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This study investigates the effectiveness of simultaneous and staged evacuation strategies using agent-based simulation. In the simultaneous strategy, all residents are informed to evacuate simultaneously, whereas in the staged evacuation strategy, residents in different zones are organized to evacuate in an order based on different sequences of the zones within the affected area. This study uses an agent-based technique to model traffic flows at the level of individual vehicles and investigates the collective behaviours of evacuating vehicles. We conducted simulations using a microscopic simulation system called Paramics on three types of road network structures under different population densities. The three types of road network structures include a grid road structure, a ring road structure, and a real road structure from the City of San Marcos, Texas. Default rules in Paramics were used for trip generation, destination choice, and route choice. Simulation results indicate that (1) there is no evacuation strategy that can be considered as the best strategy across different road network structures, and the performance of the strategies depends on both road network structure and population density; (2) if the population density in the affected area is high and the underlying road network structure is a grid structure, then a staged evacuation strategy that alternates non-adjacent zones in the affected area is effective in reducing the overall evacuation time.

Several extensive landslides have occurred in the vicinity of the Three Gorges Reservoir since its initial impoundment in June 2003. A reduction of the landslide risk is essential for the safety and security of lives and property in the region. This study analyses the deformation states of two typical colluvial landslides (the Baijiabao and Laoshewo landslides) using 6 years of monitoring data, a two-step cluster analysis, and the Apriori algorithm. The landslide displacement versus time curves exhibit step-like patterns, and the landslide deformation is highly correlated with fluctuations in the reservoir level and seasonal precipitation. To determine different types of landslide deformation, the monthly displacement curves of the colluvial landslides are classified into three types using a two-step cluster analysis: initial deformation, constant deformation, and rapid deformation. Five driving factors were selected as the antecedents for the Apriori algorithm to obtain rules that describe the relationships between the landslide deformation and the influential parameters. These factors include the cumulative precipitation over the previous month, the maximum daily precipitation during the current month, changes in the reservoir level over the previous month, cumulative increases in the reservoir level and the average reservoir level during the current month. The analytical results were validated by comparing them with observed landslide deformation characteristics using three measurement standards: support, confidence and lift. The results show that the combined method of a two-step cluster analysis with the Apriori algorithm can effectively model the landslide deformation states that are associated with the Baijiabao and Laoshewo landslides. Moreover, this method may serve as a potential reference for deformation analyses of colluvial landslides in the Three Gorges.

Lung cancer remains the leading cause for cancer mortality worldwide. While it is well-known that smoking is an avoidable high-risk factor for lung cancer, it is necessary to identify the extent to which other modified risk factors might further affect the cell’s genetic predisposition for lung cancer susceptibility, and the spreading of carcinogens in various geographical zones. This study aims to examine the association between lung cancer mortality (LCM) and major risk factors. We used Fuzzy Inference Modeling (FIM) and Random Forest Modeling (RFM) approaches to analyze LCM and its possible links to 30 risk factors in 100 countries over the period from 2006 to 2016. Analysis results suggest that in addition to smoking, low physical activity, child wasting, low birth weight due to short gestation, iron deficiency, diet low in nuts and seeds, vitamin A deficiency, low bone mineral density, air pollution, and a diet high in sodium are potential risk factors associated with LCM. This study demonstrates the usefulness of two approaches for multi-factor analysis of determining risk factors associated with cancer mortality.

