Hyaluronic acid-modified liposomes Potentiated in-vivo anti-hepatocellular carcinoma of icaritin.

Introduction: Icaritin (ICT), a promising anti-hepatocellular carcinoma (HCC) prenylated flavonoid, is hindered from being applied due to its low water solubility and high lipophilicity in poorly differentiated HCC which is associated with upregulation of CD44 isoforms. Thus, hyaluronic acid (HA), a natural polysaccharide with high binding ability to CD44 receptors, was used to formulate a modified liposome as a novel targeted ICT-delivery system for HCC treatment. Methods: The ICT-Liposomes (Lip-ICT) with and without HA were prepared by a combined method of thin-film dispersion and post-insertion. The particle size, polydispersity (PDI), zeta potential, encapsulation efficacy (%EE), drug loading content (%DLC), and in vitro drug release profiles were investigated for physicochemical properties, whereas MTT assay was used to assess cytotoxic effects on HCC cells, HepG2, and Huh7 cells. Tumor bearing nude mice were used to evaluate the inhibitory effect of HA-Lip-ICT and Lip-ICT in vivo. Results: Lip-ICT and HA-Lip-ICT had an average particle size of 171.2 ± 1.2 nm and 208.0 ± 3.2 nm, with a zeta potential of -13.9 ± 0.83 and -24.8 ± 0.36, respectively. The PDI resulted from Lip-ICT and HA-Lip-ICT was 0.28 ± 0.02 and 0.26 ± 0.02, respectively. HA-Lip-ICT demonstrated higher in vitro drug release when pH was dropped from 7.4 to 5.5, The 12-h release rate of ICT from liposomes increased from 30% at pH7.4 to more than 60% at pH5.5. HA-Lip-ICT displayed higher toxicity than Lip-ICT in both HCC cells, especially Huh7with an IC50 of 34.15 ± 2.11 µM. The in vivo tissue distribution and anti-tumor experiments carried on tumor bearing nude mice indicated that HA-Lip- ICT exhibited higher tumor accumulation and achieved a tumor growth inhibition rate of 63.4%. Discussion: The nano-sized Lip-ICT was able to prolong the drug release time and showed long-term killing HCC cells ability. Following conjugation with HA, HA-Lip-ICT exhibited higher cytotoxicity, stronger tumor targeting, and tumor suppression abilities than Lip-ICT attributed to HA-CD44 ligand-receptor interaction, increasing the potential of ICT to treat HCC.

Injectable Hydrogel To Deliver Bone Mesenchymal Stem Cells Preloaded with Azithromycin To Promote Spinal Cord Repair.

Spinal cord injury is a disease that causes severe damage to the central nervous system. Currently, there is no cure for spinal cord injury. Azithromycin is commonly used as an antibiotic, but it can also exert anti-inflammatory effects by down-regulating M1-type macrophage genes and up-regulating M2-type macrophage genes, which may make it effective for treating spinal cord injury. Bone mesenchymal stem cells possess tissue regenerative capabilities that may help promote the repair of the injured spinal cord. In this study, our objective was to explore the potential of promoting repair in the injured spinal cord by delivering bone mesenchymal stem cells that had internalized nanoparticles preloaded with azithromycin. To achieve this objective, we formulated azithromycin into nanoparticles along with a trans-activating transcriptional activator, which should enhance nanoparticle uptake by bone mesenchymal stem cells. These stem cells were then incorporated into an injectable hydrogel. The therapeutic effects of this formulation were analyzed in vitro using a mouse microglial cell line and a human neuroblastoma cell line, as well as in vivo using a rat model of spinal cord injury. The results showed that the formulation exhibited anti-inflammatory and neuroprotective effects in vitro as well as therapeutic effects in vivo. These results highlight the potential of a hydrogel containing bone mesenchymal stem cells preloaded with azithromycin and trans-activating transcriptional activator to mitigate spinal cord injury and promote tissue repair.

