Role of air pollutants in dengue fever incidence: evidence from two southern cities in Taiwan.

Air pollution may be involved in spreading dengue fever (DF) besides rainfalls and warmer temperatures. While particulate matter (PM), especially those with diameter of 10 µm (PM10) or 2.5 µm or less (PM25), and NO2 increase the risk of coronavirus 2 infection, their roles in triggering DF remain unclear. We explored if air pollution factors predict DF incidence in addition to the classic climate factors. Public databases and DF records of two southern cities in Taiwan were used in regression analyses. Month order, PM10 minimum, PM2.5 minimum, and precipitation days were retained in the enter mode model, and SO2 minimum, O3 maximum, and CO minimum were retained in the stepwise forward mode model in addition to month order, PM10 minimum, PM2.5 minimum, and precipitation days. While PM2.5 minimum showed a negative contribution to the monthly DF incidence, other variables showed the opposite effects. The sustain of month order, PM10 minimum, PM2.5 minimum, and precipitation days in both regression models confirms the role of classic climate factors and illustrates a potential biological role of the air pollutants in the life cycle of mosquito vectors and dengue virus and possibly human immune status. Future DF prevention should concern the contribution of air pollution besides the classic climate factors.

Tribological Properties of Carbon Nanocapsule Particles as Lubricant Additive.

An experimental investigation is performed into the tribological properties of mineral oil lubricants containing carbon nanocapsules (CNCs) additives with various concentrations (wt.%). Friction characteristics and wear behaviors at contact interfaces are examined by the block-on-ring tests, high-resolution transmission electron microscopy (HRTEM), and mapping (MAP) analysis. The results suggest that the addition of CNCs to the mineral oil yields an effective reduction in the friction coefficient at the contact interface. Molecular dynamics (MD) simulations clarify the lubrication mechanism of CNCs at the sliding system, indicating the tribological properties are essentially sensitive to the structural evolutions of CNCs.

Autophagy promotes radiation-induced senescence but inhibits bystander effects in human breast cancer cells.

Ionizing radiation induces cellular senescence to suppress cancer cell proliferation. However, it also induces deleterious bystander effects in the unirradiated neighboring cells through the release of senescence-associated secretory phenotypes (SASPs) that promote tumor progression. Although autophagy has been reported to promote senescence, its role is still unclear. We previously showed that radiation induces senescence in PTTG1-depleted cancer cells. In this study, we found that autophagy was required for the radiation-induced senescence in PTTG1-depleted breast cancer cells. Inhibition of autophagy caused the cells to switch from radiation-induced senescence to apoptosis. Senescent cancer cells exerted bystander effects by promoting the invasion and migration of unirradiated cells through the release of CSF2 and the subsequently activation of the JAK2-STAT3 and AKT pathways. However, the radiation-induced bystander effects were correlated with the inhibition of endogenous autophagy in bystander cells, which also resulted from the activation of the CSF2-JAK2 pathway. The induction of autophagy by rapamycin reduced the radiation-induced bystander effects. This study reveals, for the first time, the dual role of autophagy in radiation-induced senescence and bystander effects.

A note on hyper ellipse method for classifying biological and medical data.

The classification of biological and medical datasets is essential to humanity. This study proposes a hyper ellipse method based on mixed integer nonlinear program for classifying datasets. A linearization technique with a number of piecewise line segments is used to treat nonlinear constraints, which aims to obtain an approximate optimal solution. Numerical examples are presented to demonstrate the efficacy of the proposed method.

Radiation-induced senescence in securin-deficient cancer cells promotes cell invasion involving the IL-6/STAT3 and PDGF-BB/PDGFR pathways.

Securin overexpression correlates with poor prognosis in various tumours. We have previously shown that securin depletion promotes radiation-induced senescence and enhances radiosensitivity in human cancer cells. However, the underlying molecular mechanisms and the paracrine effects remain unknown. In this study, we showed that radiation induced senescence in securin-deficient human breast cancer cells involving the ATM/Chk2 and p38 pathways. Conditioned medium (CM) from senescent cells promoted the invasion and migration of non-irradiated cancer and endothelial cells. Cytokine assay analysis showed the up-regulation of various senescence-associated secretory phenotypes (SASPs). The IL-6/STAT3 signalling loop and platelet-derived growth factor-BB (PDGF-BB)/PDGF receptor (PDGFR) pathway were important for CM-induced cell migration and invasion. Furthermore, CM promoted angiogenesis in the chicken chorioallantoic membrane though the induction of IL-6/STAT3- and PDGF-BB/PDGFR-dependent endothelial cell invasion. Taken together, our results provide the molecular mechanisms for radiation-induced senescence in securin-deficient human breast cancer cells and for the SASP responses.

Formation of CC-type palladacycles with assistance from an apparently innocent NH(CO) functional group.

A novel cyclometalation pathway to form CC-type palladacycles is reported. Unlike common donor-assisted cyclometalation, the NH(CO) auxiliary group undergoes a deprotonation step to form a palladalactam intermediate. The coordinating nitrogen atom functions as an intramolecular base promoting selective C-H bond cleavage. Without the NH proton, the ortho-N-phenyl C-H is activated instead.

A DIAMOND method of inducing classification rules for biological data.

Identifying the classification rules for patients, based on a given dataset, is an important role in medical tasks. For example, the rules for estimating the likelihood of survival for patients undergoing breast cancer surgery are critical in treatment planning. Many well-known classification methods (as decision tree methods and hyper-plane methods) assume that classes can be separated by a linear function. However, these methods suffer when the boundaries between the classes are non-linear. This study presents a novel method, called DIAMOND, to induce classification rules from datasets containing non-linear interactions between the input data and the classes to be predicted. Given a set of objects with some classes, DIAMOND separates the objects into different cubes, and assigns each cube to a class. Via the unions of these cubes, DIAMOND uses mixed-integer programs to induce classification rules with better rates of accuracy, support and compact. This study uses three practical datasets (Iris flower, HSV patients, and breast cancer patients) to illustrate the advantages of DIAMOND over some current methods.