Colorectal cancer (CRC) is a common cancer among both men and women and is one of the leading causes of cancer death worldwide. It is important to identify risk factors that may be used to help reduce morbidity and mortality of the disease. We used a case-control study design to explore the association between CRC, polygenic risk scores (PRS), and other factors. We extracted data about 2,585 CRC cases and 9,362 controls from the UK Biobank, calculated the PRS for these cases and controls based on 140 single nucleotide polymorphisms, and performed logistic regression analyses for the 11,947 cases and controls, for an older group (ages 50+), and for a younger group (younger than 50). Five significant risk factors were identified when all 11,947 cases and controls were considered. These factors were, in descending order of the values of the adjusted odds ratios (aOR), high PRS (aOR: 2.70, CI: 2.27–3.19), male sex (aOR: 1.52, CI: 1.39–1.66), unemployment (aOR: 1.47, CI: 1.17–1.85), family history of CRC (aOR: 1.44, CI: 1.28–1.62), and age (aOR: 1.01, CI: 1.01–1.02). These five risk factors also remained significant in the older group. For the younger group, only high PRS (aOR: 2.87, CI: 1.65–5.00) and family history of CRC (aOR: 1.73, CI: 1.12–2.67) were significant risk factors. These findings indicate that genetic risk for the disease is a significant risk factor for CRC even after adjusting for family history. Additional studies are needed to examine this association using larger samples and different population groups.

A bibliometric analysis was conducted to evaluate landslide research from different perspectives during the period 1991–2014 based on the Science Citation Index-Expanded and Social Sciences Citation Index databases. Based on a sample of 10,567 articles that were related to landslides, the bibliometric analysis revealed the scientific outputs, science categories, source titles, global geographical distribution of the authors, productive authors, international collaborations, institutions, and temporal evolution of keyword frequencies. Landslide-related research has undergone notable growth during the past two decades. Multidisciplinary Geosciences, Geological Engineering, and Water Resources were the three major science categories, and Geomorphology was the most active journal during the surveyed period. The major author clusters and research regions are located in North America, Western Europe, and East Asia. The USA was a leading contributor to global landslide research, with the most independent and collaborative articles, and its dominance was also confirmed in the national/regional collaboration network. The Chinese Academy of Sciences, US Geological Survey, and Italian National Research Council were the three major contributing institutions. Guzzetti F from the Italian National Research Council was the most productive author, with the most high-quality articles. A keyword analysis found that landslide susceptibility assessment, rainfall- and earthquake-induced landslide stability, and effective research technologies and methods were consistent topics that attracted the most attention during the study period. Several keywords, such as “landslide susceptibility”, “earthquake”, “GIS”, “remote sensing”, and “logistic regression”, received dramatically increased attention during the study period, possibly signalling future research trends.

A spatial-temporal panel dataset was collected from 101 countries during 2006–2016. Using partial correlation (PC) and ordinary correlation (OR) analyses, this research examines the relationship between ecological footprint (EF) and subjective well-being (SWB) to measure environmental impacts on people’s happiness. Gross domestic product (GDP), urbanization rate (UR), literacy rate (LR), youth life expectancy (YLE), wage and salaried workers (WSW), political stability (PS), voice accountability (VA) are regarded as control variables. Total bio-capacity (TBC), ecological crop-land footprints (ECL), ecological grazing-land footprint (EGL), and ecological built-up land footprint (EBL) have significant positive influences on SWB, but ecological fish-land (EFL) has significant negative influences on SWB. Ecological carbon footprint (ECF) is significantly negatively related to SWB in developed countries. An increase in the amount of EF factors is associated with a country’s degree of development. Political social–economic impacts on SWB disguised environmental contribution on SWB, especially CBF impacts on SWB. The use of PC in examining the association between SWB and EF helps bridge a knowledge gap and facilitate a better understanding of happiness.

