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Pancreatic neoplasm, a highly aggressive and often fatal cancer, poses challenges due to late detection and nonspecific symptoms. Therefore, both early diagnosis and appropriate therapeutic approaches are necessary to augment the condition of these patients. Cancer cells undergo metabolic deregulation, which enables their proliferation, survival, and invasion. As a result, it is crucial to focus on the metabolic pathways in prevalent cancers and explore treatment strategies that target these pathways to control tumor growth effectively. This is particularly relevant in cancers like pancreatic cancer, which undergo numerous metabolic alterations. The ketogenic regimen, characterized by low carbohydrate and protein contents and high-fat sources, does not involve caloric restriction. This allows for the induction of ketogenesis and an increase in ketone bodies, while insulin and glucose levels remain low even after meals. This unique metabolic state may influence the tumor microenvironment. Given the lack of unanimous agreement on the precise role and mechanism of the ketogenic diet, this review aims to clarify the diagnostic value and accuracy of ketone bodies in various types of pancreatic tumors and explore the potential anti-cancer effects of the ketogenic diet when used alone or in conjunction with chemotherapy, also to determine the potential of the ketogenic diet to be used as adjuvant therapy. The outcomes of this study are instrumental in enhancing our understanding of the benefits and drawbacks associated with employing this diet for the management and diagnosis of pancreatic cancer. [ABSTRACT FROM AUTHOR]

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM 1 and PM 2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam's razor principle. For the PM 1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM 2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models. [ABSTRACT FROM AUTHOR]

The subject of this study is the interaction between 5,10,15,20-tetrakis (4-sulfonatophenyl)–porphyrin (TSPP), a potential photosensitizer for photodynamic therapy (PDT) and radiotherapy, and human serum albumin (HSA), a crucial protein in the body. The main objective was to investigate the binding mechanisms, structural changes, and potential implications of these interactions for drug delivery and therapeutic applications. Spectroscopic techniques and computational methods were employed to investigate the mechanism and effects of TSPP binding by HSA. The results suggest the possibility of simultaneous binding of three TSPP ions at binding sites of different affinity within albumin. The estimated values of the binding constant Kb for these sites were in the range of 0.6 to 6.6 μM−1. Laser flash photolysis indicated the stabilization of TSPP in the HSA structure, which resulted in prolonged lifetimes of the excited states (singlet and triplet) of porphyrin. Circular dichroism analysis was used to assess the changes in the secondary and tertiary structures of HSA upon TSPP binding. An analysis of the molecular docking results allowed us to identify the preferred TSPP binding sites within HSA and provided information on the specific interactions of amino acids involved in the stabilization of TSPP–HSA complexes. The estimated free energy of the binding of porphyrin at the three most favorable docking sites found in the HSA structure that was considered native were in the range of −80 to −41 kcal/mol. Finally, thermal unfolding studies showed that TSPP increased the stability of the secondary structure of albumin. All these findings contribute to the understanding of the interactions between TSPP and HSA, offering valuable insights for the development of novel cancer therapy approaches. [ABSTRACT FROM AUTHOR]

Disturbances in carbohydrate metabolism are suggested to be the early symptoms of pancreatic ductal adenocarcinoma (PDAC). The accumulated data suggests that endocrine function-related biomarkers may represent a breakthrough in the early detection of PDAC. Factors which may predispose one to the development of PDAC are insulin resistance and hyperinsulinemia. Elevated insulin levels induce the onset of carcinogenesis by altering the differentiation and function of islet cells through stimulating growth factors, including insulin-like growth factors (IGFs). Impaired β cell function, along with the impact of PDAC-released factors (e.g., adrenomedullin (ADM), IGF-1, and macrophage inhibitory factor (MIF) on pancreatic islets, may contribute to the induction of diabetes associated with PDAC. Recently, exosomes have attracted worldwide attention due to their role in varied features of cell function, particularly in cancer progression. Exosomes comprise of small extracellular vesicles produced by almost all cells. These vesicles contain a vast array of biomolecules, including proteins and microRNAs. Exosomes participate in cancer growth and promote angiogenesis. They promote tumorigenesis and metastasis, and are associated with the acquisition of cancer cells resistant to chemotherapy. Data have been accumulating recently on the role of exosomes in the rapid recognition, prognosis and potential therapy of pancreatic cancer. [ABSTRACT FROM AUTHOR]

Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decision-making. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities. [ABSTRACT FROM AUTHOR]

The non-coding RNAs (ncRNAs) comprise a large part of human genome that mainly do not code for proteins. Although ncRNAs were first believed to be non-functional, the more investigations highlighted tthe possibility of ncRNAs in controlling vital biological processes. The length of long non-coding RNAs (lncRNAs) exceeds 200 nucleotidesand can be present in nucleus and cytoplasm. LncRNAs do not translate to proteins and they have been implicated in the regulation of tumorigenesis. On the other hand, One way cells die is by a process called autophagy, which breaks down proteins and other components in the cytoplasm., while the aberrant activation of autophagy allegedly involved in the pathogenesis of diseases. The autophagy exerts anti-cancer activity in pre-cancerous lesions, while it has oncogenic function in advanced stages of cancers. The current overview focuses on the connection between lncRNAs and autophagy in urological cancers is discussed. Notably, one possible role for lncRNAs is as diagnostic and prognostic variablesin urological cancers. The proliferation, metastasis, apoptosis and therapy response in prostate, bladder and renal cancers are regulated by lncRNAs. The changes in autophagy levels can also influence the apoptosis, proliferation and therapy response in urological tumors. Since lncRNAs have modulatory functions, they can affect autophagy mechanism to determine progression of urological cancers. [ABSTRACT FROM AUTHOR]

To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems. [ABSTRACT FROM AUTHOR]

In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited. [ABSTRACT FROM AUTHOR]

The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors. [ABSTRACT FROM AUTHOR]

BACKGROUND: Advanced pancreatic ductal adenocarcinoma (PDAC) remains a disease with a dismal prognosis, significantly limited therapeutic options, and few innovative drugs. Inflammation plays a significant role in the development and progression of PDAC. Systemic inflammatory indexes reflect the anti-tumor inflammatory capacity of and are of prognostic and predictive value in the treatment of patients with PDAC. METHODS: In our retrospective study, we investigated the prognostic and predictive significance of inflammatory markers in chemonaive patients with locally advanced unresectable pancreatic cancer (LAPC) and metastatic pancreatic cancer (mPDAC), in relation to progression-free survival (PFS) and overall survival (OS). Survival analysis was conducted using the Kaplan-Meier method with log-rank tests in univariate analysis. We used multivariate Cox regression analysis to determine the impact of inflammatory markers on survival time. RESULTS: The present clinical study included 46 patients with LAPC and mPDAC treated with FOLFIRINOX (folinic acid, fluorouracil, irinotecan, oxaliplatin) or GEM/Nab-P (gemcitabine/nab-paclitaxel) as first-line chemotherapy regimens. Performance status (PS) ECOG 0-1, neutrophil-to-lymphocyte ratio (NLR)=2.09 and the prognostic nutritional index (PNI)=49.09 were associated with significantly longer OS in the analyzed patient cohort, Multivariate analysis confirmed PS, NLR and PNI as independent prognostic factors for OS. CONCLUSION: In our cohort of patients with advanced PDAC, PS, NLR and PNI were confirmed as independent prognostic factors for OS. [ABSTRACT FROM AUTHOR]

Stock price prediction has traditionally been known as a challenging task. However, recent advancements in machine learning and deep learning models have spurred extensive research in predicting stock returns. This study applies these predictive models to U.S. stock data to forecast stock returns and develop investment strategies based on these forecasts. Additionally, the performance of the model-based investment strategy was compared with that of a widely recognized method, market capitalization-weighted investing. The results indicate that, overall, market capitalization-weighted investing outperformed model-based investing. However, the highest returns were observed in the model-based strategy. It was also found that model-based investing exhibits higher volatility in returns, with significant disparities between years of high and low returns. While investing through machine learning methodologies may be attractive to investors seeking high risk and high return, market capitalization-weighted investing is likely more suitable for those desiring stable returns. [ABSTRACT FROM AUTHOR]

Aeroelastic simulations are employed to assess wind turbines in accordance with IEC standards in the time domain. These analyses enable the evaluation of fatigue and extreme loads experienced by wind turbine components. Such simulations are essential for several reasons, including but not limited to reducing safety margins in wind turbine component design by accounting for a wide range of uncertainties in wind and wave conditions and fulfilling the requirements of the digital twin, which necessitates a comprehensive set of simulations for calibration. Thus, it is essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. To address this challenge, we propose a data-driven approach to construct surrogate models for the damage equivalent load (DEL) based on aeroelastic simulation outputs. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need for time-consuming aeroelastic simulations. Our study focuses on utilizing a sequential machine learning (ML) method to map wind speed time series to DEL. Additionally, we demonstrate the versatility of the developed and trained surrogate models by testing them on a wind turbine in the wake and applying transfer learning to enhance their predictive accuracy. [ABSTRACT FROM AUTHOR]

Understanding the probabilistic nature of brittle materials due to inherent dispersions in their mechanical properties is important to assess their reliability and safety for sensitive engineering applications. This is all the more important when elements composed of brittle materials are exposed to dynamic environments, resulting in catastrophic fatigue failures. The authors propose the application of a non-intrusive polynomial chaos expansion method for probabilistic studies on brittle materials undergoing fatigue fracture when geometrical parameters and material properties are random independent variables. Understanding the probabilistic nature of fatigue fracture in brittle materials is crucial for ensuring the reliability and safety of engineering structures subjected to cyclic loading. Crack growth is modelled using a phase-field approach within a finite element framework. For modelling fatigue, fracture resistance is progressively degraded by modifying the regularised free energy functional using a fatigue degradation function. Number of cycles to failure is treated as the dependent variable of interest and is estimated within acceptable limits due to the randomness in independent properties. Multiple 2D benchmark problems are solved to demonstrate the ability of this approach to predict the dependent variable responses with significantly fewer simulations than the Monte Carlo method. This proposed approach can accurately predict results typically obtained through 10 5 or more runs in Monte Carlo simulations with a reduction of up to three orders of magnitude in required runs. The independent random variables' sensitivity to the system response is determined using Sobol' indices. The proposed approach has low computational overhead and can be useful for computationally intensive problems requiring rapid decision-making in sensitive applications like aerospace, nuclear and biomedical engineering. The technique does not require reformulating existing finite element code and can perform the stochastic study by direct pre/post-processing. [ABSTRACT FROM AUTHOR]

Simple Summary: The quality of life is influenced by individual's characteristics and values, personal's beliefs, physical and mental health and the quality of relationship to others. The researchers want to investigate how the quality of life of primary caregivers changes during care for patient with palliative care needs. It is a prospective longitudinal study that assesses the different aspects of the quality of life of caregivers in care process of palliative care. The caregiver burden which increased during the care of patient have a great and negative impact on quality of life as decrease significantly physical, psycho-emotional and social aspects of this. The primary caregiver is feeling the perception of deteriorating general health. Background/Objectives: The quality of life is a complex concept that is insufficiently assessed in clinical practice. It is influenced by different factors, as follows: the individual's characteristics, personal values and beliefs, physical and mental state, and relationship to other members of their community. The quality of life of the primary caregiver influences their health and the quality of their care interventions. This study aims to investigate how the quality of life of caregivers changes during palliative patients' care. Methods: This is a prospective longitudinal study that assesses the different aspects of the quality of life of primary caregivers who care for patients with palliative needs. The tool used in this study was the Medical Outcomes Scale-Short Form 36 (MOS-SF36). Results: This study included 140 caregivers, of which 63 were involved in the care of patients with cancer and 77 were involved in the care of patients with non-oncological diseases. Almost 9 out of 10 caregivers were a family member of the patient and over two-thirds of these were women. The caregivers of patients with non-malignant diseases had a decreased quality of life in the following aspects: limitations in their usual role due to emotional problems, social functioning, energy, and their perception of their general health. In the group of oncological patients, the caregivers displayed limitations in their daily role due to physical health, emotional problems, and social functioning. Conclusions: The large number of responsibilities, the long time spent caring, and the uncertainty about the evolution of the disease as well as the marginalization and lack of time for oneself are some of the elements that increase caregiver burden. Along with this, the quality of life of caregivers decreases significantly in different aspects, such as physical, psycho-emotional, and social, with the perception of deteriorating general health. [ABSTRACT FROM AUTHOR]

