estimation accuracy could potentially be improved by using multiple imaging modalities.Key Points: • Noninvasive IDH1 status estimation can be obtained with a radiomics approach. • Automatic and quantitative processes were established for noninvasive biomarker estimation. • High-throughput MRI features are highly correlated to IDH1 states. • Area under the ROC curve of the proposed estimation method reached 0.86. [ABSTRACT FROM AUTHOR]

The tropical cyclone genesis number (TCGN) in July-October (JASO) over the western North Pacific (WNP) exhibits a robust interannual variation. It shows a longitudinally tri-pole pattern with a high in the eastern WNP and South China Sea (SCS) and a low in the western WNP, which explain 42.2 and 23.4 % of total TCGN variance in the eastern WNP and SCS, respectively. The high-low-high pattern is similar to that derived from a TC genesis potential index (GPI). To understand the cause of the longitudinal distribution of the dominant interannual mode, we examine the contributions of environmental parameters associated with GPI. It is found that relative humidity and relative vorticity are important factors responsible for TC variability in the SCS, while vertical shear and relative vorticity are crucial in determining TC activity in eastern WNP. A simultaneous correlation analysis shows that the WNP TCGN in JASO is significantly negatively correlated (with a correlation coefficient of −0.5) with sea surface temperature anomalies (SSTA) in the tropical North Atlantic (TNA). The longitudinal distribution of TC genesis frequency regressed onto TNA SSTA resembles that regressed upon the WNP TCGN series. The spatial patterns of regressed environmental variables onto the SSTA over the TNA also resemble those onto TCGN in the WNP, that is, an increase of relative humidity in the SCS and a weakening of vertical shear in the eastern WNP are all associated with cold SSTA in the TNA. Further analyses show that the cold SSTA in the TNA induce a negative heating in situ. In response to this negative heating, a low (upper)-level anomalous aniti-cyclonic (cyclonic) flows appear over the subtropical North Atlantic and eastern North Pacific, and to east of the cold SSTA, anomalous low-level westerlies appear in the tropical Indian Ocean. Given pronounced mean westerlies in northern Indian Ocean in boreal summer, the anomalous westerly flows increase local surface wind speed and surface evaporation and cool the SST in situ. Cold SSTA in northern Indian Ocean further suppress local convection, inducing anomalous westerlies to its east, leading to enhanced cyclonic vorticity and low surface pressure over the WNP monsoon trough region. Idealized numerical experiments further confirm this Indian Ocean relaying effect, through which cold SSTA in the tropical Atlantic exert a remote impact to circulation in the WNP. [ABSTRACT FROM AUTHOR]

We diagnose characteristics of the quasi-balanced flow and secondary circulation (SC) of tropical storm Bilis (2006) using the potential vorticity (PV)- ω inversion method. We further analyze how secondary steering flows associated with mesoscale convective systems affected the track of tropical storm Bilis after it made landfall. The quasi-balanced asymmetric and axisymmetric circulation structures of tropical storm Bilis are represented well by the PV- ω inversion. The magnitude of the nonlinear quasi-balanced vertical velocity is approximately 75% of the magnitude simulated using the Weather Research and Forecasting (WRF) model. The SC of Bilis (2006) contained two strong regions of ascending motion, both of which were located in the southwest quadrant of the storm. The first (150-200 km southwest of the storm center) corresponded to the eyewall region, while the second (approximately 400 km southwest of the storm center) corresponded to latent heat release associated with strong precipitation in major spiral rainbands. The SC was very weak in the northeast quadrant (the upshear direction). Dynamical processes related to the environmental vertical wind shear produced an SC that partially offset the destructive effects of the environmental vertical wind shear (by 20%-25%). This SC consisted of upward motion in the southwest quadrant and subsidence in the northeast quadrant, with airflow oriented from southwest to northeast at high altitudes and from northeast to southwest at lower levels. The inverted secondary zonal and meridional steering flows associated with continuous asymmetric mesoscale convective systems were about −2.14 and −0.7 m s, respectively. These steering flows contributed substantially to the zonal (66.15%) and meridional (33.98%) motion of the storm at 0000 UTC 15 July 2006. The secondary steering flow had a significant influence on changing the track of Bilis from southward to northward. The direction of the large-scale meridional steering flow (3.02 m s) was opposite to the actual meridional motion (−2.06 m s). [ABSTRACT FROM AUTHOR]

