A Novel Cryo-controlled Growth Technique for High Performance Organometal Halide Perovskite Solar Cells

The recent trend of the high-performance perovskite solar cell (PSC) is based on multi-component perovskite materials. The reproducible perovskite growth techniques are crucial for acquiring mixed halide perovskite films with precise stoichiometry, desirable morphology, and low defect density

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201

Automatic Diagnosis of Late-Life Depression by 3D Convolutional Neural Networks and Cross-Sample Entropy Analysis From Resting-State fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy \u3e 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD

Predicting Suicidality in Late-Life Depression by 3D Convolutional Neural Network and Cross-Sample Entropy Analysis of Resting-State fMRI

BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality

Induction chemotherapy with dose-modified docetaxel, cisplatin, and 5-fluorouracil in Asian patients with borderline resectable or unresectable head and neck cancer

BackgroundSignificant ethnic differences in susceptibility to the effects of chemotherapy exist. Here, we retrospectively analyzed the safety and efficacy of induction chemotherapy (ICT) with dose-modified docetaxel, cisplatin, and 5-fluorouracil (TPF) in Asian patients with borderline resectable or unresectable head and neck squamous cell carcinoma (HNSCC).MethodsBased on the incidence of adverse events that occurred during daily practice, TPF90 (90% of the original TPF dosage; docetaxel 67.5 mg/m2 on Day 1, cisplatin 67.5 mg/m2 on Day 1, and 5-fluorouracil 675 mg/m2 on Days 1–5) was used for HNSCC patients who were scheduled to receive ICT TPF.ResultsBetween March 2011 and May 2014, 52 consecutive patients with borderline resectable or unresectable HNSCC were treated with ICT TPF90 followed by concurrent chemoradiotherapy. Forty-four patients (84.6%) received at least three cycles of ICT TPF90. The most commonly observed Grade 3–4 adverse events included neutropenia (35%), anemia (25%), stomatitis (35%), diarrhea (16%), and infections (13.5%). In an intention-to-treat analysis, the complete and partial response rates after ICT TPF90 were 13.5% and 59.6%, respectively. The complete and partial response rates following radiotherapy and salvage surgery were 42.3% and 25.0%, respectively. The estimated 3-year overall survival and progression-free survival rates were 41% [95% confidence interval (CI): 25–56%] and 23% (95% CI: 10–39%), respectively. The observed median overall survival and progression-free survival were 21.0 months (95% CI: 13.3–28.7 months) and 16.0 months (95% CI: 10.7–21.3 months), respectively.ConclusionTPF90 is a suitable option for Asian patients with borderline resectable or unresectable HNSCC who are scheduled for ICT

Correlation between three assay systems for anti-Mullerian hormone (AMH) determination

PURPOSE: Analysis of anti-Müllerian hormone (AMH) is becoming of recognized importance in reproductive medicine, but assays are not standardized. We have evaluated the correlation between the new Gen II ELISA kit (Beckman-Coutler) and the older ELISA kits by Immunotech (IOT) and Diagnostic Systems Laboratories (DSL). METHODS: A total of 56 archived serum samples from patients with subfertility or reproductive endocrine disorders were retrieved and assayed in duplicate using the three AMH ELISA kits . The samples covered a wide range of AMH concentrations (1.9 to 142.5 pmol/L). RESULTS: We observed good correlations between the new (AMH Gen II) and old AMH assay kits by IOT and DSL (R(2) = 0.971 and 0.930 respectively). The regression equations were AMH (Gen II) = 1.353 × AMH (IOT) + 0.051 and AMH (Gen II) = 1.223 × AMH (DSL) – 1.270 respectively. CONCLUSIONS: AMH concentrations using the Gen II kit are slightly higher than those from the IOT and DSL kits. Standardization of assay results worldwide is urgently required but this analysis facilitates the interpretation of values obtained historically and in future studies using any of the 3 assays available. Meanwhile, adapting clinical cut-offs from previously published work by direct conversion is not recommended

CXCR4 identifies transitional bone marrow premonocytes that replenish the mature monocyte pool for peripheral responses

It is well established that Ly6C(hi) monocytes develop from common monocyte progenitors (cMoPs) and reside in the bone marrow (BM) until they are mobilized into the circulation. In our study, we found that BM Ly6C(hi) monocytes are not a homogenous population, as current data would suggest. Using computational analysis approaches to interpret multidimensional datasets, we demonstrate that BM Ly6C(hi) monocytes consist of two distinct subpopulations (CXCR4(hi) and CXCR4(lo) subpopulations) in both mice and humans. Transcriptome studies and in vivo assays revealed functional differences between the two subpopulations. Notably, the CXCR4(hi) subset proliferates and is immobilized in the BM for the replenishment of functionally mature CXCR4(lo) monocytes. We propose that the CXCR4(hi) subset represents a transitional premonocyte population, and that this sequential step of maturation from cMoPs serves to maintain a stable pool of BM monocytes. Additionally, reduced CXCR4 expression on monocytes, upon their exit into the circulation, does not reflect its diminished role in monocyte biology. Specifically, CXCR4 regulates monocyte peripheral cellular activities by governing their circadian oscillations and pulmonary margination, which contributes toward lung injury and sepsis mortality. Together, our study demonstrates the multifaceted role of CXCR4 in defining BM monocyte heterogeneity and in regulating their function in peripheral tissues

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe