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India introduced credit scoring technology in 2007. We study its adoption by the two main types of banks operating there: new private banks (NPBs) and state-owned public sector banks (PSBs). Soon after the technology is introduced, NPBs start checking the credit scores of most borrowers before lending. PSBs do so equally quickly for new borrowers but very slowly for prior clients, although lending without checking scores is reliably associated with more delinquencies. We show that an important factor explaining the difference in adoption rates is the stickiness of past bank structures and managerial practices. Past practices inhibit better practices today. 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.

Adrenocortical cancer (ACC) is a rare and aggressive cancer (0.5-2 cases/million/year) with poor 5-year survival. Most patients inevitably succumb to widespread metastasis due to a lack of effective systemic treatment; therefore, it is critically important to identify new therapies for clinical trials. In this direction, we identified a potent synergistic combination of Heat Shock Protein 90 (HSP90) and PI3K inhibitors via quantitative high-throughput drug screening (qHTS) for ACC, using a pharmaceutical library of ~5000 drugs (FDA approved or investigational), and evaluating the drug synergy by computerized drug combination matrix analysis. Preclinical in vitro studies were performed to validate the efficacy of HSP90 inhibitors (STA9090, AUY922, HSP990), with PI3K inhibitors; PIK75 (investigational) or clinically available BGT226, and their pairwise combinations, in NCI-H295R and SW13 ACC cells. The drug combination of STA9090 and PIK75 significantly inhibited cell proliferation (monolayer and 3-dimensional culture and induced apoptosis more effectively than the individual drugs, with simultaneous upregulation of apoptotic protein markers (cleaved-Caspase3, cleaved-PARP), inhibition of the phosphorylated members of PI3K signaling pathway (AKT1, mTOR, S6K, 4EB1 and GSK3α/β), and the G2/M phase cell cycle arrest in ACC cells. Additionally, the synergetic effect of the drug combination was effective in reducing the metastatic potential as revealed by invasion/migration assay, which was confirmed by the downregulation of the epithelial-to-mesenchymal transition proteins (Vimentin, N-cadherin, SNAIL and Zeb1). An inhibition of cortisol production and altered oncogenic proteomic profiling was also observed. We validated the synergy of BGT226 with STA9090 and observed similar phenotypic changes by the drug combination in ACC cells. Interestingly, RNA sequencing and pathway analysis further revealed distinct gene expression profiles of STA9090 combined with PIK75 or BGT226, indicating different mechanism of cell death in both the PI3K drug combination treatment groups. Consistent with our in vitro findings, observations from the gene expression data from TCGA revealed significant mRNA overexpression of genes in HSP90 and PI3K signaling pathways (HSP90AA1, HSP90AB1 HSP90B1, TRAP1, PI3KCA, PI3KCB, AKT2, CDK1, CDK4, NR3C1) in 64% (n=59/92) ACC samples, with a positive correlation of mesenchymal markers TWIST1, ZEB1, VIM with HSP90AB1 and PI3KCA, suggesting our drug combination can be applicable in patients with ACC. A similar pattern of gene expressions was confirmed in independent GEO datasets. Conclusively, combination of HSP90 and PI3K inhibitors demonstrated a promising in vitro synergistic efficacy by inhibiting the key oncogenic targets of ACC. Further validation of in vivo efficacy is warranted. Citation Format: Prachi Mishra, Dipranjan Laha, Brieann Sobieski, Min Shen, Ya-Qin Zhang, Matthew Hall, Naris Nilubol. Synergistic efficacy of HSP90 and PI3K inhibitors in adrenocortical carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2684.

Background Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients ‘sera using a multi-omics discovery platform. Methods Pre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan–Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort. Results Among 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins—Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite—1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98–14.40), P 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45–32.05, P Conclusions In this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome.

The physiological mechanisms underpinning adaptations to starvation and cold stresses have been extensively studied in Drosophila, yet the understanding of correlated changes in stress-related and life-history traits, as well as the energetics of stress tolerance, still remains elusive. To answer the questions empirically in this context, we allowed D. melanogaster to evolve for either increased starvation or cold tolerance (24-generations / regime) in an experimental evolution system, and examined whether selection of either trait affects un-selected stress trait, as well as the impacts potential changes in life-history and mating success-related traits. Our results revealed remarkable changes in starvation/cold tolerance (up to 1.5-fold) as a direct effect of selection, while cold tolerance had been dramatically reduced (1.26-fold) in the starvation tolerant (ST) lines compared to control counterparts, although no such changes were evident in cold-tolerant (CT) lines. ST lines exhibited a higher level of body lipids and a reduced level of trehalose content, while CT lines accumulated a greater levels of body lipid and trehalose contents. Noticeably, we found that selection for starvation or cold tolerance positively correlates with larval development time, longevity, and copulation duration, indicating that these traits are among the most common targets of selection trajectories shaping stress tolerance. Altogether, this study highlights the complexity of mechanisms evolved in ST lines that contribute to enhanced starvation tolerance, but also negatively impact cold tolerance. Nevertheless, mechanisms foraging enhanced cold tolerance in CT lines appear not to target starvation tolerance. Moreover, the parallel changes in life history/mating success traits across stress regimes could indicate some generic pathways evolved in stressful environments, targeting life-history and mating success characteristics to optimize fitness.

Simple Summary This article reviews current treatment practices for thyroid cancer with a focus on novel targeted molecular therapy. Rapidly expanding knowledge of the molecular biology of these cancers coupled with the increased availability of genetic testing has led to exciting paradigm shifts in treatment strategies for these tumor types. We aim to provide up-to-date information on these state-of-the-art therapies as a guide for clinicians who specialize in the treatments of thyroid cancer. Abstract Thyroid cancer is the most common type of endocrine malignancy comprising 2–3% of all cancers, with a constant rise in the incidence rate. The standard first-line treatments for thyroid cancer include surgery and radioactive iodine ablation, and a majority of patients show a good response to these therapies. Despite a better response and outcome, approximately twenty percent of patients develop disease recurrence and distant metastasis. With improved knowledge of molecular dysregulation and biological characteristics of thyroid cancer, the development of new treatment strategies comprising novel targets has accelerated. Biomarker-driven targeted therapies have now emerged as a trend for personalized treatments in patients with advanced cancers, and several multiple receptor kinase inhibitors have entered clinical trials (phase I/II/III) to evaluate their safety and efficacy. Most extensively investigated and clinically approved targeted therapies in thyroid cancer include the tyrosine receptor kinase inhibitors that target antiangiogenic markers, BRAF mutation, PI3K/AKT, and MAPK pathway components. In this review, we focus on the current advances in targeted mono- and combination therapies for various types of thyroid cancer.

Adrenocortical cancer (ACC) is a rare and aggressive cancer (0.5-2 cases/ million/ year) with a poor 5-year survival ( Presentation: Saturday, June 11, 2022 1:12 p.m. - 1:17 p.m., Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query with traditional DBMS approaches. Can we estimate the cost of each query more efficiently without any computation in a SQL engine kernel? Can machine learning techniques help to estimate SQL query resource utilization? The answers are yes. We propose a SQL query cost predictor service, which employs machine learning techniques to train models from historical query request logs and rapidly forecasts the CPU and memory resource usages of online queries without any computation in a SQL engine. At Twitter, infrastructure engineers are maintaining a large-scale SQL federation system across on-premises and cloud data centers for serving ad-hoc queries. The proposed service can help to improve query scheduling by relieving the issue of imbalanced online analytical processing (OLAP) workloads in the SQL engine clusters. It can also assist in enabling preemptive scaling. Additionally, the proposed approach uses plain SQL statements for the model training and online prediction, indicating it is both hardware and software-agnostic. The method can be generalized to broader SQL systems and heterogeneous environments. The models can achieve 97.9\% accuracy for CPU usage prediction and 97\% accuracy for memory usage prediction., Comment: 2021 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2021

Tenascin C (TNC), an extra-cellular matrix (ECM) family gene, is expressed in several cancer tissues of breast, lung, colon, and gastrointestinal tract leading to proliferation, migration, invasion, angiogenesis and metastasis, but its role in tumorigenesis of prostate cancer is poorly understood. We took a meta-analysis approach to characterize the alterations of TNC gene in prostate cancer using publicly available databases (cBioportal Version 2.2.0, http://www.cBioportal.org/index.do). The analysis identified TNC alterations (gene amplification) significantly in the neuroendocrine prostate cancer dataset (Trento/Broad/Cornell, N = 114), which was further validated in other prostate cancer datasets, including The Cancer Genome Atlas (TCGA) prostate cancer (2015). In the TCGA prostate cancer dataset (N = 498), high TNC (alteration frequency, 36%) revealed a strong association with high diagnostic Gleason score. Genomic alterations of TNC was also significantly associated (P < 0.05) with expression level of genes from NOTCH, SOX and WNT family, implicating a link between TNC and poorly differentiated aggressive phenotype in NEPC. TCGA prostate adenocarcinoma cases with TNC alteration also demonstrated prominent decrease in disease-free survival (P = 0.0637). These findings indicate a possible association of TNC to the aggressive subtype of prostate cancer and warrant further functional studies to evident the involvement of TNC in prostate cancer progression.

This study was conducted to understand the behaviour of ten rice genotypes for different water deficit stress levels. The spectroscopic hyperspectral reflectance data in the range of 350–2500 nm was recorded and relative water content (RWC) of plants was measured at different stress levels. The optimal wavebands were identified through spectral indices, multivariate techniques and neural network technique, and prediction models were developed. The new water sensitive spectral indices were developed and existing water band spectral indices were also evaluated with respect to RWC. These indices based models were efficient in predicting RWC with R2 values ranging from 0.73 to 0.94. The contour plotting using the ratio spectral indices (RSI) and normalized difference spectral indices (NDSI) was done in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best index. Spectral reflectance data was also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR) and Artificial Neural Networks (ANN), support vector machine regression (SVR) and random forest (RF) models to calculate plant RWC. Among these multivariate models, PLSR-MLR was found to be the best model for prediction of RWC with R2 as 0.98 and 0.97 for calibration and validation respectively and Root mean square error of prediction (RMSEP) as 5.06. The results indicate that PLSR is a robust technique for identification of water deficit stress in the crop. Although the PLSR is robust technique, if PLSR extracted optimum wavebands are fed into MLR, the results are found to be improved significantly. The ANN model was developed with all spectral reflectance bands. The 43 developed model didn’t produce satisfactory results. Therefore, the model was developed 44 with PLSR selected optimum wavebands as independent x variables and PLSR-ANN model 45 was found better than the ANN model alone. The study successfully conducts a comparative 46 analysis among various modelling approaches to quantify water deficit stress. The methodology developed would help to identify water deficit stress more accurately by predicting RWC in the crops.

International audience; Do past employment characteristics of central bank governors affect financial regulation? To answer this question, we construct a new data set based on curriculum vitae of all central bank governors around the world in 1970–2011. We interpret work experiences as indicators of preferences toward deregulation. Over the average duration in office (5.6 years), a governor with financial sector experience is associated with three times more deregulation than a governor without experience in finance. Similar results hold for past experience at the IMF; in contrast, past experience at the BIS and the UN are associated with less deregulation.

Since Tocqueville (1835), civil society has been recognized as a cornerstone of liberal democracy. But populists claim to be the only legitimate representatives of the people, leaving no space for civil society. Are populism and civil society enemies? To answer this question, we look at voters’ choices in Europe. We find that individuals belonging to associations are less likely by 1.6–2.8 percentage points to vote for populist parties, which is large considering that the average vote share for populist parties is between 12% and 22%. This result survives a large number of robustness checks.

This paper analyzes market reactions to the 2013–2014 Fed announcements related to the tapering of asset purchases and examines how these reactions are influenced by financial depth. The study focuses on long-term government bond yields and uses daily data for all emerging markets. Controlling for all time-invariant country characteristics as well as time-varying macroeconomic fundamentals (changes in current account, fiscal balance, GDP growth, and inflation), countries with deeper domestic financial markets (as measured by higher bank credit, M2, M3, or stock market capitalization) experienced smaller increases in government bond yields during 4-day windows around Federal Open Market Committee announcements related to tapering. Countries with better macroeconomic fundamentals (measured by improvements in current account, fiscal balance, and GDP growth) also experienced smaller increases in government bond yields around such episodes.

This paper studies the political influence of individual firms on Congressional decisions to suspend tariffs on U.S. imports of intermediate goods. We develop a legislative bargaining model in which firms influence legislators by transmitting information about the value of protection, using verbal messages and lobbying expenditures. Model estimation using firm-level data on tariff suspension bills and lobbying expenditures reveals that the probability a suspension is granted decreases with each additional firm that expresses opposition. This effect is significantly larger than that of either opponent or proponent lobbying, due to the greater information content of verbal opposition and legislative bargaining costs. JEL: F13, D7.

Influence of machining process parameters on material removal rate (MRR) and chip reduction coefficient (Z) was investigated and optimized individually during turning T6 tempered Al 7075 alloy with uncoated tungsten carbide inserts in dry environment. MRR increased with increasing any of the machining parameters; however, Z reduced with spindle speed and increased with depth of cut. Analysis of Variance (ANOVA) for both the responses was conducted at 95% confidence level and linear regression models were developed. Moreover, form of chips produced during each machining run was studied.

Why do firms lobby? This paper exploits the unanticipated sequestration of federal budget accounts in March 2013 that reduced the availability of government funds disbursed through procurement contracts to shed light on this question. Following this event, firms with little or no prior exposure to the federal accounts that experienced cuts reduced their lobbying spending. In contrast, firms with a high degree of exposure to the cuts maintained and even increased their lobbying spending. This suggests that, when the same number of contractors competed for a piece of a reduced pie, the more affected firms likely intensified their lobbying efforts to distinguish themselves from the others and improve their chances of procuring a larger share of the smaller overall. These findings are stronger in government-dependent sectors and when there is intense competition. The evidence is more consistent with a rent-seeking explanation for lobbying.

INTRODUCTION AND OBJECTIVES:Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. Development of prognostic panels that include multip...

India introduced credit scoring technology in 2007. We study its adoption by the two main types of banks operating there, new private banks (NPBs) and state-owned public sector banks (PSBs). NPBs start checking the credit scores of most borrowers before lending soon after the technology is introduced. PSBs do so equally quickly for new borrowers but very slowly for prior clients, although lending without checking scores is reliably associated with more delinquencies. We show that an important factor explaining the difference in adoption is the stickiness of past bank structures and associated managerial practices. Past practices hold back better practices today.

Credit scoring was introduced in India in 2007. We study the pace of its adoption by new private banks (NPBs) and state-owned or public sector banks (PSBs). NPBs adopt scoring quickly for all borrowers. PSBs adopt scoring quickly for new borrowers but not for existing borrowers. Instrumental Variable (IV) estimates and counterfactuals using scores available to but not used by PSBs indicate that universal adoption would reduce loan delinquencies significantly. Evidence from old private banks suggests that neither bank size nor government ownership fully explains adoption patterns. Organizational culture, possibly from formative experiences in sheltered markets, explains the patterns of technology adoption.