Objective: To explore the feasibility and safety of laparoscopic radical coloproctectomy and hepatectomy for resectalble colorectal cancer with liver metastases (CRCLM), and evaluate the survival outcomes of short-middle term for these patients., Methods: Totally 36 cases of CRCLM which were evaluated to undergo laparoscopic coloproctectomy and hepatectomy preoperatively, were enrolled from January 2009 to January 2014, including 28 synchronous and 8 metachronous CLM respectively. Laparoscopic colorectal resection and hepatectomies were performed in 35 cases, including 24 male and 11 female patients, with a mean age of (64 ± 12) years and a median age of 67 years (ranging from 35 to 80 years). Management strategies were made by a board of multi-disciplinary team. Intra-operative ultrasonography was used to detect the metastases in all cases. Overall survival and disease free survival were calculated by Kaplan-Meier curve., Results: Radical total colectomy, right hemicolectomy, left hemicolectomy, sigmoidectomy, and proctectomy and were performed in 1 case of familial adenomatous polyposis with transverse colon cancer, 5 cases of cecal or asending colon cancer, 1 case of descending colon cancer, 14 cases of sigmoid colon cancer, and 14 cases of rectal cancer respectively. Metastasectomy only, anatomic hepatectomy only, and metastasectomy plus anatomic hepatectomy were done in 21, 10 and 4 cases respectively. Totally 35 colorectal tumors and 62 liver lesions were removed. The mean blood loss of colorectal and liver surgery were (80 ± 32) and (212 ± 153) ml, the median blood loss was 70 ml and 150 ml respectively. Colorectal and hepatic specific complications were not observed in all cases except a case of biliary leakage following right hemihepatectomy. Relapses were observed in 15 cases during a mean follow-up of (26 ± 16) months (median follow-up of 22 months). Four cases died of late-stage cachexia. The 1- and 3-year cumulative overall survival rates were 92.9% and 79.4% respectively. The 1- and 3-year cumulative disease free survival rates were 61.1% and 49.4% respectively., Conclusions: Laparoscopic coloproctectomy and hepatectomy for resectable CRCLM in carefully selected cases is safe and feasible, which makes simultaneous surgery possible. The oncologic outcome of short-middle term is acceptable, and long-term survival is expected.

Objective: To analyze the R1 rate of the pancreatic head carcinoma resection specimens which delt with a unified protocol by two different R1 criteria., Methods: Between November 2011 and October 2013, a unified pathological protocol was prospectively used to handle 70 consecutive pancreatioduodenectomy specimens for pancreatic ductal adenocarcinoma. Apart from the pancreatic transection margin, the bile duct and stomach/jejunum margins, different colors were used to stain the anterior surface, the superior mesenteric vein (SMV) groove margin, the superior mesenteric artery (SMA) margin, and the posterior surface. Axial slicing technique was used to dissect the pancreatioduodenectomy specimens., Results: Among the 70 patients, 3, 30 and 37 patients were classified as well, moderately and poorly differentiated respectively;7, 15 and 48 patients were classified as pT1, pT2 and pT3 respectively.Forty patients (57.1%) had metastases in regional lymph nodes (pN1) , and 16 patients (22.9%) had metastases in para-aortic nodes.Resection of the portal vein and/or the superior mesenteric vein was performed in 13 patients (18.6%) .When applying the UICC criteria, 26 cancer resections were classified R1 (37.1%) , 33 margins were turned out to be R1. The SMV groove margin and SMA margin were infiltrated in 13 specimens (13/33, 39.4%) respectively.When applying the Royal College of Pathologist's criteria, 39 cancer resections were classified R1 (55.7%) , 51 margins were turned out to be R1. The SMV groove margin and SMA margin were infiltrated in 18 (18/51, 35.3%) and 19 (19/51, 37.3%) specimen respectively.Until April 2014, the median follow-up was 18(range 6-42) months., Conclusions: Applying the unified protocol for pancreatic head ductal adenocarcinoma specimens results in an significant R1 rate of the resection margins, and the R1 rate is related to the R1 criterion. The SMV groove margin and SMA margin are the two most frequent sites of R1.

Objective: Postoperative pancreatic fistula (POPF) and readmission remain the significant sources of morbidity after distal pancreatectomy (DP). We describe a technique of drainage and postoperation management to prevent severe POPF and readmission., Methods: A retrospective analysis of 57 patients who underwent DP from January 2009 to November 2011 was conducted. The drain was placed just abutting the pancreatic stump by suturing the tissue near the stump. Each patient was discharged carrying an intraabdominal drain, and a strict follow-up was made in the outpatient clinic. Postoperative pancreatic fistula was defined using the international study group classification for pancreatic fistula definition., Results: Thirty-two patients accepted laparoscopic DP (LDP), and 25 patients accepted open DP (ODP). There was no significant difference in the rate of POPF between the 2 groups (12/32 vs 11/25, P = 0.786). All the POPF cases were grades A and B, but no case was grade C. No case needed a specific intervention. There was no reoperation, readmission, intraabdominal abscess, or surgical site infection., Conclusions: We conclude that our technique of drainage and postoperation management is associated with a low incidence of severe POPF and readmission. Continued investigation of this technique is warranted.

