Learning to explain causal rationale of stock price changes in financial reports

Department of Computer Science and EngineeringWhen a critical event occurs, it is often necessary to provide appropriate explanations. Previously, several theoretical and empirical foundations which discover causes and effects in temporal data have been established. However, for textual data, a simple causality modeling is not enough to handle variations in natural languages. To address the challenges in textual causality modeling, we annotate and create a large causality text dataset, called ???Causal Rationale of Stock Price Changes??? (CR-SPC) to fine-tune pre-trained language models. Our dataset includes 283K sentences from the 10-K annual reports of the U.S. companies, and sentence-level labels, from which we observe diverse patterns of causality from each industrial sector for stock price changes. Because of this diversity and an imbalance in training data across sectors, BERT+fine-tune baseline on Sector-only data shows a biased performance. We propose to transfer from related sectors, implemented as a two-stage fine tuning framework. First-stage fine tuning transfers from related sector, to overcome the limited training resource, then the second stage follows to fine tune for the given sector. Our proposed framework yields significantly improved results for detecting causal rationale from industrial sectors with low amounts of data. Furthermore, we generate labels for 382K unlabeled sentences and augment the size of the dataset by self-training on CR-SPC dataset.clos

CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports

In this paper, we introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports. This is a comprehensive large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. CR-COPEC contributes to two major achievements. First, it detects causal rationale from 10-K annual reports of the U.S. companies, which contain experts' causal analysis following accounting standards in a formal manner. This dataset can be widely used by both individual investors and analysts as material information resources for investing and decision making without tremendous effort to read through all the documents. Second, it carefully considers different characteristics which affect the financial performance of companies in twelve industries. As a result, CR-COPEC can distinguish causal sentences in various industries by taking unique narratives in each industry into consideration. We also provide an extensive analysis of how well CR-COPEC dataset is constructed and suited for classifying target sentences as causal ones with respect to industry characteristics. Our dataset and experimental codes are publicly available.Comment: Accepted in Findings of EMNLP 202

Expression of Pro-inflammatory Protein S100A12 (EN-RAGE) in Behçet's Disease and Its Association with Disease Activity: A Pilot Study

BACKGROUND: S100A12 is a member of the S100 family of calcium-binding proteins and is secreted either in inflamed tissues or in the bloodstream by activated neutrophils. Expression of S100A12 has been reported in various diseases, especially non-infectious inflammatory diseases, such as Kawasaki disease, giant cell arteritis and inflammatory bowel disease. OBJECTIVE: This study was conducted to determine both the tissue expression and the serum levels of S100A12 in Behçet's disease (BD) patients and the correlation of the S100A12 serum level with disease activity of BD. METHODS: We included in this study ten BD patients who fulfilled the criteria for diagnosis, according to the International Study Group for BD. The activity of BD was calculated using the BD Current Activity Form. The serum concentrations of both S100A12 and interleukin-8 were measured by the enzyme-linked immunosorbent assay, before and after treatment. Immunohistochemical studies were also performed to detect S100A12 expression in the skin. RESULTS: The serum S100A12 level was significantly increased in the active BD period (p<0.001), in the inactive BD period (p=0.041) and in patients with active Kawasaki disease (p=0.028), compared with the serum level in the healthy controls. The serum S100A12 level decreased significantly from baseline, compared to post-treatment (p=0.017). The activity score of BD was significantly correlated with the serum level of S100A12 (Spearman's coefficient=0.464, p=0.039). Immunohistochemical studies showed that S100A12 was strongly expressed in the erythema nodosum-like skin lesions of patients. CONCLUSION: S100A12 contributes to the pathogenesis of BD related to neutrophil hyperactivity and reflects the disease activity in BD patientsope

A Protective Role for Heme Oxygenase-1 in INS-1 Cells and Rat Islets that are Exposed to High Glucose Conditions

