The Cost of Innovation and Decreasing Book Equity of U.S. Firms

This study documents that book equity of U.S. firms has decreased dramatically over time and such decrease is systematic across various industries and firm size. Our analysis shows that intangible capital investment explains a significant portion of the decrease in book equity even after controlling for concurrent effect of leverage and profitability on book equity, and the effect of intangible capital investment on book equity increased in recent years. Further analysis shows that intangible capital contributes to decrease in book equity mostly through the channel of changing firm characteristics rather than changing sensitivity over time. Our findings suggest that investors must incorporate the effect of intangible capital investment into their valuation analysis, as indexes or investment strategies relying on indicators constructed by book equity may be biased and misleading

Stiffness of Substrate Influences the Distribution but not the Synthesis of Autophagosomes in Human Liver (LO2) Cells

Extracellular matrix (ECM) often becomes stiffer during tumor development, which not only gives the tumor's hardness feel but also actively contributes to the tumor formation. A good example is hepatocellular carcinoma (HCC) that usually develops within chronically stiffened liver tissues due to fibrosis and cirrhosis. On the other hand, HCC cells exhibit reduced autophagy in a malignancy dependent manner, suggesting autophagy is suppressed during tumor development. However, it is not known whether ECM stiffness would influence autophagy during tumor development. To investigate this issue, We cultured the human liver (LO2) cells that stably expressed autophagosome indicator LC3 on polydimethylsiloxane (PDMS) gels with stiffness varying from 11 to 1220 kPa. and on plastic cell culture dish as controls for up to 48h. We found that the total protein level of LC3-II in LO2 cells was not affected by the substrate stiffness. However the autophagosomes in LO2 cells cultured on the soft substrate (11 kPa PDMS gel) were localized and accumulated around the nucleus, while those on the stiff substrate (1220 kPa PDMS gel or plastic dish surface) were dispersed throughout the cytoplasmic space. Therefore, our data suggest that ECM stiffness may not directly synthesize nascent autophagosomes, but instead influence the location/translocation and ultimately distribution of autophagosomes in the cells

A real-world pharmacovigilance study of drug-induced QT interval prolongation: analysis of spontaneous reports submitted to FAERS

PurposeTo identify the most commonly reported drugs associated with QT interval prolongation in the FDA Adverse Event Reporting System (FAERS) and evaluate their risk for QT interval prolongation.MethodsWe employed the preferred term (PT) “electrocardiogram QT prolonged” from the Medical Dictionary for Regulatory Activities (MedDRA) 26.0 to identify adverse drug events (ADEs) of QT interval prolongation in the FAERS database from the period 2004–2022. Reporting odds ratio (ROR) was performed to quantify the signals of ADEs.ResultsWe listed the top 40 drugs that caused QT interval prolongation. Among them, the 3 drugs with the highest number of cases were quetiapine (1,151 cases, ROR = 7.62), olanzapine (754 cases, ROR = 7.92), and citalopram (720 cases, ROR = 13.63). The two most frequently reported first-level Anatomical Therapeutic Chemical (ATC) groups were the drugs for the nervous system (n = 19, 47.50%) and antiinfectives for systemic use (n = 7, 17.50%). Patients with missing gender (n = 3,482, 23.68%) aside, there were more females (7,536, 51.24%) than males (5,158, 35.07%) were involved. 3,720 patients (25.29%) suffered serious clinical outcomes resulting in deaths or life-threatening conditions. Overall, most drugs that caused QT interval prolongation had early failure types according to the assessment of the Weibull's shape parameter (WSP) analysis.ConclusionsOur study offered a list of drugs that frequently caused QT interval prolongation based on the FAERS system, along with a description of some risk profiles for QT interval prolongation brought on by these drugs. When prescribing these drugs in clinical practice, we should closely monitor the occurrence of ADE for QT interval prolongation

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

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

Is 100 Percent Debt Optimal? Three Essays on Aggressive Capital Structure and Myth of Negative Book Equity Firms

This dissertation comprises of three related essays in regard of puzzling negative book equity phenomenon among U.S. public firms. In essay 1, I present the evidence that there is an increasing trend of negative book equity firms over the past 50 years, from 0.3% up to over 5% among publicly traded firms in US. In contrast to previous research which generally classify these firms as distressed firms with highly likelihood of bankruptcy, I propose a new method to separate Healthy Negative Book Equity Firms (HNBEF) from relatively more distressed negative book equity firms. The results show that HNBEF have much higher net income and interest coverage ratio, they survive longer, and pay more dividends. More interestingly, these firms are often actively increase share repurchases and debt issuance. These facts, combined with their strong profitability, indicate that managers of these firms are actively increasing their leverage and choose to be negative book equity firms. To explain the existence of HNBEF, in essay 2, I investigate several possible reasons that may contribute to the extreme leverage of these firms. I find that HNBEF are substantially undervalued by their book assets as stated on the balance sheet. In addition, the value of intangible assets, especially those off-balance sheet intangible assets, is positively related to the probability of becoming HNBEF. Moreover, I find that characteristics of intangible assets and firms also play important role on existence of HNBEF. Specifically, I find that both liquidity and redeployability of intangible assets are positively related with the probability of becoming HNBEF. Also, firms associated with closer borrower-lender relationship are more likely to become HNBEF. To investigate if the aggressive capital structure adopted by HNBEF is optimal, in essay 3, I performed several tests to analyze how these firms differ from other firms in terms of operating performance, corporate governance and firm value. My research finds that compared to firms from same industry and with similar size, managers of HNBEF invest more heavily in their own firms, and HNBEF have better corporate governance. In addition, HNBEF are associated with better operating performance and higher value

Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines

Estimating High-Resolution PM_2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available