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

In this paper, we consider conditional measures of lead-lag relations between aggregate growth and industry-level cash flow growth in the United States. Our results show that firms in leading industries pay an average annualized return 3.6 |$\%$| higher than that of firms in lagging industries. Using both time-series and cross-sectional tests, we estimate an annual pure timing premium ranging from 1.2 |$\%$| to 1.7 |$\%$|⁠. This finding can be rationalized in a model in which (a) agents price growth news shocks, and (b) leading industries provide valuable resolution of uncertainty about the growth prospects of lagging industries. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We propose and test a framework of private information acquisition and decision timing for asset allocators hiring outside investment managers. Using unique data on due diligence interactions between an institutional allocator and 860 hedge fund managers, we find that the production of private information complements public information. The allocator strategically chooses how much proprietary information to collect, reducing due diligence time by 18 months and improving outcomes. Funds selected by the manager outperform those not selected by 9 |$\%$| over 20 months. The outperformance relates to the allocator learning about fund return-to-scale constraints and manager skill before other investors. [ABSTRACT FROM AUTHOR]

Background: Intraductal papillary mucinous neoplasm (IPMN) is a rare pancreatic cystic neoplasm, accounting for 1% of all exocrine pancreatic neoplasms. This study aimed to summarize the clinicopathological and biological behaviors, as well as the experience in diagnosis and treatment of IPMN., Methods: Clinicopathological data were collected from 36 cases with IPMN who were treated in Department of General Surgery, Peking University Third Hospital from May 2001 to July 2011., Results: The 36 cases of IPMN patients included 27 males and 9 females (M:F = 3:1). The age of patients ranged from 52 to 78 years, with an average of 67.3 years. Regarding tumor location, 20 tumors were located in pancreatic head, 3 in pancreatic neck, 10 in pancreatic body and tail, and 3 in the whole pancreas. All the 36 cases underwent surgical treatment, with 13 cases of pancreaticoduodenectomy, 3 cases of middle pancreatectomy, 7 cases of tumor resection plus pancreaticojejunostomy, 3 cases of distal pancreatectomy, 7 cases of distal pancreatectomy plus spleen resection, and 3 cases of total pancreaticoduodenectomy. Of the 36 patients, 9 patients underwent the operations under laparoscopy. The 36 cases included main duct type (14 cases, 38.9%), branch duct type (10 cases, 27.7%), and mixed duct type (12 cases, 33.3%). Pathologically, of the 36 cases, there were 7 IPMN adenomas, 11 borderline IPMNs, 6 IPMN with carcinomas in situ, and 12 IPMNs with invasive carcinomas. All the 36 cases were followed up. During an average of 42 months follow-up period (26-129 months), no recurrence occurred., Conclusions: IPMN, which primarily occurs in male, is a low-grade malignancy which may involve any part of the pancreas, with specific clinicopathological features. IPMN is a different malignancy type from pancreatic ductal carcinoma. Imaging and laboratory examination are helpful for the diagnosis and differential diagnosis. The prediction of invasive IPMN is still difficult. Surgical resection is recommended as the first choice of treatment. Aggressive and proper operation procedure produces better prognosis. Long-term follow-up is necessary for patients after operation. Laparoscopic distal pancreatectomy is a feasible and safe procedure for the indicated patients.

Background: Pancreatic neuroendocrine tumors (pNETs) are a type of tumors with the characteristics of easy metastasis and recurrence. Till date, the risk factors affecting the prognosis are still in the debate. In this study, several risk factors will be discussed combined with our cases and experience., Methods: Thirty-three patients diagnosed as pNETs were enrolled and the clinical features, blood tests, pathological features, surgical treatment, and follow-up data of these patients were collected and analyzed., Results: In this study, operation time of G3 cases was longer than G1/G2 cases (P = 0.017). The elevated level of tumor markers such as AFP, CEA, Ca125, and Ca19-9 may predict easier metastasis, earlier recurrence, and poor prognosis (P = 0.007). The presence of cancer embolus and nerve invasion increases along with the TNM stage (P = 0.037 and P = 0.040), and the incidence of positive surgical margin increased (P = 0.007). When the presence of nerve invasion occurs, the chance of cancer embolus and lymph node metastasis also increases (P = 0.016 and P = 0.026)., Conclusions: pNETs were tumors with the features of easy recurrence and metastasis and many risk factors could affect its prognosis such as the elevated levels of tumor markers and the presence of nerve invasion, except some recognized risk factors. If one or more of these factors existed, postoperative treatments may be needed to improve prognosis.

Background: To assess the efficacy of intraoperative ultrasound-guided implantation of 125I seeds for the treatment of unresectable pancreatic carcinoma, and analyze the associated prognostic factors., Methods: Twenty-eight patients with pancreatic carcinoma who underwent laparotomy and were considered to have unresectable tumors were included in this study. Nine patients were pathologically diagnosed with Stage II disease, and nineteen patients with Stage III disease. Twenty-eight patients received intraoperative ultrasound-guided 125I seed implantation and received a D90 (at least 90% of the tumor volume received the reference dose) ranging from 60 to 163 Gy, with a median of 120 Gy. Seven patients received an additional 35-50 Gy external beam radiotherapy after seed implantation, and ten patients received two to ten cycles of chemotherapy. Overall survival of the patients was calculated and prognostic factors were evaluated., Results: Of the patients, 94.1% (16/17) achieved good to medium pain relief. The tumor response rate was 78.6% (22/28), and local control was achieved in 85.7% (24/28) of patients. The 1-, 2- and 3-year survival rates were 30%, 11% and 4%, and the median survival was 10.1 months (95% CI: 9.0-10.9). Analysis using the Cox proportional hazards model suggested that patients younger than 60 years and patients who received a D90 higher than 110 Gy may survive for a longer period., Conclusions: I seed implantation provides a safe and effective method to relieve pain, control local tumor growth and, to some extent, prolong the survival of patients with stage II and III pancreatic disease, without additional complications. Age and accumulated dose may be factors predictive of a favorable outcome for patients with unresectable pancreatic carcinoma treated with 125I seeds. These findings need to be validated by conducting further studies with larger cohorts.

Despite the wide acceptance of laparoscopic resection for treatment of abdominal tumors, only few cases of simultaneous laparoscopic removal of the spleen and the right liver have been reported to date. Littoral cell angiosarcoma (LCAS), which arises from the littoral cells lining the sinus channels of the splenic red pulp, is a rare condition, and there is limited literature on littoral cell angiosarcoma with liver metastases. We present the case of a 28-year-old woman with postoperative pathologically-proven LCAS with right liver metastases. The patient's surgery was safely performed, and her postoperative course was uneventful until now. This case suggests that concomitant laparoscopic splenectomy (LS) and right hemihepatectomy is a suitable surgical option for selected patients.

The aim of the study was to investigate correlations between intrahepatic hepatitis B virus total DNA, covalently closed circular DNA (cccDNA), and serum HBsAg in treatment-naïve chronic hepatitis B and HBV related hepatocellular carcinoma (HCC). Liver tissues were taken from 42 HBV related HCC and 36 patients with chronic hepatitis B. A fraction of DNA extracted from liver tissue was digested with a plasmid-safe ATP-dependent DNase and used for HBV cccDNA detection. The remaining DNA was used for the detection of HBV total DNA and β-globin, the latter of which is a housekeeping gene and quantified for normalization by real-time PCR. Quantitation of serum HBsAg was performed by a chemiluminescence assay. Serum HBsAg had positive correlations with serum HBV DNA (r = 0.636, P < 0.001), intrahepatic HBV total DNA (r = 0.519, P = 0.001) and cccDNA (r = 0.733, P < 0.001) in 36 treatment-naïve chronic hepatitis B, while HBsAg correlated poorly with DNA (r = 0.224, P = 0.210), intrahepatic total DNA and cccDNA in the tumor (r = 0.351, P = 0.031; r = 0.164, P = 0.324, respectively) and non-tumor (r = 0.237, P = 0.152; r = 0.072, P = 0.667, respectively) liver tissues of 42 HCC. HBV cccDNA and total DNA were significantly higher in liver tissue from chronic hepatitis B than in tumor and non-tumor of HCC (P < 0.001). Serum HBsAg and HBV DNA were also higher in chronic hepatitis B than in HCC (P < 0.001). It was concluded that levels of serum HBsAg and intrahepatic cccDNA and total DNA were significantly higher in chronic hepatitis B than in HCC, and significant correlations among them were observed in treatment-naïve chronic hepatitis B but not in HCC., (Copyright © 2012 Wiley Periodicals, Inc.)

Objective: Obesity has become a worldwide health problem in the past decades. Human and animal studies have implicated serotonin in appetite regulation, and behavior genetic studies have shown that body mass index (BMI) has a strong genetic component. However, the roles of genes related to the serotoninergic (5-hydroxytryptamine,5-HT) system in obesity/BMI are not well understood, especially in Chinese subjects., Subjects and Design: With a sample of 478 healthy Chinese volunteers, this study investigated the relation between BMI and genetic variations of the serotoninergic system as characterized by 136 representative polymorphisms. We used a system-level approach to identify SNPs associated with BMI, then estimated their overall contribution to BMI by multiple regression and verified it by permutation., Results: We identified 12 SNPs that made statistically significant contributions to BMI. After controlling for gender and age, four of these SNPs accounted for 7.7% additional variance of BMI. Permutation analysis showed that the probability of obtaining these findings by chance was low (p = 0.015, permuted for 1000 times)., Conclusion: These results showed that genetic variations in the serotoninergic system made a moderate contribution to individual differences in BMI among a healthy Chinese sample, suggesting that a similar approach can be used to study obesity.

Bus stop passenger flow prediction is a key component in developing urban intelligent transit systems. Bus line networks are usually complex nonlinear time-varying systems, which poses a challenge for constructing the spatio-temporal correlations of passenger flows at bus stops in a region. A multigraph fusion spatio-temporal graph attention network (MF_STGAT) is proposed, which uses the spatio-temporal correlation of each bus stop to achieve regional-level passenger flow prediction. First, MF_STGAT enhances the ability to capture spatio-temporal correlations of bus stops by extracting key frames of bus passenger flow time sequence data and encoding the region into two maps (geographic feature map and functional similarity map). Second, a GAT-based graph fusion method is constructed to obtain the spatial feature of bus stops. The spatial feature information is encoded into vectors, and long short-term memory (LSTM) is used to predict the bus passenger flow at each stop in the region. Finally, the IC card data of 12 main bus lines in Beijing, China, are selected to evaluate the model. The results show that MF_STGAT outperforms baseline models. In addition, we further discuss the extent of the improvement in the accuracy of the model prediction through the spatio-temporal modeling approach and the robustness of MF_STGAT. Moreover, we output the attention weights of the model to enhance the interpretability of the spatio-temporal distribution characteristics of the bus passenger flow. [ABSTRACT FROM AUTHOR]

The probability density function (PDF), and its corresponding cumulative density function (CDF), provide direct statistical insight into the characterization of a random process or field. Typically displayed as a histogram, one can infer probabilities of the occurrence of particular events. When examining a field over some two-dimensional domain in which at each point a PDF of the function values is available, it is challenging to assess the global (stochastic) features present within the field. In this paper, we present a visualization system that allows the user to examine two-dimensional data sets in which PDF (or CDF) information is available at any position within the domain. The tool provides a contour display showing the normed difference between the PDFs and an ansatz PDF selected by the user and, furthermore, allows the user to interactively examine the PDF at any particular position. Canonical examples of the tool are provided to help guide the reader into the mapping of stochastic information to visual cues along with a description of the use of the tool for examining data generated from an uncertainty quantification exercise accomplished within the field of electrophysiology.

In this paper, we present the preparation and characterization of organic dye-coated magnetic particles and its application as magnetic resonance imaging (MRI) and photodynamic therapy (PDT) agent. We have coated the surface of Fe3O4 magnetic nanoparticles by meso-tetra(4-sulfonatophenyl) porphyrin dihydrochloride (TSPP), a photosensitive drug used in PDT, which is capable of generating singlet oxygen on the surface of paramagnetic Fe3O4 nanoparticles when irradiated at 400 nm. While the MRI was performed with in vivo mice experiment along with proper irradiation and therapeutic effect was followed by using in-situ MRI technology. The resulting conjugate is a single particle platform that can simultaneously be used as a PDT therapeutic and MRI contrasting agent.

Objective: To evaluate the feasibility and outcomes of laparoscopic surgery for primary retroperitoneal tumors (PRTs)., Methods: Between January 2007 and July 2009, laparoscopic resection was attempted of PRT in 14 patients. The histological type of the tumor, operating characteristics, and outcomes were analyzed., Results: In the 14 patients (4 male), the median age was 44 years, with a median tumor diameter of 7.6 cm. Laparoscopic resection was successfully performed in 13 patients with a solely laparoscopic approach (n=11) or combined with a hand-assistant procedure (n=2). One case (epithelioid hemangioma) was converted to laparotomy. The median operating time for laparoscopy was 139 minutes with an estimated average blood loss of 59.2 mL. There were no transfusions or major complications. After a median follow-up of 17 months, all patients remained disease free., Conclusions: Laparoscopic surgery for the resection of PRT is feasible and effective when performed by an experienced surgeon.

We introduce a real-time measure of conditional biases to firms' earnings forecasts. The measure is defined as the difference between analysts' expectations and a statistically optimal unbiased machine-learning benchmark. Analysts' conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Background: The purpose of treatment for hepatolithiasis is reducing recurrent cholangitis. This study was designed to determine the long-term outcome of Oddi sphincterotomy for hepatolithiasis., Methods: Thirty-two consecutive hepatolithiasis patients, including 15 cases that had undergone conventional open surgical transduodenal sphincteroplasty (COSTS) and 17 cases with endoscopic sphincterotomy (EST), were followed up for a long term. We compared the post-treatment rates of recurrent cholangitis and acute cholangitis severe type (ACST) with the ones of pretreatment in each group, respectively. Paired chi(2) test was used., Results: In the COSTS group, neither the post-operative rate of recurrent cholangitis (86.7%, 13/15 vs. 100%, 15/15) nor ACST (26.7%, 4/15 vs. 46.7%, 7/15) decreased (p > 0.05) compared with before the operation. In the EST group, the situation was the same: neither the rate of post-EST recurrent cholangitis (76.5%, 13/17 vs. 82.4%, 14/17) nor ACST (29.4%, 5/17 vs. 23.5%, 4/17) were reduced significantly (p > 0.05)., Conclusions: Both eradication of hepatobiliary lesions and keeping Oddi sphincter intact should be emphasized in the management of hepatolithiasis. Neither COSTS nor EST is an effective method or can achieve the goal of reducing cholangitis for hepatolithiasis.

Background: To assess the feasibility and efficacy of using 125I seed implantation under intraoperative ultrasound guidance for unresectable pancreatic carcinoma., Methods: Fourteen patients with pancreatic carcinoma that underwent laparotomy and considered unresectable were included in this study. Nine patients were pathologically diagnosed with Stage II disease, five patients with Stage III disease. Fourteen patients were treated with 125I seed implantation guided by intraoperative ultrasound and received D90 of 125I seeds ranging from 60 to 140 Gy with a median of 120 Gy. Five patients received an additional 35-50 Gy from external beam radiotherapy after seed implantation and six patients received 2-6 cycles of chemotherapy., Results: 87.5% (7/8) of patients received partial to complete pain relief. The response rate of tumor was 78.6%, One-, two-and three-year survival rates were 33.9% and 16.9%, 7.8%, with local control of disease achieved in 78.6% (11/14), and the median survival was 10 months (95% CI: 7.7-12.3)., Conclusion: There were no deaths related to 125I seed implant. In this preliminary investigation, 125I seed implant provided excellent palliation of pain relief, local control and prolong the survival of patients with stage II and III disease to some extent.

There is a controversy over whether the different outcomes of prophylaxis of hepatitis B virus (HBV) recurrence are attributable to different treatments. A systematic review and a meta-analysis were conducted to evaluate lamivudine monotherapy and combined therapy of lamivudine and hepatitis B immunoglobulin (HBIG) in HBV infected liver recipients. A fixed effects model was used for statistical pooling of relative risks (RR) for the different outcomes. Six articles (551 patients) fulfilled the inclusion criteria. Statistically significant differences were observed between lamivudine monotherapy and lamivudine + HBIG therapy in hepatitis B recurrence [P < 0.0001; RR = 0.38; 95% CI (0.25, 0.58)], YMDD mutant [P = 0.002; RR = 0.40; 95% CI (0.23, 0.72)] and hepatitis B recurrence in HBV-DNA positive patients before orthotopic liver transplantation [P < 0.00001; RR = 0.31; 95% CI (0.21, 0.45)]. No significant differences were observed in patient survival [P = 0.59; RR = 1.02; 95% CI (0.95, 1.09)], graft survival [P = 0.56; RR = 1.02; 95% CI (0.95, 1.09)] and diseases leading to death between the two groups [HBV recurrence leading to death: P = 0.05; RR = 0.47; 95% CI (0.22, 1.02); hepatocellular carcinoma recurrence leading to death: P = 0.13; RR = 0.34; 95% CI (0.09, 1.36)]. In conclusion, combination of lamivudine and HBIG can effectively decrease the recurrence rate of HBV and the incidence of YMDD mutant, but it can not improve patient survival and graft survival significantly. Well-designed large-sample trials are needed to evaluate the efficiency of combined therapy of lamivudine and HBIG in prophylaxis of HBV recurrence in liver graft recipients.

Despite the wide acceptance of laparoscopic resection of abdominal tumors, few cases of laparoscopic removal of retroperitoneal schwannoma have been reported to date. We present a case of retroperitoneal schwannoma, encircled with the celiac artery, common hepatic artery, splenic artery, splenic vein, and portal vein, which was resected successfully using laparoscopic surgical technique. During the procedure, an attempt was made to approach the neoplasm via the inferior border of the pancreas, but it was hard to separate the superior mesenteric vein. We changed the position to separate the tumor from the anterior border of the splenic vein with success. Without any complications, the patient was discharged on the sixth postoperative day. In contrast to the open procedure for resection of a tumor in this location, laparoscopy has the advantage of precise visualization, which helps to resect retroperitoneal schwannoma completely.

Markovian models of ion channels have proven useful in the reconstruction of experimental data and prediction of cellular electrophysiology. We present the stochastic Galerkin method as an alternative to Monte Carlo and other stochastic methods for assessing the impact of uncertain rate coefficients on the predictions of Markovian ion channel models. We extend and study two different ion channel models: a simple model with only a single open and a closed state and a detailed model of the cardiac rapidly activating delayed rectifier potassium current. We demonstrate the efficacy of stochastic Galerkin methods for computing solutions to systems with random model parameters. Our studies illustrate the characteristic changes in distributions of state transitions and electrical currents through ion channels due to random rate coefficients. Furthermore, the studies indicate the applicability of the stochastic Galerkin technique for uncertainty and sensitivity analysis of bio-mathematical models.

Glucagon-like peptide-1 receptor agonist (GLP-1RA), a novel hypoglycemic agent for the treatment of type 2 diabetes, has well-known effects such as lowering blood sugar, ameliorating inflammation, reducing weight, and lowering blood lipids. It has also been shown that it can influence the proliferation and survival of cells and has a certain effect on the prognosis of some neoplastic diseases. In this study, the potential effects of GLP-1RAs on the occurrence and development of tumorswere reviewed to provide new ideas for the prevention and treatment of tumors in patients. [ABSTRACT FROM AUTHOR]

A comprehensive modeling framework utilizing hydrodynamic and sediment transport models (MIKE Hydro River, MIKE 21 FM, MIKE 21C) was applied to the 33.57 km stretch of the Banshadhara River from Banshadhara bridge, Gunupur to Kashinagar town. Daily discharge, water levels, and sediment data from 2013 to 2022 of Gunupur and Kashinagar gauge stations was used as model input, and calibration–validation of models. Additionally, satellite remote sensing data used for determined Manning's roughness (n) values as well compared the model outputs. Calibration procedures ensured high accuracy, with MIKE Hydro River achieving 98.3% agreement between observed and simulated water levels. 1D sediment transport model showing significant variability in total bed load values ranging from 0.3 to 3.83 m^3/s. The Wilcock & Crowe gravel bed load formula accurately predicted bed load transport within the expected magnitude, albeit tending to overestimate transport, indicative of supply-limited conditions. 2D MIKE 21 FM simulations highlighted flow dynamics, with rapid flood arrival at Kashinagar and varying current speeds along the river, especially at bends. MIKE 21C, focused on the central portion, indicated erosion processes at river bends with high discharge. Sediment transport model accuracy was approximately 86.7%, capturing general trends despite occasional discrepancies. Over a 10-year simulation, erosion trends and potential river course shifting were observed, particularly near bends. The analysis provides valuable insights for river management, flood risk mitigation, water resource management, infrastructure planning, and environmental preservation in the Banshadhara River basin and similar environments. Article Highlights: Accurate modeling predicts river erosion and possible course shifts, vital for flood risk planning. Sediment transport varies widely, indicating complex riverbed dynamics impacting river management. Study offers insights into managing river ecosystems, infrastructure, and environmental preservation. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is known to be the most lethal cancer. Fewer new treatments are being developed for pancreatic cancer as compared to other cancers. The bioactive lipid S1P, which is mainly regulated by sphingosine kinase 1 (SK1) and sphingosine kinase 2 (SK2) enzymes, plays significant roles in pancreatic cancer initiation and exacerbation. S1P controls many signaling pathways to modulate the progression of pancreatic cancer through the G-coupled receptor S1PR1-5. Several papers reporting amelioration of pancreatic cancer via modulation of S1P levels or downstream signaling pathways have previously been published. In this paper, for the first time, we have reviewed the results of previous studies to understand how S1P and its receptors contribute to the development of pancreatic cancer, and whether S1P can be a therapeutic target. In addition, we have also reviewed papers dealing with the effects of SK1 and SK2, which are kinases that regulate the level of S1P, on the pathogenesis of pancreatic cancer. We have also listed available drugs that particularly focus on S1P, S1PRs, SK1, and SK2 for the treatment of pancreatic cancer. Through this review, we would like to suggest that the SK/S1P/S1PR signaling system can be an important target for treating pancreatic cancer, where a new treatment target is desperately warranted. [ABSTRACT FROM AUTHOR]

We compare sparse grid stochastic collocation and Gaussian process emulation as surrogates for the parameter-to-observation map of a groundwater flow problem related to the Waste Isolation Pilot Plant in Carlsbad, NM. The goal is the computation of the probability distribution of a contaminant particle travel time resulting from uncertain knowledge about the transmissivity field. The latter is modelled as a lognormal random field which is fitted by restricted maximum likelihood estimation and universal kriging to observational data as well as geological information including site-specific trend regression functions obtained from technical documentation. The resulting random transmissivity field leads to a random groundwater flow and particle transport problem which is solved realization-wise using a mixed finite element discretization. Computational surrogates, once constructed, allow sampling the quantities of interest in the uncertainty analysis at substantially reduced computational cost. Special emphasis is placed on explaining the differences between the two surrogates in terms of computational realization and interpretation of the results. Numerical experiments are given for illustration. [ABSTRACT FROM AUTHOR]

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is a global health concern, presenting significant risks to human health. Atherosclerosis is among the most prevalent CVD, impacting the medium and large arteries in the kidneys, brain, heart, and other vital organs, as well as the lower limbs. As the disease progresses, arterial obstruction can result in heart attacks and strokes. Therefore, patients with atherosclerosis should receive accurate diagnosis and timely therapeutic intervention. With the advancements in nanomedicine, researchers have proposed new research strategies and methods for atherosclerosis imaging. This paper summarizes some current research findings on the use of nanomaterials in diagnosing atherosclerosis and offers insights for optimizing the imaging applications of nanomaterials in the future. [ABSTRACT FROM AUTHOR]

This article predicts the returns of S&P 500 constituents using long short-term memory (LSTM) and gradient boosting (GB) algorithms, incorporating technical indicators and principal component analysis (PCA). Models are evaluated using root-mean-squared error (RMSE), and portfolio performance is assessed through continuously compounded returns, contrasting growing and sliding window setups. The findings reveal that GB achieves superior forecasting performance compared to LSTM, with both models benefiting from the set of technical indicators. Additionally, the models that use principal components tend to have higher RMSE and yield lower portfolio returns. Importantly, portfolio returns emerge as a more indicative measure of potential profitability compared to RMSE. Lastly, the analysis reveals that the machine learning (ML) models can outperform the benchmark S&P 500 index, offering significant economic gains and affirming their potential in enhancing investment strategies. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this study is to investigate the predictive performance of the machine and deep learning methods in predicting the CAC 40 index and its 40 constituent prices of the French stock market during the COVID-19 pandemic. The study objective in forecasting the CAC 40 index is to analyze if the index and the individual prices will preserve the continuous increase they acquired at the beginning of the administration of vaccination and containment measures or if the negative effect of the pandemic will be reflected in the future. Design/methodology/approach: The authors apply two machine and deep learning methods (KNN and LSTM) and compare their performances to ARIMA time series model. Two scenarios have been considered: optimistic (high values) and pessimistic (low values) and four periods are examined: the period before COVID-19 pandemic, the period during the COVID-19, and the period of vaccination and containment. The last period is divided into two sub-periods: the test period and the prediction period. Findings: The authors found that the KNN method performed better than LSTM and ARIMA in forecasting the CAC 40 index for both scenarios. The authors also identified that the positive effect of vaccination and containment outweighs the negative effect of the pandemic, and the recovery pattern is not even among major companies in the stock market. Practical implications: The study empirical results have valuable practical implications for companies in the stock market to respond to unexpected events such as COVID-19, improve operational efficiency and enhance long-term competitiveness. Companies in the transportation sector should consider additional investment in R&D on communication and information technology, accelerate their digital capabilities, at least in some parts of their businesses, develop plans for lights out factories and supply chains to keep pace with changing times, and even include big data resources. Additionally, they should also use a mix of financing sources and securities in order to diversify their capital structure, and not rely only on equity financing as their share prices are volatile and below the pre-pandemic level. Considering portfolio allocation, the transportation sector was severely affected by the pandemic. This displays that transportation equities fail to be a candidate as a good diversifier during the health crisis. However, the diversification would be worth it while including assets related to the banking and industrial sectors. On another strand, the instability of this period induced an informational asymmetry among investors. This pessimistic mood affected the assets' value and created a state of disequilibrium opening up more opportunities to benefit from potential arbitrage profits. Originality/value: The impact of COVID-19 on stock markets is significant and affects investor behavior, who suffered amplified losses in a very short period of time. In this regard, correct and well-informed decision-making by investors and other market participants requires careful analysis and accurate prediction of the stock markets during the pandemic. However, few studies have been conducted in this area, and those studies have either concentrated on some specific stock markets or did not apply the powerful machine learning and deep learning techniques such as LSTM and KNN. To the best of our knowledge, no research has been conducted that used these techniques to assess and forecast the CAC 40 French stock market during the pandemic. This study tries to close this gap in the literature. [ABSTRACT FROM AUTHOR]

Rapidly developing machine learning methods have stimulated research interest in computationally reconstructing differential equations (DEs) from observational data, providing insight into the underlying mechanistic models. In this paper, we propose a new neural-ODE-based method that spectrally expands the spatial dependence of solutions to learn the spatiotemporal DEs they obey. Our spectral spatiotemporal DE learning method has the advantage of not explicitly relying on spatial discretization (e.g., meshes or grids), thus allowing reconstruction of DEs that may be defined on unbounded spatial domains and that may contain long-ranged, nonlocal spatial interactions. By combining spectral methods with the neural ODE framework, our proposed spectral DE method addresses the inverse-type problem of reconstructing spatiotemporal equations in unbounded domains. Even for bounded domain problems, our spectral approach is as accurate as some of the latest machine learning approaches for learning or numerically solving partial differential equations (PDEs). By developing a spectral framework for reconstructing both PDEs and partial integro-differential equations (PIDEs), we extend dynamical reconstruction approaches to a wider range of problems, including those in unbounded domains. [ABSTRACT FROM AUTHOR]