POE: Process of Elimination for Multiple Choice Reasoning

Language models (LMs) are capable of conducting in-context learning for multiple choice reasoning tasks, but the options in these tasks are treated equally. As humans often first eliminate wrong options before picking the final correct answer, we argue a similar two-step strategy can make LMs better at these tasks. To this end, we present the Process of Elimination (POE), a two-step scoring method. In the first step, POE scores each option, and eliminates seemingly wrong options. In the second step, POE masks these wrong options, and makes the final prediction from the remaining options. Zero-shot experiments on 8 reasoning tasks illustrate the effectiveness of POE, and a following analysis finds our method to be especially performant on logical reasoning tasks. We further analyze the effect of masks, and show that POE applies to few-shot settings and large language models (LLMs) like ChatGPT.Comment: Accepted as a short paper at EMNLP 202

Iterative Network Pricing for Ridesharing Platforms

Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based "surge pricing", however, does not take into consideration market conditions at trip destinations, leading to inefficient driver flows in space and incentivizes drivers to strategize. In this work, we introduce the Iterative Network Pricing mechanism, addressing a main challenge in the practical implementation of optimal origin-destination (OD) based prices, that the model for rider demand is hard to estimate. Assuming that the platform's surge algorithm clears the market for each origin in real-time, our mechanism updates the OD-based price adjustments week-over-week, using only information immediately observable during the same time window in the prior weeks. For stationary market conditions, we prove that our mechanism converges to an outcome that is approximately welfare-optimal. Using data from the City of Chicago, we illustrate (via simulation) the iterative updates under our mechanism for morning rush hours, demonstrating substantial welfare improvements despite significant fluctuations of market conditions from early 2019 through the end of 2020

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways

BACKGROUND Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. RESULTS We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. CONCLUSIONS Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases

Source Apportionment of Gaseous and Particulate PAHs from Traffic Emission Using Tunnel Measurements in Shanghai, China

Understanding sources and contributions of gaseous and particulate PAHs from traffic-related pollution can provide valuable information for alleviating air contamination from traffic in urban areas. On-road sampling campaigns were comprehensively conducted during 2011–2012 in an urban tunnel of Shanghai, China. 2–3 rings PAHs were abundant in the tunnel\u27s gas and particle phases. Diagnostic ratios of PAHs were statistically described; several were significantly different between the gas and particle phases. Principal component analysis (PCA), positive matrix factorization (PMF), bivariate correlation analysis and multiple linear regression analysis (MLRA) were applied to apportion sources of gaseous and particulate PAHs in the tunnel. Main sources of the gaseous PAHs included evaporative emission of fuel, high-temperature and low-temperature combustion of fuel, accounting for 50–51%, 30–36% and 13–20%, respectively. Unburned fuel particles (56.4–78.3%), high-temperature combustion of fuel (9.5–26.1%) and gas-to-particle condensation (12.2–17.5%) were major contributors to the particulate PAHs. The result reflected, to a large extent, PAH emissions from the urban traffic of Shanghai. Improving fuel efficiency of local vehicles will greatly reduce contribution of traffic emission to atmospheric PAHs in urban areas. Source apportionment of PM10 mass was also performed based on the organic component data. The results showed that high-temperature combustion of fuel and gas-to-particle condensation contributed to 15–18% and 7–8% of PM10 mass, respectively, but 55–57% of the particle mass was left unexplained. Although the results from the PCA and PMF models were comparable, the PMF method is recommended for source apportionment of PAHs in real traffic conditions. In addition, the combination of multivariate statistical method and bivariate correlation analysis is a useful tool to comprehensively assess sources of PAHs

Epigenetic liquid biopsies for minimal residual disease, what’s around the corner?

Liquid biopsy assays for minimal residual disease (MRD) are used to monitor and inform oncological treatment and predict the risk of relapse in cancer patients. To-date, most MRD assay development has focused on targeting somatic mutations. However, epigenetic changes are more frequent and universal than genetic alterations in cancer and circulating tumor DNA (ctDNA) retains much of these changes. Here, we review the epigenetic signals that can be used to detect MRD, including DNA methylation alterations and fragmentation patterns that differentiate ctDNA from noncancerous circulating cell-free DNA (ccfDNA). We then summarize the current state of MRD monitoring; highlight the advantages of epigenetics over genetics-based approaches; and discuss the emerging paradigm of assaying both genetic and epigenetic targets to monitor treatment response, detect disease recurrence, and inform adjuvant therapy

Cancer-associated mesothelial cells promote ovarian cancer chemoresistance through paracrine osteopontin signaling

Ovarian cancer is the leading cause of gynecological malignancy-related deaths, due to its widespread intraperitoneal metastases and acquired chemoresistance. Mesothelial cells are an important cellular component of the ovarian cancer microenvironment that promote metastasis. However, their role in chemoresistance is unclear. Here, we investigated whether cancer-associated mesothelial cells promote ovarian cancer chemoresistance and stemness in vitro and in vivo. We found that osteopontin is a key secreted factor that drives mesothelial-mediated ovarian cancer chemoresistance and stemness. Osteopontin is a secreted glycoprotein that is clinically associated with poor prognosis and chemoresistance in ovarian cancer. Mechanistically, ovarian cancer cells induced osteopontin expression and secretion by mesothelial cells through TGF-β signaling. Osteopontin facilitated ovarian cancer cell chemoresistance via the activation of the CD44 receptor, PI3K/AKT signaling, and ABC drug efflux transporter activity. Importantly, therapeutic inhibition of osteopontin markedly improved the efficacy of cisplatin in both human and mouse ovarian tumor xenografts. Collectively, our results highlight mesothelial cells as a key driver of ovarian cancer chemoresistance and suggest that therapeutic targeting of osteopontin may be an effective strategy for enhancing platinum sensitivity in ovarian cancer

Structural, thermal and dissolution properties of MgO- and CaO-containing borophosphate glasses: effect of Fe2O3 addition

This paper investigated manufacture of high-durability phosphate glass fibres for biomedical applications. Five different borophosphate glass formulations in the systems of 45P2O5–5B2O3–5Na2O–(29 − x)CaO–16MgO–(x)Fe2O3 and 45P2O5–5B2O3–5Na2O–24CaO–(21 − x)MgO–(x)Fe2O3 where x = 5, 8 and 11 mol% were produced via melt quenching. The compositions and amorphous nature of the glasses were confirmed by ICP-MS and XRD, respectively. FTIR results indicated depolymerisation of the phosphate chains with a decrease in Q2 units with increasing Fe2O3 content. DSC analyses showed an increase in Tg by ~5 °C with an increment of 3 mol% in Fe2O3 content. The thermal properties were also used to calculate processing window (i.e. Tc,ons—Tg) and another parameter, Kgl, to determine the suitability for fibre drawing directly from melt, which equals (Tc,ons—Tg)/(Tl—Tc,ons). The degradation study conducted in PBS solution at 37 °C showed a decrease of 25–47% in degradation rate with increasing Fe2O3 content. This confirmed that the chemical durability of the glasses had increased, which was suggested to be due to Fe2O3 addition. Furthermore, the density measured via Archimedes method revealed a linear increase with increasing Fe2O3 content

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd