Association of Leisure-Time Physical Activity and Mortality Risk in High Cardiovascular Risk Population with and without Left Ventricular Hypertrophy.

Background: Increased leisure-time physical activity (LTPA) is linked with decreased mortality risk, while also with increased left ventricular mass, which may induce left ventricular hypertrophy (LVH). We investigated whether LVH modifies the association between higher LTPA and lower mortality risk in population at high cardiovascular risk. Methods: In a prospective national cohort, we used the left ventricular mass/body surface area (LVM/BSA) method to define LVH. Baseline LTPA was self-reported and divided into: low ( < 500 metabolic equivalent of task [MET]) min/week), moderate (500-1999 MET min/week) and high ( ≥ 2000 MET-min/week). Analyses of the dose-response relationship between LTPA and left ventricular mass were performed using restricted cubic spline regression. A multivariate adjusted Cox proportional hazards regression analysis was used to estimate hazard ratios (HRs). Results: A total of 163,006 participants (55.3% females, mean [standard deviation] age, 62.4 [7.4] years) were included. During a median of 4.8 years of follow-up, 6586 (4.0%) died from all causes and 3024 (1.9%) from cardiovascular causes. Multivariate adjusted Cox proportional hazards regression analyses revealed that moderate and high LTPA were linked with less cardiovascular and all-cause mortality risk than low LTPA in the absence of LVH. In those with LVH, the association of high (0.83, 0.69-0.99) or moderate (0.72, 0.56-0.91) LTPA with cardiovascular mortality risk persisted. For all-cause mortality risk, this association was only significant in high LTPA (0.73, 0.61-0.86), while marginal in moderate LTPA (0.96, 0.84 to 1.08). Overall, the correlation patterns between LTPA and mortality risk appears distinct between those with LVH and those without LVH; the modification of LVH was not significant regarding mortality risk among the high cardiovascular risk population (all-cause: p-value for interaction = 0.074; cardiovascular cause: p-value for interaction = 0.581), except in females regarding all-cause mortality risk (p-value for interaction = 0.006). Conclusions: The association between higher LTPA and lower mortality risk was not modified by LVH in high cardiovascular risk population. However, the presence of LVH altered this association in females regarding the all-cause mortality risk.

Decabromodiphenyl Ethane Mainly Affected the Muscle Contraction and Reproductive Endocrine System in Female Adult Zebrafish.

The novel brominated flame retardant decabromodiphenyl ethane (DBDPE) has become a widespread environmental pollutant. However, the target tissue and toxicity of DBDPE are still not clear. In the current study, female zebrafish were exposed to 1 and 100 nM DBDPE for 28 days. Chemical analysis revealed that DBDPE tended to accumulate in the brain other than the liver and gonad. Subsequently, tandem mass tag-based quantitative proteomics and parallel reaction monitoring verification were performed to screen the differentially expressed proteins in the brain. Bioinformatics analysis revealed that DBDPE mainly affected the biological process related to muscle contraction and estrogenic response. Therefore, the neurotoxicity and reproductive disruptions were validated via multilevel toxicological endpoints. Specifically, locomotor behavioral changes proved the potency of neurotoxicity, which may be caused by disturbance of muscular proteins and calcium homeostasis; decreases of sex hormone levels and transcriptional changes of genes related to the hypothalamic-pituitary-gonad-liver axis confirmed reproductive disruptions upon DBDPE exposure. In summary, our results suggested that DBDPE primarily accumulated in the brain and evoked neurotoxicity and reproductive disruptions in female zebrafish. These findings can provide important clues for a further mechanism study and risk assessment of DBDPE.

High precision even-power phase modulation method for a self-mixing displacement sensor under the weak feedback regime.

A novel method called even-power phase modulation is proposed in a self-mixing displacement sensor to improve measuring accuracy, to the best of our knowledge, which is realized by combining the even-power fast algorithm with the sinusoidal phase-modulation method. By performing the even-power fast algorithm in the self-mixing interference system, the spectrum of harmonic components is broadened. In this case, the extracted first and second harmonic components in the frequency domain contain rich information, and the displacement of the target can be accurately reconstructed. The principle and signal processing approach are introduced in detail, and the simulation results show that the reconstruction error can be effectively reduced compared with the electro-optic modulator phase modulation method. A series of experiments at different vibration amplitudes is conducted to confirm the feasibility and effectiveness of the method. An amplitude of 120 nm is proved to be measurable, and the absolute error is 10 nm, which shows great potential in the field of non-contact nanometer vibration measurement sensors.

