Exosomal proteomics and cytokine analysis distinguish silicosis cases from controls.

Occupational silica exposure caused a serious disease burden of silicosis. There is currently a lack of sensitive and effective biomarkers for silicosis, and the pathogenesis of silicosis is unclear. Exosomes were significant in the pathogenesis of silicosis, and our study was carried out from exosomal proteomics and cytokine analysis. Firstly, the plasma levels of cytokines were detected using a Luminex multiplex assay, and the results indicated that the plasma levels of TNF-α, IL-6, CCL2, CXCL10, and PDGF-AB were significantly higher in silicosis patients than in silica-exposed workers and controls (p < 0.05). After correlation analysis, the plasma levels of cytokines were positively correlated with exosomal protein concentration. Secondly, data-independent acquisition (DIA) was performed on plasma-derived exosomes in the screening population, which identified 88, 151, 293, and 53 differentially expressed proteins (DEPs) in exposure/control, silicosis/control, silicosis/exposure, and silicosis stage Ⅲ/silicosis stage Ⅰ groups respectively. After parallel reaction monitoring (PRM) in an independent verification population, the results indicated that the changing trend of 15 DEPs was coincident in screening and verification results. The result of correlation analysis indicated that the plasma level of TNF-α was negatively correlated with the expression of exosomal DSP, KRT78, SERPINB12, and CALML5. The AUC of combined determination of TNF-α and CALML5 reached 0.900, with a sensitivity of 0.714 and a specificity of 0.933. Overall, our study revealed the exosomal proteomic profiling of silicosis patients, silica-exposed workers, and controls, indicating that exosomes were significant in the pathogenesis of silicosis. It also revealed that the combined of the plasma levels of cytokines and the expression of exosomal DEPs could increase determination efficiency. This study provided directions for the development of silicosis biomarkers and a scientific basis for the pathogenesis research of silicosis in the future.

Modified Uni-Traveling-Carrier Photodetector with Its Optimized Cliff Layer.

We have designed the MUTC-PD with an optimized thickness of cliff layer to pre-distort the electric field at the front side of the collection layer. With the optimized MUTC-PD design, the collapse of the electric field will be greatly suppressed, and the electrons in its collection layer will gradually reach their peak velocity with the growing incident light power. Moreover, as the incident light intensity increases, the differential capacitance also declines, thus the total bandwidth grows. It will make the MUTC-PD achieve high-speed and high-power response performance simultaneously. Based on simulation, for 16µm MUTC-PD with a 70 nm cliff layer, the maximum 3 dB bandwidth at -5 V is 137 GHz, compared with 64 GHz for the MUTC-PD with a 30 nm cliff layer. The saturation RF output power is 27.4 dBm at 60 GHz.

Effects of the SEMA4B gene on hexavalent chromium [Cr(VI)]-induced malignant transformation of human bronchial epithelial cells.

Our previous study identified the potential of SEMA4B methylation level as a biomarker for hexavalent chromium [Cr(VI)] exposure. This study aimed to investigate the role of the SEMA4B gene in Cr(VI)-mediated malignant transformation of human bronchial epithelial (BEAS-2B) cells. In our population survey of workers, the geometric mean [95% confidence intervals (CIs)] of Cr in blood was 3.80 (0.42, 26.56) µg/L. Following treatment with various doses of Cr(VI), it was found that 0.5 µM had negligible effects on the cell viability of BEAS-2B cells. The expression of SEMA4B was observed to decrease in BEAS-2B cells after 7 days of treatment with 0.5 µM Cr(VI), and this downregulation continued with increasing passages of Cr(VI) treatment. Chronic exposure to 0.5 µM Cr(VI) enhanced the anchorage-independent growth ability of BEAS-2B cells. Furthermore, the use of a methylation inhibitor suppressed the Cr(VI)-mediated anchorage-independent growth in BEAS-2B cells. Considering that Cr levels exceeding 0.5 µM can be found in human blood due to occupational exposure, the results suggested a potential carcinogenic risk associated with occupational Cr(VI) exposure through the promotion of malignant transformation. The in vitro study further demonstrated that Cr(VI) exposure might inhibit the expression of the SEMA4B gene to promote the malignant transformation of BEAS-2B cells.

Hexavalent chromium [Cr(VI)]-induced ribosomal DNA copy number variation and DNA damage responses and their associations with nucleolar protein HRAS in humans and cells.

The carcinogenicity of hexavalent chromium [Cr(VI)] and its compounds has been widely recognized, yet the mechanism of genetic damage is still not fully understood. The ribosomal DNA (rDNA) copy number is recently considered a potential marker of cancer-associated stress. To investigate the roles of rDNA copy number variation (CNV) in DNA damage responses (DDRs) induced by Cr(VI) and the potential mechanism from nucleolar protein HRAS, a cross-sectional study in Cr(Ⅵ)-exposed workers and an in vitro experiment using HeLa cells were conducted. Our results showed increased levels of rDNA CNV, DDRs, and HRAS expression in Cr(VI)-exposed workers. Generalized linear regression analyses showed that Cr(VI) exposure was significantly positively associated with increased levels of rDNA CNV, DDRs, and HRAS expression in Cr(VI)-exposed workers. Moreover, there were pairwise associations between rDNA CNV, DDRs, and HRAS levels. Mediation analyses found that rDNA CNV significantly mediated the association between Cr(VI) exposure and DDRs. The in vitro experiments further confirmed that Cr(VI) treatment induced increased levels of rDNA CNV, DDRs, and HRAS expression in HeLa cells. Cr(VI)-induced rDNA CNV, ATM activation, and apoptosis damage were then strongly enhanced by HRAS depletion with siRNA in vitro, suggesting the important role of HRAS in CNV and DDRs caused by Cr(VI). The combined results of the human and cell line studies indicated that Cr(VI) exposure might enhance rDNA CNV by regulation of HRAS expression, which leads to Cr(VI)-induced genetic damage.

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters.

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Optical-electronic hybrid Fourier convolutional neural network based on super-pixel complex-valued modulation.

An optical-electronic hybrid convolutional neural network (CNN) system is proposed and investigated for its parallel processing capability and system design robustness. It is regarded as a practical way to implement real-time optical computing. In this paper, we propose a complex-valued modulation method based on an amplitude-only liquid-crystal-on-silicon spatial light modulator and a fixed four-level diffractive optical element. A comparison of computational results of convolutions between different modulation methods in the Fourier plane shows the feasibility of the proposed complex-valued modulation method. A hybrid CNN model with one convolutional layer of multiple channels is proposed and trained electrically for different classification tasks. Our simulation results show that this model has a classification accuracy of 97.55% for MNIST, 88.81% for Fashion MNIST, and 56.16% for Cifar10, which outperforms models using only amplitude or phase modulation and is comparable to the ideal complex-valued modulation method.

CoCoCs: co-optimized compressive imaging driven by high-level vision.

Compressive imaging senses optically encoded high-dimensional scene data with far fewer measurements and then performs reconstruction via appropriate algorithms. In this paper, we present a novel noniterative end-to-end deep learning-based framework for compressive imaging, dubbed CoCoCs. In comparison to existing approaches, we extend the pipeline by co-optimizing the recovery algorithm with optical coding as well as cascaded high-level computer vision tasks to boost the quality of the reconstruction. We demonstrate the proposed framework on two typical compressive imaging systems, i.e., single pixel imaging and snapshot video compressive imaging. Extensive results, including conventional image quality criteria, mean opinion scores, and accuracy in image classification and motion recognition, confirm that CoCoCs can yield realistic images and videos, which are friendly to both human viewing and computer vision. We hope CoCoCs will give impetus to bridge the gap between compressive imagers and computer vision and the perception of human.

Interaction data are identifiable even across long periods of time.

Fine-grained records of people's interactions, both offline and online, are collected at large scale. These data contain sensitive information about whom we meet, talk to, and when. We demonstrate here how people's interaction behavior is stable over long periods of time and can be used to identify individuals in anonymous datasets. Our attack learns the profile of an individual using geometric deep learning and triplet loss optimization. In a mobile phone metadata dataset of more than 40k people, it correctly identifies 52% of individuals based on their 2-hop interaction graph. We further show that the profiles learned by our method are stable over time and that 24% of people are still identifiable after 20 weeks. Our results suggest that people with well-balanced interaction graphs are more identifiable. Applying our attack to Bluetooth close-proximity networks, we show that even 1-hop interaction graphs are enough to identify people more than 26% of the time. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union's General Data Protection Regulation.

