To further explore the phenomenon of impaired self-awareness of motor symptoms in patients with Parkinson’s Disease by using an evaluated measurement approach applied in previous studies, while also examining its connection with dispositional mindfulness and possible correlates of functional connectivity.Recently, the phenomenon of impaired self-awareness has been studied more intensively by applying different measurement and imaging methods. Existing literature also points towards a possible connection with mindfulness, which has not been examined in a cross-sectional study. There is no data available concerning correlates of functional connectivity.Non-demented patients with idiopathic Parkinson’s Disease without severe depression were tested for impaired self-awareness for motor symptoms following a psychometrically evaluated approach. Mindfulness was measured by applying the German version of the Five Facet Mindfulness Questionnaire. A subset of eligible patients underwent functional MRI scanning. Spearman correlation analyses were performed to examine clinical data. Whole-brain voxelwise regressions between seed-based connectivity and behavioral measures were calculated to identify functional connectivity correlates of impaired self-awareness scores.A total of 41 patients with Parkinson’s Disease were included. 15 patients successfully underwent resting-state fMRI scanning. Up to 88% of patients showed signs of impaired self-awareness. Awareness for hypokinetic movements correlated with total mindfulness values and three facets, while awareness for dyskinetic movements did not. Three significant clusters between scores of impaired self-awareness in general and for dyskinetic movements were identified linking behavioral measures with the functional connectivity of the inferior frontal gyrus, the right insular cortex, the supplementary motor area, and the precentral gyrus among others. Impaired self-awareness for hypokinetic movements did not have any neural correlate.Clinical data is comparable with results from previous studies applying the same structured approach to measure impaired self-awareness in Parkinson’s Disease. Functional connectivity analyses were conducted for the first time to evaluate neural correlates thereof. This data does not support a connection between impaired self-awareness of motor symptoms and dispositional mindfulness. Self-awareness is defined as a complex multidimensional phenomenon of “becoming the object of one’s own attention” . ImpairePrevious studies reported a varying incidence of ISAm ranging from approximately 23%  to a totIn previous studies, ISAm-Hypo was present in up to 54% of PD patients, when measured in the ON-state with adequate dopaminergic medication, while no significant clinical or neurobiological correlates were identified . HoweverIn contrast, ISAm-LID was found in up to 91% of patients with dyskinesias , 11. A hMindfulness can be classically described as “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally” . MindfulSeveral neuroimaging studies have examined dispositional mindfulness and its neural correlates in healthy subjects. For example, in a task-based fMRI study Dickenson et al. (2013) observed, that people with greater dispositional mindfulness scores showed greater activity in the right temporo-parietal junction, superior parietal lobule and DLPFC during a focused breathing task . Lu and Previous articles pointed out, that mindfulness (meditation) has an influence on self-awareness in general as well as body-related processes like body awareness and interoception e.g. , 26. AltThe aim of this study was to further explore the phenomenon of ISAm in PD, including analyses of its clinical and resting-state fMRI correlates. Also, a possible connection to dispositional mindfulness is probed. To the best of our knowledge, no previous study has investigated resting-state fMRI correlates of ISAm, and the connection between ISAm and dispositional mindfulness in PD. In comparison to previous work by Maier et al. (2016), we examined ISAm and its clinical correlates in a separate group of PD patients . StudiesThe study has been approved by the local ethics committee of the medical faculty of the Philipps-University of Marburg (study number: 119/18) and has been pre-registered at the German Clinical Trials Register (DRKS00015807). The data presented in this study was obtained as part of a mixed-method, randomized clinical trial comparing an intervention group and a waitlist-control group in a pre-post design to evaluate the effectiveness of a newly developed mindfulness training in PD. In the current study, the clinical, and neurobiological baseline data were analyzed. For a more detailed description see Buchwitz et al. (2020) .41 patients with idiopathic PD, diagnosed according to the Movement Disorder Society PD criteria , were reAdditional exclusion criteria were left-handedness or ambidexterity, and deep brain stimulation. All patients had no prior experience in meditation, and are therefore described as naïve to meditation. Patients, who fulfilled criteria for an MRI scan, were asked for their willingness to also take part in a structural and functional MRI scan. Clinical and imaging data of patients, who successfully participated in an MRI scan, were used to form a subsample for further analyses. All patients gave written informed consent in accordance with the Declaration of Helsinki.All measurements were performed in the medication ON-state. Dose of medication was unchanged for at least two weeks prior to data collection. ISAm, disease severity according to Unified Parkinson’s Disease Rating Scale part III [UPDRS ] as wellSitting on a chair: resting tremor (left and right hand), dyskinesiaRight hand pronation-supination: speed and amplitude of movement, dyskinesiaLeft hand pronation-supination: speed and amplitude of movement, dyskinesiaArising from a chair: resting tremor (left and right hand), dyskinesiaWalking down an aisle: resting tremor (left and right hand), dyskinesiaAs the whole procedure was videotaped, the patient’s performance could later be evaluated by two independent raters. Whenever a discrepancy between the rater’s and the patient’s evaluation occurs , ISAm was present. In a second step, for each motor task the severity of motor impairment is defined in accordance to the UPDRS-III scale . Lastly, two global scores could be summarized: a total ISAm score , which only includes all tasks with a discrepancy, and a total motor severity score. Both scores vary between 0–60 with higher scores indicating stronger impairment of self-awareness. Additionally, the total ISAm score was divided into subscores for ISA for hypokinetic symptoms (ISAm-Hypo for tremor and bradykinesia) and hyperkinetic symptoms (ISAm-LID) for further analysis.The Montreal Cognitive Assessment [MoCA ] was useDepression scores were assessed by using the BDI-II. Dispositional mindfulness was assessed using the Five Facet Mindfulness Questionnaire–German version  with hiBehavioral and clinical data was analyzed using SPSS, version 27.0 . Level of significance was set at 5% for all outcomes.For the total scores of ISAm and motor symptom severity, intraclass correlation coefficients  were calculated to determine the degree of agreement between independent raters. Neuropsychological raw scores were converted into z-scores according to respective normative data . A global mean cognition z-score [similar to All ISAm scores were not normally distributed. Therefore, non-parametric statistical methods were used (Spearman correlations). Correlation analyses were performed between ISAm scores and demographic, neuropsychological and questionnaire data. As age and education likely influence the performance in neuropsychological tests, analysis of neuropsychological data was corrected for both variables to rule out confounding effects. After calculating Spearman correlations of the FFMQ-D with basic sample characteristics , years of education and UPDRS motor scores were selected as covariates because of their respective highly significant test results. Therefore, correlation analyses of ISAm scores and mindfulness scores were corrected for the influence of education and motor severity. Due to the mostly exploratory character, no correction for multiple testing was performed.d = .20 , the two one-sided t-tests approach for equivalence testing with the variables age, education, LEDD, UPDRS-III scores and depression scores were performed using Microsoft Excel. The equivalence bounds were set at a benchmark of ohen’s d ) to deteohen’s d .Structural and functional MRI data were acquired on a 3-Tesla Tim Trio MR scanner  with a 32 channel headcoil at the Department of Psychiatry, University of Marburg. After the initial localizer scan, structural images were acquired using a high resolution T1-weighted magnetization-prepared rapid gradient-echo sequence. The following parameters were utilized for this scan: 1 mm slice thickness, 0 mm gap, TR/ TE: 1900 ms/ 2.52 ms, matrix size of 256×256 voxel, field of view (FOV) 256 mm. Subsequently, a resting-state functional MRI scan was performed with a whole brain echo planar imaging (EPI) sequence and the following parameters: 490 volumes were obtained using 3-mm slice thickness, 0 mm gap, TR/TE = 1040/30 ms, flip angle = 55°, 70×70 voxel matrix size, FOV 210 mm –43.https://www.fil.ion.ucl.ac.uk/spm/). Preprocessing was performed according to CONN’s default preprocessing pipeline. Following coregistration, T1-weighted structural images were segmented and normalized to a template in Montreal Neurological Institute (MNI) space with a 1 x 1 x 1 mm resolution. T2*-weighted functional images were spatially realigned after coregistration and normalized to a MNI template with a 2 x 2 x 2 mm resolution using direct spatial segmentation and normalization. Functional scans were spatially smoothed using a 6 mm full-width half-maximum (FWHM) Gaussian filter. Outliers were defined using the artifact detection tool implemented in CONN. Here, outliers are identified using the observed BOLD signal and subject-motion in the scanner. Outliers were defined as a framewise displacement of at least 0.9 mm or a change of the global BOLD signal above five standard deviations. The framewise displacement is calculated based on 140 x 180x 115mm bounding box around the brain and estimating the largest displacement among six control points. The global BOLD signal change is defined as a change in the average BOLD signal within SPM’s global-mean mask scaled to standard deviation units. Framewise displacement as well as the global BOLD signal are computed at each timepoint. To deal with outliers, a variable number of noise components (one for each of the identified outlier) is used as potential confounding effect to account for any influence of the previously identified outlier scans on the BOLD signal [Preprocessing and analysis of resting-state functional MRI data was carried out using the CONN toolbox  and SPM1D signal . FurtherD signal . We alsoSeed-based whole-brain correlations (Fisher-transformed bivariate Pearson correlation coefficient) were calculated in CONN using seed regions of interest (ROIs) from the Harvard-Oxford atlas. Based on the results of Maier et al. (2016), the following seeds were included in the analysis: Inferior frontal gyrus left and right, medial frontal gyrus left and right, superior frontal gyrus left and right, precentral gyrus left and right, insular cortex left and right, anterior and posterior cingulate cortex . SignifiSample characteristics of the 41 patients  included in the present study are depicted in Both the total sample as well as the subsample appear to be comparable due to their similar demographics and characteristics. For a summary of statistical test results to check the comparability of the current study sample and the sample analyzed by Maier et al. (2016) see Intraclass correlation coefficients were high for both ISAm total score (ICC = 0.892) and total motor severity score (ICC = 0.873). Thirty-six of 41 patients did not perceive at least one symptom. Therefore, ISAm was present in about 88% of patients. Detailed results for ISAm total score and all subscales can be found in For ISAm-total scores, significant correlations were found with age , education years , UPDRS-III values  and global cognition z-score . ISAm-total scores also strongly correlated with total ISAm symptom severity  as well as ISAm-Hypo  and ISAm-LID . Regarding neuropsychological raw scores, correlations of ISAm-total with RWT verbal fluency , ECFT-MI recognition total score  and digit span backwards score  were significant. However, we did not find any significant correlations with neuropsychological test scores or global cognition z-score after correction for age and education. There were no significant correlations between ISAm-total and depression scores.ISAm-Hypo did not correlate significantly with neither age nor education years. Positive correlations were found with UPDRS-III values  as well as with ISAm-Hypo symptom severity . Several negative correlations with neuropsychological raw scores were significant, including ECFT-MI recognition total score , lexical verbal fluency , alternating semantic fluency  and digit span backwards . None of these correlations remained significant after correction for age and education. ISAm-Hypo did not correlate with global cognition z-score. Additionally, ISAm-Hypo significantly correlated with depression scores .ISAm-LID did not correlate with age, education years, UPDRS-III values, depression, global cognition, or most neuropsychological test raw scores, except for ECFT-MI Matching . After correction for age and education, there were no significant correlations between ISAm-LID and any neuropsychological raw score. The only significant correlations were found between ISAm-LID and ISAm-LID symptom severity  as well as ISAm-LID and LEDD . It is worth mentioning, that ISAm-LID did not significantly correlate with ISAm-Hypo . An overview of the ISAm correlation analyses results is illustrated in Quality assurance checks of the normalized and realigned structural and functional data were interpreted as sufficient for this preliminary analysis. The number of valid scans was M = 477.60 (SD = 31.52), while the number of invalid scans (or outliers) was M = 13.40 (SD = 31.52). On average, the observed motion was M = .22 mm (SD = .10). The largest motion was M = .81 mm (SD = .48). Changes of the BOLD signal was on average M = .83 (SD = .05). The largest change was M = 8.00 (SD = 4.41). In total, multiple regression analyses between seed-based resting-state functional connectivity and ISAm scores revealed three significant clusters. Detailed results are depicted in ISAm total scores were positively associated with the functional connectivity between the left IFG and the right IC, right frontal operculum, and right frontal orbital cortex. Significant negative associations were observed between ISAm-LID scores and the functional connectivity between both IFG seeds and the right precentral gyrus. These findings are illustrated in To explore the concept of mindfulness in our study sample, we first correlated the FFMQ-D total score with basic sample characteristics. Hereby, we identified several significant correlations, namely with age , education , the MoCA total score , the global cognition score , and the UPDRS motor score . Only LEDD  did not significantly correlate with the total mindfulness score.Initially, out of all six FFMQ-D scales , only the subscale acting with awareness was significantly correlated with ISAm-total scores . ISAm-LID did not correlate with any subscale. Several negative correlations were found for ISAm-Hypo, including negative correlations with FFMQ-D total score , subscales describing , acting with awareness  and non-reactivity to inner experience . It is noteworthy, that subscale non-judging of inner experience and observing did not correlate with any of the three ISAm scores. However, after correcting for the influence of education and UPDRS motor scores, none of these results remained significant. A few years ago, ISAm in PD including its clinical and neurobiological correlates based on FDG-PET have been evaluated by Maier et al. (2016) for the first time. In this study, we evaluated ISAm using the same structured approach  in anothOne goal of this study was to compare our data with the results of Maier et al. (2016), which presupposes a general comparability of the samples used. Unfortunately, despite a statistically proven equivalence in terms of age, both samples are rather disparate. Therefore, the general level of comparability is impaired. Also, there are a few differences, that need to be highlighted in the following: The total sample of Maier et al. (2016) consisted of 31 patients. Our total sample was larger with 41 patients included and showed only minimal differences concerning clinical and demographic variables, with slightly more advanced age and education, higher motor symptom severity, and LEDD. The current study included a higher proportion of female patients. Both samples were cognitively healthy according to mean values of their respective screening tools . While we performed all tests under patients’ regular medication, Maier et al. (2016) obtained neuropsychological and imaging data in the regular ON-state but measured motor symptom severity and ISAm in clearly defined medication ON- and OFF states.In total, a higher percentage of patients showed signs of ISAm (88%) in our sample compared to 61% of patients in the previous sample. Respectively, a similar case applied to ISAm-Hypo (78% vs. 41%). At least, a part of this increase might be due to an increase of age and motor severity in our sample, as both ISAm scores significantly correlated with their respective motor severity score. ISAm-total also correlated with the patients’ age. The latter has to be highlighted, as Maier et al. (2016) only found a positive correlation between age and ISAm-total in the OFF-state, but not in the ON-state. It has to be noted, that similar to Maier et al. (2016), we also did not correct for multiple testing. Therefore, the results must be interpreted within a highly explorative context.Moreover, Maier et al. (2016) did not find any significant correlations of ISAm-total and any clinical or neuropsychological score. ISAm-Hypo significantly correlated with higher disease duration and worse verbal fluency (uncorrected). However, in our study both ISAm-total and ISAm-Hypo correlated with nearly the same neuropsychological raw scores . Initially, ISAm total score did correlate with global cognition z-score. In contrast, ISAm-Hypo score did not correlate with the global cognition z-score. However, similar to the study of Maier et al. (2016), none of the correlations with neuropsychological scores or global cognition remained significant after correction for age and education.For ISAm-total, voxelwise regression analysis revealed a significant positive cluster for the left IFG seed, which included the right IC, right frontal operculum, and right frontal orbital cortex. This provides a link between ISAm and the prefrontal cortex and the insular cortex and surrounding structures, which have previously been linked with processes of reward learning, language, and empathy , 48. In ISAm-Hypo, especially in earlier stages of PD, is expected to occur, because of symptoms being too mild and not causing enough impairment in daily life , which mIn our sample, ISAm-LID was present in 85% of patients with dyskinesia, which is about the same proportion as 81% of patients in the sample of Maier et al. (2016) . In our On a neurobiological level, we found evidence for a role of functional connections between the left and right IFG and cingulate areas, the pre- and postcentral gyri, the supplementary motor area and the precuneus for ISAm-LID, which previously have been associated with the development of LID in PD –59 or fuWhile the presented clinical data does not suggest, that ISAm-LID and executive dysfunctioning are mostly intertwined, the neurobiological results of this study further support existing theories surrounding the pathological mechanisms of ISAm-LID. We identified a negative association between the connectivity of bilateral inferior frontal gyri and the cingulate cortex and ISAm-LID, which are congruent with findings by Palermo et al. (2018) concerning a reduced activity of the anterior cingulate cortex to play an important part in the appropriate monitoring and integration of one’s movements. ISAm-LID has previously been linked to different brain regions including several frontal regions and the (anterior) cingulate cortex , 13 withdorsolateral prefrontal cortex, the inferior frontal junction, the anterior IC as well as the dorsal pre-motor cortex and the posterior parietal cortex [e.g. On a functional level, the aforementioned brain areas were identified to be part of a wider cognitive control network, also known as the “task positive” network , 61. It tex e.g. . This futex e.g. . This inFirst, we wanted to better understand the basic role of mindfulness in our sample. Interestingly, mindfulness correlated with education, as well as the MoCA total score and the global level cognition. This indicates a strong association with general level of cognitive functioning for PD patients. It is unclear, if this is different from the general population as mindfulness samples often tend to consist of highly educated people . Total mTo better understand the connection of ISAm and mindfulness, the diversity of the associations of ISAm scores and mindfulness facets might be of particular interest. At first, all mindfulness facets correlated with ISAm, except for FFMQ-D subscales non-judging of inner experience and observing. Initially, ISAm-total only correlated with FFMQ-D subscale acting with awareness, which refers to the person’s ability to be fully present in the current moment with regard to internal and external stimuli and therefore avoiding automatic pilot . None ofWe only assessed ISAm during the patients’ daily medication intake (ON-state). Moreover, we did not perform OFF-state measurements, as this would likely have been too burdensome for the patients as part of a larger clinical trial, from which this data was taken. Secondly, only a relatively small subsample (15 of 41 patients) was able to undergo a resting-state fMRI scan. Therefore, we have referred to it as a preliminary analysis with limited generalization of our results. Lastly, while we included an elaborate neuropsychological test battery, we did not include a GO-NoGO-task, which somewhat limits comparability to other studies in the field. To specifically study the possible connection of ISAm, executive functioning and the role of the ACC, future studies should therefore include a GO-NoGO-task. It has to be noted, that we compared data generated by two different imaging methods (fMRI vs. FDG-PET). Therefore, it can also be of further interest to combine MRI and FDG-PET imaging methods in another cohort of PD patients. On a general level, the comparability of clinical data with previous work conducted by Maier et al. (2016) is limited. As the clinical and neuropsychological results were not corrected for multiple testing (in both studies), all results must be interpreted within an exploratory context.In conclusion, the results of this study are in line with and sensibly complement the existing literature on the phenomenon of ISAm in PD. On a descriptive level, clinical correlates in our independent and slightly larger sample mostly matched the results from Maier et al. (2016) with few exceptions like age being correlated with ISAm-total in the ON-state. Seed-based functional connectivity analysis revealed functional correlates of ISAm and ISAm-LID, which mainly included an involvement of the IFG, insula, precentral cortex, the ACC and medial frontal gyrus, and therefore supported the hypothesis of cingulate areas being involved in the development of ISAm-LID by Palermo et al. (2018). Lastly, we explored the connection between ISAm and dispositional mindfulness. Our results do not suggest a possible connection between both constructs.S1 TableAbbreviations: BDI-II, Beck Depression Inventory-2; LEDD, levodopa equivalent daily dose; UPDRS, Unified Parkinson’s Disease Rating Scale; *, significant finding.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings  were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings. Instant starch noodles are made from various starch , water and other ingredients, and processed into dry, no-cooking, convenience food . It usuaSeasonings play a key role in determining sensory attributes of instant starch noodles. Seasonings are mainly used for improving aroma and taste. Aroma is an important sensory attribute of flavor in food and volatile compounds plays an important role in it . NowadayConsumers are mainly concerned about the flavor of instant starch noodles and the major difference of flavors is the variety of instant starch noodles seasonings. Different brands of instant starch noodles seasonings with the same taste vary greatly in flavor. To a certain extent, brand is an indicator of the product’s quality and flavor. Different brands of instant starch noodles seasonings possessed variant chemical ingredients, aroma, taste, and price . BasicalElectronic nose (e-nose) and electronic tongue (e-tongue) are sensor-based apparatus which imitate the olfactory and gustatory perception of human. They provide fast detection and comprehensive information about the sample, and they can monitor and differentiate samples that are otherwise difficult for human sensory panels to discriminate . The e-nThe main objectives of this study was to establish an easy, rapid and objective identification method for instant starch noodles seasonings based on different flavor profiles. Therefore, we used e-nose and e-tongue independently and in combination to discriminate different brands of instant starch noodles seasonings. Then, recognition of the seasonings was performed by principal components analysis (PCA), cluster analysis (CA), and multilayer perceptron neural networks analysis (MLPN), with the intention of testing whether e-nose and e-tongue can provide a rapid and accurate approach for flavor recognition and quality assurance of instant starch noodles seasonings.Five different brands of instant starch noodles with braised beef were obtained at the commercial market in China. The five brands were marked as C, D, G, L, and Z, respectively. Each brand contained three categories: seasoning powder (P), seasoning mixture sauce (S), and the mixture of powder and sauce (M). The PEN3 portable e-nose  was applied to classify and characterize the instant starch noodles seasonings. The apparatus is composed of a sampling unit and a gas detection system consisting of 10 metal oxide semiconductor (MOS) sensors with varying sensitivity to each volatile compound . Prior tBased on the preliminary experimental results, 7 g of seasoning powder, 4 g of seasoning mixture sauce, and 11 g of the mixture of powder and sauce were put into three 150 mL beakers, respectively. The seasoning solution was prepared after pouring 100 mL boiling water into the beaker and stirring for 2 min. A volume of 10 mL sample solution was put into a 30 mL sealed glass vial and incubated for 30 min at 40°C. The chamber and injection flow rate was 400 mL/min. The measurement time, sample interval time, flush time, and automatic zero adjustment time were 60, 1, 300, and 5 s. Each sample was measured 10 times.Instant starch noodles seasonings were analyzed using ASTREE e-tongue . The instrument consists of an automatic sampler, chemical sensor array, and chemometric software package . The senAccording to preliminary experiments, 6 g of seasoning powder, 8 g of seasoning mixture sauce, and 14 g of the mixture of powder and sauce were put into three 500 mL beakers, respectively. The seasoning solution was prepared after pouring 400 mL boiling water into the beaker and stirring for 2 min. The seasoning solution was first filtered using a piece of gauze and the filtrate was then filtered through a 0.45 μm nylon syringe filters. Then, 80 mL of filtrate of each sample was analyzed using the e-tongue. Before the test begins, 0.01 mol/L HCl, NaCl, and sodium glutamate were applied to activate, calibrate, and diagnose the chemical sensors . The e-tData fusion involves different levels of data abstraction , with loThe individual e-nose and e-tongue data and their fusion were applied to PCA, CA, and MLPN. PCA is the most popular feature extraction method in pattern recognition. It is used to differentiate samples and extract the most important information from data. PCA reduces the dimensionality of datasets while retaining most of the information of the original data . PCA intCluster analysis is an unsupervised classification technology which can cluster samples according to their similarity in multidimensional space . It idenMultilayer perceptron neural networks analysis is a feed-forward supervised classification method consisting of an input layer, several hidden layers, and an output layer . It can Principal component analysis and MLPN were performed using Statistical Product and Service Solutions (SPSS) 20.0. CA was conducted using Minitab 16.2.The typical e-nose response curves of CP, CS, and CM are shown in The radar plots of e-nose sensor responses are presented in The e-tongue sensor responses of CP, CS, and CM are shown in The radar plots of e-tongue sensor responses are presented in The e-nose PCA plots are presented in In multidimensional space, the samples were clustered into five non-overlapping groups. Samples with more similar flavor would appear closer to each other. The CP and DP samples were located very close to each other, suggesting that CP and DP had similar aromas. The characteristic aroma attributes of CS, DS, and GS were also similar. The results showed that principal components analysis of e-nose data can be used to classify different brands of instant starch noodles seasonings.The e-tongue PCA plots are presented in As shown in Combination of e-nose and e-tongue data has been applied in various foods analysis and quality control . PCA andRelationships between samples were visualized with a dendrogram based on a nearest neighbor method that used Euclidean distances. The combined e-nose and e-tongue data were analyzed using CA, for comparison to PCA. As shown in In this paper, e-nose, e-tongue, and the combination of their data were used to discriminate instant starch noodles seasonings from five different brands. PCA explained over 85.00% of the total variance in e-nose and e-tongue data, suggested that both individual e-nose and e-tongue could discriminate different brands of instant starch noodles seasonings. For the combination data of seasoning powder, seasoning mixture sauce, and mixture of powder and sauce, the first three PCs explained 86.96, 98.09, and 94.38% of the total variance and the results of CA were in agreement with those from PCA. PCA and CA results suggested that the combination of e-nose and e-tongue showed much better classification ability than either alone. MLPN modeling demonstrated that the identification rate of the combined data was basically 100%, further indicated that the combination of e-nose and e-tongue produced more optimal discrimination and analysis of samples.In summary, e-nose and e-tongue can provide integrated information of flavors to distinguish among samples. The combination of e-nose and e-tongue an effective method for rapid, easy, and accurate food odor analysis that is objective, highly automated and lower cost. This work may be helpful in providing some valuable information on evaluating and discriminating the quality of instant starch noodles seasoning to consumers.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.RM: conceptualization, investigation, writing – original draft, and data curation. HS: methodology and writing – review and editing. HC: software and formal analysis. GZ: project administration and supervision. JZ: supervision and resources. All authors contributed to the article and approved the submitted version.


A two-year research trial was conducted to evaluate progression of common bean (Phaseolus vulgaris) growth, Rhizoctonia root rot and weed development in association with productivity when treated with different cultivars, planting dates and weed control methods in north-western part of Iran.To determine the best descriptors, six standard curves were examined to model development of bean dry matter, Rhizoctonia root rot incidence, and weed density during two growing seasons across 256 field plots. Exponential and linear-by-linear models were fitted to bean-disease-weed progression data, and then model parameters representing over-season progress curve elements were used in multivariate regression analyses to estimate bean production. Furthermore, using herbicides  restricted weed density by 28% in early (mid-spring) and 42% in late (late spring to early summer) plantings. Late plantings of two bean cultivars decreased disease progress up to 36% for herbicide use, hand-weeding and control. Although bean dry matter, pod and seed production for herbicide use and hand-weeding treatments were 6-17% greater than control, late planting improved productivity in control by 10-24%. Findings suggested that late planting of bean improved efficiency of herbicides to control weeds. Late planting also restricted Rhizoctonia root rot progress and thus, improved bean yield. There were significant correlations between bean-disease-weed development descriptors. According to principal component analysis, bean-disease-productivity-weed variables accounted for 80% of total data variance. Such information extends our understanding of bean-disease-weed progress in interaction with planting date to develop more effective and sustainable integrated Rhizoctonia management programs.  Rhizoctonia root rot and weed development as damaging agents in common bean (Phaseolus vulgaris L.) cultivation worldwide  planted under various sowing date and weed control treatments during two growing seasons.vailable . Howeverge-scale  findingselopment , and impRhizoctonia root rot, and weed density were examined across experimental plots naturally infested with R. solani and weed populations, confirmed by the pre-test of soil  samples collected from the experimental plots. The experiments were performed at Kheirabad Research Site . The experiment was a split-split-plot designed as follows: planting date as the main plots which consisted of four dates of 10-15 May, 26-31 May, 10-15 June, and 25-30 June, and weed-control practices as the subplots: Imazethapyr application, Trifluralin application, hand-weeding and control, and two commercial common bean cultivars as the sub-subplots: Talash (climbing bean) and COS16 (bush bean). The two chemical weed control treatments involved the pre-emergence use of Imazethapyr (Pursuit™ 10% SL) at 1 l ha-1, and pre-planting Trifluralin (Treflan™ 48% EC) at 2.5 l ha-1. In this two-year research, each experimental plot was replicated four times. Each experimental plot was consisted of six 5-m-length rows spaced by 0.30 m with plant spacing of 0.075 m resulting in an approximate density of 40 plant/m2. To avoid herbicide-mixing, plots were spaced by 5 m.In 2015 and 2016 years, bean dry matter, Rhizoctonia root rot incidence, five bean plants per quadrat were dug up randomly to assess red-brown cankers on the root (Rhizoctonia root cankers in five examined plants per quadrat (the same quadrats tested for weed density) was recorded as disease incidence. Then, the isolation and confirmation of the pathogen was performed in the laboratory  at seedling (with one and three leaflets or cotyledon leaves opened), 50% flowering, podding and physiological maturity stages of bean plants per plot. To detect the root . The perboratory . The disRhizoctonia root rot, and weed density progress based on the parameters estimated by the best model fitted to the disease, bean and weed development datasets. To improve the degrees of freedom, the datasets for the two study years were pooled. To fit the best regression model, the seasonal measurements of the development of either variable for the two bean cultivars grown under diverse sowing-date and weed-control treatments were examined using the six following standard models: exponential, logistic, Gompertz, linear-by-linear, quadratic-by-linear, and Gaussian (2), Fisher’s test, and associations of fitted values with observed values  experimental plots were examined for bean dry matter, Gaussian Table 1.d values .Rhizoctonia root rot, and weed density descriptors were examined. Loading values indicating the associations of variables with the principal component , were regarded significant if they were ≥ 0.35  test, and is used when the assumptions of one-way ANOVA  are not met. The normality of data variance was determined using kurtosis and skew tests. However, a high heterogeneity in datasets was required to improve predictive values of variables involved in the regression model . Thus, te ≥ 0.35 .Rhizoctonia-weed interaction were involved in the regression model. Stepwise process used the two criteria of adjusted coefficient of determination (R2) and Mallows Cp to fit the best predictors ), a refers to the constant term, b to the bean-dry-matter increase factor, r to the rate of bean-dry-matter increase, and x is the time intervals (day) between assessments. This function represents a slow increase of bean dry matter at first, followed by a more rapid increase without bound when r is greater than 1.Due to the lack of significant effect of study year, bean-disease-weed datasets for the two years of current study were pooled. This study examined six types of standard regressions to model the development of bean dry matter,  seasons Table 1.a + b/(1 + d*x)) was fitted to Rhizoctonia root rot incidence data as the best curve. This model with a bimodal distribution described a as the constant term, b as the disease increase factor, d as the strength of regression model (disease progress rate), and x as the time (day) between measurements. The linear-by-linear model represents a rapid initial increase/decline of Rhizoctonia root rot incidence, and then a subsequently slow decline/recovery to an approximate equilibrium.The linear-by-linear regression model (a + b/(1 + d*x)) was also fitted to weed density data as the best weed development curve. In this model, a is the constant term, b is the weed density increase factor, d is the strength of regression (density progress rate), and x is the time intervals. This regression curve indicates a rapid initial increase/decline of weed density followed by a slow decline/recovery.The linear-by-linear model  and linear-by-linear  parameters were estimated for each field plot based on the bean-disease-weed datasets. These model fitted parameters were considered as predictors to estimate bean dry matter, Rhizoctonia root rot, and weed density development over the growing season for each field plot. The average, standard deviation, minimum and maximum values were provided for the three exponential parameters of bean dry matter and three linear-by-linear parameters of disease incidence and weed density estimators in two cultivars of bean planted at different dates  by planting date and weed control factors (r) at both of the early and late plantings. Hand-weeding classes defined for both levels of the planting date factor showed greater rankings of dry matter increase rate when compared with the herbicide use classes. The constant term of Rhizoctonia root rot development (the linear-by-linear parameter a) was also affected by the planting date and weed control factors . This suggested lower initial disease rankings for either herbicide use or hand-weeding classes than the class of not treated plots (control) planted at early and late dates. Furthermore, late plantings indicated lower initial disease rankings than early plantings at each class of the weed control factor. The planting date and weed control factors affected all the three weed density development descriptors, linear-by-linear parameters a, b and d. The initial weed density or the constant term of the linear-by-linear model of weed density development over the growing seasons had the lowest and highest rankings for the hand-weeding and not treated classes, respectively, at both early and late planting dates. Due to negative estimations of linear-by-linear parameters b and d by the weed density development model, the lowest and highest rankings were detected for not treated and hand-weeding classes, respectively, at both planting date levels. For pod and seed production, planting date and weed control factors affected pod number per plant  and seed number per pod . Lower and higher pod and seed production rankings at each planting date were obtained for not treated and hand-weeding classes of the weed control factor, respectively. In addition, herbicide use and hand-weeding classes indicated a greater pod and seed productivity in early plantings than late plantings  between the exponential parameters a and b estimated for the development of bean dry matter  to the exponential parameters a and d. From the weed density progression variables, only linear-by-linear parameter b negatively corresponded (P ≤ 0.05) with linear-by-linear parameter d. The seed number per pod variable was negatively correlated (P ≤ 0.05) with the linear-by-linear parameter a estimated for the weed density progress variable. There was a significantly positive correlation between the pod number per bean plant and the seed number per pod  with greater linear-by-linear parameter a  and d (weed density progress rate).The PCA using a correlation matrix indicated associations of relevant bean-disease-weed development, pod-seed production indicators with principal components estimating linear combinations of the variables. From the PCA, the five principal components accounted for 79.5% of the variation in bean dry matter, a) and the dry matter increase factor  to characterize a diverse range of bean-disease-weed development curves developed during two growth seasons  models with a higher exponential parameter a .The second principal component, accounting for 19.2% of the total bean-disease-weed data variance, demonstrated the moderately significant contributions of the initial bean dry matter  and weed density increase factor (linear-by-linear parameter b), and negative contributions of the disease increase factor (linear-by-linear parameter b) and weed density increase rate (linear-by-linear parameter d). This suggested the indirect association of parameter b (Rhizoctonia root rot or weed density increase factor) with the parameters a  and d (weed density increase rate) of linear-by-linear models.The third principal component justified 16.9% of the total bean-disease-weed data variance Table 5.a; r (the bean dry matter increase rate) and a moderately significant contribution from linear-by-linear parameter a estimated for weed density. This suggested the correspondence of the initially less dense weed populations with a greater bean dry matter increase rate  demonstrated that 49% of variations in pod and seed production in bean cultivars treated with different planting dates and weed control methods across 256 experimental plots were explained according to the bean dry matter, Rhizoctonia root rot and weed density development descriptors  treated with different planting dates and weed control practices across experimental field plots. Hence, we firstly needed to determine the best indicators of plant dry matter, Rhizoctonia root rot, and weed density development in bean cultivars treated with diverse planting dates and weed control methods. This experimental design provided a diverse range of variations in the bean-disease-weed progression over time due to treating field plots with different planting dates and weed control practices. Such a noticeable variability in data helps to increase the predictive value of a disease progress curve element  cultivars grown in naturally infested field soils  of variations in the bean-disease-weed datasets. This suggested that those model parameters estimated in the current research were responsible for about half of the variability in bean growth, Rhizoctonia root rot and weed density during the two growing season. Such information may assists with more accurately monitoring bean-disease-weed development and predicting bean productivity for sustainable crop management programs in future.It could be concluded that a main (80%) part of variations in the crop growth and production, disease and weed development in bean cultivars grown under different planting dates and weed control treatments was explained according to the eleven bean-disease-weed descriptors in the current study. In addition to the above-mentioned findings, the present study determined the similar descriptive values for bean dry matter, The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.SN: Designing and performing the work, and BN: interpreting the data and writing the paper. All authors contributed to the article and approved the submitted version.This study was funded by the Iranian Agricultural Research, Education & Extension Organization, projects 4-47-16-88113 and 4-47-16-90070.Authors appreciate the technical assistance in the field.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


With the development of noninvasive imaging technology, collecting different imaging measurements of the same brain has become more and more easy. These multimodal imaging data carry complementary information of the same brain, with both specific and shared information being intertwined. Within these multimodal data, it is essential to discriminate the specific information from the shared information since it is of benefit to comprehensively characterize brain diseases. While most existing methods are unqualified, in this paper, we propose a parameter decomposition based sparse multi-view canonical correlation analysis (PDSMCCA) method. PDSMCCA could identify both modality-shared and -specific information of multimodal data, leading to an in-depth understanding of complex pathology of brain disease.Compared with the SMCCA method, our method obtains higher correlation coefficients and better canonical weights on both synthetic data and real neuroimaging data. This indicates that, coupled with modality-shared and -specific feature selection, PDSMCCA improves the multi-view association identification and shows meaningful feature selection capability with desirable interpretation.The novel PDSMCCA confirms that the parameter decomposition is a suitable strategy to identify both modality-shared and -specific imaging features. The multimodal association and the diverse information of multimodal imaging data enable us to better understand the brain disease such as Alzheimer’s disease. Alzheimer’s Disease (AD) –5, the mThe statistical pairwise correlation analysis has been widely used for medical image analysis. For example, researchers use both PET and functional magnetic resonance imaging(fMRI) data to study the relationship between brain and genes metabolism indicators . With thThe existing correlation methods are mostly designed for two views , 20. Forrding to , this inWith above observations, to better identify the complex multi-way correlations among multimodal imaging data, we propose a novel sparse multi-view canonical correlation analysis (PDSMCCA) method based on the parameter decomposition. On the one hand, to improve interpretability, PDSMCCA contains two types of regularization as the benchmark method. The experiment adopts the nested fivefold cross-validation and the grid search strategy to tune suitable e denotes the noise level.Data 1: Data 2: In summary, we construct simulation data under different conditions to compare the proposed algorithm with the benchmark method.In this simulation study, we use two synthetic data sets which contain different ground truth and noise intensity. We first generate three canonical weight vectors Figure https://adni.loni.usc.edu). and the primary goal of ADNI is to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. For up-to-date information, see www.adni-info.org.The brain imaging data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database  non-Hispanic Caucasian participants. Three modalities of brain imaging data, including sMRI, FDG-PET and AV45-PET were used in this paper. FDG-PET and AV45-PET scans were co-registered to the standard MNI space. sMRI scans were processed with voxel-based morphometry (VBM) , 36 by tFigure pocampus , 37, thepocampus , the lefofrontal , 39. Meaentclass1pt{minimap < 0.01). This indicated imaging QTs were significantly related to the diagnosis. Moreover, in order to verify the biological effects of the selected imaging QTs. We further analyzed the prominent imaging QT of each modality, which were Frontal-Med-Orb-Left in AV45 [To further show the meaning of these selected imaging QTs, the ANOVA and population stratification analysis were conducted. The one-way ANOVA results showed that the top selected imaging QTs reached the level of significance  based on parameter decomposition. In our model, the canonical weights were decomposed into modality-shared and modality-specific components, resulting in a flexible and meaningful interpretability. We also introduced an efficient optimization algorithm to solve PDSMCCA, and proved the convergence. The results on both synthetic and real neuroimaging data showed that compared with SMCCA, PDSMCCA accurately selected the modality-shared and -specific features, and obtained higher or comparable correlation coefficients. The diverse feature selection might provide a new insight for revealing AD pathology.i-th row and j-th column of In this paper, italic letters indicate scalars, boldface lowercase letters and boldface capitals represents column vectors and matrices respectively. Specifically, the k-th modality of imaging data with n samples and p imaging quantitative traits (QTs) and K is the number of imaging modalities. k-th modality, and SMCCA extends the conventional two-view SCCA model to multi-view oriented, which can handle the association identification among multiple data sets. Generally, the definition of SMCCA is as follows:rding to , 44, 2)2\documenhe model . What’s In order to better identify the relationship between multimodal brain imaging data and overcome the drawbacks of SMCCA, we propose a novel SMCCA (PDSMCCA) model. PDSMCCA is defined as follows:Specifically, by using different regularization functions for 2,1-norm  on \docuThe merits of PDSMCCA are as follows. First of all, our model directly calculates the multi-way association among multiple data modalities, which holds a powerful modeling capability. Besides, we use ith diagonal element being According to Lemma 2.2 in , the optith diagonal element is Similarly, the optimal  solving 10\documeOnce every Theorem The value of (keeps decreasing througout the iteration of Algorithm 1.value of  keeps detth iteration. Next, we will prove that the value of (We use value of  is contictive of  as \docufined in  can be eessed in  and deno


Background: Accumulating evidence suggests that anti-estrogens have been effective against multiple gynecological diseases, especially advanced uterine corpus endometrial carcinoma (UCEC), highlighting the contribution of the estrogen response pathway in UCEC progression. This study aims to identify a reliable prognostic signature for potentially aiding in the comprehensive management of UCEC.Methods: Firstly, univariate Cox and LASSO regression were performed to identify a satisfying UCEC prognostic model quantifying patients’ risk, constructed from estrogen-response-related genes and verified to be effective by Kaplan-Meier curves, ROC curves, univariate and multivariate Cox regression. Additionally, a nomogram was constructed integrating the prognostic model and other clinicopathological parameters. Next, UCEC patients from the TCGA dataset were divided into low- and high-risk groups according to the median risk score. To elucidate differences in biological characteristics between the two risk groups, pathway enrichment, immune landscape, genomic alterations, and therapeutic responses were evaluated to satisfy this objective. As for treatment, effective responses to anti-PD-1 therapy in the low-risk patients and sensitivity to six chemotherapy drugs in the high-risk patients were demonstrated.Results: The low-risk group with a relatively favorable prognosis was marked by increased immune cell infiltration, higher expression levels of HLA members and immune checkpoint biomarkers, higher tumor mutation burden, and lower copy number alterations. This UCEC prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective.Conclusion: Our study provides molecular signatures for further functional and therapeutic investigations of estrogen-response-related genes in UCEC and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses. Uterine corpus cancer is the sixth most commonly diagnosed cancer in women globally, with an estimated 417,000 new cases and 97,000 deaths in 2020 worldwide . Among tEstrogens are a group of steroid compounds that function in a myriad of physiologic and pathologic processes . EstrogeImmunotherapy has become a potential therapeutic strategy for treating advanced cancer in recent years . CurrentThis study captured the expression profiles of 200 genes related to estrogen-response from the TCGA-UCEC dataset. The single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was performed to obtain the enrichment scores of the estrogen response pathway of each UCEC patient. Subsequently, correlation analysis among estrogen response ssGSEA score, prognosis, and clinical features of patients was performed. Next, an estrogen-response-related risk model was established, and the UCEC patients were classified into low- and high-risk groups in light of their risk scores. Additionally, we established a nomogram that integrated the prognosis model with clinicopathological factors to predict the overall survival of patients with UCEC. Finally, integrated analyses of pathway enrichment, immune landscape, somatic mutation and copy number variation (CNV), and immuno-/chemotherapeutic response prediction were conducted in the two risk groups.https://xena.ucsc.edu/public-hubs). The mRNAs with a normalized count equal to or above 1 in at least 10% of the samples were left for further analysis . 570 samples, including 535 tumor samples and 35 normal samples, were obtained with integrated clinical information. These tumor samples (the Entire set) were randomly classified into the Training set (n = 268) and the Validation set (n = 267). The Training set was used for constructing the prognostic model, while both the Entire set and the Validation set were used for validation. The data of somatic mutations and CNV was obtained through the “TCGAbiolinks” package. The mutation data was processed and visualized using the “maftools” package. The CNV data was processed and visualized using GISTIC 2.0.The transcriptome data  of the TCGA-UCEC was downloaded from the UCSC Xena browser . To obtain the enrichment scores of gene sets with these hallmarks, we used the ssGSEA algorithm according to our previous studies   Cox regression by using the “glmnet” package with the penalty parameter estimated by 10-fold cross-validation. So far, the UCEC prognostic signature consisting of 13 genes has been established. The risk score calculation formula is:A Univariate Cox regression analysis was performed to evaluate the correlation between OS and differentially expressed genes in the estrogen response pathway. According to the criteria of ” package with default parameters. Differences in these scores were analyzed in the low- and high-risk groups. Specifically, the ESTIMATE algorithm was used to evaluate Tumor Purity, ESTIMATE Score, Immune Score, and Stromal Score. The differential expression levels of human leukocyte antigen (HLA) family members and immune checkpoint biomarkers between the high- and the low-risk group were further analyzed.https://www.cancerrxgene.org/), UCEC patients’ sensitivity to six chemotherapeutic agents was estimated, including docetaxel, lenalidomide, doxorubicin, cisplatin, vinorelbine, and gefitinib, by using the “pRRophetic” package  or cytotoxic T lymphocyte antigen-4 (CTLA4) . Based o package , which i package .p -value < 0.05 as the cut-off. The Chi-square test, or Fisher’s exact test, was used for categorical data. Unpaired Student’s t-test, Wilcoxon rank-sum test, or Kruskal–Wallis tests was used for continuous data. All p -values of statistical data were based on two-sided statistical tests, and data with p < 0.05 was considered to be statistically significant.Statistical analyses were implemented using R software (version 3.6.3). Survival analysis was performed by the Kaplan-Meier method using the “survminer” package, with the log-rank test used for comparisons. The prognostic nomogram was built based on the result of the multivariate Cox proportional hazards regression analysis, which was used to predict the 1-, 3- and 5-years OS by representing the sum of points for each factor. Next, the calibration curves were used to assess the relationship between the predicted probabilities and actual outcomes, and the calibration was evaluated by bootstrapping 1,000 times. Further, the time-dependent area under the ROC curve (AUC) were calculated to evaluate the accuracy values of prognostic models. Also, decision curve analysis (DCA) was used to evaluate the clinical benefit of the nomogram model by quantifying the net benefit of nomogram-assisted decisions. The “limma” package was used to identify differentially expressed genes with | log2(Fold change (FC)) | > 0.5 and adj The overview workflow of our research is shown in p -value < 0.05, the expression of these 200 genes was analyzed between UCEC and normal samples. We found 132 differentially expressed ERGs (DEERGs), of which 57 and 75 were up-regulated and down-regulated, respectively  to investigate whether estrogen response has a bearing on the prognosis of UCEC patients. According to the criteria of |log2FC| > 0.5 and adj ectively . Based oectively . Next, wectively . Most DEectively . Meanwhiectively . These dp -value < 0.05  into two sets: the Training set (n = 268) and the Validation set (n = 267). To screen out DEERGs with potential prognostic value and develop one robust model to predict the prognosis of patients with UCEC, we performed a univariate Cox regression analysis in the entire set and identified 34 genes significantly associated with OS according to the criteria of e < 0.05 . Next, we < 0.05 . At thisIn this formula, the gene with a positive coefficient was a risk factor for UCEC patients, whereas the gene with a negative coefficient was a protective factor .Next, the median risk score was used for grouping UCEC patients into low- and high-risk groups in all three groups. The Scatterplots and Heatmap were drawn to show the distribution of risk scores and the correlation between risk scores and OS, prognostic risk gene expression in the entire group. High-risk individuals were more likely to die. Some of their risk genes were overexpressed, whereas the protective genes were under-expressed . The surThrough analyzing the difference in risk scores of patient groups with different UCEC grades, stages, and TCGA subtypes, we discovered that patients with higher grades and/or stages consistently had higher risk scores . SpecifiThe above results strongly suggested that high-risk scores were significantly correlated with OS and DFS of patients with UCEC. Combined with both univariate and multivariate Cox regression analyses, the screening process identified a signature consisting of 13 DEERGs as a UCEC prognostic factor independent of clinicopathological factors such as age, grade, stage, and histological type. It proved that the risk score based on 13 DEERGs could be an independent prognostic factor for patients with UCEC, whether in the Training, Validation, or Entire set .To establish a clinically applicable model for predicting the survival probability of UCEC patients, we created a prognostic nomogram based on the risk score, age, group, and stage in the Entire set (TCGA-UCEC) to estimate the probability of the 1-, 3- and 5-years OS . The calp -value < 0.05  and tumor-associated macrophages (TAMs) can contribute to immune escape and tumor growth . The lowesponses . The resesponses . TherefoAn assessment of the mutated genes underlying human tumors is essential to cancer diagnosis, therapy, and rational treatment selection . Tumors Despite some exciting discoveries, some problems remain. First, this study is a retrospective study only covering the TCGA dataset and thus should be validated by an external dataset and further confirmed by preliminary experiments. Second, the predictive capacity of the prognostic signature composed of 13 genes and its potential relationship with immune status, requires further verification testing in other clinical samples.In this study, we developed an estrogen-response-related signature that could act as an independent prognostic factor for patients with UCEC. According to the prognostic model based on 13 DEERGs, we comprehensively evaluated the biological behaviors, immune status, genomic alterations, and therapeutic responses in different risk groups. In summary, our study provides a novel insight into potential strategies for diagnosing, monitoring, and treatment of UCEC.


Background: Although many genes related to epithelial-mesenchymal transition (EMT) have been explored in hepatocellular carcinoma (HCC), their prognostic significance still needs further analysis.Methods: Differentially expressed EMT-related genes were obtained through the integrated analysis of 4 Gene expression omnibus (GEO) datasets. The univariate Cox regression and Lasso Cox regression models are utilized to determine the EMT-related gene signature. Based on the results of multivariate Cox regression, a predictive nomogram is established. Time-dependent ROC curve and calibration curve are used to show the distinguishing ability and consistency of the nomogram. Finally, we explored the correlation between EMT risk score and immune immunity.Results: We identified a nine EMT-related gene signature to predict the survival outcome of HCC patients. Based on the EMT risk score’s median, HCC patients in each dataset were divided into high and low-risk groups. The survival outcomes of HCC patients in the high-risk group were significantly worse than those in the low-risk group. The prediction nomogram based on the EMT risk score has better distinguishing ability and consistency. High EMT risk score was related to immune infiltration.Conclusion: The nomogram based on the EMT risk score can reliably predict the survival outcome of HCC patients, thereby providing benefits for medical decisions. Liver cancer is a common malignant tumor that has become a danger to public health globally. Hepatocellular carcinoma is well-established as the predominant pathological type and is mainly caused by hepatitis virus infection, alcoholism, and liver cirrhosis . CurrentEpithelial-mesenchymal transition (EMT) refers to the transformation process of cells from epithelial cells to mesenchymal cells, crucial in wound healing, tumor progression, and embryonic development . Its maiGiven the close association between EMT and the occurrence and development of HCC, the present research sought to systematically evaluate the prognostic role of EMT-related genes. Four HCC microarrays from the GEO dataset were integrated, and differentially expressed EMT-related genes were analyzed. A prognostic EMT-related gene signature was established through the univariate and LASSO Cox regression model, and a nomogram model was constructed based on the EMT risk score. Finally, we explored the correlation between EMT risk score and tumor immunity.https://portal.gdc.cancer.gov/), ICGC (https://icgc.org/), and GEO (https://www.ncbi.nlm.nih.gov/geo/) website, processed the raw data, and finally obtained the expression matrix of hepatocellular carcinoma tissues (HCC). Datasets GSE22058, GSE25097, GSE36376, and GSE39791 containing data on HCC and para-cancerous tissues were used to screen differentially expressed genes (DEGs). All datasets were normalized by Robust Multi-Array Average (RMA), and different probes were converted into gene symbols. The dbEMT2 database (http://dbemt.bioinfo-minzhao.org/download.cgi) was used to download EMT-related genes. Perl language was used to extract the expression matrix of EMT-related genes from the TCGA, ICGC, and GSE14520 HCC datasets in batches.We downloaded the raw data from TCGA  cox regression model to further screen the EMT-related risk genes in the TCGA dataset and finally determined 9 EMT gene signatures could predict hepatocellular carcinoma prognosis. The EMT risk score is equal to the sum of the product of EMT gene expression and its coefficient. We calculated the EMT risk score of each patient in the TCGA, GSE14520, and ICGC datasets and divided HCC patients into a high and a low EMT risk score group based on the median value in each dataset. The TCGA dataset was used to construct the EMT gene signature, while the GSE14520 and ICGC datasets were used to validate the constructed signature. The survival curve between high and low-risk groups of HCC patients was generated using the survminer package, and the difference in survival times was assessed by the log-rank test. The survival ROC package assessed the predictive performance of the EMT risk score for survival outcomes.The nomogram was based on multivariate regression analysis, which integrates multiple predictors, and scaled line segments were drawn on the same plane according to a certain proportion to ensure that the survival probability of patients can be predicted objectively. In the TCGA dataset, we included patients with exact age, sex, tumor differentiation, tumor stage, and survival information. Univariate and multivariate cox regression models were used to screen variables that affect survival outcomes (including OS and RFS). Therefore, the risk score and tumor stage data from the TCGA dataset were included to build a predictive nomogram model for OS and RFS. Datasets GSE14520 and ICGC were used for external validation. The time-dependent ROC curve was used to assess the predictive ability, and the consistency of the nomogram model was visually represented by the 1-year, 3-year, and 5-year calibration curve. The larger the area under the time-dependent ROC curve, the better the discriminative ability of the nomogram model. The higher the degree of coincidence between the actual 1-year, 3-year, and 5-year calibration curves and the lines at the 45-degree angle, the better the consistency.http://www.proteinatlas.org/) database is a well-recognized online database that provides tissue and cell distribution information of all 24,000 human proteins. It can provide the expression of a specific protein in tumor tissues and normal tissues. In the present study, we analyzed differences in protein expression of these 9 EMT risk genes between HCC and normal liver tissues.The Human Protein Atlas (https://cistrome.shinyapps.io/timer/) is an online database that provides information on the infiltration status of the six immune cells  in the TCGA database (https://bioinformatics.mdanderson.org/estimate). Correlation analysis was used to explore the correlation between EMT risk score and immune-related indexes . Immune checkpoint genes included CD274 (PD-L1), PDCD1(PD-1), TIGIT, CTLA4, HAVCR2, and ICOS. Finally, we analyzed the differences in mRNA expression levels of immune checkpoint genes between high and low-risk groups.The TIMER (Tumor Immune Estimation Resource) database  (p < 0.001)  . Similar< 0.001) . HCC pat< 0.001) . In the < 0.001) , and HCC< 0.001) . Finally< 0.001) . The sur< 0.001) . Supplemp < 0.001), tumor stage (p < 0.001), and survival status (p < 0.001). In the GSE14520 dataset, the EMT risk score was significantly correlated to age (p = 0.043), TNM stage (p < 0.001), and survival status (p < 0.001). In the ICGC dataset, the EMT risk score was significantly correlated to the TNM stage (p < 0.001) and survival status (p = 0.001)  .p < 0.001; RFS: p = 0.005) and tumor stage  were independent risk factors that affected the overall survival and recurrence-free survival of HCC patients (p < 0.001) and tumor stage (p < 0.001) were independent risk factors that affected the overall survival of HCC patients (p = 0.001) was an independent predictor of recurrence-free survival in HCC patients (p = 0.002) was an independent predictor of overall survival in HCC patients  and recurrence-free survival  of HCC patients  of HCC patients at 1, 3, and 5 years. In the TCGA dataset, the EMT risk score and tumor stage were independent risk factors that affected the overall survival and recurrence-free survival of HCC patients. Therefore, OS and RFS nomogram models were constructed based on these two variables . The resWe analyzed the mRNA expression of 9 EMT-related genes in the TCGA, GSE14520, and ICGC datasets and found that these nine genes were dysregulated in these three HCC datasets . The HPAGSEA was used to explore significantly enriched KEGG pathways associated with the EMT gene signature in the TCGA dataset. The high EMT risk group was significantly enriched in the spliceosome, RNA degradation, oocyte meiosis, pyrimidine metabolism, and cell cycle (Top 5) . In contWe downloaded the expression of tumor-infiltrating immune cells, tumor immune score, stroma score, and ESTIMATE score from the TIMER and ESTIMATE databases. Correlation analysis showed a significant correlation between the EMT risk score and six tumor immune cells in the TCGA dataset . MoreoveHCC is a common malignant tumor well-acknowledged to exert increasing public health and socio-economic burden. Given the limitations of the TNM staging system for predicting prognosis, there is an urgent need for biomarkers to predict the survival of HCC patients. With the rapid development of high-throughput technologies, much emphasis has been placed on gene chip technology since it can be used to identify biomarkers associated with the heterogeneity of HCC . Herein,In recent years, tumor-associated fibroblasts and exosomes have attracted significant attention for their potential role in tumorigenesis. It has been established that cancer-associated fibroblasts (CAFs) are one of the most important cellular components in the tumor stroma and are in a persistently activated state. Indeed, fibroblasts represent an essential and abundant component of the tumor microenvironment, which affects angiogenesis, extracellular matrix remodeling, immunosuppression, and stem cell properties to promote the occurrence and development of HCC . CurrentHerein, we integrated four HCC microarrays in the GEO dataset through the metaDE package and finally identified 317 differentially expressed EMT-related genes. Then, univariate Cox regression analysis was used to explore the prognostic-related EMT genes in the TCGA and GSE14520 datasets and the Lasso Cox regression model to identify the EMT gene signature. Next, we validated the diagnostic and prognostic value of the EMT-related gene signature for predicting survival outcomes in the three HCC datasets and constructed a predictive nomogram of OS and RFS based on the EMT risk score and tumor stage. The constructed nomogram exhibited good consistency during prediction in the training and validation datasets. A high EMT risk score correlated with poor prognosis, high TNM stage, poor histopathological grade, and immune infiltration in the HCC dataset.Among the screened EMT-related genes, some have already been extensively studied in liver cancer, especially HDAC2. In this regard, Ji Heon Noh et al. found that HDAC2 was upregulated in liver cancer tissues and could promote the proliferation of HCC cells through the expression of G1/S cyclin . Yang etOver the years, the advent of high-throughput technology has resulted in the emergence of multi-omics integrated analysis. Nowadays, researchers can obtain large-scale omics data from distinct molecular levels, including genome, transcriptome, proteome, interactome, epigenome, metabolome, liposome, and microbiome data, to conduct in-depth disease research. Interestingly, an omics analytical method can provide information on biological processes that differ in different life processes or disease groups. However, these analyses often have limitations. Multi-omics approaches are well-established to integrate information from several omics levels to provide evidence to better understand biological mechanisms and candidate key factors, leading to a deeper understanding of the molecular mechanisms and genetic basis of complex traits in biological processes and disease processes . In the Our study identified a nine-gene signature to predict the overall survival and recurrence-free survival of HCC patients. It yielded good prediction accuracy, providing potential targets for individualized treatment of HCC. At the same time, we acknowledge there were limitations and shortcomings in this study. In this regard, the molecular mechanism of the selected EMT-related proteins in hepatocellular carcinoma was not explored. The relatively small sample size of our study population highlights the need for a large and multiple HCC cohort to validate the established gene signature and increase the robustness of our findings.


This systematic review investigated randomized controlled trials evaluating cognitive behavioral therapy (CBT) and graded exercise therapy (GET) for adults with chronic fatigue syndrome (CFS). The objective was to determine prognosis following treatment. Studies were eligible if they were peer-reviewed and investigated treatment at least 12 weeks in duration. Studies were excluded if they used co-morbid diagnoses as entry criteria or if they did not measure fatigue, disability, or functioning. Literature published between 1988 and 2021 was searched using MEDLINE, EMBASE, PsycINFO, and Web of Science. Study quality was assessed using the Effective Public Health Practice Project assessment tool. Outcomes were synthesized when three or more studies reported outcomes obtained from the same validated measurement tool. The review included 15 publications comprising 1990 participants. Following CBT, and at short-term to medium-term follow-up, 44% considered themselves better and 11% considered themselves worse. Following GET, and at post-treatment to short-term follow-up, 43% considered themselves better and 14% considered themselves worse. These outcomes were 8–26% more favorable compared to control conditions. Two-thirds of studies were of moderate quality and the remainder were of weak quality. Limitations of this review relate to the clinical heterogeneity of studies and that most outcomes were self-reported. Results suggest some support for the positive effects of CBT and GET at short-term to medium-term follow-up although this requires further investigation given the inconsistent findings of previous reviews. Findings may not be generalizable to severe CFS. This review was registered with PROSPERO (CRD42018086002). Chronic fatigue syndrome (CFS) is a serious illness characterized by persistent and medically unexplained fatigue which is severe enough to result in substantial disability. Other symptoms such as musculoskeletal pain, sleep disturbance, and cognitive dysfunction are common  and graded exercise therapy (GET) are the most promising treatments, both of which yield improvements in fatigue and functioning , may also lead to misclassification and yield findings that do not apply to CFS  was conducted following PRISMA guidelines  contained original data from an English-language, peer-review journal; (2) were randomized and controlled; (3) included participants who met at least one case definition for CFS, including the Oxford criteria  included participants <18 years old; (2) used idiopathic chronic fatigue or co-morbid diagnoses as entry criteria; (3) did not include at least one outcome related to fatigue, disability, functioning, or quality of life; and (4) were economic evaluations, ecological or case-control studies, or cross-over trials without an independent control group.Published literature was searched using MEDLINE, EMBASE, PsycINFO, and Web of Science, between 1988, the year of first case definition of CFS, and March 2021. Other databases listed in the PROSPERO protocol were not used as four were considered sufficient. The search was performed using the PICO (patient/intervention/comparator/outcome) model with keywords such as chronic fatigue syndrome, cognitive behavioral or graded exercise therapy, randomized controlled trials and prognosis combined with the ‘AND’ Boolean operator. Searches in MEDLINE, EMBASE, and PsycINFO were limited to human studies and searches in Web of Science excluded studies of children or adolescents. Online Supplementary eAppendix 1 shows the search strategy for each database.Authors TI and AS independently reviewed all titles and abstracts for eligibility using the Rayyan citation website , and during a discrete time period following treatment. Outcomes were reported at various time-points and categorized as post-treatment, short-term follow-up (1–6 months post-treatment), medium-term follow-up (7–12 months post-treatment), and long-term follow-up (>12 months post-treatment).Extracted data were entered onto a Microsoft Excel (Version 16) spreadsheet by TI and checked for accuracy and completeness by AS. The data extraction sheet template can be found in online Supplementary eAppendix 2. To increase validity and focus, outcomes obtained before or after the primary outcome point, which concerned participant subsets, or which were measured using unvalidated measurement tools, and predictive factors, were not extracted. Fulcher and White  was a crStudy quality was assessed using the Effective Public Health Practice Project (EPHPP) quality assessment tool for quantitative studies  were excluded, resulting in 50 full texts. Reasons for exclusion and references to excluded studies can be found in online Supplementary eAppendix 3. Reviewing full texts led to further exclusion of 35 studies, resulting in 15 included publications. White et al. , 2013 reReviewers were initially discrepant on 32 (1%) occasions when screening titles and abstracts for study eligibility. These discrepancies were resolved through discussion.n = 7), the Netherlands (n = 6), the USA (n = 1), and New Zealand (n = 1).Study characteristics are shown in n = 8, 50%), the Oxford criteria , both the CDC and the Oxford criteria , or the NICE .Eligible studies comprised 1990 participants, excluding 1266 reported in another included study or in an excluded treatment arm. As seen in the NICE  criteriaAs shown in Participants were offered a median of 14 sessions (range: 13–16) in eight trials with a face-to-face CBT condition. Participants in White et al. , 2013 weThe control condition in five trials  in trials comparing higher and lower resource or intensity CBT  physical functioning subscale .When excluding three studies of weak quality used in the synthesis of fatigue . All data used in the synthesis of each of the above outcomes were drawn from GET studies of moderate quality.This paper reviewed studies reporting the prognosis of CFS following CBT and GET. The review included 15 publications, comprising 11 studies of CBT and six of GET. Results, implications, and limitations will now be discussed.Findings from the synthesis revealed that, at post-treatment to short-term follow-up, fatigue significantly improved in 43% of participants, 23% more than in control conditions. These results are consistent with Price et al.  who founFindings from the data synthesis revealed that at post-treatment to medium-term follow-up, 61% of participants demonstrated clinically significant improvements in physical functioning, 23% more than in control conditions, and at post-treatment to short-term follow-up, 43% considered themselves significantly better, 26% more than in control conditions. These findings are consistent with a previous review of GET (Larun et al., The synthesis also showed that, at post-treatment to short-term follow-up, 14% of participants considered themselves worse, 10% fewer than in control groups. This is consistent with a review of GET (White & Etherington, Our analysis indicates that, at post-treatment to medium-term follow-up, CBT and GET result in a better prognosis compared to control conditions. These findings are consistent with previous reviews reporting post-treatment outcomes and may provide some support for the positive effects of CBT and GET at short-term to medium-term follow-up, although this requires further investigation given that a meta-analysis was not conducted and the inconsistent findings of previous reviews. Nonetheless, our findings are consistent with previous findings that CBT and GET yield similar improvements in fatigue and functioning. Commissioners may want to consider this when providing treatment.A limitation of this review is that one-third of included studies were of weak quality. All studies were penalized for a lack of blinding, which increases the risk of performance or detection bias. However, blinding is not possible in trials of psychotherapeutic treatments while it has been suggested that concerns over bias caused by lack of blinding may be exaggerated (Moustgaard et al., Another limitation relates to the clinical heterogeneity of included studies, which used different diagnostic criteria, investigated different intensities and doses of treatments, and assessed outcomes at different follow-ups. This may have contributed to between-study differences in prognosis. Indeed, even though they are similar, studies using Oxford criteria for CFS may yield non-significantly higher effect sizes than those using CDC criteria (Castell et al., A further limitation is that most studies reported subjective, self-report measures, which may have increased the risk of observer or detection bias. However, two included studies (Jason et al., The risk of review-level bias, and missing relevant articles, was minimized by using four databases to search the literature, in addition to manual and reference list searches. Publication bias was not assessed, and although Castell et al.  found liA final limitation is that treatment in most trials involved at least some face-to-face sessions, thereby excluding less mobile or housebound participants, therefore findings may not be generalizable to severe CFS.Future trials should report objective outcomes, in addition to self-report measures, which are essential as fatigue is subjective and individuals are best placed to judge how they feel. Further work also needs to investigate the statistical significance of between-treatment differences in prognosis, particularly in the longer term, and whether differences are moderated by diagnostic criteria and treatment intensity. There is a need to further define and measure deterioration and recovery. Further trials, not only of CBT and GET, but also other treatments, are required, particularly for individuals with severe CFS.Findings of this review indicate that CBT and GET lead to a more favorable prognosis compared to control groups comprising relaxation, medical care, and wait-list conditions. These findings correspond with existing reviews reporting improvements in post-treatment symptoms; however, previous findings concerning short-term to medium-term follow-up are inconsistent and require further investigation. Further work also needs to improve existing treatments and to explore new treatments, particularly for those with severe CFS.