The key nuclear export protein CRM1/XPO1 may represent a promising novel therapeutic target in human multiple myeloma (MM). Here we showed that chromosome region maintenance 1 (CRM1) is highly expressed in patients with MM, plasma cell leukemia cells and increased in patient cells resistant to bortezomib treatment. CRM1 expression also correlates with increased lytic bone and shorter survival. Importantly, CRM1 knockdown inhibits MM cell viability. Novel, oral, irreversible selective inhibitors of nuclear export (SINEs) targeting CRM1 (KPT-185, KPT-330) induce cytotoxicity against MM cells (ED 50 <200 n M ), alone and cocultured with bone marrow stromal cells (BMSCs) or osteoclasts (OC). SINEs trigger nuclear accumulation of multiple CRM1 cargo tumor suppressor proteins followed by growth arrest and apoptosis in MM cells. They further block c-myc, Mcl-1, and nuclear factor κB (NF-κB) activity. SINEs induce proteasome-dependent CRM1 protein degradation; concurrently, they upregulate CRM1, p53-targeted, apoptosis-related, anti-inflammatory and stress-related gene transcripts in MM cells. In SCID mice with diffuse human MM bone lesions, SINEs show strong anti-MM activity, inhibit MM-induced bone lysis and prolong survival. Moreover, SINEs directly impair osteoclastogenesis and bone resorption via blockade of RANKL-induced NF-κB and NFATc1, with minimal impact on osteoblasts and BMSCs. These results support clinical development of SINE CRM1 antagonists to improve patient outcome in MM.

Many studies have investigated associations between maternal residential exposures to air pollutants and low birth weight (LBW) in offspring. However, most studies focused on the criteria air pollutants (PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 , CO, and Pb), and only a few studies examined the potential impact of other air pollutants on LBW. This study investigated associations between maternal residential exposure to industrial air emissions of 449 toxics release inventory (TRI) chemicals and LBW in offspring using a case-control study design based on a large dataset consisting of 94,106 LBW cases and 376,424 controls in Texas from 1996 to 2008. Maternal residential exposure to chemicals was estimated using a modified version of the emission-weighted proximity model (EWPM). The model takes into account reported quantities of annual air emission from industrial facilities and the distances between the locations of industrial facilities and maternal residence locations. Binary logistic regression was used to compute odds ratios measuring the association between maternal exposure to different TRI chemicals and LBW in offspring. Odds ratios were adjusted for child’s sex, birth year, gestational length, maternal age, education, race/ethnicity, and public health region of maternal residence. Among the ten chemicals selected for a complete analysis, maternal residential exposures to five TRI chemicals were positively associated with LBW in offspring. These five chemicals include acetamide (adjusted odds ratio [aOR] 2.29, 95% confidence interval [CI] 1.24, 4.20), p -phenylenediamine (aOR 1.63, 95% CI 1.18, 2.25), 2,2-dichloro-1,1,1-trifluoroethane (aOR 1.41, 95% CI 1.20, 1.66), tributyltin methacrylate (aOR 1.20, 95% CI 1.06, 1.36), and 1,1,1-trichloroethane (aOR 1.11, 95% CI 1.03, 1.20). These findings suggest that maternal residential proximity to industrial air emissions of some chemicals during pregnancy may be associated with LBW in offspring.

The transcription factor forkhead box M1 (FOXM1) is a validated oncoprotein in solid cancers, but its role in malignant plasma cell tumors such as multiple myeloma (MM) is unknown. We analyzed publicly available MM data sets and found that overexpression of FOXM1 prognosticates inferior outcome in a subset (~15%) of newly diagnosed cases, particularly patients with high-risk disease based on global gene expression changes. Follow-up studies using human myeloma cell lines (HMCLs) as the principal experimental model system demonstrated that enforced expression of FOXM1 increased growth, survival and clonogenicity of myeloma cells, whereas knockdown of FOXM1 abolished these features. In agreement with that, constitutive upregulation of FOXM1 promoted HMCL xenografts in laboratory mice, whereas inducible knockdown of FOXM1 led to growth inhibition. Expression of cyclin-dependent kinase 6 (CDK6) and NIMA-related kinase 2 (NEK2) was coregulated with FOXM1 in both HMCLs and myeloma patient samples, suggesting interaction of these three genes in a genetic network that may lend itself to targeting with small-drug inhibitors for new approaches to myeloma therapy and prevention. These results establish FOXM1 as high-risk myeloma gene and provide support for the design and testing of FOXM1-targeted therapies specifically for the FOXM1 High subset of myeloma.