Dentin, the predominant mineralized tissue of the tooth, comprises an extracellular matrix of collagen and a heterogeneous mixture of non-collagenous components, many of which have cellular signaling properties. These properties may be important in signaling stem cell involvement in tissue regeneration following injury and the present study investigates their morphogenic effects on differentiation of Bone Marrow Stromal Stem Cells (BMMSCs) in vitro. Non-collagenous dentin matrix proteins (DMPs) were isolated from healthy human teeth and their effects on BMMSCs behavior examined during in vitro culture. In vitro, DMPs enhanced alkaline phosphatase activity and mineralization in BMMSCs cultures as well as increasing the expression of dentinogenic and osteogenic differentiation markers (including runt-related transcription factor 2, osterix, bone sialoprotein, dentin sialophosphoprotein and osteocalcin) at both transcript and protein levels, with 10 μg/mL DMPs being the optimal stimulatory concentration. Expression of phosphor-ERK/phosphor-P38 in BMMSCs was up-regulated by DMPs and, in the presence of the ERK1/2- and p38-specific inhibitors, the differentiation of BMMSCs was inhibited. These data indicate that DMPs promote the dentinogenic/osteogenic differentiation of BMMSCs via the ERK/p38 MAPK pathways. [ABSTRACT FROM AUTHOR]

Endogenous estrogen deficiency usually causes the systemic osteoporosis including the jaw bones. However, it remains unclear whether estrogen deficiency can affect the tooth mineralization. In this study, the classical osteoporotic rat model was established via the ovariectomy, which was subsequently confirmed by the detection of serum estradiol levels and body weights. The mineralization-related assays were performed to observe the potential changes in mineralized tissues of rat lower incisors. The clinical crown length, compressive strength, radiodensity, and calcium content in the ovariectomy group (OVX) were significantly downregulated ( P < 0.01), as compared with the sham operation group (Sham). Histological results revealed that OVX incisors presented the thinner predentin structures than Sham incisors. Immunohistochemical staining and western blot assay further demonstrated that the odonto/osteoblast specific proteins (e.g., dentin sialoprotein, runt-related transcription factor 2, osterix, and osteocalcin) in the dentin-pulp complex of OVX incisors were significantly decreased in comparison with Sham counterparts. Together, estrogen deficiency reduces the dentinogenic capacity and calcium deposition in rat incisors, indicating that estrogen plays an important role in the dentinogenesis. [ABSTRACT FROM AUTHOR]

Various factors can affect the functions of dental pulp stem cells (DPSCs). However, little knowledge is available about the effects of estrogen deficiency on the differentiation of DPSCs. In this study, an estrogen-deficient rat model was constructed and multi-colony-derived DPSCs were obtained from the incisors of ovariectomized (OVX) or sham-operated rats. Odonto/osteogenic differentiation and the possible involvement of the nuclear factor kappa B (NF-κB) pathway in the OVX-DPSCs/Sham-DPSCs of these rats were then investigated. OVX-DPSCs presented decreased odonto/osteogenic capacity and an activated NF-κB pathway, as compared with Sham-DPSCs. When the cellular NF-κB pathway was specifically inhibited by BMS345541, the odonto/osteogenic potential in OVX-DPSCs was significantly upregulated. Thus, estrogen deficiency down-regulated the odonto/osteogenic differentiation of DPSCs by activating NF-κB signaling and inhibition of the NF-κB pathway effectively rescued the decreased differentiation potential of DPSCs. [ABSTRACT FROM AUTHOR]

Insulin-like growth factor 1 (IGF-1) is a potent mitogenic protein which can enhance the osteogenic differentiation of periodontal ligament (PDL) fibroblasts. However, it remains unclear whether IGF-1 can stimulate the osteogenic differentiation and osteogenesis of human periodontal ligament stem cells (PDLSCs). In this study, STRO-1 PDLSCs were isolated from human PDL tissues, treated with IGF-1, and their osteogenic capacity was investigated in vitro and in vivo. Dimethyl-thiazol-diphenyl tetrazolium bromide assay and flow cytometry results demonstrated that 10-200 ng/mL IGF-1 can stimulate the proliferation ability of PDLSCs and 100 ng/mL is the optimal concentration. Exogenous IGF-1 can modify the ultrastructure, enhance the alkaline phosphatase activity, the mineralization ability of PDLSCs, and increase the expression of osteogenic markers (runt-related transcription factor 2, osterix, and osteocalcin) at mRNA and protein levels. In vivo transplantation illustrated that IGF-1 treated implants generated more mineralized tissues, and presented stronger expression of RUNX2, OSX, and OCN than control group. Moreover, the expression of phosphor-ERK and phosphor-JNK in these stem cells was upregulated by IGF-1, indicating that MAPK signaling pathway was activated during the osteogenic differentiation of PDLSCs mediated by IGF-1. Together, the results showed that IGF-1 can promote the osteogenic differentiation and osteogenesis of STRO-1 PDLSCs via ERK and JNK MAPK pathway, suggesting that IGF-1 is a potent agent for stem cell-based periodontal tissue regeneration. [ABSTRACT FROM AUTHOR]