Mobility patterns are associated with experienced income segregation in large US cities.

Traditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual's tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Sentiment correlation in financial news networks and associated market movements.

In an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period of 7 years. We investigate the propagation of such sentiment in company networks and evaluate the associated market movements in terms of stock price and volatility. Our results suggest that, in certain sectors, strong media sentiment towards one company may indicate a significant change in media sentiment towards related companies measured as neighbours in a financial network constructed from news co-occurrence. Furthermore, there exists a weak but statistically significant association between strong media sentiment and abnormal market return as well as volatility. Such an association is more significant at the level of individual companies, but nevertheless remains visible at the level of sectors or groups of companies.

Postoperative Antifungal Treatment of Pulmonary Cryptococcosis in Non-HIV-Infected and Non-Transplant-Recipient Patients: A Report of 110 Cases and Literature Review.

BACKGROUND: To explore the efficacy of postoperative antifungal treatment for preventing the recurrence of pulmonary cryptococcosis (PC) and occurrence of cryptococcal meningitis (CM), a retrospective study was conducted in 112 hospitalized PC patients with or without antifungal treatment following surgery. METHODS: The treatment failure rate, PC recurrence rate, and CM incidence were compared. Additionally, the effectiveness of postoperative antifungal therapy was assessed by gathering and analyzing the published literature. RESULTS: The failure rate (P = .054) and recurrence rate (P = .178) were similar in the 2 groups, but the incidence of CM was lower in the group that received postoperative antifungal treatment (P = .039). CONCLUSIONS: This study did not show any difference in the PC recurrence rate or failure rate in the different treatment duration groups. Thus, a shorter antifungal treatment course of 2 months may be an optional treatment. In addition, upon review of the literature, no case of CM occurrence was reported among the 169 cases given postoperative antifungal treatment.

Segregation and polarization in urban areas.

Social behaviours emerge from the exchange of information among individuals-constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning.

Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its equivalence to constrained Bayesian variational inference and demonstrate its superiority over existing approaches in two applications, namely, fast log determinant estimation and information-theoretic Bayesian optimisation.

Heavy Metal Accumulation/Excretion in and Food Utilization of Lymantria dispar Larvae Fed With Zn- or Pb-Stressed Populus alba berolinensis Leaves.

Heavy metal contaminations have attracted increasing concern worldwide due to their potential damages to the whole ecosystem. This study investigated the heavy metal-accumulation and excretion in, and food utilization of the gypsy moth (Lymantria dispar) larvae that were fed with leaves plucked from poplar seedlings (Populus alba berolinensis) grown in either noncontaminated soil (control), Zn-contaminated soil (500mg/kg), or Pb-contaminated soil (500mg/kg). Our results showed that excretion of heavy metals via insect feces and exuvia is an effective approach to reduce the internal Zn and Pb concentrations, and result in the decrease of Zn and Pb concentrations in the gypsy moth larvae with the increased larval age. In addition, the gypsy moth larvae seemed to have a strong homeostatic adjustment mechanism [between approximate digestibility (AD) and efficiency of conversion of digested food (ECD)] that maintains a stable level of "efficiency of conversion of ingested food (ECI)" regardless of heavy metal (Zn or Pb) contaminations or not, except the fifth instar larvae in which the increase in AD was insufficient to compensate for the decrease of ECD. These results suggest that heavy metal excretions could help the gypsy moth larvae cope with Zn or Pb stress, and the increased digestion of food could meet their energy requirements for both detoxification and growth. However, further increase in Zn or Pb exposure time seemed to inhibit the larval food utilization.

Controllable synthesis of ordered mesoporous NiFe2O4 with tunable pore structure as a bifunctional catalyst for Li-O2 batteries.

