Dispersal modes affect Rhamnaceae diversification rates in a differentiated manner.

The evolution of dispersal modes has been proposed to promote the diversification of angiosperms. However, little is known about the relative impact of different dispersal modes on plant diversification. We test the association between dispersal modes and diversification rates using Rhamnaceae, the cosmopolitan buckthorn family, as a model. We found that species with diplochory have the highest diversification rates followed by those with myrmecochory and ballistic dispersal, while lineages dispersed by vertebrates and wind have relatively low diversification rates. The difference in diversification rates may be closely linked to the difference in dispersal distance and ecological interactions implied by each dispersal mode. Species which disperse over larger geographical distances may have much higher speciation rates due to the increased chance of establishing isolated populations due to geological barriers or habitat fragmentation. However, long-distance dispersal may also increase the chance of extinction. By contrast, species with short-distance dispersal modes may have low speciation rates. Complex interactions with the surrounding environment may, however, impact diversification rates positively by increasing plant survival and reproductive success.

Survival of 48866 cancer patients: results from Nantong area, China.

Objective: This study aimed to provide a realistic observation of survival by major site for 48,866 cancer patients treated at a tertiary cancer hospital in a rural area of China. Methods: Patients with cancer registered between 2007 and 2017 in the Nantong rural area were followed up. The starting date for survival calculation was the date of the first diagnosis of cancer at the Nantong Tumor Hospital, and the closing date was December 31, 2020. Observed survival (OS) was analyzed according to ICD-10 site, sex, age, region, and hospitalization period using the life table method and compared using the Wilcoxon (Gehan) statistic. Results: The overall 5-year OS rate was 40.48% for all 48,866 patients, 30.19% for males, and 51.90% for females. The top five cancer sites, accounting for 60.51% of the total cases, were the esophagus, lung, stomach, liver, and cervix, with 5-year OS rates of 33.72%, 18.64%, 32.10%, 19.04%, and 71.51%, respectively. The highest 5-year OS was observed in the thyroid (87.52%) and the lowest was in the pancreas (6.37%). Survival was significantly higher in younger patients than in older patients, with 5-year OSs of 69.26% and 19.84% in those aged 20-29 and 90-99 years, respectively. Five-year OSs improved significantly from 39.35% in 2007-2011 to 41.26% in 2012-2017. Conclusion: Overall survival improved over the years, although the improvement at some sites was not significant. The observed survival varies from region to region, reflecting differences in the patterns of major sites, disparities in proportions of hospitalization, and demographic characteristics.

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography.

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of the Alberta Stroke Program Early CT Score (ASPECTS) are usually time-consuming and require experienced neuroradiologists. To this end, this study proposes DGA3-Net, a convolutional neural network (CNN)-based model for ASPECTS assessment via detecting early ischemic changes in ASPECTS regions. DGA3-Net is based on a novel parameter-efficient dihedral group CNN encoder to exploit the rotation and reflection symmetry of convolution kernels. The bounding volume of each ASPECTS region is extracted from the encoded feature, and an attention-guided slice aggregation module is used to aggregate features from all slices. An asymmetry-aware classifier is then used to predict stroke presence via comparison between ASPECTS regions from the left and right hemispheres. Pre-treatment NCCTs of suspected AIS patients were collected retrospectively, which consists of a primary dataset (n = 170) and an external validation dataset (n = 90), with expert consensus ASPECTS readings as ground truth. DGA3-Net outperformed two expert neuroradiologists in regional stroke identification (F1 = 0.69) and ASPECTS evaluation (Cohen's weighted Kappa = 0.70). Our ablation study also validated the efficacy of the proposed model design. In addition, class-relevant areas highlighted by visualization techniques corresponded highly with various well-established qualitative imaging signs, further validating the learned representation. This study demonstrates the potential of deep learning techniques for timely and accurate AIS diagnosis from NCCT, which could substantially improve the quality of treatment for AIS patients.

Rapid screening of antioxidant from natural products by AAPH-Incubating HPLC-DAD-HR MS/MS method: A case study of Gardenia jasminoides fruit.

A new AAPH-Incubating HPLC-DAD-HR MS/MS method was developed for the rapid and high-throughput screening of antioxidants directly in natural products and applied to Gardenia jasminoides fruit. This method was assumed that the peak areas of compounds with potential antioxidant activity in HPLC chromatograms would be significantly reduced or disappeared after incubating with the AAPH which can release ROO at physiological conditions (37 °C, pH 7.4). Additionally, the activity of antioxidants can be evaluated by comparing the peak reduction rates and the screened components can be further identified by HRMS/MS. Then, 17 potential natural antioxidants from the crude extract of GJF was screened. Among them, three major components including crocin I, crocin II and crocetin showed excellent ROO scavenging activity, which were further validated by the ORAC assay. In conclusion, our study provided a simple and effective strategy to rapidly screen antioxidants in natural products.

Identification of the New Psychoactive Substance Eutylone.

OBJECTIVES: To establish a method to identify unknown sample based on the combined use of Fourier transform infrared spectroscopy (FTIR), gas chromatography-quadrupole time-of-flight mass spectrometry (GC-QTOF-MS), ultra-high performance liquid chromatography-linear ion trap quadrupole-orbitrap mass spectrometry (UPLC-LTQ-Orbitrap MS) and 1H-nuclear magnetic resonance spectroscopy (1H-NMR) technique. METHODS: The unknown sample was directly analyzed by FTIR. The unknown sample was dissolved in methanol solution containing internal standard SKF525A and the supernatant was detected by GC-QTOF-MS and UPLC-LTQ-Orbitrap MS. The unknown sample was dissolved in methanol-d4 solution for structural analysis of 1H-NMR. RESULTS: The characteristic absorption peaks of FTIR spectra obtained from unknown sample were 1 682 (C=O bond), 1 503, 1 488, 1 436, 1 363, 1 256, 1 092, 1 035, 935, 840 and 800 cm-1, the characteristic fragment ions (m/z) of GC-QTOF-MS were 86.096 4 (base peak), 58.065 1, 149.023 5, 121.028 6 and 65.038 6, the accurate mass [M+H]+ detected by UPLC-LTQ-Orbitrap MS was 236.127 7. The sample was identified as synthetic cathinone new psychoactive substance Eutylone by 1H-NMR. CONCLUSIONS: The method established in this study can be used for structural confirmation of Eutylone.

MEG-based Classification and Grad-CAM Visualization for Major Depressive and Bipolar Disorders with Semi-CNN.

Major depressive disorder (MDD) and bipolar disorder (BD) are two major mood disorders with partly overlapped symptoms but different treatments. However, their misdiagnosis and mistreatment are common based on the DSM-V criteria, lacking objective and quantitative indicators. This study aimed to develop a novel approach that accurately classifies MDD and BD based on their resting-state magnetoencephalography (MEG) signals during euthymic phases. A revisited 3D CNN model, Semi-CNN, that could automatically detect brainwave patterns in spatial, temporal, and frequency domains was implemented to classify wavelet-transformed MEG signals of normal controls and MDD and BD patients. The model achieved a test accuracy of 96.05% and an average of 95.71% accuracy for 5-fold cross-validation. Furthermore, saliency maps of the model were estimated using Grad-CAM++ to visualize the proposed classification model and highlight disease-specific brain regions and frequencies. Clinical Relevance - Our model provides a stable pipeline that accurately classifies MDD, BD, and healthy individuals based on resting-state MEG signals during the euthymic phases, opening the potential for quantitative and accurate brain-based diagnosis for the highly misdiagnosed MDD/BD patients.

Deep Learning-Based Automatic Detection of ASPECTS in Acute Ischemic Stroke: Improving Stroke Assessment on CT Scans.

(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.

PADAr: physician-oriented artificial intelligence-facilitating diagnosis aid for retinal diseases.

Purpose: Retinopathy screening via digital imaging is promising for early detection and timely treatment, and tracking retinopathic abnormality over time can help to reveal the risk of disease progression. We developed an innovative physician-oriented artificial intelligence-facilitating diagnosis aid system for retinal diseases for screening multiple retinopathies and monitoring the regions of potential abnormality over time. Approach: Our dataset contains 4908 fundus images from 304 eyes with image-level annotations, including diabetic retinopathy, age-related macular degeneration, cellophane maculopathy, pathological myopia, and healthy control (HC). The screening model utilized a VGG-based feature extractor and multiple-binary convolutional neural network-based classifiers. Images in time series were aligned via affine transforms estimated through speeded-up robust features. Heatmaps of retinopathy were generated from the feature extractor using gradient-weighted class activation mapping++, and individual candidate retinopathy sites were identified from the heatmaps using clustering algorithm. Nested cross-validation with a train-to-test split of 80% to 20% was used to evaluate the performance of the screening model. Results: Our screening model achieved 99% accuracy, 93% sensitivity, and 97% specificity in discriminating between patients with retinopathy and HCs. For discriminating between types of retinopathy, our model achieved an averaged performance of 80% accuracy, 78% sensitivity, 94% specificity, 79% F1-score, and Cohen's kappa coefficient of 0.70. Moreover, visualization results were also shown to provide reasonable candidate sites of retinopathy. Conclusions: Our results demonstrated the capability of the proposed model for extracting diagnostic information of the abnormality and lesion locations, which allows clinicians to focus on patient-centered treatment and untangles the pathological plausibility hidden in deep learning models.

Genome-Wide Identification, Evolution and Expression Analysis of the Glutathione S-Transferase Supergene Family in Euphorbiaceae.

Euphorbiaceae, a family of plants mainly grown in the tropics and subtropics, is also widely distributed all over the world and is well known for being rich in rubber, oil, medicinal materials, starch, wood and other economically important plant products. Glutathione S-transferases (GSTs) constitute a family of proteins encoded by a large supergene family and are widely expressed in animals, bacteria, fungi and plants, but with few reports of them in Euphorbiaceae plants. These proteins participate in and regulate the detoxification and oxidative stress response of heterogeneous organisms, resistance to stress, growth and development, signal transduction and other related processes. In this study, we identified and analyzed the whole genomes of four species of Euphorbiaceae, namely Ricinus communis, Jatropha curcas, Hevea brasiliensis, and Manihot esculenta, which have high economic and practical value. A total of 244 GST genes were identified. Based on their sequence characteristics and conserved domain types, the GST supergene family in Euphorbiaceae was classified into 10 subfamilies. The GST supergene families of Euphorbiaceae and Arabidopsis have been found to be highly conserved in evolution, and tandem repeats and translocations in these genes have made the greatest contributions to gene amplification here and have experienced strong purification selection. An evolutionary analysis showed that Euphorbiaceae GST genes have also evolved into new subtribes (GSTO, EF1BG, MAPEG), which may play a specific role in Euphorbiaceae. An analysis of expression patterns of the GST supergene family in Euphorbiaceae revealed the functions of these GSTs in different tissues, including resistance to stress and participation in herbicide detoxification. In addition, an interaction analysis was performed to determine the GST gene regulatory mechanism. The results of this study have laid a foundation for further analysis of the functions of the GST supergene family in Euphorbiaceae, especially in stress and herbicide detoxification. The results have also provided new ideas for the study of the regulatory mechanism of the GST supergene family, and have provided a reference for follow-up genetics and breeding work.

Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography.

PURPOSE: Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT. METHODS: We used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations. RESULTS: The proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data. CONCLUSION: This study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.

Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection.

This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP is weakened as deeper backbones with more layers are used. In addition, it cannot keep up accurate detection of both small and large objects at the same time. To address these issues, we propose a new parallel FP structure with bi-directional (top-down and bottom-up) fusion and associated improvements to retain high-quality features for accurate localization. We provide the following design improvements: 1) parallel bifusion FP structure with a bottom-up fusion module (BFM) to detect both small and large objects at once with high accuracy; 2) concatenation and re-organization (CORE) module provides a bottom-up pathway for feature fusion, which leads to the bi-directional fusion FP that can recover lost information from lower-layer feature maps; 3) CORE feature is further purified to retain richer contextual information. Such CORE purification in both top-down and bottom-up pathways can be finished in only a few iterations; 4) adding of a residual design to CORE leads to a new Re-CORE module that enables easy training and integration with a wide range of deeper or lighter backbones. The proposed network achieves state-of-the-art performance on the UAVDT17 and MS COCO datasets.

Enzymatic acylation of lutein with a series of saturated fatty acid vinyl esters and the thermal stability and anti-lipid oxidation properties of the acylated derivatives.

Lutein was enzymatically acylated with saturated fatty acid vinyl esters of different lengths of carbon chain (C6 -C14 ) under the action of Candida antarctica lipase B (Novozyme 435). The acylation reaction was optimized by considering substrate molar ratio, reaction solvent, type of enzyme, and reaction time. The highest yield (88%) was obtained using the Novozyme 435 to catalyze the acylation reaction of lutein and vinyl decanoate (lutein/vinyl decanoate molar ratio of 1/10) for 16 h in methyl tert-butyl ether. Ten lutein esters were synthesized, isolated, and purified, which were characterized by Fourier-transform infrared spectroscopy, high-resolution mass spectrometry, and nuclear magnetic resonance spectroscopy. We found that the acylation of lutein improved its antioxidant capacity in lipid system and thermal stability. Our study extended the potential application of lutein in lipophilic food, cosmetic, and pharmaceutical industries. Practical Application: Enzyme acylation of lutein improved its antioxidant capacity in lipid system and thermal stability, extended its potential application in food, cosmetic, and pharmaceutical industries. In addition, our study also provided a new perspective and cognition for the further development and utilization of lutein.

Liver Cancer Survival: A Real World Observation of 45 Years with 32,556 Cases.

BACKGROUND AND AIMS: To explore the long-term trend of liver cancer survival, based on the real-world data (RWD) in the past 45 years from a population-based cancer registry, in Qidong, China. METHODS: A number of 32,556 patients with liver cancer were registered during the period of 1972 to 2016. Mixed methods by active and passive follow-up were performed. Life table method was employed for survival analysis by SPSS22 software. Wilcoxon (Gehan) statistics was considered as a significant test. Relative survival was calculated by using SURV software, and its annual percent change (APC) was estimated by the Joinpoint Regression Program. RESULTS: The overall observed survival (OS) rates of 1-, 5-, 10-, and 20-year rates from the data series were 18.51%, 6.28%, 4.03%, and 2.84%, and their relative survival (RS) rates were 18.88%, 6.95%, 4.96%, and 4.49%, respectively. For 24,338 male cases, the 5-year OS and RS rates were 5.93% and 6.54%, and for 8218 female cases, 7.34% and 8.15%, respectively, with P values less than 0.01. Survival rates of liver cancer from three 15-year periods of 1972-1986, 1987-2001, and 2002-2016 have increased significantly, with 5-year OS rates of 2.02%, 4.40%, and 10.76%, 5-year RS rates of 2.18%, 4.83%, and 12.18%; 10-year OS and RS rates of 0.95%, 3.00%, and 7.02%, vs 1.13%, 3.65%, and 8.96%, respectively, showing a very significant upward trend (P<0.01). There are significant differences among age groups (P<0.01): those aged 55-64 demonstrated the best OS and RS rates of 5-year, being 8.44% and 9.09%, respectively. CONCLUSION: There are significant gender and age differences in the survival rate of liver cancer in Qidong. RWD indicates the relative lower survival rate of liver cancer in this area, but great improvement has been achieved over the past decades.

Congruence between ocean-dispersal modelling and phylogeography explains recent evolutionary history of Cycas species with buoyant seeds.

