Phonon Landau Quantization and Enhanced Lifetime in Deformed Graphene.

The pseudomagnetic field effect may offer unique opportunities for the emergence of intriguing phenomena. To date, investigations into pseudomagnetic field effects on phonons have been limited to sound waves in metamaterials. The revelation of this exotic effect on the atomic vibration of natural materials remains elusive. Our simulations of twisted graphene nanoribbons reveal well-defined Landau spectra and sublattice polarization of phonon states, mimicking the behavior of Dirac Fermions in magnetic fields. Both valley-specified helical edge currents and snake orbits are obtained. Analysis of dynamics indicates that phonon Landau states have extended lifetimes, which are crucial for the realization of Landau-level lasing. Our findings demonstrate the occurrence of the phonon pseudomagnetic field effect in natural materials, which has important implications for the mechanical tuning of phonon quantum states at the atomic scale.

Preparation of Biomimetic Selenium-Baicalein Nanoparticles and Their Targeted Therapeutic Application in Nonsmall Cell Lung Cancer.

In this study, we prepared bionic selenium-baicalein nanoparticles (ACM-SSe-BE) for the targeted treatment of nonsmall cell lung cancer. Due to the coating of the A549 membrane, the system has homologous targeting capabilities, allowing for the preparation of target tumor cells. The borate ester bond between selenium nanoparticles (SSe) and baicalein (BE) is pH-sensitive and can break under acidic conditions in the tumor microenvironment to achieve the targeted release of BE at the tumor site. Moreover, SSe further enhances the antitumor effect of BE by increasing the production of ROS in tumor cells. Transmission electron microscopy (TEM) images and dynamic light scattering (DLS) showed that the ACM-SSe-BE had a particle size of approximately 155 ± 2 nm. FTIR verified the successful coupling of SSe and BE. In vitro release experiments indicated that the cumulative release of ACM-SSe-BE at pH 5.5 after 24 h was 69.39 ± 1.07%, which was less than the 20% release at pH 7.4, confirming the pH-sensitive release of BE in ACM-SSe-BE. Cell uptake experiments and in vivo imaging showed that ACM-SSe-BE had good targeting ability. The results of MTT, flow cytometry, Western blot, and cell immunofluorescence staining demonstrated that ACM-SSe-BE promoted A549 cell apoptosis and inhibited cell proliferation. The in vivo antitumor results were consistent with those of the cell experiments. These results clearly suggested that ACM-SSe-BE will be a promising bionic nanosystem for the treatment of nonsmall cell lung cancer.

Stark Effects of Rydberg Excitons in a Monolayer WSe2 P-N Junction.

The enhanced Coulomb interaction in two-dimensional semiconductors leads to tightly bound electron-hole pairs known as excitons. The large binding energy of excitons enables the formation of Rydberg excitons with high principal quantum numbers (n), analogous to Rydberg atoms. Rydberg excitons possess strong interactions among themselves as well as sensitive responses to external stimuli. Here, we probe Rydberg exciton resonances through photocurrent spectroscopy in a monolayer WSe2 p-n junction formed by a split-gate geometry. We show that an external in-plane electric field not only induces a large Stark shift of Rydberg excitons up to quantum principal number 3 but also mixes different orbitals and brightens otherwise dark states such as 3p and 3d. Our study provides an exciting platform for engineering Rydberg excitons for new quantum states and quantum sensing.

Response of mineral particles in inland lakes to water optical properties and its influence on chlorophyll-a estimation.

Many chlorophyll-a (Chl-a) remote sensing estimation algorithms have been developed for inland water, and they are proposed always based on some ideal assumptions, which are difficult to meet in complex inland waters. Based on MIE scattering theory, this study calculated the optical properties of mineral particles under different size distribution and refractive index conditions, and the Hydrolight software was employed to simulate remote sensing reflectance in the presence of different mineral particles. The findings indicated that the reflectance is significantly influenced by the slope (j) of particle size distribution function and the imaginary part (n') of the refractive index, with the real part (n) having a comparatively minor impact. Through both a simulated dataset containing 18,000 entries and an in situ measured dataset encompassing 2183 data from hundreds of lakes worldwide, the sensitivities of band ratio (BR), fluorescence baseline height (FLH), and three-band algorithms (TBA) to mineral particles were explored. It can be found that BR showed the best tolerance to mineral particles, followed by TBA. However, when the ISM concentration is less than 30 g m-3, the influence of CDOM cannot be ignored. Additionally, a dataset of over 400 entries is necessary for developing the BR algorithm to mitigate the incidental errors arising from differences in data magnitude. And if the amount of developing datasets is less than 400 but greater than 200, the TBA algorithm is more likely to obtain more stable accuracy.