Investigating safety and cost-effectiveness of cable median barriers in Louisiana.

OBJECTIVES: Cable median barriers (CMBs) are installed on freeway medians to prevent cross-median crashes and reduce the severity of median-related crashes. Though CMBs are effective in preventing cross-median crashes, they are also known to increase the number of property damage-only (PDO) crashes. The higher frequency of PDO crashes could result in increased CMB maintenance and repair expenses. The aim of this study is to evaluate the safety impact and economic justification of CMBs in Louisiana. METHODS: Initially, a flowchart was developed using Louisiana crash data to identify targeted crashes, such as median-related and cross-median crashes. This was followed by a 3-year observational before-and-after crash analysis with an emphasis on head-on collisions and crashes involving large trucks. Using a 4-step improved prediction method, crash modification factors were then developed to quantitatively assess the impact of CMBs on crash outcomes, accounting for and adjusting to changes in the annual average daily traffic (AADT) and relevant crash frequencies before and after CMB implementation. Finally, an exhaustive benefit-cost analysis was conducted to determine the cost-effectiveness of CMBs. RESULTS: The results revealed that CMBs significantly reduced cross-median crashes of all severities. However, an increase in PDO crashes was observed in both total and median-related crashes. Large truck cross-median crashes and head-on collisions also decreased significantly after CMB implementation. Testing Level 4 (TL-4) CMBs were found to be more effective in preventing vehicles from crossing the median and in reducing crashes of higher severity levels. The benefit-cost ratios, calculated using economic crash unit costs for both total and targeted crashes, were greater than 1. Notably, the estimated benefit-cost ratios were considerably higher, demonstrating that CMBs are cost-effective countermeasures for enhancing traffic safety. CONCLUSION: This study contributes to the understanding of CMB performance from both traffic safety and economic perspectives. The findings may assist transportation agencies in making decisions regarding the management of CMB systems. Based on the comprehensive analysis of CMBs on Louisiana freeways, this project has revealed that CMBs are an effective and economically justified crash countermeasure. Thus, further implementation of CMBs is recommended until better alternatives are available.

Zein-Based Triple-Drug Nanoparticles to Promote Anti-Inflammatory Responses for Nerve Regeneration after Spinal Cord Injury.

Defects in autophagy contribute to neurological deficits and motor dysfunction after spinal cord injury. Here a nanosystem is developed to deliver autophagy-promoting, anti-inflammatory drugs to nerve cells in the injured spinal cord. Celastrol, metformin, and everolimus as the mTOR inhibitor are combined into the zein-based nanoparticles, aiming to solubilize the drugs and prolong their circulation. The nanoparticles are internalized by BV2 microglia and SH-SY5Y neuron-like cells in culture; they inhibit the secretion of inflammatory factors by BV2 cells after insult with lipopolysaccharide, and they protect SH-SY5Y cells from the toxicity of H2O2. In a rat model of spinal cord injury, the nanoparticles mitigate inflammation and promote spinal cord repair. In the in vitro and in vivo experiments, the complete nanoparticles function better than the free drugs or nanoparticles containing only one or two drugs. These results suggest that the triple-drug nanoparticles show promise for treating spinal cord injury.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means.

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedestrians', drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not 'typical pedestrians', a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35-44 years, and spring season. The interaction of 'pedestrian impairment' and 'weekend' was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the 'unintended pedestrians' about the crash scenarios on the interstate and reduce these unexpected incidents.

Exploring nighttime pedestrian crash patterns at intersection and segments: Findings from the machine learning algorithm.

INTRODUCTION: Pedestrian safety at nighttime is an ongoing critical traffic safety concern. Although poor visibility is primarily associated with nighttime pedestrian crashes, other contributing factors such as humans, vehicles, roadways, and environmental factors interact with each other to cause a crash. Additionally, the pattern of nighttime pedestrian crashes differs significantly according to the intersection and segment location, which requires further exploration. DATA: This study applied Association Rules Mining (ARM), a rule-based machine learning method, to reveal the association of nighttime pedestrian crash risk factors according to the intersection and segment locations using 2,505 nighttime pedestrian fatal and injury crashes in Louisiana (2015-2019). RESULTS AND CONCLUSIONS: Based on the generated rules, the results show that nighttime pedestrian crashes at the intersection are associated with right-turn vehicle movement, older drivers (>64 years) at the high-speed intersection, senior pedestrians (>64 years) in rainy weather conditions, violation by pedestrian age group '<15 years', and alcohol-intoxicated pedestrian violation in business/industrial areas. Additionally, 'careless operation' at the intersection is associated with alcohol-involved drivers. Most of the nighttime pedestrian crashes at segments are associated with roadways with no physical separation and the absence of streetlights. Driver alcohol involvement and their physical condition (inattentive/distracted) are also associated with pedestrian crashes associated at the segment location at night. Other segment pedestrian crashes are linked to the interstate in dark conditions, open country locations, and high-speed roadways. Additionally, the crash site investigation identified several critical pedestrian safety concerns including the lack of crosswalk facilities, high driveway density, and pedestrian behavioral patterns (e.g., crossing at roadway segments close to the intersection location). PRACTICAL APPLICATIONS: The findings of this study can be used for selecting the appropriate countermeasures based on a case-by-case basis. The exposure patterns can be used in educational campaigns to strategically reduce nighttime pedestrian crashes.

Long-Circulating Lipid Nanospheres Loaded with Flurbiprofen Axetil for Targeted Rheumatoid Arthritis Treatment.

Background: Flurbiprofen axetil (FA) is a non-steroidal anti-inflammatory drug with good analgesic and anti-inflammatory effects. However, it suffers from poor solubility, short circulation time, and off-target binding profile, which significantly limit its clinical application. Here, we loaded FA into stealth lipid microspheres modified with the arginine-glycine-aspartic acid (RGD) peptide (cRGD-FA-SLM), and examined the therapeutic potential of the resulting platform for the treatment of rheumatoid arthritis (RA). Methods: cRGD-FA-SLM was prepared by high pressure homogenization, and its toxicity and uptake by macrophages were examined using cultures of RAW264.7 cells. Hemolysis and hepatotoxicity tests were performed to assess the safety of the developed platform, while its pharmacokinetics, biodistribution, and therapeutic efficacy were investigated in a collagen-induced arthritis rat model. Results: cRGD-FA-SLM showed homogeneous spherical morphology and efficient encapsulation of FA. The developed platform was non-toxic to normal macrophages and was selectively internalized by lipopolysaccharide-activated macrophages in vitro, while it distributed mainly to arthritic joints and significantly prolonged FA in circulation in vivo. cRGD-FA-SLM also significantly reduced the expression of prostaglandin E2 and alleviated joint edema and bone erosion, showing prolonged analgesic effects in arthritic rats. Conclusion: cRGD-FA-SLM shows good inflammation-targeting ability and prolongs drug circulation in vivo, suggesting promise as an anti-inflammatory and analgesic agent for targeted RA treatment.

Nanoparticles that target the mitochondria of tumor cells to restore oxygen supply for photodynamic therapy: Design and preclinical validation against breast cancer.

Photodynamic therapy, in which photosensitizers locally generate cytotoxic reactive oxygen species, can treat tumor tissue with minimal effects on surrounding normal tissue, but it can be ineffective because of the anoxic tumor microenvironment. Here we developed a strategy to inactivate the mitochondria of tumor cells in order to ensure adequate local oxygen concentrations for photodynamic therapy. We conjugated the photosensitizer 5-aminolevulinic acid to the lipophilic cation triphenylphosphine, which targets mitochondria. Then we packaged the conjugate into nanoparticles that were based on biocompatible bovine serum albumin and coated with folic acid in order to target the abundant folate receptors on the tumor surface. In studies in cell culture and BALB/c mice bearing MCF-7 xenografts, we found that the nanoparticles helped solubilize the cation-photosensitizer conjugate, prolong its circulation, and enhance its photodynamic antitumor effects. We confirmed the ability of the nanoparticles to target tumor cells and their mitochondria using confocal laser microscopy and in vivo assays of pharmacokinetics, pharmacodynamics, and tissue distribution. Our results not only identify a novel nanoparticle system for treating cancer, but they demonstrate the feasibility of enhancing photodynamic therapy by reducing oxygen consumption within tumors.

Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: Association knowledge using data mining approach.

INTRODUCTION: More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed. DATA: Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and dark (with and without streetlight). METHOD: This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. To accomplish this, an unsupervised data mining algorithm association rules mining (ARM) was utilized. RESULTS AND CONCLUSIONS: Based on the generated rules, the findings reveal several interesting crash patterns in the daylight, dark-with-streetlight, and dark-no-streetlight, emphasizing the importance of investigating RwD crash patterns depending on the lighting conditions. In daylight condition, fatal RwD crashes are associated with cloudy weather conditions, distracted drivers, standing water on the roadway, no seat belt use, and construction zones. In dark lighting condition (with and without streetlight), the majority of the RwD crashes are associated with alcohol/drug involvement, young drivers (15-24 years), driver condition (e.g., inattentive, distracted, illness/fatigued/asleep), and colliding with animal(s). PRACTICAL APPLICATIONS: The findings also reveal how certain driver behavior patterns are connected to RwD crashes, such as a strong association between alcohol/drug intoxication and no seat belt usage in the dark-no-streetlight condition. Based on the identified crash patterns and behavioral characteristics under different lighting conditions, the findings could aid researchers and safety specialists in developing the most effective RwD crash mitigation strategies.

Understanding the drowsy driving crash patterns from correspondence regression analysis.

Drowsy driving-related crashes have been a key concern in transportation safety. In Louisiana, 14% (1,758 out of 12,512) of police-reported drowsy driving-related crashes during 2015-2019 resulted in injury (fatal, severe, or moderate). Amid the calls for action against drowsy driving by national agencies, it is of paramount importance to explore the key reportable attributes of drowsy driving behaviors and their potential association with crash severity. METHOD: This study used 5-years (2015-2019) of crash data and utilized the correspondence regression analysis method to identify the key collective associations of attributes in drowsy driving-related crashes and interpretable patterns based on injury levels. RESULTS: Several drowsy driving-related crash patterns were identified through crash clusters - afternoon fatigue crashes by middle-aged female drivers on urban multilane curves, crossover crashes by young drivers on low-speed roadways, crashes by male drivers during dark rainy conditions, pickup truck crashes in manufacturing/industrial areas, late-night crashes in business and residential districts, and heavy truck crashes on elevated curves. Several attributes - scattered residential areas indicating rural areas, multiple passengers, and older drivers (aged more than 65 years) - showed a strong association with fatal and severe injury crashes. PRACTICAL APPLICATIONS: The findings of this study are expected to help researchers, planners, and policymakers in understanding and developing strategic mitigation measures to prevent drowsy driving.

Using unsupervised learning to investigate injury-associated factors of animal-vehicle crashes.

Animal vehicle crash is a critical yet often under-emphasized safety concern of Louisiana. During 2014-2018, over 14,000 animal-related crashes cost Louisiana more than $520 million. To identify multiple key contributing factors and their association patterns, this study applied association rules mining in the dataset of animal-related roadway crashes that occurred during 2014-2018. Since high proportions of animal-related crashes involve complaint and no injury of vehicle occupants, separate analyses were performed for KAB (fatal, severe, and moderate injury) and CO (possible/complaint and no injury) crashes. Top rules ordered by higher lift values were interpreted and compared to implicate the quantified likelihood of crash patterns. KAB rules presented the likelihood of associations of characteristics such as unlighted dark conditions, interstate and parish roads, a wide range of speed limits, residential and open country locations, normal and rainy weather conditions, light trucks, young drivers, etc. The majority of CO crash patterns were associated with interstates, straight segments, normal driver conditions, clear weather, unlighted dark conditions, open country locations, a speed limit of 97 km/h or higher, etc. Findings in this study and their implications supported by prior studies are expected to be beneficial in strategic planning for identifying implementable countermeasures for animal-vehicle crashes.