Ocean currents play a significant role in driving the long-distance dispersal (LDD), spatial distribution and phylogeographic patterns of many organisms. Integrating phylogeographic analyses and mechanistic ocean current modelling can provide novel insights into the evolutionary history of terrestrial littoral species but has been rarely applied in this context. We focused on a group of Cycas that have buoyant seeds and occupy coastal habitats. By integrating evidence from mechanistic simulations and whole plastomic data, we examined the role of ocean circulation in shaping the phylogeography of these Cycas species. Plastomes of the studied Cycas species showed extreme conservatism, following a post-Pleistocene divergence. Phylogenies revealed three subclades, corresponding to the Pacific Ocean, Sunda Shelf and Indian Ocean. The ocean modelling results indicate that hotspots of seed stranding coincide well with the contemporary distribution of the Cycas species and that drifting trajectories from the three subclades are largely confined to separate regions. These findings suggest that ocean current systems, by driving long-distance dispersal, have shaped the distribution and phylogeography for Cycas with buoyant seeds. This study highlights how the combination of genomic data and ocean drift modelling can help explain phylogeographic patterns and diversity in terrestrial littoral ecosystems.

Long Noncoding RNA KCNMB2-AS1 Promotes SMAD5 by Targeting miR-3194-3p to Induce Bladder Cancer Progression.

PURPOSE: Bladder cancer is a common malignant tumor of the urinary system, with the fourth-highest incidence of male malignant tumors in Europe and the United States. So far, the mechanism of bladder cancer progression and metastasis has not been clarified. The aim of our study was to validate the way of long noncoding RNA (lncRNA) KCNMB2-AS1 on the metabolism and growth of bladder cancer cells by miR-3194-3p/SMAD5. PATIENTS AND METHODS: The Gene Expression was analyzed by qRT-PCR in bladder cancer tissues and cell lines, with the highly expressed KCNMB2-AS1 screened out. Cell proliferation was detected by Edu staining and clone formation assay, cell migration, and invasion by wound healing and transwell assays. Cell stemness was determined by assessing sphere-forming ability and stemness marker. Correlation between miRNA and lncRNA/gene was verified by dual-luciferase assay and RIP, and the effect of KCNMB2-AS1 on bladder cancer growth by nude mice tumor formation experiment. RESULTS: Here, we revealed the increased level of KCNMB2-AS1 in bladder cancer for the first time. Knockdown of KCNMB2-AS1 in vitro prevented the ability of proliferation, metastasis, and stemness of cancer cells. In vivo, the silencing of KCNMB2-AS1 also prevented tumor growth in vivo. Next, we revealed that KCNMB2-AS1 could interact with miR-3194-3p and uncovered that SAMD5 was a downstream target of miR-3194-3p. CONCLUSION: In conclusion, KCNMB2-AS1 mediated the bladder cancer cells progress by regulating the miR-3194-3p/SAMD5 signal pathway, which would provide a new target for bladder cancer research.

ABO blood groups and liver cancer: prospective results from an HBsAg cohort study.

OBJECTIVE: The association between ABO blood group and risk of liver cancer is unclear, although few studies have reported positive results. This study examined the relationship between ABO blood group and liver cancer in hepatitis B surface antigen (HBsAg)-positive individuals. DESIGN: A high-risk population-based cohort study. SETTING: The study was started in 2007 and closed in 2019; the number of observed person-years as obtained by ABO blood group. PARTICIPANTS: The study included 3663 individuals with positive HBsAg, including men aged 30-70 and women aged 40-70. OUTCOME MEASURES: The frequencies of ABO group in the cohort population and patients with liver cancer were calculated, respectively. χ2 test was used to compare differences, and the relative risk (95% CI) for development of liver cancer was evaluated. RESULTS: The frequency distribution of blood types A, B, O and AB was 1118 (30.52%), 1073 (29.29%), 1104 (30.14%) and 368 (10.05%), respectively, among 3663 cohort individuals. In the cohort, patients with liver cancer (n=336) were of the following frequencies: type A: 104 (30.95%); type B: 97 (28.87%); type O: 95 (28.27%); and type AB: 40 (11.90%). No significant difference was found between patients with liver cancer and other individuals. The annual incidence rate of liver cancer was 906.34 per 100 000 person-years, and for blood type A, B, O and AB the rates were 917.76, 893.78, 846.02 and 1093.43 per 100 000 person-years, respectively. The relative risk (95% CI) was 0.97 (0.74 to 1.29), 0.92 (0.70 to 1.22) and 1.19 (0.82 to 1.72) for blood types B, O and AB, respectively, compared with blood type A. CONCLUSION: There were no significant differences in the frequency distribution of ABO blood groups in patients with liver cancer within this high-risk cohort, which demonstrates lack of positive association between ABO blood group and risk of liver cancer.

Enzymatic acylation of rutin with benzoic acid ester and lipophilic, antiradical, and antiproliferative properties of the acylated derivatives.

Rutin (3',4',5,7-tetrahydroxy-flavone-3-rutinoside) was enzymatically acylated with benzoic acid and its esters (methyl benzoate and vinyl benzoate) using Thermomyces lanuginosus lipase (Lipozyme TLIM). The acylation reaction was optimized by varying the reaction medium, reaction temperature, acyl donor, substrate molar ratio, and reaction time. The highest conversion yield (76%) was obtained in tert-amyl alcohol (60 °C, 72 hr) using vinyl benzoate (molar ratio of 1:10) as acyl donor. The acylation occurred at the 2'''-OH and 4'''-OH of the rhamnose unit and the 2''-OH position of the glucose moieties. Three novel rutin acylated derivatives (compounds 1-3) were purified and characterized by HR-MS and 1D and 2D NMR spectroscopy. We found that acylation significantly improved lipophilicity, capacity to inhibit lipid peroxidation, anticancer capacity and substantially maintained the antioxidant activity of rutin. This research provides important insights in the acylation of flavonoids with different glycosyl moieties. PRACTICAL APPLICATION: In this study, three novel rutin derivatives were successfully synthesized and the highest conversion yield (76%) was obtained by reacting the rutin and vinyl benzoate at molar ratio of 1:10 in tert-amyl alcohol for 72 hr at 60 °C. Introducing a benzoic acid substituent into rutin molecule significantly improved their lipophilicity and inhibition of lipid peroxidation in lipophilic system. Furthermore, this study demonstrated that acylation significantly improved anticancer capacity and substantially maintained the antioxidant activity.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea.

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Liver cancer mortality over six decades in an epidemic area: what we have learned.

BACKGROUND AND AIMS: Liver cancer is one of the most dominant malignant tumors in the world. The trends of liver cancer mortality over the past six decades have been tracked in the epidemic region of Qidong, China. Using epidemiological tools, we explore the dynamic changes in age-standardized rates to characterize important aspects of liver cancer etiology and prevention. METHODS: Mortality data of liver cancer in Qidong from 1958 to 1971 (death retrospective survey) and from 1972 to 2017 (cancer registration) were tabulated for the crude rate (CR), and age-standardized rate and age-birth cohorts. The average annual percentage change was calculated by the Joinpoint Regression Program. RESULTS: The natural death rate during 1958-2017 decreased from 9‰ to 5.4‰ and then increased to 8‰ as the population aged; cancer mortality rates rose continuously from 57/105 to 240/105. Liver cancer mortality increased from 20/105 to 80/105, and then dropped to less than 52/105 in 2017. Liver cancer deaths in 1972-2017 accounted for 30.53% of all cancers, with a CR of 60.48/105, age-standardized rate China (ASRC) of 34.78/105, and ASRW (world) of 45.71/105. Other key features were the CR for males and females of 91.86/105 and 29.92/105, respectively, with a sex ratio of 3.07:1. Period analysis showed that the ASRs for mortality of the age groups under 54 years old had a significant decreasing trend. Importantly, birth cohort analysis showed that the mortality rate of liver cancer in 40-44, 35-39, 30-34, 25-29, 20-24, 15-19 years cohort decreased considerably, but the rates in 70-74, and 75+ increased. CONCLUSIONS: The crude mortality rate of liver cancer in Qidong has experienced trends from lower to higher levels, and from continued increase at a high plateau to most recently a gradual decline, and a change greatest in younger people. Many years of comprehensive prevention and intervention measures have influenced the decline of the liver cancer epidemic in this area. The reduction of intake levels of aflatoxin might be one of the most significant factors as evidenced by the dramatic decline of exposure biomarkers in this population during the past three decades.