Heme oxygenase-1 (HO-1) has been described as an inducible protein that is capable of cytoprotection via radical scavenging and the prevention of apoptosis. Chronic exposure to hyperglycemia can lead to cellular dysfunction that may become irreversible over time, and this process has been termed glucose toxicity. Yet little is known about the relation between glucose toxicity and HO-1 in the islets. The purposes of the present study were to determine whether prolonged exposure of pancreatic islets to a supraphysiologic glucose concentration disrupts the intracellular balance between reactive oxygen species (ROS) and HO-1, and so this causes defective insulin secretion; we also wanted to evaluate a protective role for HO-1 in pancreatic islets against high glucose levels. The intracellular peroxide levels of the pancreatic islets (INS-1 cell, rat islet) were increased in the high glucose media (30 mM glucose or 50 mM ribose). The HO-1 expression was induced in the INS-1 cells by the high glucose levels. Both the HO-1 expression and glucose stimulated insulin secretion (GSIS) was decreased simultaneously in the islets by treatment of the HO-1 antisense. The HO-1 was upregulated in the INS-1 cells by hemin, an inducer of HO-1. And, HO-1 upregulation induced by hemin reversed the GSIS in the islets at a high glucose condition. These results suggest HO-1 seems to mediate the protective response of pancreatic islets against the oxidative stress that is due to high glucose conditions

An Engineered Viral Protease Exhibiting Substrate Specificity for a Polyglutamine Stretch Prevents Polyglutamine-Induced Neuronal Cell Death

BACKGROUND: Polyglutamine (polyQ)-induced protein aggregation is the hallmark of a group of neurodegenerative diseases, including Huntington's disease. We hypothesized that a protease that could cleave polyQ stretches would intervene in the initial events leading to pathogenesis in these diseases. To prove this concept, we aimed to generate a protease possessing substrate specificity for polyQ stretches. METHODOLOGY/PRINCIPAL FINDINGS: Hepatitis A virus (HAV) 3C protease (3CP) was subjected to engineering using a yeast-based method known as the Genetic Assay for Site-specific Proteolysis (GASP). Analysis of the substrate specificity revealed that 3CP can cleave substrates containing glutamine at positions P5, P4, P3, P1, P2', or P3', but not substrates containing glutamine at the P2 or P1' positions. To accommodate glutamine at P2 and P1', key residues comprising the active sites of the S2 or S1' pockets were separately randomized and screened. The resulting sets of variants were combined by shuffling and further subjected to two rounds of randomization and screening using a substrate containing glutamines from positions P5 through P3'. One of the selected variants (Var26) reduced the expression level and aggregation of a huntingtin exon1-GFP fusion protein containing a pathogenic polyQ stretch (HttEx1(97Q)-GFP) in the neuroblastoma cell line SH-SY5Y. Var26 also prevented cell death and caspase 3 activation induced by HttEx1(97Q)-GFP. These protective effects of Var26 were proteolytic activity-dependent. CONCLUSIONS/SIGNIFICANCE: These data provide a proof-of-concept that proteolytic cleavage of polyQ stretches could be an effective modality for the treatment of polyQ diseases

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Measurement and Analysis of Gait Pattern during Stair Walk for Improvement of Robotic Locomotion Rehabilitation System

Background. Robotic locomotion rehabilitation systems have been used for gait training in patients who have had a stroke. Most commercialized systems allow patients to perform simple exercises such as balancing or level walking, but an additional function such as stair-walk training is required to provide a wide range of recovery cycle rehabilitation. In this study, we analyzed stair-gait patterns and applied the result to a robotic rehabilitation system that can provide a vertical motion of footplates. Methods. To obtain applicable data for the robotic system with vertically movable footplates, stair-walk action was measured using an optical marker-based motion capture system. The spatial position data of joints during stair walking was obtained from six healthy adults who participated in the experiment. The measured marker data were converted into joint kinematic data by using an algorithm that included resampling and normalization. The spatial position data are represented as angular trajectories and the relative displacement of each joint on the anatomical sagittal plane and movements of hip joints on the anatomical transverse plane. Results. The average range of motion (ROM) of each joint was estimated as (−6.75°,48.69°) at the hip, 8.20°,93.78° at the knee, and −17.78°,11.75° at the ankle during ascent and as 6.41°,31.67° at the hip, 7.38°,91.93° at the knee, and −24.89°,24.18° at the ankle during descent. Additionally, we attempted to create a more natural stair-gait pattern by analyzing the movement of the hip on the anatomical transverse plane. The hip movements were estimated to within ±1.57 cm and ±2.00 cm for hip translation and to within ±2.52° and ±2.70° for hip rotation during stair ascent and stair descent, respectively. Conclusions. Based on the results, standard patterns of stair ascent and stair descent were derived and applied to a lower-limb rehabilitation robot with vertically movable footplates. The relative trajectory from the experiment ascertained that the function of stair walking in the robotic system properly worked within a normal ROM

Aerial Imaging-Based Fuel Information Acquisition for Wildfire Research in Northeastern South Korea

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires