Fine particle trace elements at a mountain site in southern China: Source identification, transport, and health risks.

Trace elements in atmospheric particulate matter play a significant role in air quality, human health, and biogeochemical cycles. In this study, the trace elements (Ca, Al, K, Fe, Na, Mg, Zn, Pb, Mn, Ti, Cu, Cr, Sr, Ni) in PM2.5 samples collected at the summit of Mt. Lushan were analyzed to quantify their abundance, source, transport, and health risks. During the whole sampling period, the major trace elements was Ca, Al, and K. While the trace metals with the lowest concentrations were Sr, Ni, Rb, and Cd. The trace elements were influenced by air mass transport routes, exhibiting an increasing trend of crustal elements in the northwesterly airmass and anthropogenic elements (Zn, Mn, Cu, and Ni) in the easterly air masses. Construction dust, coal + biomass burning, vehicle emission, urban nitrate-rich + urban waste incineration emissions, and soil dust + industry emissions were common sources of PM2.5 on Mt. Lushan. Different air mass transport routes had various source contribution patterns. These results indicate that trace elements at Mt. Lushan are influenced by regional anthropogenic emissions and monsoon-dominated trace element transport. The total resulting cancer risk value that these elements posed were below the acceptable risk value of 1 × 10-6, while the non-carcinogenic risk value (1.72) was higher than the safety level, suggesting that non-carcinogenic effects due to these trace elements inhalation were likely to occur. Vehicle emission and coal + biomass burning were the common dominant sources of non-cancer risks posed by trace elements at Mt. Lushan.

Towards a simple mathematical model for the legal concept of balancing of interests.

We propose simple nonlinear mathematical models for the legal concept of balancing of interests. Our aim is to bridge the gap between an abstract formalisation of a balancing decision while assuring consistency and ultimately legal certainty across cases. We focus on the conflict between the rights to privacy and to the protection of personal data in Art. 7 and Art. 8 of the EU Charter of Fundamental Rights (EUCh) against the right of access to information derived from Art. 11 EUCh. These competing rights are denoted by (i1) right to privacy and (i2) access to information; mathematically, their indices are respectively assigned by u1∈[0,1] and u2∈[0,1] subject to the constraint u1+u2=1. This constraint allows us to use one single index u to resolve the conflict through balancing. The outcome will be concluded by comparing the index u with a prior given threshold u0. For simplicity, we assume that the balancing depends on only selected legal criteria such as the social status of affected person, and the sphere from which the information originated, which are represented as inputs of the models, called legal parameters. Additionally, we take "time" into consideration as a legal criterion, building on the European Court of Justice's ruling on the right to be forgotten: by considering time as a legal parameter, we model how the outcome of the balancing changes over the passage of time. To catch the dependence of the outcome u by these criteria as legal parameters, data were created by a fully-qualified lawyer. By comparison to other approaches based on machine learning, especially neural networks, this approach requires significantly less data. This might come at the price of higher abstraction and simplification, but also provides for higher transparency and explainability. Two mathematical models for u, a time-independent model and a time-dependent model, are proposed, that are fitted by using the data. Supplementary Information: The online version contains supplementary material available at 10.1007/s10506-022-09338-3.

Association of lifestyle habits and cardiovascular risk among sedentary adults.

This study aimed to analyze the association of lifestyle habits (physical activity, sleep habits, and eating habits) with cardiovascular risk (arterial stiffness and autonomic nervous system function) among sedentary adults. Sixty adults of sedentariness and physical activity were evaluated by accelerometers; sleep and eating habits were assessed by questionnaires; cardiovascular risks were assessed by pulse wave velocity (PWV), ankle-brachial index, flow mediated dilation, and heart rate variability; circulating biomarkers were also determined. Prolonged sitting (represented by longer maximum length of sedentary bouts, lower length of sedentary breaks, and more total time of sitting) were (P < .05) significantly associated with matrix metalloproteinases, neuropeptide Y, C-reactive protein, peptide Y, ghrelin, and leptin; significant associations (P < .05) were also observed of total time in physical activity with most circulating biomarkers except interleukin-6, tumor necrosis factor-α, and adiponectin. Sleep habits, especially sleep efficiency, were (P < .05) significantly associated with PWV, ankle-brachial index, and circulating biomarkers. Eating habits (including emotional overeating and enjoyment of food) were (P < .05) significantly associated with PWVs and flow mediated dilation; satiety responsiveness and enjoyment of food were (P < .05) significantly associated with low-frequency spectral component expressed in normalized units, high frequency spectral component expressed in normalized units, and ratio between low-frequency/high frequency spectral component expressed in normalized units. The findings indicated that several lifestyle habits among sedentary adults were closely associated with increased cardiovascular risk. Sedentary people were encouraged to live with sufficient physical activity, good sleep, and healthy eating habits for decreasing arterial stiffness and balancing autonomic nervous function.

Tumor microenvironment characterization in cervical cancer identifies prognostic relevant gene signatures.

OBJECTIVE: The aim of this study is to systematically analyze the transcriptional sequencing data of cervical cancer (CC) to find an Tumor microenvironment (TME) prognostic marker to predict the survival of CC patients. METHODS: The expression profiles and clinical follow-up information of CC were downloaded from the TCGA and GEO. The RNA-seq data of TCGA-CESC samples were used for CIBERSORT analysis to evaluate the penetration pattern of TME in 285 patients, and construct TMEscore. Other data sets were used to validate and evaluate TMEscore model. Further, survival analysis of TMEscore related DEGs was done to select prognosis genes. Functional enrichment and PPI networks analysis were performed on prognosis genes. RESULTS: The TMEscore model has relatively good results in TCGA-CESC (HR = 2.47,95% CI = 1.49-4.11), TCGA-CESC HPV infection samples (HR = 2.13,95% CI = 1-4.51), GSE52903 (HR = 2.65, 95% CI = 1.06-6.6), GSE44001 (HR = 2.1, 95% CI = 0.99-4.43). Patients with high/low TMEscore have significant difference in prognosis (log-rank test, P = 0.00025), and the main difference between high TMEscore subtypes and low TMEscore subtypes is immune function-related pathways. Moreover, Kaplan-Meier survival curves found out a list of identified prognosis genes (n = 86) which interestingly show significant enrichment in immune-related functions. Finally, PPI network analysis shows that highly related nodes such as CD3D, CD3E, CD8A, CD27 in the module may become new targets of CC immunotherapy. CONCLUSIONS: TMEscore may become a new prognostic indicator predicting the survival of CC patients. The prognostic genes (n = 86) may help provide new strategies for tumor immunotherapy.

Riemannian means on special euclidean group and unipotent matrices group.

Among the noncompact matrix Lie groups, the special Euclidean group and the unipotent matrix group play important roles in both theoretic and applied studies. The Riemannian means of a finite set of the given points on the two matrix groups are investigated, respectively. Based on the left invariant metric on the matrix Lie groups, the geodesic between any two points is gotten. And the sum of the geodesic distances is taken as the cost function, whose minimizer is the Riemannian mean. Moreover, a Riemannian gradient algorithm for computing the Riemannian mean on the special Euclidean group and an iterative formula for that on the unipotent matrix group are proposed, respectively. Finally, several numerical simulations in the 3-dimensional case are given to illustrate our results.