Temporal lobe epilepsy is a group of neurological diseases caused by the repeated abnormal discharge of brain neurons. Patients with this disease are often accompanied with cognitive impairment. However, the pathogenesis of the cognitive impairment remains unclear. Resting state functional magnetic resonance imaging is a kind of magnetic resonance imaging method based on blood oxygen level dependence. This can reflect the spontaneous brain functional activity of a human brain in the resting state. In recent years, a number of researchers have used resting state functional magnetic resonance imaging to study the changes in resting spontaneous brain function in patients with temporal lobe epilepsy with cognitive impairment (TLE-CI). However, due to the differences in sample and methodology, the results of these studies were inconsistent. Therefore, the present study aimed to investigate the characteristics of the resting spontaneous brain function in patients with TLE-CI through a meta-analysis.A search was conducted on electronic databases, including PubMed, Cochrane Library, EMBASE, Web of Science, China National Knowledge Infrastructure, WANGFANG DATA and Chinese Biomedical Literature Database, and Baidu scholar Database, from the establishment of the database to April 20, 2021. Randomized controlled trials that employed amplitude of low-frequency fluctuations/regional homogeneity to investigate the changes in resting spontaneous brain function in patients with TLE-CI were selected. Anisotropic effect size version of signed differential mapping was applied to perform the data analysis.The study summarized the changes in spontaneous brain function in patients with TLE-CI.The conclusion for the functional cerebral alterations based on the latest studies will be provided. Although under the control of anti-epileptic drugs, this can still affect human brain function in a slow and long-term manner. Furthermore, cognitive impairment is the major concern for these patients, such as damaging memory, attention, language, and executive control function.–4 But precise data on its incidence remains unclear. At present, a number of studies have investigated the factors that contribute to cognitive dysfunction. It has been considered that these cognitive deficits are caused by complicated reasons, including anti-epileptic drugs, recurrent seizures, seizure severity, and organic brain disorders of the temporal lobe, seizure-induced head trauma, age of onset of epilepsy, and long duration of epilepsy.,6,7 However, the pathogenesis of the cognitive impairment remains unclear.Epilepsy is a group of neurological diseases caused by the repeated abnormal discharge of brain neurons. This is a prevalent neurological disorder that affects approximately 50 million people worldwide. thalamus, and fornix. Depending on voxel-based morphometry, white matter reductions ipsilateral to the seizure focus were also found in the temporopolar, entorhinal, and perirhinal areas. Based on these studies, TLE originates unilaterally from the medial temporal lobe; they may be propagated from other regions which project to limbic areas.,13In patients with TLE, MRI studies have reported abnormal brain structures in hippocampus, entorhinal cortex, Various studies have investigated the resting-state networks in temporal lobe epilepsy with cognitive impairment (TLE-CI) and TLE with the default mode network: activation of the left dorsolateral pre-frontal cortex in patients with verbal memory; substantial decrease in activation in the neocortical, hippocampal, and parahippocampal regions in patients with 24-hour delayed verbal memory retrieval; greater activation in the contralateral hippocampus in patients with worse memory performance. However, the sample variables were small, and the results were inconsistent. Therefore, it is necessary to conduct a systematic review of the literature that evaluated the changes in brain functional activities in patients with TLE-CI in fMRI.The changes of whole brain function caused by epilepsy have also become a research hotspot. At present, functional magnetic resonance imaging (fMRI) is being gradually applied in the diagnosis and research of epileptic brain function, providing neurophysiological and pathological evidence for the changes in brain function in epileptic patients. Among these, resting state fMRI has been extensively used to analyze the differences in activation characteristics of various brain regions in resting-state epilepsy patients and normal control groups through regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFF).22.1 The results of this meta-analysis will be published in a journal or conferences.This protocol was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY). The registration number is INPLASY202130092 (Doi: 10.37766/inplasy2021.3.0092). The protocol is according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) statement guidelines.2.22.2.1Adolescents and adult patients diagnosed with TLE (left or right) and cognitive impairment, according to any recognized diagnostic criteria, were included into the study. All participants were treated for their condition.2.32.3.1Randomized controlled trials that compared the fMRI cerebral alterations in TLE of patients with cognitive impairment and healthy controls; studies that reported the whole-brain results in 3-dimensional coordinates  for changes in standard stereotactic space ; studies that used thresholds for significance corrected for multiple comparisons or uncorrected with spatial extent thresholds.2.3.2Studies that only reported findings in regions of interests; studies that used coordinates relative to the analysis that employed small volume corrections in pre-selected regions of interests.Types of interventions: None.2.42.4.1A systematic literature search was performed in the following electronic databases, from the establishment of database to April 20, 2021: PubMed, Cochrane Library, EMBASE, Web of Science, China National Knowledge Infrastructure, WANGFANG DATA and Chinese Biomedical Literature Database, and Baidu scholar Database. Merely studies in the English and Chinese language were included for the study.∗”, “Disorder∗”, “fMRI”, “functional MRI”, “ReHo”, and “ALFF”. The sample for the search strategy for PubMed is summarized in Table The following search terms were used: “Epilepsy, Temporal Lobe”, “Cognitive Dysfunction”, “Neurocognitive”, “Deterioration2.4.2The EndNote X9 document management software was used to filter the articles. Two researchers independently and carefully browsed the selected articles according to the inclusion and exclusion criteria. The reasons for the exclusion of articles was recorded, and any disagreements were resolved through the consensus of all authors. The diagram of the selection of studies is shown in Figure 2.52.5.1Two reviewers independently browsed the selected articles, and extracted and recorded the following information on Microsoft Excel: essential information: title, first author, publisher of the journal, year of publication, MRI type, and country; study design: inclusion and exclusion criteria, randomization method, the definition of cognitive impairment, statistical analysis technique and patient characteristics, and sample size; participants: gender, age, and disease duration; results and conclusion. At all stages, any disagreement was resolved by discussion with a senior author.2.5.2The anisotropic effect size version of signed differential mapping (AES-SDM) software was used to analyze patients with TLE-CI and healthy controls through the ReHo or ALFFs between subjects. The peak coordinates in Talarach space were transformed into MNI space. In the experiment, if the result was a Z value, this was converted to the T value. AES-SDM was used to reconstruct the effect scales and statistical parameters for the increase and decrease in brain activation in each original study.P < .005 and voxel number ≥10 were used as the main thresholds.The Monte Carlo random number was set to 20. The full width at the half height of 20 mm was used to reconstruct the brain area difference map, and each voxel with a difference was given a corresponding weight value, which was as close as possible to the results of each study . A meta-analysis was immediately performed after image reconstruction to calculate the voxel mass, with the statistical difference between TLE-CI patients and normal controls. In order to balance the sensitivity and specificity, the uncorrected 2.6Separate analyses on functional brain response abnormalities were conducted using AES-SDM. This method uses the peak coordinates to recreate a statistical parametric map for each study. Then, a map would be created using the effect sizes of the differences between patients and controls, and a standard random-effects variance-weighted meta-analysis was conducted for each voxel.2.7The methodological quality of the randomized controlled trials was assessed using the risk of bias assessment tool, Newcastle-Ottawa scale. The total score of the scale was 9 points. If the score of the included literature was >6 points, this would be included into the study, while if the score was <6 points, it was excluded. This process was independently completed by 2 professional researchers. In case of any disputes, this was discussed by 2 researchers or resolved by a third researcher.2.8The Jack-knife sensitivity analysis method was used to confirm the repeatability of the results. That is, the statistical analysis was repeated, and a different dataset was removed each time. This tested the repeatability of the results.2.9P < .005 was set, and merely brain regions with a significant correlation between spontaneous brain function changes, and age and disease duration were reported. Finally, if adequate trials would be included, the following potential sources of heterogeneity would be explored using subgroup analyses or meta-regression: studies with low risk of bias, when compared to trials with high risk of bias; methods: ALFF\ReHo; scan T: 1.5\3.0.The meta-linear regression analysis method based on AES-SDM was used to explore the influence of age and disease duration in TLE-CI patients on the research results. The threshold range of 2.10If more than 10 studies were included, a funnel plot would be used to evaluate the publication bias. Egger or Begg test would be conducted to analyze the potential publication bias, and the results would be estimated based on the Cochrane Handbook for Systematic Reviews of Interventions.2.11No human or animal subjects or samples were/will be used. The results will be published in a peer-reviewed journal, and will be disseminated at local and international neurology conferences.2.12Patients were not involved in the development of the research question, outcome measures, and study design.3Nowadays, the mechanism of TLE cognitive impairment has not been fully elucidated, and studies suggest that cognitive impairment is closely related to the abnormal brain structure and function of patients. Resting state functional magnetic resonance imaging is used to observe the neural activities of various brain regions in the resting state, showing the interaction of brain neural networks. This paper aims to summarize the functional characteristics of the whole brain in patients with cognitive impairment of TLE, and to help us further understand the mechanism of cognitive impairment.Conceptualization: Qing Ye.Data curation: Yiming Sun, Yuxuan Peng, Xueping Yang.Formal analysis: Yu Dai.Funding acquisition: Quan Wen.Investigation: Xinru Liu, Qing Ye.Methodology: Yuxuan Peng.Project administration: Yiming Sun, Yu Dai.Software: Yu Dai.Supervision: Quan Wen.Validation: Xinru Liu.Visualization: Yiming Sun, Quan Wen.Writing – original draft: Qing Ye.Writing – review & editing: Yiming Sun, Qing Ye.


Green growth is an exceptional strategy for sustainable development. It provides a pathway to combat environmental issues and the use of natural resources. This study investigates the effects of green technology and environmental factors on green growth in high-gross domestic product (GDP) countries from 2000 to 2020. In addition, it also probes the linear and nonlinear effects of GDP on green growth. To do so, we employ an advanced econometric approach, e.g., a cross-sectional autoregressive distributed lags estimator for long and short runs. The outcomes demonstrate that the linear effect of GDP is positive for green growth. On the contrary, the nonlinear effect of GDP has a negative magnitude for green growth. Besides, green technology substantially increases green growth. Energy consumption is found to be an important influencer, and it decreases green growth. Environmental factors such as emissions, according to the findings, also reduce green growth in the sample countries. It is worth noting that the joint effects of energy consumption and emissions deteriorate green growth in countries. Based on empirical findings, for policy makers, this study suggests that high-GDP countries should manage their economic and environmental activities in order to increase the amount of green growth that may protect the ecological environment. The term “green growth” has been used to refer to ecological protection. An American marine biologist (R. Carson) published an article entitled “Silent Spring” in 1987, demonstrating that excessive pesticide input would have a detrimental effect on the environment and people's understanding of the harmful effects caused by pollution on human development. It has had significant global ramifications. Besides, a British economist, namely Pierce, introduced the notion of “green economy” for the first time in 1989 with the publication of the “Green Economy Blue Book.” The author explains that natural resource depletion will cause economic growth to permanently stagnate if economic expansion exceeds the limit of available natural resources .Green growth has received a lot of attention since the consideration of climate change and environmental degradation. Several organizations, including the World Bank, the Organization for Economic Cooperation and Development (OECD), and the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), are taking the green economy seriously.The reason for the continuous rise in global temperature and its concomitant effect on the planet is that it has been prioritized in the intergovernmental panel on climate change , 4. In oWe assess the environmental factors and green technology that affect green growth. According to the , green gThis article addresses the challenges regarding green growth that still exist and vary from country to country due to the diverse economic system and environmental policies. Thus, countries need to identify the factors that influence green growth and provide better strategies to tackle the issues. More analytically, environmental taxes (ENTs) have a substantial impact on green growth. In explaining, such factors encourage the market's concern over economic activities . SimilarThis study investigates whether environmental factors and green technology affect green growth in high-GDP countries. In addition, we question how environmental factors, e.g., emissions, influence green growth through the use of energy consumption. Furthermore, does green technology influence green growth? What strategies should be adopted to avoid the reduction of green growth in order to make it environmentally friendly?Why high-GDP economies? For some reasons, the research concentrates on high-GDP economies. For instance, economies significantly contribute to the world's GDP. These economies are highly responsible for deteriorating green growth due to continuous economic growth. Economies have an abundance of financial resources to invest in mega projects. Their economic complexity index is high compared with low-GDP economies. Because of a lack of evidence in the current literature, the exclusion of significant factors requires further investigation to explicitly understand the association between green growth and environmental factors. Last, whether environmental factors affect the relationship between energy consumption and green growth is still unclear.2), sulfur dioxde (SO2), nitrogen dioxide (NO2), and phosphorus (PH15). Previous studies did not emphasize environmental productivity, particularly sulfur dioxide, nitrogen, and phosphorus, but we explored simultaneously the exact identification of challenges for the concerned countries and (ii) ENTs. Third, this study also estimates the linear and nonlinear effects of GDP on green growth. Furthermore, the joint effect of energy consumption and emissions is also investigated in the current model. Last, this research employs an advanced econometric approach, e.g., Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL), for long-run and short-run estimation.This article motivates by making several contributions to the existing literature: first, examining the role of green technology in green growth, e.g., patents. Second, we included the environmental factors in order to investigate the impact on green growth, i.e., (i) environmental productivity, e.g., carbon dioxide . It integrates the value of capital natural resources concerning the economic decisions for development planning. In addition, green growth also prevents the depletion of capital natural resource's value. However, energy consumption (EC) may affect the green growth –55.This study proposes the following model:s indicate the slope of explanatory variables, and cross sections are denoted by “i”, , whereas, “t” show time period from 2000 to 2020. In addition, the terms: GGR, GDP, ENC, ENT, GTN, and EMS indicate green growth, GDP, energy consumption, ENTs, patents, and emissions.Where β′The Equation (1) reveals that green growth is a function of GDP, ENC, ENT, GTN, and EMS. Previous arguments indicate that GDP has an expected positive or negative sign for green growth. It is anticipated that GDP has a positive sign 2) to estimate the nonlinear effect on green growth. The square of GDP also has expected positive or negative effect on green growth Additionally, we extend Equation (1) by including the square of GDP (GDP*ENC) is included in the based model in order to estimate the joint effect on green growth. Thus, the interaction term is also anticipated to have negative effect on green growth Besides, Equation (3) shows that the interaction term of emissions and energy consumption . “delta” test statistics; Swamy  developeNdenotes number of cross-section unit; Sdenotes the Swamy test statistic; kdenotes independent variables. If pvalue of the test is larger than 5%, then the null hypothesis is accepted at a 5% significance level and the cointegrating coefficients are considered homogenous. ΔHAC and (ΔHAC )adj:Where, dence of , 72, devThe null hypothesis of slope homogeneity can be rejected in all cases because the probability values are smaller than 0.05 in all of the cases. The slope coefficients are not homogeneous. Heterogeneity exists across sample countries; we should employ heterogeneous panel techniques.In order to detect the cross-dependency among the selected variables, the outcomes from cross-dependence (CD) test are reported in Tests for Cross-Sectional Dependence in ResidualsTests for Cross-Sectional Dependence in the Variables.GGRi, t, GDPi, t, ENCi, t, ENTi, t, GTNi, t & EMSi, t). Most of the variables show statistically significant at 1%, showing cross-sectional dependence in the variables of estimable model.Based on Bailey, Kapetanios, and Pesaran  and BailTo test for stationarity, in the presence of cross-sectional dependence, we use second generation unit root tests. The cross-sectional dependence has a strong presence in residuals (and in variables) as shown in ai is a deterministic term, t, and ρ is the lag order. ti denotes the corresponding t ratio of αi and is known as cross-sectional Augmented Dickey Fuller (ADF) . The avbuted to ]. In TabGGT, GDP, ENC, ENT, GTN, and EMS. The equation is given as:After checking the stationarity of the variables, we apply the CS-ARDL model. Attributed to , CS-ARDLTo solve the issue of cross-sectional dependency and slope heterogeneity, the extended version of the last equation is given as:pD, pX, pZ: Dit is dependent variable (here GGT), followed by Xi, t for all the independent variables  show that around 7.9% disequilibrium is corrected every year.In addition, emissions have negative impact on green growth. It implies that a 4.3% decrease in green growth is due to a 1% change in emissions in the long run. On the contrary, the short-run outcomes also validate that there is a negative correlation between GGR and EMS. In contrast, Hao et al.  argue th2 has a negative impact on green growth in the sample countries, e.g., 2 reduces 22.1% green growth in the long run. On the contrary, the short-run results also validate that a 33% decrease in green growth is due to a 1% change GDP2.Considering the nonlinear effect of GDP, the coefficient of GDP*EMS), the coefficient magnitude of EC*EMS is negative and statistically significant in the sample countries e.g., *EMS reduces the 63.3% green growth. Thus, there is a negative correlation among the interaction of EC, EMS, and GGR.Concerning over the joint effect of energy consumption and emissions (ECj(= β+ωj), which implies a common parameter β across the countries while ωj~IID. CCEMG has the tendency to asymptotically eliminate CD. Moreover, it allows heterogeneous slope coefficients across group members, which are captured simply by taking the average of each country's coefficient.To check the robustness of the results obtained by CS-ARDL, we deploy two additional techniques that cater cross-sectional dependence. Pesaran  forwardeAttributed to , augmentThe estimable model can be written as follows:i stands for cross-sectional dimension i=1,…,n and time period t=1,…,t and αi represents country specific effects and dit denotes heterogeneous country specific deterministic trends. αi is related with the coefficient of respective independent variables via error term ft characterizes the vector of unobserved common shocks. ft can be either stationary or nonstationary, which does not influence the validity of the estimation  is also significant at 1%, Since green growth relationship is empirically established, it is important to know the cause-effect role in this relationship. Following statistical tools shall help us in furnishing it. Work of  laid theDepending on the assumptions about homogeneity of the coefficients across cross-sections, there are two forms of panel causality test. First and conventional type treats the panel data as one large stacked set of data and performs the causality test in the standard way, that assumes all coefficients same across all cross-sections.Results of panel Granger causality are shown in GDPi, t), EMS , EC, and Green Growth  are evident from results in ECi, t) and green growth  also shows that any policy in energy consumption may affect the green growth. The reason behind that green growth is also influenced by the energy consumption , which was discovered from nature-based assets.Bi-causality between GDP  causality outcomes suggest that any policy to target gross domestic product (GDP), energy consumption (EC), and emissions (EMS) significantly changes growth (GGR) and Based on empirical findings, this study recommends some policy implications: (1) the findings highlight that continuous economic growth in high-GPD countries is not favorable to green growth. However, it is noteworthy that linear effect of economic growth is positive. The policymakers should consider the economic planning concern over green growth. (2) High-GDP countries should adopt alternative strategies of energy consumption such as fossil fuels . More precisely, countries must reduce the consumption level of energy in order to increase the green growth that may protect ecological environment. (3) Policymakers should focus on green technology by innovating the new methods or products related to environmental, which directly enhance the green growth. (4) High-GDP countries should manage their economic activities in order to reduce the emissions that pollute the environment and decrease the green growth. (5) Countries should avoid the imposition of aggressive ENTs on environmental institutions. On the contrary, the progressive ENTs should be imposed on environmental institutions, because such taxes enhance the green growth.The scope of this study is limited to high-GDP countries and only a few variables are considered in the model. This study emphasizes on energy consumption that comes from fossil fuel such as oil, coal, and gas. Likewise, it also considers the patent related to environment. Furthermore, total emissions from all sector of the economy are included. Lastly, ENTs are considered to be analyzed. Thus, future studies can be extending the model by including economic complexity, green financing, and industrialization. Besides, the study can be assessed by covering the geographical areas such as BRICS, OECD, EU, and OBOR.stats.oecd.org.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found here: ZH conceived the concept, developed the model and analyzed. BM employed the estimation models. MK and RT critically discussed and reviewed the draft to accomplishment.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.


X_ cookies), estimation of time and weight does not . Therefore, estimation of the latter categories has another level of difficulty, requiring extensive involvement of cognitive control. During a functional magnetic resonance imaging (fMRI) scan, 18 students performed estimations with three estimation categories: number, time, and weight. Estimations elicited activity in multiple brain regions, mainly: (1) visual regions including bilateral lingual gyrus), (2) parietal regions including the left angular gyrus and right supramarginal gyrus, and (3) the frontal regions . Continuous magnitude estimations (mostly time) produced different frontal activity than discrete numerical estimations did, demonstrating different profiles of brain activations between discrete numerical estimations and estimations of continuous magnitudes. The activity level in the right middle and inferior frontal gyrus correlated with the tendency to give extreme responses, signifying the importance of the right prefrontal lobe in estimations.The current study examined whether discrete numerical estimation is based on the same cognitive process as estimation of continuous magnitudes such as weight and time. While the verbal estimation of numerical quantities has a contingent unit of measurement (e.g., how many cookies fit in a cookie jar? _ How do we estimate magnitudes? Do we use different mechanisms and different neural regions to estimate different domains such as weight, time, or numerical quantities? The current study suggests a new cognitive estimation task (CET) to examine this issue.One of the accepted views in the field of numerical cognition suggested an innate mechanism in charge of estimation, that is, the approximate number system (ANS) ,2,3. ConHowever, discrete and continuous properties of physical stimuli are most of the time-correlated ,9. For eAdditionally, some very early developmental indications suggest that the understanding of continuous variables develops before the understanding of numerical discrete quantities and affects it. For example, in his classical experiment, Piaget  presenteAccordingly, Leibovich et al.  suggesteCognitive estimation is a process that is critical to many daily life activities, such as estimating the weight of a suitcase, estimating the time of driving to arrive at work, or the cost of several products in a store. One applied task that was designed to examine estimation is CET. CET examines verbal estimations of continuous magnitudes and numerical entities. It provides estimations for answers to questions for which relevant exact knowledge, but not the specific answer, is available to the subject  . Working memory is one of the most investigated EF tasks and includes: (1) the phonological loop for maintenance of verbal information—the ventrolateral PFC (VLPFC) is thought to be involved in short-term maintenance of verbal information ; (2) theX_ minutes), understanding the unit of measurement, fitting it to the estimation, together with the mixture of continuous entities and discrete measurement units, all add extra steps and difficulty and require domain-general processes such as EF. However, in discrete numerical quantities estimation, such as the number of cookies or children, the unit of measurement is part of the question and is discrete  and thus very intuitive to use. For example, how many cookies fit in a cookie jar? _X_ cookies. In the estimation of continuous variables, working memory and cognitive control are needed to remember and to manage the multiple stages leading to a solution. Namely, even if the estimations of all magnitudes originate from a common mechanism [The use of measurement units is hard because units of measurement impose discrete units onto continuous measurements . For exaechanism , there iechanism  examinedechanism . In anotechanism  found thechanism .The current study presents a new approach to test CET that is based on our previous studies ,23 and cMoreover, we wanted to evaluate the respective role of EF and numerical estimation in the CET process. We examined brain regions that were activated during CET and explained individual differences in CET estimations. Prefrontal or orbital frontal activation might indicate involvement of EF in CET, while inferior and posterior parietal cortex activation might indicate involvement of numerical estimation in CET .Another important question of the current study was whether the different types of magnitudes  have shared or distinct cognitive representations. To examine this, we presented participants with estimation questions regarding those magnitudes. The use of different units of measurement varies among various contexts of magnitudes. While for numerical quantity estimation, the unit of measurement is part of the question and both are discrete (unit of measurement and magnitude), for time and weight estimation, which are continuous magnitudes, the use of independent units of measurement is required, such as minutes, hours or kilograms (discrete unit of measurement). Hence, we hypothesized that numerical quantity estimations would be distinct from weight estimation and time estimation, which would recruit more regions related to cognitive control.Twenty-two students from Ben-Gurion University of the Negev, Beer-Sheva, Israel, were recruited to participate in the experiment. All of the participants were university students and most of them were studying psychology. All participants were right-handed and monolingual native Hebrew speakers, with intact or corrected vision and no reported learning disabilities or attention deficits. Participants were compensated for their participation in the experiment with a monetary reimbursement ($25). Four participants were excluded from the analysis due to either (a) excessive motion during scanning , or (b) low accuracy rates in one or more conditions .SD = 6 years and 8 months). All experimental procedures were approved by the Helsinki Committee of Soroka Medical Center, Beer Sheva, Israel.For the remaining 18 (12 women) participants, the average age was 22 years and 9 months .The pilot study was conducted to create a range and percentiles for the estimation questions. The questionnaire included background questions . Participants were then presented with questions in which they were instructed to estimate the correct answer. The 64 questions were composed of four types of questions. Each question was presented with the units of measure of the expected answer in brackets ; weight: What is the weight of the fattest man in Israel? (kilograms); time: How much time does it take for flowers in a vase to dry? (days); numerical estimation: How many cookies can be contained in a cookie jar? (cookies)). Participants were instructed to type their numerical answers using the keyboard. We created the questionnaire using google-docs. A link to the questionnaire was sent to all first-year psychology students in the Hebrew University of Jerusalem, with a letter that thanked them in advance for taking part in the research. Participation was voluntary, with no identification of the participant. The results were based on the answers of 48 students. We computed four quartiles of the estimations, which were the basis of the ranges in the fMRI study (see below).The tasks were programmed in E-Prime 2.1. The fMRI experiment was an event-related design with four task conditions—exact knowledge, weight, time, and numerical estimation—of 16 trials each, divided into two blocks . As in the pilot study, in each trial, participants were presented with a question and unit of measure of the expected answer in brackets. Participants were instructed to estimate the answer to the presented question and were told in advance that for most questions, any estimated answer would be correct, meaning that there was not just one, unitary correct estimation but a range of estimations were correct. Then participants were presented with four ranges of answers  and were asked to choose the range that included their estimation . The questions and answers were presented in Courier New font, size 72. Each trial began with a fixation point in the middle of the screen, presented for 1 s, followed by a black screen for 300 ms. Then, an estimation question was presented for 8 s, followed by the four ranges presented for 3 s. During the presentation of the ranges, participants responded using a response box. Only the first eight seconds were included in the analysis of the fMRI time window, to avoid differentiating response time (RT) between conditions and motor activity. RT was measured in milliseconds from the onset of the ranges until the participant’s keypress. The next trial started after a jitter period that varied between 4997 ms and 20,246 ms, with an average of 13,300 ms. The total length of the experimental block was 13 min and 35 s see  below. TWhole-brain functional data were acquired using a 3-Tesla Philips ingenia MRI scanner using a gradient echo-planar imaging (EPI) sequence . The order of imaging acquisition was ascending—interleaved, covering the entire brain of participants. For each functional volume, 33 slices  = 230 mm × 245 mm × 115 mm, matrix = 76 × 83) were collected, resulting in a spatial resolution of 3 mm isotropic voxels. Each scanning session included the acquisition of a T1-weighted three-dimensional volume (voxel dimension = 1 mm × 1 mm × 1 mm) for co-registration and anatomical location of functional data.Data analysis was conducted using the BrainVoyager QX software package . The first four images of each functional scan were discarded. Preprocessing of functional scans included 3D motion correction, slice scan time correction, and removal of low frequencies, up to three cycles per scan . The anatomical and functional images were transformed to the Talairach coordinate system using trilinear interpolation. For whole-brain group analyses, preprocessing of functional scans additionally included an 8 mm spatial smoothing. The whole-brain group contrasts were computed using a random effect generalized linear model (GLM) analysis, corrected for multiple comparisons with a false discovery rate of q(FDR) < 0.05.To further investigate cognitive estimation and different categories, we conducted a multi-voxel pattern analysis. We calculated a whole-brain support vector machine (SVM)-based searchlight mapping  between First, we describe the response frequencies according to category and then the analysis of variance (ANOVA) with category as a within-subject variable—performed once with the number of extreme responses as the dependent variable and once with RT. We calculated the number of extreme responses (more than percentile 75 and less than percentile 25) by category using an ANOVA with category as a within-subject variable.F = 2.28, p < 0.05. The number of extreme responses was lower in the exact knowledge condition  compared to the time condition , t(17) = −3.45, p < 0.001. Additionally, the number of extreme responses was lower in the numerical estimation condition  than in the time condition, t(17) = −2.28, p < 0.05. There were no significant differences between number of extreme responses in the weight category  and any other category (min p = 0.12). All the other comparisons were not significant  = 13.6, p < 0.002. RTs were lower in the exact knowledge category  compared to the numerical estimation , time  and weight  categories, t(16) = −4.89, p < 0.001; t(16) = −4.5, p < 0.001; t(16) = −4.2, p < 0.001, respectively. There were no significant differences between the estimation categories .There was a main effect of category, t-test statistic pitting the brain signal related to CET against the brain signal associated with the exact knowledge category across all estimation types . The results of this analysis We calculated a whole-brain SVM-based searchlight mapping between the brain activation of the numerical estimation versus the brain activation of the weight and time categories.Results revealed differences in various frontal regions: the right inferior frontal gyrus, right and left middle frontal gyrus (R Brodmann Area (BA) 6, 8 and L BA 8, 9), right and left superior frontal gyrus , right and left anterior cingulate and left precentral gyrus. Moreover, this analysis revealed differences in the left angular gyrus and left lingual gyrus and thalamus .Results revealed differences in the right middle frontal gyrus, right postcentral gyrus, left putamen, left caudate, and the right cerebellum .Correlation Analysis on the Contrast of CET and Exact Knowledge with Extreme Responsesr(16) = 0.74, p < 0.01, the right middle frontal gyrus (BA 9), r(16) = 0.78, p < 0.01, and the left superior temporal gyrus (BA 22), r(16) = 0.81, p < 0.01, .In line with this view, multi-voxel pattern analysis found activation differences between numerical estimation and time estimations in various frontal regions including bilateral middle and inferior frontal gyri . Moreover, MVPA found activation differences between numerical estimations and weight estimations in the right medial frontal gyrus (BA 9).CET was originally designed to evaluate patients with EF weakness, and an extensive study was done on CET in atypical populations; however, studies testing CET and the neural correlations of CET in the normal population are rare. Hence, another goal that guided the present study was to test the neural correlations of CET in the normal population. The results indicated that in the normative population, CET elicits activity in multiple regions. The loci of activity can be categorized into three major brain systems—the visual system , the parietal regions (left angular gyrus and right SMG gyrus), and frontal regions —suggesting that CET involves brain regions that are associated to complex problem solutions, including numerical estimation, verbal understanding, and attention.In atypical populations, CET is usually used to look for extreme responses, which are an indication for non-effective estimations. To test the role of extreme responses in the typically developed population, we correlated the number of extreme responses and brain activity during CET. Activity level in the right middle and inferior frontal gyrus correlated with the tendency to give extreme responses, demonstrating the importance of the right prefrontal lobe in CET. Moreover, the correlation between activity level in the right middle and inferior frontal gyrus, domain-specific brain regions that are related to EF, and the tendency to give extreme responses, demonstrate that EF plays a significant role in CET, especially, in the tendency to give extreme responses.The last goal of the present study was to evaluate the respective role of numerical estimation and EF in the CET process. It is widely accepted that to solve CET questions, one is required to activate EF  ,25. MoreIn the parietal lobe, the bilateral IPS in the posterior parietal cortex is believed to hold innate, preverbal approximate quantity representation ,26,27. TThe left angular gyrus, which is an essential part of the language-based code, was found to be more deactivated during estimation than exact knowledge ,27. SomeAnother important area whose activation was modulated during CET was the right SMG. The right SMG was previously found to be related to CET: in a study that tested patients with parietal damage, the size of the lesion in the right inferior parietal cortex (approximately next to the SMG) was found to predict extreme performance in CET . The rigFrontal lobe activation is traditionally related to EF demands. CET requires multi unknown stages to be solved that require the involvement of EF, such as working memory, attention, and planning. Hence, it comes as no surprise that CET elicits activation in the anterior and posterior cingulate gyrus bilaterally—an important region related to EF, error monitoring , integraHere we examined the effect of CET for different categories. While questions that estimate numerical quantities have no unit of measurement, CET of time and weight require the use of a unit of measurement. Therefore, estimation of the latter categories has another level of difficulty, requiring extensive involvement of cognitive control.Both behavioral and multi-voxel pattern analysis confirm this assumption. Regarding the multi-voxel pattern analysis, extensive prefrontal cortex regions differentiate between numerical and time estimation, including the bilateral inferior and middle frontal gyrus and the bilateral anterior cingulate. These activated areas in the prefrontal cortex involve EF .Note, however, that the widespread differences between the activation elicited by numerical and time estimation were not found for the comparison between weight and numerical estimation. The comparison between weight and numerical estimation using multi-voxel pattern analysis elicited a different set of regions. In the prefrontal lobe, only the right middle frontal gyrus differentiated between the conditions. We suggest that while both time and weight require the usage of units of measurements, the use of units of measurements of time is unique compared to all the other units of measurements. While units of measurements of weight, for example, follow the base-ten structure , the time units of measurement do not present internal consistency .The present results demonstrate that CET is not a unified construct and that the estimation category plays a significant role in the activation observed.Additionally, we wanted to find regions that could predict individual differences between participants in CET. We created a regression model that combined the behavioral data (number of extreme responses in the percentile range ≤ 24 or ≥ 75) and brain activity during CET.As was found in a previous study with groups of patients , the preDistinct brain activation characterized different estimation processes, demonstrating that estimation of various discrete and continuous magnitudes is not a unified construct. The results demonstrate that various magnitudes are processed in separate brain pathways, based on the need to use units of measurements or not. Verbal estimations of continuous magnitude but not discrete numerical values involve the usage of units of measurement that require extensive cognitive control and recruitment of widespread frontal networks.An additional goal of the present study was to examine the neural correlations of CET in the typical population. We found a few interesting results. First, the frontoparietal network, which is involved both in EF and in numerical estimation, is strongly activated during CET. Second, we found that brain regions related to the verbal representation of numerical information are involved in CET. Last, we found relations between the behavioral tendency to give extreme estimations and the activity level in the right middle and inferior frontal gyrus, demonstrating the importance of the right prefrontal lobe in CET.


Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care.Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients (n = 1054) was also conducted.Results: In the very old patients, the median length of stay was shorter , and the rate of a prolonged ICU stay was lower  than the old patients. The mortality from sepsis was higher in very old patients , and after multi-variable adjustment being very old was associated with higher odds for ICU mortality . In patients with septic shock, mortality was also higher in the very old patients .Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible. Sepsis is common and is associated with a high morbidity and mortality –5. DurinThis risk is of great importance for intensive care medicine as old and very old patients are among the fastest-growing subgroups of all patients admitted to the intensive care unit (ICU) . FurtherThese issues result in ongoing conflict for the intensive care physician: There are increasingly older and frailer patients, more and more intensive care treatments available, but at the same time a decreasing overall capacity due to economic constraints and more recently the covid pandemic.However, in reality, in situations, such as a pandemic, the chronological age often serves as a key factor used to estimate the predicted outcome for a critically ill patient and thus whether they are admitted to ICU . DespiteTherefore, this study aimed to investigate and compare the mortality of old and very old patients with sepsis. Furthermore, we compared the rates of organ support and the length of stay between these two groups. We conducted this analysis using the multi-center eICU Collaborative Research Database .The eICU-Database was originally drawn from the eICU telehealth system. This system complemented on-site ICU teams with remote support. This multi-center ICU database, comprised over 200,000 admissions to 335 ICUs from 208 hospitals across the USA in 2014 and 2015 . Patientvia billing codes using the method established by Angus et al.  on day one. The (pre-defined) site of primary infection and the ethical background were extracted. The database was released under the Health Insurance Portability and Accountability Act (HIPAA) safe harbor provision. The re-identification risk was certified as meeting safe harbor standards by Privacert  (HIPAA Certification no. 1031219-2).Continuous data are expressed as median ± interquartile range. We assessed differences between independent groups using Kruskal-Wallis equality-of-populations rank test. We expressed categorical data as numbers (percentage) and used the Chi-square test to calculate univariate differences between groups.n = 6,184), and very old patients, i.e., patients aged 80 years and older . The primary outcome of our analysis was ICU mortality. As secondary outcomes we evaluated the frequency of mechanical ventilation and vasopressor use. We used multi-level logistic regression to fit three sequential regression models for the binary primary outcome to assess the impact of the age category on ICU-mortality. First, a baseline model with the age category as a fixed effect and ICU as a random effect (model-1) was fitted. Second, to model-1, patient characteristics  (model-2) were added as independent variables to the model. Third, to model-2, management strategies  were added to the model. Model-1 and model-2 were used to evaluate the primary and secondary outcomes, whereas model-3 was only used to assess the primary outcome. We chose the independent variables based on previous reports and our own clinical experience. We calculated adjusted odds ratios (aOR) with respective 95% confidence intervals (95%CI).The primary exposure was the age dichotomised in two age strata: old patients, i.e., patients aged 65–79 years .We conducted all analyses in the total cohort of 9,385 patients diagnosed with sepsis and a sub-group of patients with septic shock , lactate above and below 2.0 mmol/L (arbitrary cut-off), SOFA > 1 (Sepsis-3 criteria) and SOFA > 6 (median SOFA score), with and without mechanical ventilation, with and without the vasopressor use, and patients with a primary pulmonary focus vs. non-pulmonary , and Caucasian patients (being the most frequent ethnic group) vs. non-Caucasian patients. We performed the stratified sensitivity analyses using model-1. Length of stay was divided into <72, 72–168, and >168 h.p-value of <0.05 was considered statistically significant. We used Stata/IC 16.1  for all the statistical analyses.All tests were two-sided, and a p < 0.001), and a higher serum creatinine concentration . The baseline serum lactate concentration , as well as the rate of patients with an increased serum lactate , were higher in the very old. The primary source of infection differed between groups. Very old patients suffered from urinary tract infections significantly more frequently , but significantly less from skin infections . Ethnic background also differed. For example, the proportion of African Americans was higher in the old patients , while the proportion of Hispanics was greater in the very old patients . However, these differences were not detected when the subgroup of patients with septic shock was analyzed.In the total cohort of 9,385 patients, 6,184 were old patients (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). The baseline characteristics of old vs. very old patients are shown in p < 0.001) was higher in the very old (p < 0.001). We could not detect significant differences in the relative amounts of fluid administered , and the rate of short-term stay ; model-2  and model-3 .ICU-mortality was higher in the very old , non-ventilated patients (aOR 1.50 95% CI 1.26–1.78) and patients without vasopressor use  and lower SOFA scores . The median baseline creatinine , and lactate  concentrations were similar , and the rate of short-term stay  was higher, and the rate of long-term stay  was lower. There was no difference in fluid management (p < 0.001).The length of stay was lower in the very old patients , model-2  and model-3  and being very old remained associated with a higher odds of ICU mortality after adjustments in model-1  vs. very old (aged 80 and older) critically ill patients with sepsis admitted to ICU, we found a slightly higher ICU mortality in the very old. Recently, in Europe during the Covid-19 pandemic, as part of triage, numerous ICUs set an age cut-off for ICU admission . This stSepsis is a common acute illness associated with a high mortality–some authors estimate that sepsis is the third leading cause of death in the Western world –4. The kOur results are at odds with previous analyses, which demonstrated an effect of age on mortality in septic patients , 23. HowIn our analysis, very old patients were intubated and ventilated significantly less frequently than “old only” patients. This may indicate that in these very old patients–even after adjusting for numerous confounders–the decision to limit therapy was made more frequently on the basis of age. In fact, we found a significantly lower rate of mechanical ventilation in the very old, although the median SOFA and the numbers with a pulmonary focus for sepsis were similar in both the old and the very old. These findings indicate a less intensive approach to treatment in the very old and support previous analysis. Boumendil et al. , reporteIt is interesting that, despite the fact that the very old patients received less intensive care treatment than the old patients, even after adjustment for severity of illness, the mortality is only marginally higher. If could be that the more aggressive treatment in the old (the younger patients) may not have been indicated, and therefore it was not associated with an improved outcome. Alternatively, the very old patients may have received less intensive care and been discharged from the ICU for further palliative treatment–making them formally ICU survivors as their mortality would not have been captured in our analysis. If we had looked at longer term follow up, mortality may have been much higher in this patient group. In support of this, Biston et al.  showed tbonmot that it is difficult to make predictions, especially about the future, applies to predicting the life expectancy of an individual patient.Due to the absolute mortality difference of 2% points in both groups, the number needed to “harm” would be 50. Therefore, based on these data, for patients with a chronological age over 80 years, we would not consider a generic withholding of intensive medical therapy to be justified. However, there may be other reasons or scenarios when withholding and “rationing” intensive care treatment may be deemed appropriate, especially for old people . Health This study aimed to look at mortality as the primary outcome. However, for older people who are close to their natural end of life, functional outcomes, such as quality of life are of equal or greater importance , 34. It Intensive care medicine requires robust and reliable parameters which enable us to predict outcomes in intensive care, particularly when treatment is deemed futile. Established scores, such as SOFA, biomarkers, such as lactate and novel developments including machine learning algorithms are helpful –40. HoweOf note, in some subgroups we found a pronounced association between being very old and ICU mortality. In very old patients, mortality was higher in female patients and in those who did not receive mechanical ventilation or vasopressors. We are aware of the limitations of subgroup analyses , and we Although we found a higher mortality in the subgroup of patients with septic shock and numerically a higher mortality in the very old patients, even in this subgroup, the absolute difference between old and very old patients was 2%points, a level that we would not consider clinically relevant. In this subgroup, the functional outcomes would be of particular interest as after prolonged intubation and immobilization very old patients may suffer from significant morbidity resulting in the inability to lead an independent life. Unfortunately, we do not have data on this, another relevant limitation of our data.Interestingly, and in contrast to previous studies, such as Heyland et al. , in our It is open to debate to what extent this effect of chronological age on mortality, although statistically significant and detectable even after multi-variable correction, is clinically relevant. The absolute differences were, in our opinion, relatively small both in the total cohort of septic patients (13 vs. 11%), and in the subgroup of patients with septic shock (38 vs. 36%). From the clinicians' point of view, we interpret our data along with other preliminary studies in very old intensive care patients that the blanket denial of intensive care treatment based on the calendar age alone does not seem justifiable. From our perspective, a combination of pre-admission risk factors (such as frailty), markers of disease severity on admission (such as acute organ failure) and solid clinical judgement should be used to assess patients in all age categories in order to formulate a bespoke and realistic clinical plan. In the (frequent) case of ambiguity, an “ICU trial” is an opportunity to gain further information or at least time to consider the individual patient's prognosis , 48. We From a scientific perspective, the association of chronological age and intensive care outcomes seems less robust than perhaps intuitively assumed. However, due to the retrospective nature of this study these data do not allow for any generalization of the findings. With this analysis, we hope to gain a better understanding and to propose suggestions for future prospective studies evaluating this issue. Future studies should focus on different aspects of aging, such as frailty, and on different outcome measures, such as independent daily living or quality of life.This study found a 2% absolute difference in mortality between old and very old septic patients, which translates into a relative risk difference of ~20% in a vulnerable patient population. This finding is statistically significant but probably clinically irrelevant. This study underlines the pivotal importance of concepts, such as frailty that involve the biological age of patients and not the chronological age alone for outcome prediction. Based on these data, being old or very old alone are insufficient to define therapeutic goals.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was an analysis of two third-party anonymized publicly available databases with a pre-existing institutional review board (IRB) approval. The database was released under the Health Insurance Portability and Accountability Act (HIPAA) safe harbor provision.RB, BW, CJ, and BM analyzed the data and wrote the first drought of the manuscript. SL, HF, and VO contributed to statistical analysis and improved the paper. RR, SB, PB, GW, MK, BG, DDL, DD, AK, TD, WS, SS, PvH, and MB gave guidance and improved the paper. All authors read and approved the final manuscript.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.


Cayratia and Pseudocayratia, heart curves of the Squared Heart Curve (SqHC) type of Ampelocissus and Ampelopsis and Elongated Superellipse-Heart Curves (ESHCs), frequent in Tetrastigma species and observed also in Cissus species and Rhoicissus rhomboidea). The utilities of the application of geometric models for seed description and shape quantification in this family are discussed.The Vitaceae Juss., in the basal lineages of Rosids, contains sixteen genera and 950 species, mainly of tropical lianas. The family has been divided in five tribes: Ampelopsideae, Cisseae, Cayratieae, Parthenocisseae and Viteae. Seed shape is variable in this family. Based on new models derived from equations representing heart and water drop curves, we describe seed shape in species of the Vitaceae. According to their similarity to geometric models, the seeds of the Vitaceae have been classified in ten groups. Three of them correspond to models before described and shared with the Arecaceae , while in the seven groups remaining, four correspond to general models  and three adjust to the silhouettes of seeds in particular genera (heart curves of  The Vitaceae Juss. contains sixteen genera with ca. 950 species of lianas primarily distributed in the tropics with some genera in the temperate regions. The Leeaceae Dumort., with a single genus of 34 species, mostly shrubs and small trees rather than lianas, included in the family in the APG IV , was latAmpelopsis Michx., Nekemias Raf., Rhoicissus Planch., Clematicissus Planch.); (II) Cisseae Rchb. (Cissus L.); (III) Cayratieae J.Wen and L.M.Lu  Alston, Pseudocayratia J.Wen, L.M.Lu and Z.D.Chen, Tetrastigma Planch.); (IV) Parthenocisseae J.Wen and Z.D.Chen (Parthenocissus Planch.) and (V) Viteae Dumort . Cayratia and Cyphostemma were included in Cissus by Linné and Planchon considered the former as a section of Cissus [The Vitaceae has been divided in five tribes  Table 1Table 1: f Cissus ,7.Cissus is the largest genus in the family with 300 species of complex classification [Cyphostemma is second, with 200 species of an interesting diversity in their range of distribution as well as in growth habits  [Vitis has seventy-five inter-fertile wild species distributed in three continents under subtropical, Mediterranean and continental-temperate climatic conditions. Vitis vinifera L. is the species with highest economic importance in the family with some taxonomic uncertainty about the differentiation between V. vinifera L. subsp. vinifera and V. vinifera L. subsp. sylvestris (Willd.) Hegi [V. vinifera are used worldwide in Viticulture. Species of other genera are widely cultivated, such as Parthenocissus quinquefolia (L.) Planch., the Virginia creeper, in temperate areas, and Cissus incisa Des Moul., the grape ivy, in tropical areas. Species of the genus Tetrastigma are the only host plants for the parasitic plant Rafflesia arnoldii R.Br., Rafflesiaceae, which is native only to a few areas within the Malay Archipelago [fication . Cyphost a tree) . Vitis hd.) Hegi ,11. Thouhipelago .Vitis vinifera [The taxonomy and phylogenetic relationships of the Vitaceae are far from complete and will benefit from an accurate description of seeds in the unambiguous terms of geometry. From a practical point of view, the classification based on geometric models may contribute to the distinction between wild and crop grapes of vinifera .The main objective of this review is to provide a framework for the description of seed morphology in the Vitaceae based on geometric models. A recent review of this subject in the Arecaceae described morphological types in the seeds of this family based on the similarity of seed images to geometric figures, like ellipses, ovals and others . MembersJ index, is calculated with the data obtained in ImageJ [J index measures the percent of similarity between two images (the seed and the model) and provides information on overall seed shape [Arabidopsis thaliana (L.) Heynh., those of the model legumes Lotus japonicus (Regel) K.Larsen and Medicago truncatula Gaertn., as well as the seeds of Capparis spinosa L., in the Capparaceae and Rhus tripartita DC. in the Anacardiaceae adjust well to cardioids or modified cardioids [Ricinus communis L. and Jatropha curcas L. in the Euphorbiaceae and those of cultivars of Triticum sp. in the Poaceae adjust well to ellipses of varied x/y ratio [The silhouettes of bi-dimensional images of seeds often resemble geometric figures that can be used as models for the description and quantification of seed shape in plant families. A recent review of the geometry of seeds in the Arecaceae described a series of models useful for the analysis of seed shape in this family . Geometrn ImageJ . J indexed shape ,17. Bidied shape . The seeardioids ,21,22,23/y ratio ,25,26. O/y ratio ,28, whil/y ratio . In the /y ratio .The seeds of the Vitaceae share structural characteristics. The endosperm presents in transversal section a typical “M” shape coincident with a pair of ventral in-folds and a dorsal chalaza knot allowing the identification of fossil seeds in this family .Leea, Cissus, Cyphostemma, Tetrastigma, Rhoicissus, and Cayratia have a long or linear chalaza, visible from the ventral side and terminated very near the beak at the dorsal side, a condition that was termed ‘‘perichalaza’’ [Tetrastigma and species of Rhoicissus have peculiar characteristics in their linear chalaza, which is located near the apical notch and extends their beak; their long, narrow, sometimes divergent ventral in-folds and their rugose surface. The authors conclude that, by comparison of selected sets of characters, the seeds can be distinguished to the generic level [For the application of morphology in taxonomy, characters of the seeds are selected and compared between different taxonomic groups. Frequently, the data concern measurements of defined structural components and the distances between well referenced seed positions. The results depend on the characters selected and the method used for comparison. Chen and Manchester applied successive PCA based on 57 characters to 252 seeds representative of 238 species belonging to 15 genera . The conhalaza’’ , whereasic level .Ampelocissus are pyriform, oval, or round in dorsal or ventral view [Cissus species are often described as globose with a pointed base, elliptic in outline or oblong . V. vinifera. In subsequent work, Model 7 revealed particularly useful, because models derived from it adjusted well to the shape of many cultivars of V. vinifera in the Spanish collection at IMIDRA [Seven geometric models for the description and quantification of seed shape in the Vitaceae were described before . Models t IMIDRA . The folThe comparison of well-oriented seeds with geometric figures reveals the diversity of shapes in a family, adds precision to the description and permits quantification of seed shape. The geometric analysis of seed shape in the Vitaceae shows a variety of forms shared with the Arecaceae, such as lenses, superellipses and water drops , while tLenses and superellipses  can be dr lenses ,67; . Additionally, in some Tetrastigma species the seeds resemble superellipses, for example: Tetrastigma henryi [Tetrastigma campylocarpum [T. caudatum [The seeds of a henryi , Tetrastlocarpum  and T. ccaudatum   to give:Vitis amurensis, V. labrusca, V. rupestris and Cissus granulosa; Model AGL1, a rounded Water drop, . A slightly narrowed model adjusts well to seeds of Tetrastigma hensleyanum (not shown).The three models represented in Simultaneous modifications in both terms of Equation (2) result in a variety of heart curves. Heart curves are obtained with selected values of a, reduces the size of the lower entry; increasing b, reduces the upper beak.For example, Model ACO1 see  resulteda = 1/3; b = 1/2.Model PHI1 see  was obtaa = b = 1/3. (Data Availability Statement section).Model PPE1 resulted from:Ampelocissus robinsonii, A. javalensis A. erdvendbergiana, respectively; and (ii) Models AGR1 and ADE1 that resemble the seeds of Ampelopsis grossdentata and A. denudata (AGR1) and A. delavayana and A. cantoniensis (ADE1) respectively, see Changes in the above equations give modified water drop and heart curves. For example, changes in Equation (3) result in broadened heart curves, giving (i) Models ARO1, AJA1 and AER1 that describe the seeds of Ampelocissus cavicaulis, A. macrocirrha, and A. ochracea share with A. javalensis the Squared Heart Curve (SqHC) type related to Model AJA1. Other models, such as AGR2, may fit better the shape of seeds of A. grantii and A. latifolia , Cayratia oligocarpa (COL1), Yua chinensis and Vitis aestivalis (VAE1) and Ampelopsis megalophylla (AME1). These four models share similar values of aspect ratio that justify the inclusion of model COL1 here with preference to the Squared Heart Curve (SqHC) group. Seeds of some species of Ampelocissus ).Some seed images resemble water drops in their overall shape; nevertheless, they show broader basis than waterdrops. Rhoicissus rhomboidea and of many species of Tetrastigma resemble polarized ellipses with an end bi-lobulated and the other acute. We have termed this morphological group as the Elongated Superellipse-Heart Curve (ESHC) (The seeds of e (ESHC) .Cayratia and Pseudocayratia types, with a marked entry at the basis and an acute protuberance a G X in .Tetrastigma and have been observed in Rhoicissus and Cissus, but not in species of other genera. While many seeds in species of Ampelopsis, Parthenocissus and Vitis share the typical shapes of water drop and heart curves, the squared heart curve (SqHC) type (Group IX) has been predominantly observed in Ampelocissus and Ampelopsis. A number of species remain undefined due to one of these two reasons: First, their irregular seed shape making difficult the identification of an adequate model  and, second, the seed images have geometric shapes but the identification of the model with the corresponding equation is pending . In addition, further work will be done on the seeds of Vitis species.In general, the distribution of morphological types is not in close agreement with the current taxonomic classification; nevertheless, some results may be summarized in this aspect. First, the seeds of the Elongated Superellipse-Heart Curves (ESHCs) type (Group X) are more frequent in Morphology has not received the attention due in recent decades due to the increased emphasis on molecular approaches, but the importance of descriptive aspects is rising . Seed moA distinctive aspect of seed morphology in the Vitaceae is the adjustment of their seeds to a diversity of water drops, heart curves and related figures. A series of variations derived from the equation of an ellipse have been described; their graphical representations give water drops and heart curves resembling with precision the seeds of diverse species of the Vitaceae. Both types of figures can be described as products from the modification of ellipses to obtain one pole acute and the other rounded or bi-lobulated.Cayratia and Pseudocayratia types, with a marked entry at the basis and an acute protuberance; (2) heart curves of the Squared Heart Curves (SqHCs) type in Ampelocissus and broadened models of Ampelocissus and Ampelopsis, and (3) Elongated Superellipse-Heart Curves (ESHCs), frequent in Tetrastigma species and observed also in Cissus species and R. rhomboidea.The new models here described define the seed silhouettes of species in the diverse subfamilies of the Vitaceae. In addition to three groups based on models shared with the Arecaceae, and three other groups described before  , four neThe importance of seed morphology in taxonomy has been described for a long time, see, for example, . NeverthIn addition to taxonomy, classification based on seed shape acquires relevance in other research areas. Members of both families, the Arecaceae and the Vitaceae, were present in the Neotropical flora in the Eocene , and theL’Évolution créatrice, Henri Bergson recognized the importance of Geometry stating that nôtre intelligence triomphe dans la géometrie, où se révelè la parenté de la pensée logique avec la matière inerte [In the first paragraph of the introduction to his book entitled e inerte . Unfortue inerte , quoted e inerte .Cayratia and Pseudocayratia types, heart curves of the Squared Heart Curve (SqHC) type of Ampelocissus and Ampelopsis, and Elongated Superellipse-Heart Curves (ESHCs), frequent in Tetrastigma species and observed also in Cissus species and R. rhomboidea. All these groups are defined by geometric models obtained by the representation of algebraic equations. Modifications in the equations result in models adjusting to the shape of seeds for each species.Ten morphological types are described in the Vitaceae. Seven of them are general and three specific. Among the general types, three are shared with the Arecaceae and correspond to geometric figures well described . Four additional groups include waterdrops, normal or rounded, heart curves, normal or rounded, elongated heart curves and other elongated curves, respectively. Finally, the three specific types correspond to heart curves of the


CI 0.41, 0.81) and decreased negative affect . However, there was extreme heterogeneity between the studies, and the risk of bias was high. According to the subgroup analysis, study region, study design, mean age of the sample, sample size, and type of natural and built environment were found to be important factors during exposure to the natural environment. The implications of these findings for the existing theory and research are discussed. These findings will help convince the health professionals and policymakers to encourage the residents to increase their time spent in the natural environment. These findings of this systematic review also suggested that the creation, maintenance, and enhancement of accessible greenspaces or existing natural environments may form part of a multidimensional approach to increasing emotional well-being of the local populations.There is increasing evidence that the natural environment provides substantial benefits to human emotional well-being. The current study synthesized this body of research using the meta-analysis and assessed the positive and negative effects of exposure to both the natural and built environments. We searched four databases and 20 studies were included in the review. The meta-analysis results showed the most convincing evidence that exposure to the natural environment could increase positive affect (standardized mean difference, SMD = 0.61, 95%  A considerable number of theoretical and empirical studies support the notion that natural exposure contributes to both physiological and psychological health –5. ExposSeveral existing theories demonstrate that the natural environment is beneficial to the mental health of the population. Two existing theories focus on the effects of the natural environment on human health. The stress reduction theory  proposedAs the above literature indicates, ample empirical evidence suggests that exposure to the natural environment is associated with increased emotional well-being. However, few studies have provided a quantitative synthesis of effect of the natural environment on the positive and negative emotions, especially for comparison with built environments. The hypotheses predict that if the individuals are stressed, an encounter with most unthreatening natural environments will have a stress reduction or restorative influence, whereas many built environments will hamper recuperation . GeneralThe protocol was registered using PROSPERO . We condaprior eligibility criteria were based on the PECO framework:The following Participants: any human adult populationExposure: exposure to the natural environmentComparator: exposure to the built environmentOutcomes: positive and negative affect.vs. built environment) and a self-report assessment of current emotional state that was administered following exposure to the natural and built conditions. The studies that did not include an emotion assessment and examined only cognitive and/or psychophysiological responses to nature were excluded.In terms of the populations considered, any adult population was eligible, regardless of the physical or mental health conditions. The studies involving children were excluded from the review . ExposurThe reviewers (XZ) initially screened titles and abstracts to remove obviously irrelevant articles, and then the two reviewers (WY and CF) independently screened all the full texts for eligibility. The discrepancies were resolved through discussion.Two reviewers (WY and XZ) independently extracted the following information from each included study: the names of authors, publication year, location, study design, sample, environment type, exposure procedure, and effect measures. Data were extracted into a coding frame using Microsoft Excel, synthesized, and tabulated. We used the risk of bias tool employed by Twohig-Bennett and Jones  and OgilA narrative summary was compiled after a critical review of each study. All the studies were considered to be included in the meta-analysis. Mean, SD, and cell count (N) for all positive and negative affect the outcomes in each included study were extracted. In the first instance, data were extracted directly from the studies.The studies that included the participants exposed to the natural environment only were excluded from the review , 32–34. CIs. The I2-statistic was calculated to quantify the degree of heterogeneity between the studies   . A randogeneity) . A sensigeneity) , 39.Begg's and Egger's tests  as the measure of study size on the vertical axis and mean difference on the horizontal axis. A symmetrical, inverted funnel indicates the absence of bias. In addition, a funnel plot asymmetry was tested using 's tests , which eQuality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group guideline , 42. TheThe subgroup analyses were conducted based on the study characteristics (study region and study design), participant characteristics , and process of exposure . Missingness was very limited. Two studies did not show the gender of the participants , 36. To p-value <0.05 was defined as statistically significant.All the statistical analyses were performed using the “metan” command in the STATA package version 15.1 software program, and a The initial database search yielded 17,620 articles, of which 6,237 were removed as repeats, and 11,266 were removed as clearly irrelevant after reviewing the titles and/or abstracts. We retrieved the full text of the 83 relevant papers. A further three studies were retrieved from the reference lists of the review articles. After independent assessment by the reviewer (WY), 20 articles met the inclusion criteria and were eligible for inclusion in the synthesis. A review flowchart is shown in n = 10), followed by Europe (n = 8), and Asia (n = 2).Although there was no date restriction on the search, 90% of the studies were published in the past 10 years. The studies were conducted in nine different countries. Most were conducted in North America , the Zuckerman Inventory of Personal Reactions , and the Physical Activity Affective Scale . All the 20 studies assessed the positive affect changes and 18 studies assessed the negative affect changes after exposure to the natural environment, two studies reported on positive affect only  or urban parks (n = 6) as their natural environment. Four studies used the natural environments characterized by their biodiversity, which included the arboretum (n = 8) and building site (n = 7). Three studies used the indoor environments . One used a parking lot ?” and “5. Representativeness: Were the study samples shown to be representative of the study population?”For the 20 studies detailed in CI 0.41, 0.81, p < 0.001) and decreased negative affect . The observed positive affect sizes ranged from −0.17 to 1.81, and the negative affect sizes ranged from −2.41 to 0.15. The I2 scores for both the positive and negative affect meta-analyses were higher than 75%, indicating extreme heterogeneity between the studies. To test whether significant the meta-analysis results were due to the inclusion of poor-quality studies, a sensitivity analysis was conducted where possible. The sensitivity analyses excluding single studies did not materially change the pooled results. The results of the 20 studies and the meta-analysis estimates are shown in tau2 reduced from 0.1534 to 0.0940. The mean age  and sample size  were significantly associated with negative affect. The other variables did not reach significance. The results from the Galbraith plot , and approximately 14.5% of the heterogeneity was attributed to the study of Browning et al. , and 10.5% of the heterogeneity was attributed to the study by Reeves et al.  and Begg's (PPOS = 0.206) tests of funnel asymmetry were non-significant, suggesting that the effects of publication bias on positive affect were negligible. However, both Egger's and Begg's (PNEG = 0.019) tests indicated the presence of publication bias on negative affect. The trim and fill analysis suggested that the three studies were missing from our dataset , suggesting that the effects of publication bias on the negative affect were negligible.Using the funnel plots , 6, the  dataset . After aThe subgroup analyses of the study characteristics, the participant characteristics and process of exposure identified the effect of the natural environment on positive and negative affect. A summary of the subgroup analysis is given in p = 0.016), sample size (p = 0.006), type of natural (p = 0.003), and built (p < 0.001) environments. A larger effect size was observed in the studies conducted in Europe (effect size = −0.833). The older samples (effect size = −0.873) and the smaller samples (effect size = −0.853) were found to be more effective during exposure to the natural environment. A subgroup analysis based on the process of exposure revealed that exposure of the participants to the biodiverse areas was associated with a significant reduction in negative affect (effect size = −1.019).For the effect on negative affect, the results indicated a significant difference between the study region  and a decrease in negative affect  relative to the comparison conditions, which is consistent with a prior study . Our findings also converge with previous research that demonstrated that exposure to nature is beneficial to health  and sample size (p = 0.011) were related to negative affect between natural and built environment. Other variables did not reach the significance level , 0.980 (95% CI 0.486–1.474), and 0.401 (95% CI 0.235–0.567), respectively. The study design with within-subject and between-subject got the higher positive affect. This finding was a supplement to the results of previous reports (p = 0.027), which was consistent with the results of the meta-regression models. On the other hand, study region, sample size, type of natural, and built environment were all found significantly heterogeneity between the groups in the subgroup analysis of negative affect, this might be explained by the potential bias in the included studies and “low” or “very low” quality evidence. To identify the participant characteristic changes in positive and negative affect between natural and built environment, mean age, sample size and gender were considered and the studies were divided into 2–3 groups, i.e., the studies were divided into two groups  according to mean age of the participants. The older samples exposed to natural environment were found to have more positive (effect size = 0.941) and fewer negative (effect size = −0.873) emotions than those exposed to a built environment. This finding was consistent with a previous study  walk along their assigned routes. Interference between the participants was inevitable. Another subgroup meta-analysis was based on the process of exposure. The positive affect was 0.654 (95% CI 0.319–0.989), 0.682 (95% CI 0.171–1.193), 0.452 (95% CI 0.147–0.758), and 0.569 (95% CI 0.140–0.999) for biodiverse area, forest, urban park, and university campus, respectively. Thus, the discrepancy was stable regardless of the type of natural environment for positive affect, suggesting that contact with managed nature and wild nature produces similar effect for positive emotion. In contrast, the negative affect was −1.019 (95% CI −2.279 to 0.241), −0.523 (95% CI −0.757 to −0.289), −0.006 (95% CI −0.170 to 0.158), and −0.165 (95% CI −0.662 to 0.333) for biodiverse area, forest, urban park, and university campus, respectively. The findings indicated significant moderation, with exposure to wild natural environments having a greater effect on decreasing negative affect than exposure to the managed natural environments. As a result, managed nature could serve as substitute for wild nature to increase positive emotions, while this is not suitable for alleviating negative emotions.During the subgroup analysis, we examined factors that may moderate the effect of nature on the positive and negative emotions. The results of these analyses indicated that the study region, study design, mean age of the sample, sample size, and type of natural and built environment were all important factors contributing to increased positive affect and decreased negative affect. As shown in  reports , 58. Howus study  made allThe current study provides the most comprehensive and up-to-date findings on the effect of exposure to the natural environment on positive and negative affect. Its key strengths are its broad range of included studies, and its fully reproducible and transparent meta-analysis approach. Second, we assessed the quality and risk of bias for each included study according to the validated scales and determined confidence of our pooled estimates according to the GRADE system. Thus, our results may be valuable for the researchers in this area and improve quality of future research. Third, we conducted the subgroup analyses to show the positive and negative affect between natural and built environment, potential moderators of the effect of natural on positive and negative affect were discussed, thus, to help clarify the manner in which nature contributes to human well-being.However, our study had some limitations. First, there was extreme heterogeneity in all of the pooled studies. Second, according to the GRADE system, the credibility of the cumulative evidence was “low” or “very low.” The main causes may include high heterogeneity, and inconsistent results across the studies. Third, the included studies were not representative of global populations, as half were from North America and, the others were form Europe and Asia, so our results may not be generalizable to the other areas. Fourth, several included studies used the small samples, which tend to provide a less stable estimate of the effect size. Fifth, nearly all our included studies controlled for potential confounders, yet confounding factors varied among the studies, and some important confounding factors, such as the quantity of greenspace were not considered.In summary, the present systematic review and meta-analysis indicated an association between the natural environment and positive and negative affect. However, owing to extreme heterogeneity, it is difficult to draw a robust conclusion. Future research in this area should take appropriate steps to reduce bias and improve quality to build a stronger evidence-based medical foundation. The research focus should also be extended to other geographic areas. The advanced methods should be applied to assess the positive and negative effects. Further technological refinement is required to become a viable tool to support evidence-based decision-making for public health and greenspace provision. Some important confounding variables, such as meteorological factors and, quantity and quality of greenspace should be collected and adjusted. Finally, although our systematic review uncovered a large body of research on the relationship between the environment and positive and negative affect, there is a paucity of studies on the mechanisms underlying this relationship. The mechanistic studies are needed to clarify the manner in which nature contributes to the development of human feelings and functioning, which could possibly answer more questions regarding the nature of the relationship between human health and the environment.The original contributions presented in the study are included in the article/WY: conceptualization, project administration, funding acquisition, and writing. WY, SW, and XZ: assessment and analysis. WY and FC: data retrieval, curation, and methodology. All authors made substantial contributions to the study. All authors contributed to the article and approved the submitted version.This work was funded by the National Natural Science Foundation of China (No.32001367) and Open Fund of innovation institute for Sustainable Maritime Architecture Research and Technology (iSMART), Qingdao University of Technology (No. C2020-037).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.