Decabromodiphenyl ethane induced hyperactivity in developing zebrafish at environmentally relevant concentrations.

Decabromodiphenyl ethane (DBDPE), a widely used novel brominated flame retardant, is gaining concerns due to rapidly increased contents in various environmental and biota samples. In the present study, zebrafish (Danio rerio) embryos were exposed to 2.91, 9.71, 29.14 and 97.12 µg/L of DBDPE until 120 h post-fertilization (hpf) to investigate the potential developmental neurotoxicity and underlying mechanisms. Chemical analysis revealed concentration-dependently increased body burdens of DBDPE in zebrafish larvae, with bioaccumulation factors (BCFs) ranging from 414 to 726. Embryonic exposure to DBDPE caused hyperactivity without affecting the development of secondary motoneuron axons and muscle fibers. However, further results implicated that DBDPE may affect the locomotor regulatory network via different mechanisms at lower and higher concentrations. On the one hand, embryonic exposure to 2.91 µg/L DBDPE transiently promoted spontaneous coiling contractions, but showed no effects on touch-response and swimming activity in zebrafish larvae. The whole-body contents of neurotransmitters were significantly decreased. Significant decreased protein abundances of α1-TUBULIN and SYN2a and molecular docking results pointed out possible interactions of DBDPE with these two proteins. However, these changes may be unconcerned with the transient hyperactivity, and the exact molecular mechanisms need further investigation. On the other hand, 29.14 and 97.12 µg/L DBDPE exposure caused longer-lasting effects in promoting spontaneous coiling contractions, and also touch-response and swimming activity. At the same time, increased ACh contents (without changes of other neurotransmitters) and ChAT activity and inhibited transcription of nAChRs were observed at higher concentrations. Molecular docking indicated direct interaction of DBDPE with ChAT. The results suggested that DBDPE induced hyperactivity at higher concentrations was probably involved with disrupted cholinergic system, with ChAT as a potential target. Given that the body burden of DBDPE in lower concentration group was comparable with those detected in wild fish, the current results may provide useful information for ecological risk assessment.

Improved method for damping coefficient measurement based on spectral analysis of a self-mixing signal.

In this paper, the self-mixing interference subject to weak optical feedback has been used to measure the damping vibration. By analyzing the spectrum of the signal, the damping coefficient can be extracted precisely from the nth-order Bessel functions, which are determined by the dominant harmonic order of the frequency spectrum. Theoretical derivation and signal processing are presented. Four kinds of vibrating targets with different damping coefficients are measured. Experimental results show that standard deviation and root mean square error of data are less than 0.2 and 0.1, respectively, which means fitted values are stable as well as having a very high fitting precision.

Physiological and Proteomic Evidence for the Interactive Effects of Post-Anthesis Heat Stress and Elevated CO2 on Wheat.

Elevated CO2 promotes leaf photosynthesis and improves crop grain yield. However, as a major anthropogenic greenhouse gas, CO2 contributes to more frequent and severe heat stress, which threatens crop productivity. The combined effects of elevated CO2 and heat stress are complex, and the underlying mechanisms are poorly understood. In the present study, the effects of elevated CO2 and high-temperature on foliar physiological traits and the proteome of spring wheat grown under two CO2 concentrations (380 and 550 µmol mol-1 ) and two temperature conditions (ambient and post-anthesis heat stress) are examined. Elevated CO2 increases leaf photosynthetic traits, biomass, and grain yield, while heat stress depresses photosynthesis and yield. Temperature-induced impacts on chlorophyll content and grain yield are not significantly different under the two CO2 concentrations. Analysis of the leaf proteome reveals that proteins involved in photosynthesis as well as antioxidant and protein synthesis pathways are significantly downregulated due to the combination of elevated CO2 and heat stress. Correspondingly, plants treated with elevated CO2 and heat stress exhibit decreased green leaf area, photosynthetic rate, antioxidant enzyme activities, and 1000-kernel weight. The present study demonstrates that future post-anthesis heat episodes will diminish the positive effects of elevated CO2 and negatively impact wheat production.

Study on the simultaneous reduction of methylmercury by SnCl2 when analyzing inorganic Hg in aqueous samples.