Inhibition of A1 Astrocytes and Activation of A2 Astrocytes for the Treatment of Spinal Cord Injury.

Spinal cord injury (SCI) is a serious injury to the central nervous system that causes significant physical and psychological trauma to the patient. SCI includes primary spinal cord injuries and secondary spinal cord injuries. The secondary injury refers to the pathological process or reaction after the primary injury. Although SCI has always been thought to be an incurable injury, the human nerve has the ability to repair itself after an injury. However, the reparability is limited because glial scar formation impedes functional recovery. There is a type of astrocyte that can differentiate into two forms of reactive astrocytes known as 'A1' and 'A2' astrocytes. A1 astrocytes release cytotoxic chemicals that cause neurons and oligodendrocytes to die and perform a harmful role. A2 astrocytes can produce neurotrophic factors and act as neuroprotectors. This article discusses ways to block A1 astrocytes while stimulating A2 astrocytes to formulate a new treatment for spinal cord injury.

Cellphone-distracted crashes of novice teen drivers: Understanding associations of contributing factors for crash severity levels and cellphone usage types.

OBJECTIVE: As novice teen drivers are uniquely susceptible to the harmful effects of secondary activities on cellphones, 38 states and Washington D.C. have banned all types of cellphone usage for drivers younger than 18 years or in the learner/intermediate phase of driving. Despite the prevalence of such cellphone prohibitions, several surveillance studies have highlighted the persistent engagement of teenagers in cellphone-distracted driving, which increases the related crash risk. Most of the prior studies broadly consider cellphone usage as a general distraction instead of investigating different distraction-related tasks associated with cellphone use. This study analyzed the cellphone crashes of novice teenagers (aged 15-17 years) to discover the grouping of contributing factors by crash severity levels and cellphone usage types. METHODS: The current study collected five years (2015-2019) of related crash data from the Louisiana Department of Transportation and Development. A manual effort was carried out to recognize the type of cellphone tasks before collision by reading the narratives of police-investigated crash reports. Association rule mining was applied to explore the associations between numerous crash attributes in multiple circumstances without relying on any predetermined hypotheses. RESULTS: The cumulative effect of cellphone distraction and no seatbelt usage is frequently visible in confirmed injury crash scenarios. Cellphone crashes of novice teenagers at intersections are strongly associated with talking/listening rather than texting/browsing/dialing and reaching for/answering/locating. The associations among environmental factors and modes of cellphone usage significantly influence the manner of collisions. Single-vehicle crashes are associated with cellphone manipulation while driving on weekends in cloudy weather, whereas sideswipe collisions are frequent in evening hours during reaching for/answering/locating the cellphones. In relation to texting/browsing/dialing, novice teenagers operating vans/SUVs are strongly associated with traffic control violations. CONCLUSIONS: The findings are expected to be beneficial for policymakers and other safety officials to develop strategic planning and implementable countermeasures when dealing with cellphone-distracted novice teenagers. The association of factors identified from the analysis exhibits real-world crash scenarios critical to strengthening driver education programs to mitigate teen driver crashes. Moreover, cellphone crashes and related casualties can be reduced by eliminating or improving one of the attributes involved in the crash patterns.

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data.

Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013-2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00-5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

Bone-targeting drug delivery system of biomineral-binding liposomes loaded with icariin enhances the treatment for osteoporosis.