Three-dimensional ordered mesoporous (3DOM) NiFe2O4 materials with tunable pore size ranging from 5.0 to 25.1 nm have been synthesized via a hard template and used as bifunctional electrocatalysts for rechargeable Li-O2 batteries. Characterization of the catalysts by X-ray diffraction and transmission electron microscopy confirms the formation of a single-phase 3DOM NiFe2O4 structure. Linear scanning voltammetry measurements reveal that Ketjen black (KB) carbon-supported 3DOM NiFe2O4 exhibits a decreased overpotential for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) than commonly used KB. A reduction in both the ORR and OER overpotentials increases with the mean pore size of 3DOM NiFe2O4 materials. Importantly, Li-O2 batteries with 3DOM NiFe2O4 materials as the cathode catalysts exhibit a significant enhancement in the discharge capacity, rate capability, and cyclability, and these performances increases with the mean pore size of 3DOM NiFe2O4 materials. For a Li-O2 battery equipped with a 3DOM NiFe2O4 catalyst with a maximum mean pore size of 25.1 nm, a long cycling life of up to 100 cycles under the limiting capacity of 1000 mAh gC(-1) is achieved, strongly indicating that the mesoporous size of the bifunctional catalysts plays a crucial role in enhancing the performance of Li-O2 batteries. The combined use of 3DOM NiFe2O4 with a maximal pore size of 25.1 nm and a poly(vinylidene difluoride hexafluoropropylene) separator with a tuned pore structure further improves the Li-O2 battery performance, highlighting the importance of the pore structure in the development of bifunctional catalysts and separators.

Study of the correlation between serum ferritin levels and the aggregation of metabolic disorders in non-diabetic elderly patients.

The present study aimed to explore the correlation between serum ferritin (SF) levels and the aggregation of metabolic disorders in non-diabetic elderly patients. A total of 2,600 patients were enrolled in the study. Various parameters, including blood pressure (BP), height, weight, lipid profiles, blood glucose (BG), body mass index (BMI), fasting insulin (FINS), serum uric acid (SUA), the urinary albumin/creatinine ratio (UACR) and SF levels were measured. A homeostatic model was used to evaluate insulin resistance (HOMA-IR) and ß-cell function (HOMA-ß). The quantitative insulin sensitivity check index (QUICKI) and disposition index (DI) were calculated. The QUICKI and DI decreased significantly and other parameters increased significantly when the number of metabolic disorders increased. Patients with high triglycerides (TG), high total cholesterol (TC), high SUA and obesity demonstrated higher SF levels than those with normal TG, normal TC, normal SUA and normal weight, respectively (P<0.01). Male patients with metabolic disorders (high TG, high TC, high BP, high SUA and obesity) had higher SF levels than female patients with the corresponding disorders (P<0.01). BG, FINS, BMI, TC, TG, SUA, HOMA-IR and HOMA-ß were positively correlated with SF, while DI and QUICKI were negatively correlated with SF (P<0.01). Stepwise regression analysis showed that HOMA-IR, BMI, TC, TG and SUA were risk factors for elevated SF levels. In conclusion, the SF levels in non-diabetic, elderly individuals with metabolic disorders may be significantly related to the clustering of the metabolic disorders. Dyslipidemia, obesity, disorders of purine metabolism and insulin resistance may be important risk factors for higher SF levels in the elderly.

The template-free synthesis of square-shaped SnO(2) nanowires: the temperature effect and acetone gas sensors.

Square-shaped single-crystalline SnO(2) nanowires and their sphere-like hierarchical structures were synthesized successfully with a template-free hydrothermal approach. It was found that an intermediate phase-Na(2)Sn(OH)(6)-is first produced because it is slow to dissolve in ethanol/water media. The intermediate phase gradually decomposes and converts into SnO(2) at temperatures higher than 200 °C. The reaction temperature also affects the microstructure of SnO(2) nanomaterials. Uniform square-shaped SnO(2) nanowires, which form sphere-like hierarchical structures in 100% structure yield, can be produced at 285 °C on a large scale. The diameter of the nanowires shows a decrease accompanying the increase of the reaction temperature. The temperature effect could be a result of the faster and oriented growth of SnO(2) nanowires along their [Formula: see text] direction at higher temperature. Chemical sensors constructed with square-shaped SnO(2) nanowires exhibit excellent stability, good sensitivity and selectivity, as well as a quick response and short recovery times under exposure to acetone gas in practical applications.