CCND1 plays a critical role in cell cycle control and may contribute to head and neck cancer. We performed a meta-analysis of eleven case-control studies that examined the association between CCND1 G870A polymorphism and head and neck cancer risk. Overall, no significant association of this polymorphism with head and neck cancer was found (for AA vs. GG: OR=0.96, 95% CI=0.59-1.58, P<0.01 for heterogeneity; for GA vs. GG: OR=1.00, 95% CI=0.74-1.35, P<0.01 for heterogeneity; for the dominant model GA/AA vs. GG: OR=0.98, 95% CI=0.69-1.39, P<0.01 for heterogeneity; for the recessive model AA vs. GG/GA: OR=0.94, 95% CI=0.66-1.33, P<0.01 for heterogeneity). In subgroup analysis by ethnicity, we also did not find any significant association in European and Asians populations. All the results were not materially altered in any genetic model after the studies which did not fulfill Hardy-Weinberg equilibrium were excluded. In conclusion, our meta-analysis strongly suggested that the CCND1 G870A polymorphism is not associated with head and neck cancer risk. [ABSTRACT FROM AUTHOR]

Objective: Compared with normal birth weight fetuses (2,500-4,000 g), accurate fetal weight estimation for fetuses with low or excessive weight is considered more important for fetal compromise prediction and labor management. New formulas were developed to estimate weight for low birth weight (LBW) fetuses and macrosomic fetuses, respectively. Methods: A total of 523 fetuses with birth weight less than 2,500 g, 652 fetuses with normal birth weight, and 239 fetuses with birth weight more than 4,000 g were included in the study. As much as 25 existing formulas which incorporate regularly defined fetal measurements were evaluated and compared. Performance evaluation of existing formulas showed that no formulas can provide consistently accurate weight estimation both for LBW fetuses and macrosomic fetuses. A total of 1,034 cases were utilized to generate an overall regression formula. If the pre-estimated weight fell into the suspicious LBW and macrosomia range, the value was then updated by using the new regression formulas for LBW fetuses and macrosomic fetuses. As the training group, 262 LBW fetuses and 120 macrosomic fetuses were employed in a stepwise linear regression to obtain two update regression formulas for suspicious LBW fetuses and macrosomic fetuses. As the validation group, another 261 LBW fetuses and 119 macrosomic fetuses were assessed. Results: The new overall formula has the form of Log(10)BW = 0.180(HC) + 0.00628(AC) - 0.00318(HC)(2) + 0.00173(AC)(FL) + 0.0000430(BPD)(HC)(2). The update formula for suspicious LBW fetuses is LnBW = 1.470(BPD) + 0.0169(HC) - 0.0873(BPD)(2) + 0.00518(AC)(FL) and for macrosomic fetuses is Log(10)BW = 0.730(BPD) - 0.0375(BPD)(2) + 0.000264(AC)(FL). For LBW fetuses, the new method gave 7.6 ± 209.0 (g) of estimation error and 8.3 ± 7.8 (%) of absolute percentage error, while the best existing formula provided -0.7 ± 226.0 (g) and 9.1 ± 8.3 (%). With the new method, 71.3% of estimates fell within ±10% of the actual birth weight, while the best existing formula gave 65.5%. For macrosomic fetuses, the new method gave -87.9 ± 231.0 (g) of estimation error and 4.4 ± 3.9 (%) of absolute percentage error, while the best existing formula provided 115.6 ± 345.1 (g) and 6.8 ± 5.4 (%). With the new method, 89.1% of estimates fell within ±10% of the actual birth weight, while the best existing formula gave 75.6%. Conclusions: To improve the weight estimation accuracy for low or excessive weight fetuses, separate formulas are necessary. The new method provides significant improvement on fetal weight estimation for LBW fetuses and macrosomic fetuses. [ABSTRACT FROM AUTHOR]

Interleukin-1β (IL-1β), which is involved in inflammatory and immunological responses, plays an important role in the development and progression of breast cancer. Three functional single nucleotide polymorphisms (SNPs) identified in IL-1β gene are thought to influence breast cancer risk. The results of the association between IL-1β polymorphisms and breast cancer remain inconsistent. Therefore, we conducted a meta-analysis of eight case-control studies with rs1143627 (T > C), rs16944 (C > T), and rs1143634 (C > T). We found that the variant CC genotype of rs1143627 was associated with a significantly increased breast cancer risk (CC vs. TT: OR = 1.37, 95% CI = 1.10-1.70, P = 0.22 for heterogeneity; the recessive model CC vs. TT/TC: OR = 1.40, 95% CI = 1.17-1.67, P = 0.49 for heterogeneity). For rs16944 (C > T) and rs1143634 (C > T), no significant associations were found in all genetic models. In conclusion, the present meta-analysis suggests that rs1143627 is associated with breast cancer risk. [ABSTRACT FROM AUTHOR]