Unraveling the influence of biogenic volatile organic compounds and their constituents on ozone and SOA formation within the Yellow River Basin, China.

Biogenic volatile organic compounds (BVOC) assume a pivotal role during the formation stages of ozone (O3) and secondary organic aerosols (SOA), serving as their primary precursors. We used the latest MEGAN3.1 model, updated vegetation data and emission factors, combined with MODIS data analysis to simulate and estimate the integrated emissions of BVOC from nine provinces in China's Yellow River Basin in 2018. Following an extensive evaluation of the WRF-CMAQ model utilizing diverse parameters, the simulated and observed values had correlation coefficients between them that ranged from 0.94 to 0.99, implying a favorable outcome in terms of simulation efficacy. The findings from the simulation analysis reveal that the combined BVOC emissions from the nine provinces in the Yellow River Basin reached a total of 6.51 Tg in 2018. Among these provinces, Sichuan, Henan, and Shaanxi ranked highest, with emissions of 1.28 Tg, 1.04 Tg, and 0.96 Tg, respectively. BVOC emissions led to concentrations of 36.72 µg/m³ in the daily maximum 8-h ozone and 0.59 µg/m³ in the average SOA in nine provinces of the Yellow River Basin in July. Isoprene contributed the most to the O3 production with 6.31 µg/m3, and monoterpenes contributed the most to SOA production with 0.45 µg/m3. ΔSOA and ΔOzone are mainly distributed in the belts of central Sichuan Province, southern Shaanxi Province, western Henan Province, northern Qinghai Province, central Inner Mongolia, and southern Shanxi Province, and most of these areas are located 50 km around the Yellow River. O3 and SOA in Taiyuan, Xi'an, Chengdu, and Zhengzhou cities are strongly influenced by the generation of BVOCs. This study provides a reliable scientific basis for the prevention and control of air pollution in the Yellow River Basin.

Temporal patterns and driving factors of sediment carbon, nitrogen, and phosphorus stoichiometry in a eutrophication plateau lake.

Stoichiometry determines the key characteristics of organisms and ecosystems on a global scale and provides strong instructions on the fate of sediment carbon, nitrogen, and phosphorus (C-N-P) during the sedimentation process, contributing to the Earth's C-N-P balance. However, the mechanisms underlying C-N-P stoichiometry in response to intensive human activity and organic matter sources remain underexplored, especially in freshwater ecosystems. This study identifies the temporal patterns of C-N-P stoichiometry, reveals the inner driving factors, and clarifies its impact path, especially in eutrophication (the late 1970s). The results revealed that sediment RCP and RNP increased significantly and were controlled by TCAR and TNAR, respectively, indicating the direct impact of burial rate on C-N-P stoichiometry. Based on redundancy analysis and the STM model, autochthonous origin, GDP, and population had positive effects on sediment TCAR, TNAR, and TPAR, which, in turn, affected RCN, RCP, and RNP. Organic matter sources and human activities have a significant influence on RCN, RCP, and RNP, possibly regulated by the variation of TCAR and TNAR. Autochthonous origin had an indirect positive impact on RCN and RCP through the mediating effect of TCAR. Similarly, through the mediating effect of TNAR, it had an indirect negative impact on RCN and an indirect positive impact on RNP. This study showed that TCAR, TNAR, TPAR, GDP, autochthonous, allochthonous and population better explained the changes in RCN, RCP, and RNP over a-hundred-year deposition, highlighting an in-depth understanding of the dynamic change mechanism of sediment C-N-P stoichiometry during the lake deposition process.

Humification levels of dissolved organic matter in the eastern plain lakes of China based on long-term satellite observations.

Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite-ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average ± standard deviation = 33.6 % ± 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986-2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.

[Construction of integrated platform for emergency clinical scientific research based on big data].

OBJECTIVE: To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. METHODS: Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. RESULTS: (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. CONCLUSIONS: The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters.

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 µg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

An operational approach for large-scale mapping of water clarity levels in inland lakes using landsat images based on optical classification.

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

Remote sensing identification of urban water pollution source types using hyperspectral data.

Owing to accelerated urbanisation, increased pollutants have degraded urban water quality. Timely identification and control of pollution sources enable relevant departments to effectively perform water treatment and restoration. To achieve this goal, a remote sensing identification method for urban water pollution sources applicable to unmanned aerial vehicle (UAV) hyperspectral images was established. First, seven fluorescent components were obtained through three-dimensional excitation-emission matrix fluorescence spectroscopy of dissolved organic matter (DOM) combined with parallel factor analysis. Based on the hierarchical cluster analysis of the seven fluorescence components and three spectral indices, four pollution source (PS) types were determined, namely, domestic sewage, terrestrial input, agricultural and algal, and industrial wastewater sources. Second, several water colour and optical parameters, including the absorption coefficient of chromophoric DOM at 254 nm, humification index, chlorophyll-a concentration, and hue angle, were utilised to develop an identification method with a recognition accuracy exceeding 70% for the four PSs that is suitable for UAV hyperspectral data. This study demonstrated the potential of identifying PSs by combining the fluorescence characteristics of DOM with the optical properties of water, thus expanding the application of remote sensing technologies and providing more comprehensive and reliable information for urban water quality management.

Contributions of meteorology and nutrient to the surface cyanobacterial blooms at different timescales in the shallow eutrophic Lake Taihu.

Quantitative assessments of the contributions of various environmental factors to cyanobacterial blooms at different timescales are lacking. Here, the hourly cyanobacterial bloom intensity (CBI) index, a proxy for the intensity of surface cyanobacterial biomass, was obtained from the geostationary satellite sensor Geostationary Ocean Color Imager (GOCI) over the years 2011-2018. Generalized additive model was applied to determine the responses of monthly and hourly CBI to the perturbations of meteorological factors, water stability and nutrients, with variation partitioning analysis used to analyze the relative importance of the three groups of variables to the inter-monthly variation of diurnal CBI in each season. The effects of environmental factors on surface cyanobacterial blooms varied at different timescales. Hourly CBI increased with increasing air temperature up to 18 °C but decreased sharply above 18 °C, whereas monthly CBI increased with increasing air temperature up to 30 °C and stabilized thereafter. Among all the environmental factors, air temperature had the largest contribution to the intra-daily variation in CBI; water stability had the highest explanation rate for the inter-monthly variation of diurnal CBI during summer (42.3 %) and autumn (56.9 %); total phosphorus explained the most variation in monthly CBI (18.5 %). Compared with cyanobacterial biomass (CB) in the water column, high light and low wind speed caused significantly lower CBI in July and higher CBI in November respectively. Interestingly, cyanobacterial blooms at the hourly scale were aggravated by climate warming during winter and spring but inhibited during summer and autumn. Collectively, this study reveals the effects of environmental factors on surface cyanobacterial blooms at different timescales and suggests the consideration of the hourly effect of air temperature in short-term predictions of cyanobacterial blooms.

Higher allochthonous organic carbon increases polycyclic aromatic hydrocarbon concentration whereas fossil fuel combustion alters the composition: Evidence from a eutrophic plateau lake in southwest China.

Understanding the source of polycyclic aromatic hydrocarbons (PAHs) is crucial for determining their structural, degradational, and burial characteristics in lake sediments. Here, we used a sediment core to determine the changing sources and burial characteristics of 16 PAHs from Dianchi Lake, southwest China. The ∑16PAH concentrations ranged from 105.10 to 1248.05 ng g-1 (448.97 ± 351.25 ng g-1), exhibiting a sharp increase since 1976. Our results showed that the depositional flux of PAHs has increased by approximately 3.72 times over the past 114 years (1895-2009). The C/N ratio, stable isotopes (δ13Corg and δ15N), and n-alkanes data all indicated that allochthonous contributors of organic carbon have substantially increased since the 1970s, playing an important role in the increase in sedimentary PAHs. Positive matrix factorization indicated that petrogenic sources, coal and biomass combustion, and traffic emissions were the main sources of PAHs. The relationships between PAHs from different sources and total organic carbon (TOC) varied with the sorption characteristics. The effect of TOC on the absorption of high-molecular-weight aromatic PAHs from fossil fuels was significant. A higher risk of lake eutrophication is accompanied by higher allochthonous organic matter imports, which might stimulate an increase in sedimentary PAHs through algal biomass blooms.

A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images.

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were used for water type 1; [Formula: see text], [Formula: see text], and [Formula: see text] were adopted for water type 2; and [Formula: see text], [Formula: see text], and [Formula: see text] were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05 mg/L and 2.24 mg/L to 1.75 mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.

Serum Irisin is Associated with Bone Mineral Density in Postmenopausal T2dm Patients Complicated with Osteoporosis and in Mice with Diabetic Osteoporosis.

BACKGROUND: Osteoporosis is a systemic bone disease with low bone mass, destruction of bone microstructure, and increased bone fragility. Gender and metabolic status are well-known risk factors for osteoporosis. Irisin is a newly discovered myokine that is secreted by skeletal muscle and adipose tissue. Serum Irisin was reported to be decreased in type 2 diabetes mellitus (T2DM) and/or osteoporosis patients, and it is correlated with bone mineral density (BMD) of neck bone, but its role in postmenopausal T2DM with osteoporosis remains largely unknown. METHODS: Postmenopausal T2DM patients with or without osteoporosis were recruited, and 50 agematched healthy postmenopausal women were employed as healthy control. C57BL/6J mice were intraperitoneally injected with 65 mg/kg Streptozotocin (STZ) daily for consecutive 5 days to induce diabetes, and 1 mg/kg recombinant Irisin protein was injected into diabetic mice through the tail vein once a week for 4 months. RESULTS: Compared to that of healthy control, serum Irisin levels and BMD in L1-L4 lumbar spine, femoral neck, total hip, and Wards were decreased in postmenopausal T2DM patients and further decreased in T2DM patients with osteoporosis. Moreover, serum Irisin levels were also correlated with BMD in the above body parts in T2DM patients. Furthermore, recombinant Irisin protein improved diabetic osteoporosis and inflammation in STZ-induced diabetic mice with osteoporosis. CONCLUSION: Serum Irisin levels in postmenopausal T2DM patients with osteoporosis were significantly decreased, which may be related to the decreased BMD and the occurrence of osteoporosis in postmenopausal T2DM patients. The combined measurement of serum Irisin levels and BMD in patients with T2DM in the early stage has a certain effect on the diagnosis and treatment of osteoporosis.

Quantum interference between dark-excitons and zone-edged acoustic phonons in few-layer WS2.

Fano resonance which describes a quantum interference between continuum and discrete states, provides a unique method for studying strongly interacting physics. Here, we report a Fano resonance between dark excitons and zone-edged acoustic phonons in few-layer WS2 by using the resonant Raman technique. The discrete phonons with large momentum at the M-point of the Brillouin zone and the continuum dark exciton states related to the optically forbidden transition at K and Q valleys are coupled by the exciton-phonon interactions. We observe rich Fano resonance behaviors across layers and modes defined by an asymmetry-parameter q: including constructive interference with two mirrored asymmetry Fano peaks (weak coupling, q > 1 and q < - 1), and destructive interference with Fano dip (strong coupling, ∣q∣ < < 1). Our results provide new insight into the exciton-phonon quantum interference in two-dimensional semiconductors, where such interferences play a key role in their transport, optical, and thermodynamic properties.

A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China.

Chemical oxygen demand concentration (CCOD) is widely used to indicate the degree of organic pollution of lakes, reservoirs and rivers. Mastering the spatiotemporal distribution of CCOD is imperative for understanding the variation mechanism and controlling of organic pollution in water. In this study, a hybrid approach suitable for Sentinel 3A/Ocean and Land Colour Instrument (OLCI) data was developed to estimate CCOD in inland optically complex waters embedding the interaction between CCOD and the absorption coefficients of optically active constituents (OACs). Based on in-situ sampling in different waters, the independent validations of the proposed model performed satisfactorily in Lake Taihu (MAPE = 23.52 %, RMSE = 0.95 mg/L, and R2 = 0.81), Lake Qiandaohu (MAPE = 21.63 %, RMSE = 0.50 mg/L and R2 = 0.69), and Yangtze River (MAPE = 29.34 %, RMSE = 0.83 mg/L, and R2 = 0.64). In addition, the approach not only showed significant superiority compared with previous algorithms, but also was suitable for other common satellite sensors equipped same or similar bands. The hybrid approach was applied to OLCI images to retrieve CCOD of Lake Taihu from 2016 to 2020 and reveals substantial interannual and seasonal variations. The above results indicate that the proposed approach is effective and stable for studying spatiotemporal dynamic of CCOD in optically complex waters, and that satellite-derived products can provide reliable information for lake water quality management.

Sediment record in pollution, toxicity risk, and source assignment of polycyclic aromatic hydrocarbons (PAHs) in Erhai Lake, Southwest China.

Surface sediments and sediment core had been collected from Erhai Lake, Southwest China to study the concentrations, toxicity risks, and sources of polycyclic aromatic hydrocarbons (PAHs). The average concentrations of Σ16PAHs, seven carcinogenic PAHs (carPAHs), and carcinogenic toxic equivalents (TEQcar) in the surface sediments and sediment core were 1634.50 ± 488.56 ng g-1 and 436.72 ± 128.17 ng g-1, 67.18-293.65 ng g-1 and 91.07-265.90 ng g-1, and 34.89 ± 13.17 ng g-1 and 36.99 ± 7.52 ng g-1, respectively. The Σ16PAHs and carPAHs concentrations in surface sediments were higher in the southern lake. The Σ16PAHs and TEQcar in the sediment core peaked in the 2010s and 1980s. The spatiotemporal variations in TEQcar and carPAHs were similar. Positive matrix factorization revealed that traffic emissions contributed 35.71 % of the TEQcar, whereas coal and biomass combustion contributed 12.89 % in the surface sediments. The contribution of gasoline and fossil fuel to TEQcar significantly increased from 19.2 % (1890s) to 66.5 % (1990s), that of benz[a]pyrene (coal combustion) decreased, and those of benz[b]fluoranthene and indeno[1,2,3-cd]pyrene (petroleum combustion and traffic emissions) increased from 1.92 % to 3.93 % and from 1.54 % to 2.52 % in the sediment cores, respectively, owing to changes in energy consumption.

Recent estimates and predictions of 5-year survival rate in patients with pancreatic cancer: A model-based period analysis.

Background: The 5-year survival rate for pancreatic cancer (PC) is incredibly low, resulting in this often being a fatal disease. Timely and accurate assessment of the survival rate and prognosis of patients with PC is of great significance for the development of new programs for prevention, monitoring, and treatment. Methods: Period analysis and further stratified analysis were used to determine the 5-year relative survival rate (RSR) of patients with PC from 2002 to 2016 using the Surveillance, Epidemiology, and End Results (SEER) project database of the National Cancer Institute. Based on this, a generalized linear model was created to predict the survival rate of patients from 2017 to 2021. Result: During 2002-2016, the 5-year RSR of patients with PC increased from 7.9 to 23.7%. The generalized linear model predicted that the survival rate had increased to 33.9% during 2017-2021, and hence, it was still unacceptably low. The survival rate of patients aged ≥75 years at diagnosis was the lowest among all age groups and was predicted to be only 21.4% during 2017-2021. Notably, the survival rate of patients with differentiation grade III at diagnosis remains particularly low at 7.6%. Conclusion: The survival rates of patients with PC, although slightly improved, remain extremely low. Timely assessment of the trend of survival rate changes in patients with PC further improves the prognosis of tumor patients and provides data support for relevant medical works to formulate effective tumor prevention and control policies.