Mercury (Hg) is among the most concerned contaminants in the world. It has three major chemical forms in the environment, including Hg0, Hg2+, and methylmercury (MeHg). Due to their differences in toxicity, mobility, and bioavailability, speciation analysis is critical for understanding Hg cycling and fate in the environment. SnCl2 reduction-atomic fluorescence spectrometry detection is the most commonly used method for analyzing inorganic Hg. However, it should be noted that MeHg may also be reduced by SnCl2, which would result in the overestimation of inorganic Hg. In this study, the reduction of MeHg by SnCl2 in both de-ionized (DI) water and four natural waters was investigated. The results showed that MeHg could be reduced by SnCl2 in DI water whereas this reaction was hard to occur in tested natural waters. By investigating the effects of water chemical characteristics (dissolved organic matter, pH and common anions and cations) on this reaction, SO42- was identified to be the dominant factor prohibiting SnCl2 induced MeHg reduction in natural waters. SO42- in natural waters was evidenced to be reduced to S2- by SnCl2 and the generated S2- can complex with MeHg to form MeHgS- which is hard to be reduced by SnCl2. Findings of this study indicate that the effect of MeHg reduction by SnCl2 on inorganic Hg analysis is negligible in natural waters; however, at simulated experimental systems without SO42-, SO42- should be added as protecting agents to prevent MeHg reduction when analyzing inorganic Hg if it would not cause any other unwanted effects.

Dual-wavelength filters based on two-dimensional photonic crystal degenerate modes with a ring dielectric rod inside the defect cavity.

A tunable dual-wavelength filter based on degenerate modes with a ring dielectric rod inside the single point defect cavity is proposed. The band structures and mode profiles are computed by the plane-wave expansion method. The normalized transmission spectra for this structure are investigated by using the two-dimensional finite-difference time-domain method. The two orthogonal output modes of the filter can be regarded as the combination of the original degenerate cavity modes and both excited due to the introduced perturbation rod. The influences of the perturbation rod on the localized modes, band separations, and the tuning ranges are all investigated and analyzed based on the perturbation theory. With the ring dielectric rod being introduced into the defect, a narrower wavelength bandwidth and a wider wavelength tuning range can be obtained, which are superior to that of the filter based on a cavity with a larger radius rod. Besides, the perturbation rod can also be used for splitting degenerate modes for multiwavelength filters. The proposed filter has a simple structure and may be potentially applied in various integrated circuits, such as multiwavelength filters.

Enzyme-free ratiometric fluorescence and colorimetric dual-signal determination of glyphosate based on copper nanoclusters (ZIF/CuNCs) combined with blue carbon dots (bCDs).

A novel ratio fluorescent and colorimetric dual-signal sensing platform for detecting glyphosate based on blue carbon dots (bCDs) combined with ZIF/CuNCs nanomaterials that encapsulate copper nanoclusters (CuNCs) in a metal-organic framework (MOF). In principle, the immobilization of Cu2+ in ZIF/CuNCs results in complexation with imidazole in ZIF, leading to fluorescence quenching of ZIF/CuNCs, while the reference fluorophore bCDs remains unaffected. In addition, the colorimetric sensing strategy was based on the efficient peroxidase-like activity of bCDs binding to Cu2+, catalyzing H2O2 to generate OH. Under this condition, TMB could be oxidized to form blue oxTMB. However, when glyphosate was involved in the system, the fluorescence of ZIF/CuNCs was restored upon due to the strong chelation between Cu2+ and glyphosate, while the peroxidase-like activity of bCDs/Cu2+ decreased and resulted in the generation of fewer oxTMB, accompanied by a lighter blue color. The sensing platform was successfully applied to the determination of glyphosate in real samples of lake water and cabbage, demonstrating reliable and sensitive performance in practical applications.

Thalamic segmentation based on diffusion tensor imaging in patients with trigeminal neuralgia.

Classical trigeminal neuralgia (CTN) refers to episodic pain that is strictly confined to the trigeminal distribution area, and the thalamus is an important component of the trigeminal sensory pathway. Probabilistic tracking imaging algorithm was used to identify specific connections between the thalamus and the cortex, in order to identify structural changes in the thalamus of patients with CTN and perform thalamic segmentation. A total of 32 patients with CTN and 32 healthy controls underwent DTI-MRI scanning (3.0 T). Differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) between the groups were studied. Correlation analysis was performed with clinical course and pain level. Compared to the healthy controls, patients in the CTN group had significantly reduced FA, increased AD, RD and MD in somatosensory subregion of the bilateral thalamus, increased RD in frontal subregion, increased RD and MD in motor subregion. Correlation analysis showed that patient history was positively correlated with pain grading, and that medical history was positively correlated with significantly reduced FA in somatosensory subregion, negatively correlated with increased RD and MD in motor subregion. We used DTI-based probabilistic fiber tracking to discover altered structural connectivity between the thalamus and cerebral cortex in patients with CTN and to obtain a thalamic segmentation atlas, which will help to further understand the pathophysiology of CTN and serve as a future reference for thalamic deep brain stimulation electrode implantation for the treatment of intractable pain.

Governance pathway for coastal eutrophication based on regime shifts in diatom-dinoflagellate composition of the Bohai and Baltic Seas.

Regime shifts in the diatom-dinoflagellate composition have occurred in the Baltic Sea (BS) and Bohai Sea (BHS) under eutrophication and have affected the entire coastal ecosystem, damaging the regulatory, provisioning, cultural, and supporting service functions of marine ecosystems. Therefore, finding a solution to restore the balance of phytoplankton community composition and mitigate eutrophication is of utmost importance. In this study, the Driver (per capita gross domestic product)-Pressure (terrestrial inputs)-State (seawater environmental parameters)-Impact (proportions of diatoms and dinoflagellates)-Response (eutrophication governance projects) framework served as a guide for our analysis of the causal relationship among various environmental components in the coastal system. The relevant data in BS and BHS spanning from the 1950s to the 2010s were collected and used to construct a diatom-dinoflagellate composition single index, which allowed us to identify the shifts in regimes (mutation points and phases) of the diatom-dinoflagellate composition and environmental factors using sequential t-test analysis. We also identified key environmental factors that moderated the diatom-dinoflagellate composition using redundancy analysis and analyzed the partial effects of the main environmental factors on the diatom-dinoflagellate composition using a generalized additive model. Finally, the regulation of the eutrophication governance investment on diatom-dinoflagellate composition was investigated. We found that (1) BS is a "time machine," with coastal eutrophication governance and regime shift of diatom-dinoflagellate composition and environmental factors two decades earlier than that in BHS; (2) in BS, the key moderation factor of diatom proportion is SiO3-Si and those of dinoflagellates are sea surface salinity and N:P ratio; in BHS, the key moderation factors of diatom proportion are PO4-P and Si:N ratio and those of dinoflagellate are dissolved inorganic nitrogen and N:P and Si:P ratios; (3) it is projected that BHS will enter its recovery phase from eutrophication after mid-2020s. In summary, the N/P/Si stoichiometric relationships should be given greater consideration, with the exception of the "dose-response" relationship in both sea areas. Our results indicate an urgent need for an improved mechanistic understanding of how phytoplankton biodiversity changes in response to changes in nutrient load and how we should ultimately deal with the challenges that arise.

Pr2Ni0.8Co0.2O4+δ impregnated La0.6Sr0.4CoO3-δ oxygen electrode for efficient CO2 electroreduction in solid oxide electrolysis cells.

The solid oxide electrolysis cell (SOEC) is an advanced electrochemical device with a promising future in reducing CO2 emissions. Currently, the insufficient oxygen evolution reaction activity in conventional anode materials severely restricts the development of electrolytic CO2. Herein, the PNCO-LSC composite oxygen electrode was exploited by impregnating Pr2Ni0.8Co0.2O4+δ (PNCO) on the surface of La0.6Sr0.4CoO3-δ (LSC) oxygen electrode. The results of electrochemical tests and various physicochemical characterizations indicate that the infiltration of PNCO can lead to a significant improvement in the performance of the cell for CO2 electroreduction by increasing the surface oxygen exchange. The current density of the PNCO-LSC oxygen electrode infiltrated twice at 800 °C and 1.5 V reaches 0.917 A cm-2, which is about 40% higher than that of the bare LSC oxygen electrode. In addition, the single cell did not show significant degradation in a long-term stability test at a current density of 0.4 A cm-2 for 100 h of electrolysis. Therefore, the PNCO-LSC composite oxygen electrode material is effective in enhancing electrolytic CO2 performance.

An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network.

Deep learning networks are state-of-the-art approaches for 3D brain image segmentation, and the radiological characteristics extracted from tumors are of great significance for clinical diagnosis, treatment planning, and treatment outcome evaluation. However, two problems have been the hindering factors in brain image segmentation techniques. One is that deep learning networks require large amounts of manually annotated data. Another issue is the computational efficiency of 3D deep learning networks. In this study, we propose a vector quantization (VQ)-based 3D segmentation method that employs a novel unsupervised 3D deep embedding clustering (3D-DEC) network and an efficiency memory reserving-and-fading strategy. The VQ-based 3D-DEC network is trained on volume data in an unsupervised manner to avoid manual data annotation. The memory reserving-and-fading strategy beefs up model efficiency greatly. The designed methodology makes deep learning-based model feasible for biomedical image segmentation. The experiment is divided into two parts. First, we extensively evaluate the effectiveness and robustness of the proposed model on two authoritative MRI brain tumor databases (i.e., IBSR and BrainWeb). Second, we validate the model using real 3D brain tumor data collected from our institute for clinical practice significance. Results show that our method (without data manual annotation) has superior accuracy (0.74±0.04 Tanimoto coefficient on IBSR, 97.5% TP and 97.7% TN on BrainWeb, and 91% Dice, 88% sensitivity and 87% specificity on real brain data) and remarkable efficiency (speedup ratio is 18.72 on IBSR, 31.16 on BrainWeb, 31.00 on real brain data) compared to the state-of-the-art methods. The results show that our proposed model can address the lacks of manual annotations, and greatly increase computation speedup with competitive segmentation accuracy compared to other state-of-the-art 3D CNN models. Moreover, the proposed model can be used for tumor treatment follow-ups every 6 months, providing critical details for surgical and postoperative treatment by correctly extracting numerical radiomic features of tumors.

Seasonal dynamics of dissolved organic matter bioavailability coupling with water mass circulation in the South Yellow Sea.

As a typical shelf-marginal sea, the South Yellow Sea (SYS) is significantly influenced by various factors such as land-based inputs and water mass movements, leading the complex biogeochemical processes of dissolved organic matter (DOM) to become highly dynamic. However, the bioavailability of dissolved organic matter (DOM) coupled with water mass circulation has not been accurately assessed, despite being crucial for understanding the source-sink pattern of organic carbon in marginal sea. In this study, four cruises were conducted in the SYS to analyze the spatial and temporal distribution characteristics of dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and total dissolved amino acids (TDAA). Combined with the bioassay experiments, TDAA carbon normalized yield [TDAA (%DOC)] and TDAA degradation index (DIAA) were used as indicators to explore the bioavailability of DOM across different water masses. Results show that the DOC of the SYS exhibits higher average value in late autumn and early winter, and lower value in spring and summer due to the seasonal alternation of water mass and biological activities. The collective results indicate that DOM bioavailability is higher in the Changjiang River diluted water (CDW) and lower in the Yellow Sea warm current (YSWC) and the Yellow Sea cold water mass (YSCWM). Approximately 20 % of DON can be degraded in the YSCWM during autumn. Notably, although the YSCWM constitutes merely constitutes 10 % of the SYS volume, it stores 18.1 % dissolved inorganic nitrogen (DIN) and 23.9 % PO43- of total nutrients, indicating that the YSCWM is a significant nutrient reservoir within the SYS.

[Studies on micelle behaviors of sophorolipid biosurfactant by steady-state fluorescence probe method].

The critical micelle concentration (CMC) of sophorolipid was determined by steady-state fluorescence probe method in which pyrene was used as fluorescence probe. Meanwhile, the changes in the sophorolipid CMC were examined in the addition of NaCl and aliphatic alcohol, respectively. The results showed that the CMC of sophorolipid was 1.3 x 10(-4) mol x L(-1) and had a slight decrease as NaCl concentration increased. However, the CMC of sophorolipid appeared to increase in the presence of aliphatic alcohol. The aggregation numbers and the micelle size of sophorolipid solution were investigated by fluorescence quenching and laser light scattering method. The aggregation numbers were 4-8 with the concentrations of sophorolipid in the range of 4 -8 CMC and the micelle mean diameter for sophorolipid with the concentration of 6 CMC was about 90 nm. These experimental results indicated that the micelle should be incompact.

Exploring Oscillatory Dysconnectivity Networks in Major Depression During Resting State Using Coupled Tensor Decomposition.

Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time × frequency × connectivity × subject are constructed for the healthy group and the major depression group. We assume that the two groups share the same features for group similarity and retain individual characteristics for group differences. Considering that the constructed tensors are nonnegative and the components in spectral and adjacency modes are partially consistent among the two groups, we formulate a double-coupled nonnegative tensor decomposition model. To reduce computational complexity, we introduce the low-rank approximation. Then, the fast hierarchical alternative least squares algorithm is applied for model optimization. After clustering analysis, we summarize four oscillatory networks characterizing the healthy group and four oscillatory networks characterizing the major depression group, respectively. The proposed model may reveal novel mechanisms of pathoconnectomics in MDD during rest, and it can be easily extended to other psychiatric disorders.

Exposure to tris(1,3-dichloro-2-propyl) phosphate for two generations aggravates the adverse effects on survival and growth of zebrafish at environmentally relevant concentrations.

It was reported that tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) could inhibit the growth of F0-generation fish. However, multi-generation effects of TDCIPP on survival and growth of fish remain unknown. In this study, the effects of TDCIPP on survival and growth in F1 generation zebrafish were evaluated after two-generation exposure. Results demonstrated that TDCIPP inhibited the survival and growth of F1-generation zebrafish at 96 hpf and 30 dpf. Moreover, compared with the F0 generation, two-generation exposure resulted in a greater accumulation of TDCIPP in F1 generation zebrafish, and strongly down-regulated the expression of genes related to the GH/IGF axis (gh, igf1, igf2b) and HPT axis (tshß). Taken together, for the first time, this study revealed that exposure to TDCIPP for two generations at environmentally relevant concentrations aggravated the adverse effects on growth and survival in zebrafish.

Two dimensional twin T-graphene: monolayer for visible-light photocatalytic water splitting and bulk for anode material of magnesium batteries.

Two-dimensional (2D) carbon allotropes with all-sp2-hybridization have demonstrated great potential in nano-photoelectric devices, but the application of semiconductor photocatalysts for water splitting and anodes in magnesium batteries remains unoptimistic. Motivated by this, we theoretically study a highly stable all-sp2-hybridized 2D carbon allotrope twin T-graphene (TTG) via first-principles simulations. And through the calculations of the HSE06 functional, we find that TTG has a wide bandgap (2.70 eV) and suitable band edge positions satisfying the criteria of photocatalysts for overall water splitting. Additionally, TTG exhibits excellent photocatalytic properties for overall water splitting reflecting a high STH efficiency (12.34%), strong absorption coefficient in the visible-light region and the carrier mobility being high for electrons but low for holes. By investigating the strain effects, we get that, with biaxial compressive strain, the ability of overall photocatalytic water splitting can be effectively improved including STH up to ∼30%. Moreover, the bulk TTG also exhibits great potential as an anode material of magnesium batteries with a theoretical capacity of 556 mA h g-1, average voltage of 0.74 V and diffusion energy barrier of ∼0.96 eV. Our results would broaden the application of all-sp2-hybridized 2D carbon allotropes in the semiconductor photocatalytic and magnesium batteries field.

Effects of organic nitrogen components from terrestrial input on the phytoplankton community in Jiaozhou Bay.

Dissolved organic nitrogen (DON) from terrestrial input exacerbates eutrophication and induces harmful algal blooms. We investigated the effects of hydrophilic (Hic) and low molecular weight (LMW) DON on the phytoplankton community in Jiaozhou Bay during autumn (October 2017) and spring (May 2018). Our results showed DON additions significantly increased algal growth while decreasing community biodiversity and provide a competitive advantage for Skeletonema costatum. These situations were further intensified by increasing temperature in autumn. Additionally, Hic DON had a higher bioavailability than LMW DON. Based on emission-excitation matrix spectra, we identified protein-like components as the main components of Hic DON whereas humus-like components were the principal components of LMW. Correlation analysis confirmed a positive correlation between DON bioavailability and protein-like components. Therefore, our results indicate DON from terrestrial input disrupts the structural stability of the phytoplankton community and increases the risk of harmful algal blooms, which in turn threaten coastal ecosystems.

A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study.

Objective: To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST). Methods: We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA). Results: The mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models (P < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets. Conclusion: The combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.