The effects of environmental vertical wind shear (VWS) on the intensity and rainfall asymmetries in Tropical Storm (TS) Bilis (2006) have been analyzed based on TRMM/TMI-estimated surface rainfall data, QuikSCAT wind fields, 850- and 200-hPa winds of the NCEP-NCAR reanalysis, precipitation data at 5-min intervals from automatic weather stations over mainland China, and the best track data of TS Bilis (2006). The results show that the simultaneous and 6-hour-lagged correlation coefficients between VWS and storm intensity (the minimum central sea level pressure) are 0.59145 and 0.57438 ( P < 0.01), respectively. The averaged VWS was found to be about 11 m s−1 and thus suppressed the intensification of Bilis (2006). Distribution of precipitation in Bilis (2006) was highly asymmetric. The azimuthally-averaged rainfall rate in the partial eyewall, however, was smaller than that in a major outer rainband. As the storm intensified, the major rainband showed an unusual outward propagation. The VWS had a great impact on the asymmetric distribution of precipitation. Consistent with previous modeling studies, heavy rainfall generally occurred downshear to downshear-left of the VWS vector both near and outside the eyewall, showing a strong wavenumber-one asymmetry, which was amplified as the VWS increased. [ABSTRACT FROM AUTHOR]

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods. A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario. [ABSTRACT FROM AUTHOR]

Pursuing better imaging quality and miniaturizing imaging devices are two trends in the current development of ultrasound imaging. While the first one leads to more complex and expensive imaging equipment, poor image quality is a common problem of portable ultrasound imaging systems. In this paper, an image reconstruction method was proposed to break through the imaging quality limitation of portable devices by introducing generative adversarial network (GAN) model into the field of ultrasound image reconstruction. We combined two GAN generator models, the encoder-decoder model and the U-Net model to build a sparse skip connection U-Net (SSC U-Net) to tackle this problem. To produce more realistic output, stabilize the training procedure, and improve spatial resolution in the reconstructed ultrasound images, a new loss function which combines adversarial loss, L1 loss, and differential loss was proposed. Three datasets including 50 pairs of simulation, 40 pairs of phantom, and 72 pairs of in vivo images were used to evaluate the reconstruction performance. Experimental results show that our SSC U-Net is able to reconstruct ultrasound images with improved quality. Compared with U-Net, our SSC U-Net is able to preserve more details in the reconstructed images and improve full width at half maximum (FWHM) of point targets by 3.23%. [ABSTRACT FROM AUTHOR]

Insulin-like growth factor-1 (IGF-1) and its receptor IGF-1R play a paramount role in tooth/bone formation while hsa-let-7c actively participates in the osteogenic differentiation of mesenchymal stem cells. However, the interaction between IGF-1/IGF-1R and hsa-let-7c on the committed differentiation of stem cells from apical papilla (SCAPs) remains unclear. In this study, human SCAPs were isolated and treated with IGF-1 and hsa-let-7c over/low-expression viruses. The odonto/osteogenic differentiation of these stem cells and the involvement of mitogen-activated protein kinase (MAPK) pathway were subsequently investigated. Alizarin red staining showed that hsa-let-7c low-expression can significantly promote the mineralization of IGF-1 treated SCAPs, while hsa-let-7c over-expression can decrease the calcium deposition of IGF-1 treated SCAPs. Western blot assay and real-time reverse transcription polymerase chain reaction further demonstrated that the expression of odonto/osteogenic markers (ALP, RUNX2/RUNX2, OSX/OSX, OCN/OCN, COL-I/COL-I, DSPP/DSP, and DMP-1/DMP-1) in IGF-1 treated SCAPs were significantly upregulated in Let-7c-low group. On the contrary, hsa-let-7c over-expression could downregulate the expression of these odonto/osteogenic markers. Moreover, western blot assay showed that the JNK and p38 MAPK signaling pathways were activated in Let-7c-low SCAPs but inhibited in Let-7c-over SCAPs. Together, the IGF-1/IGF-1R/hsa-let-7c axis can control the odonto/osteogenic differentiation of IGF-1-treated SCAPs via the regulation of JNK and p38 MAPK signaling pathways. [ABSTRACT FROM AUTHOR]