BACKGROUND: Osteoporosis is a bone-incapacitating malady and it is characterized by obvious bone mass loss and bone microarchitecture deterioration. Current treatments for osteoporosis have many limitations, including the non-obvious therapeutic effect and long-term safety issues. Icariin is a pharmacologically active flavonoid glycoside, which shows potential application in treatment of osteoporosis. But its clinical application is limited by the inherent disadvantages such as poor water solubility, first pass effect after oral administration, and low bioavailability. Moreover, due to lack of targeting ability, icariin cannot accumulate at the local diseased region to provide early protection from fractures. To solve the application problems of icariin and enhance its therapeutic effects on osteoporosis, this work aimed to design a targeting drug delivery system of biomineral-binding liposomes (BBL) mediated by pyrophosphate ions. RESULTS: Biomineral-binding liposomes enhanced the binding ability of liposomes with hydroxyapatite particles. It increased the serum level of alkaline phosphatase and reduced that of tartrate-resistant acid phosphatase 5b. Meanwhile, BBL increased the mechanical strength of femoral midshaft, preserving the trabecular bone microarchitecture. Moreover, BBL could initiate bone turnover/remodeling of rats with osteoporosis. CONCLUSIONS: This drug targeting delivery system of BBL loading with icariin showed more therapeutic advantages than the free icariin for the treatment of osteoporosis, which may be a kind of valid candidate in future osteoporosis therapy.

Pedestrian crash analysis with latent class clustering method.

Pedestrians are the most vulnerable users of the highway transportation system. While encouraging "Green Transportation", a concerning fact emerges in the United States: pedestrian deaths are climbing faster than motorist fatalities, reaching nearly 6000 in 2016 - the highest in over two decades. In 2015, pedestrian fatalities reached 110, 14.6% of total traffic fatalities in Louisiana for that year. Consequently, the Louisiana pedestrian fatality rate (fatalities per 100,000 population) was 2.18, exceeding the U.S. average of 1.67. In an effort to effectively reduce the pedestrian crashes, this paper investigates this problem for Louisiana. However, with the heterogeneity of provided crash data, it is difficult to identify major causation that contribute to these crashes. This study will reveal the findings of the Latent Class Cluster (LCC) model, utilizing it as a preliminary tool for the segmentation of 14,236 pedestrian crashes in Louisiana, between the years of 2006-2015. Next, Multinomial Logit (MNL) models are used to identify the main factors in pedestrian crash severity, shown in the original dataset, by further analyzing the clusters previously obtained by the LCC model. The results shed lights on the crash characteristics that are not apparent without these combined data analysis methods. Certain variables that have not been identified as significant in whole data analysis are identified as significant for a specific cluster, such as pedestrian crossing and entering roads, crash hours between midnight to 6 pm, dark-unlighted conditions, dark-lighted conditions, and non-intersection locations. The study suggests that the LCC regression approach can reveal important, formerly hidden relationships in traffic safety analyses.

Optimization of Scanning and Counting Sensor Layout for Full Route Observability with a Bi-Level Programming Model.

Utilizing the data obtained from both scanning and counting sensors is critical for efficiently managing traffic flow on roadways. Past studies mainly focused on the optimal layout of one type of sensor, and how to optimize the arrangement of more than one type of sensor has not been fully researched. This paper develops a methodology that optimizes the deployment of different types of sensors to solve the well-recognized network sensors location problem (NSLP). To answer the questions of how many, where and what types of sensors should be deployed on each particular link of the network, a novel bi-level programming model for full route observability is presented to strategically locate scanning and counting sensors in a network. The methodology works in two steps. First, a mathematical program is formulated to determine the minimum number of scanning sensors. To solve this program, a new 'differentiating matrix' is introduced and the corresponding greedy algorithm of 'differentiating first' is put forward. In the second step, a scanning map and an incidence matrix are incorporated into the program, which extends the theoretical model for multiple sensors' deployment and provides the replacement method to reduce total cost of sensors without loss of observability. The algorithm developed at the second step involved in two coefficient matrixes from scanning map and incidence parameter enumerate all possibilities of replacement schemes so that cost of different combination schemes can be compared. Finally, the proposed approach is demonstrated by comparison of Nguyen-Dupuis network and real network, which indicates the proposed method is capable to evaluate the trade-off between cost and all routes observability.

Characterization of aptamer-mediated gene delivery system for liver cancer therapy.

Liver cancer is a fatal disease with limited therapy options. The recombinant adenovirus expressing tumor-suppressor gene of PTEN (Ad5-PTEN) showed effective antitumor activity against liver cancer. However, its disadvantages produced great limitation on its application, especially its nonspecific and toxicity to normal cells and tissues. The epithelial cell adhesion molecule (EpCAM) is over-expressed in some liver cancer cells and an RNA aptamer EpDT3 could specially target to EpCAM-positive cells. Based on this founding, we aimed to design a kind of gene delivery system of EpDT3-mediated Ad5-PTEN (EpDT3-PEG-Ad5-PTEN, EPAP) in which polyethylene glycol was used to be a linker to conjugate EpDT3 with Ad5-PTEN. This strategy may overcome the disadvantages of naked Ad5-PTEN and enhance the antitumor effect on liver cancer. The SDS-PAGE electrophoresis, TBE-PAGE electrophoresis and fluorescence detection were conducted to confirm the successful preparation of EPAP. Compared with the naked Ad5-PTEN, EPAP showed significant anti-proliferative and anti-migratory activities against HepG2 cells. EPAP also showed selective and precise target ability to EpCAM-positive HepG2 cells in vivo. Therefore, EPAP may be further explored as a novel effective anticancer drug for malignant liver cancer.

Investigation on the wrong way driving crash patterns using multiple correspondence analysis.

Wrong way driving (WWD) has been a constant traffic safety problem in certain types of roads. Although these crashes are not large in numbers, the outcomes are usually fatalities or severe injuries. Past studies on WWD crashes used either descriptive statistics or logistic regression to determine the impact of key contributing factors. In conventional statistics, failure to control the impact of all contributing variables on the probability of WWD crashes generates bias due to the rareness of these types of crashes. Distribution free methods, such as multiple correspondence analysis (MCA), overcome this issue, as there is no need of prior assumptions. This study used five years (2010-2014) of WWD crashes in Louisiana to determine the key associations between the contribution factors by using MCA. The findings showed that MCA helps in presenting a proximity map of the variable categories in a low dimensional plane. The outcomes of this study are sixteen significant clusters that include variable categories like determined several key factors like different locality types, roadways at dark with no lighting at night, roadways with no physical separations, roadways with higher posted speed, roadways with inadequate signage and markings, and older drivers. This study contains safety recommendations on targeted countermeasures to avoid different associated scenarios in WWD crashes. The findings will be helpful to the authorities to implement appropriate countermeasures.

Land use, transport, and population health: estimating the health benefits of compact cities.

Using a health impact assessment framework, we estimated the population health effects arising from alternative land-use and transport policy initiatives in six cities. Land-use changes were modelled to reflect a compact city in which land-use density and diversity were increased and distances to public transport were reduced to produce low motorised mobility, namely a modal shift from private motor vehicles to walking, cycling, and public transport. The modelled compact city scenario resulted in health gains for all cities (for diabetes, cardiovascular disease, and respiratory disease) with overall health gains of 420-826 disability-adjusted life-years (DALYs) per 100 000 population. However, for moderate to highly motorised cities, such as Melbourne, London, and Boston, the compact city scenario predicted a small increase in road trauma for cyclists and pedestrians (health loss of between 34 and 41 DALYs per 100 000 population). The findings suggest that government policies need to actively pursue land-use elements-particularly a focus towards compact cities-that support a modal shift away from private motor vehicles towards walking, cycling, and low-emission public transport. At the same time, these policies need to ensure the provision of safe walking and cycling infrastructure. The findings highlight the opportunities for policy makers to positively influence the overall health of city populations.