Given that the full word meaning remains activated, the picture is thus judged as being less related to the full word. To a first approximation, it seems that cascading processes cannot account for the preferential access of the first “constituent” over the second. However, the activation of embedded constituents can be triggered by initially identifying the leftmost constituent . Thus, the meaning of the first followed by the second “constituent” can be quickly accessed and integrated with the full word meaning resulting in an inhibition for semantically unrelated “constituents”. Our results seem to be at odds with the predictions of models positing a prelexical parser that is entirely blind to the semantics of morpho-orthographic segments. A semantically blind system wouldn’t distinguish between compounds and pseudocompounds, not at least when scanning one of its constituents. If the system reads left-to-right, a semantically blind morpho-orthographic mechanism should treat both compounds and pseudocompounds as yielding potential morphemes. If the system is morphologically savvy and a “head hunter” it should treat the second morpho-orthographic sequence as a potential morpheme. But our results show significant differences in RTs and accuracy between compounds and pseudocompounds at both segments. However, it is possible that the lexical parsing mechanism first identifies the leftmost constituent but with semantic information quickly ruling out misparses. That would be the case of a pseudocompound whose “constituent” fan is accessed but quickly ruled-out upon accessing the full word fanfare. The whole word meaning can be accessed via a postlexical route or following a semantically anomalous composition of parsed “constituents”.
This in effect would reflect in faster and more accurate judgements for compounds given that the full word meaning is related to the meaning of the head,plant pot with drainage even if the referent picture stands for the head only . It is clear that the time-course of visual word recognition is faster than the time it takes to make a semantic judgement about a word and a picture. With our method, we aimed to capture the early moments of visual word recognition and lexical access by relying on a short presentation of words and pictures dichoptically, that is, without foveation. So, it is possible that the lexical targets—and thus their morpho-orthographic parsing—were degraded, limiting the scope of our results. However, it is important to note that the judgement accuracy for single constituents at the same spatial position as their embedded counterparts yielded high accuracy suggesting that parafoveal viewing was sufficiently clear to enable morpho-orthographic parsing. Taken together, our findings support a model whereby the visual word recognition system produces left-to-right constituent recognition, with these constituents accessing their lexical entries, but with morpho-semantic processes rapidly ruling out misparses. In other words, “constituent” meanings are accessed, though only the representation of semantically related “constituents” remain active. Our suggestion is that both constituents and full words token their concepts; but while the concept BED is compatible with the complex word bedroom , the concept FAN is incompatible with the monomorphemic word fanfare and is rapidly suppressed. This view is compatible with a model of visual word recognition that produces multiple parses that are initially insensitive to the semantics of morpho-orthographic sequences, but which are quickly evaluated by semanticcomposition processes.
With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. Clear graphical representation and visual communication are effective ways to present large datasets and dense information to learners. Heatmaps with hierarchical clustering analysis and principal component analysis are commonly used cluster analysis methods for omics studies. Wang et al. investigated the interaction of fruity aromas with polyphenols by the use of heatmap cluster analysis by Origin Pro 9.0; Varunjikar et al. analyzed proteomics from tandem mass spectrometry by the use of heatmap cluster analysis through Omics Explorer V 3.6 software for food-grade insect protein analysis; Lin et al. analyzed the glycome profile of blueberry using a heatmap via R software. Yang et al. combined head space-gas chromatography-ion mobility spectrometry with PCA to detect the flavor compounds of fermented soybean products by the use of a software package with a dynamic PCA plug-in. Green & Selina employed both PCA and hierarchal cluster analysis without a heatmap to classify fatty acid and sterol profiles for analyzing avocado oil quality by the use of OriginPro2016 software. Zhao et al. implemented PCA in R software and machine learning algorithms in Python to classify up to ten types of major edible oils based on fatty acid profiles and Raman spectra datasets; Zhao et al. also applied PCA based on R software to analyze phenolic compound profiles of different cultivars of the US midwestern grapes with selenium and lithium fertilizer treatments. Richter et al. used PCA and heatmap cluster analysis to analyze inductively coupled plasma mass spectrometry data in R software for identifying food authentication of German asparagus. Zou et al. analyzed a multidimensional dataset of HS-SPMEGC×GC-TOFMS of coffee using ChromaTOF® , ChromaTOF Tile , R version 4.0.2, and MATLAB® .
However, processing high-dimensional data from raw food omics datasets is time consuming and remains a challenging task for data mining and untargeted foodomics studies. To achieve multiple data analysis methods, different software or code packages may be needed. For instance, by the use of R software, packages ‘ggbiplot’ and ‘ggplot’ are usually used for PCA analysis, while another package ‘heatmap20 is usually applied for heatmap cluster analysis. However, it takes time for researchers to learn and operate different software and code packages with confidence. The objective of the study is to develop an integrated code basis program based on MATLAB® software to give a 3D heatmap, heatmap hierarchical clustering analysis, and PCA all at once by directly reading datasets from Excel® files. The code has been optimized for figure qualities such as resolution, color code, and label font size. The code also adjusts the size of the 3D bars of the heatmap in accordance with the values, which gives readers better data visualization and differentiation. In addition, we have provided proper code annotations and completed user guidance in the supplementary materials for future learning and educational proposes. The original dataset of our previous publication about the US California olive pomace phenolics was used as an example dataset in this study. As can be seen from Table S1, the data matrix contained 7 extracts * 22 olive pomace phenolic compounds. Data were saved in an ‘.xlsx’ file format by the use of Microsoft® Excel; in this case, the full file name was ‘olive phenolics.xlsx’. Here, Hadley Wickham’s ‘Tidy Data’ conceptwas referred, where each variable was a column and each sample observation was a row, because the input data must be tidy for the best results. It can be seen from Figure S1a that the names of 7 olive pomace extracts were listed in the first column from A2 to A8,growing blueberries in pots and the names of 22 olive pomace phenolic compounds were listed in the first row from B1 to W1. The text ‘NAME’ was placed in cell A1. The file was saved as ‘olive phenolics.xlsx’ in a MATLAB work folder. The data area in the excel file can be expanded in both rows and columns; however, there should be no blank cells in any places in the data area. The sample observation name should also be listed in the first column and the compound variables names should be listed in the first row. Omics data of each sample must be listed in each row, and variables/compounds must be listed in columns; otherwise, the program will still run, but output meaningless results. The excel data file and ‘.m’ code in the MATLAB files have been uploaded to the file exchange website as a secondary way to obtain the dataset and code. Readers can download from there in the MATLAB software, as shown in the ‘screenshot’ in Figure S1b, or via the MATLAB file exchange website.In perennial plants, masting marks one extreme end of the spectrum of population-level variation, and constant yield marks the opposite end. To date, much of the research on synchronous seed production has been focused on mast-seeding by wind-pollinated trees in temperate regions. It may be that mast-seeding is more common in wind-pollinated taxa; theory suggests selection for enhanced pollination efficiency through synchronous flowering with conspecifics is more likely in wind-pollinated species .
In insect-pollinated species, synchronous flowering may saturate insect pollinators and high pollination efficiency at low flowering density may select for a more constant production of flowers . An alternative explanation of the over representation of wind-pollinated species in the synchronous seeding literature is that much of the masting work, and indeed the bulk of ecological and evolutionary research, has been done in temperate regions where wind is the predominant pollination syndrome among forest trees. Early reviews on whether pollination syndrome predicts the tendency for masting had difficulty gathering sufficient data on insect pollinated and animal-dispersed taxa. A recent meta-analysis included data with approximately equal numbers of animal- and wind pollinated species, but there were more time series per species for the wind- than animal-pollinated ones . To help to fill this gap, we make use of an analogous pattern of highly variable reproduction in perennial crop plants which, unlike mast-seeding forest trees, are biased toward insect-pollinated taxa and span tropical, Mediterranean and temperate climates . Alternate bearing in fruit and nut crops is an intermediate pattern of perennial reproductive variability in which a year of high reproduction is followed by a year of low reproduction. While media and trade reports have cited alternate bearing in discussions of national crop yield, literature on the extent and drivers of synchrony among alternate-bearing individuals is scarce. Despite evidence of similar plant-level mechanisms in masting and alternate bearing, ecological research on the synchrony of mast-seeding has largely ignored, or explicitly excluded, alternate-bearing crops . This may be because breeding and management actions are generally assumed to outweigh any natural conditions that could result in alternate bearing at farm-, region- or nationwide scales. Similarly, agricultural research on yield and alternate bearing rarely integrates insights from masting literature. Such insights include the possibility that factors which increase yield in one year may result in a more severe reduction in the following year and the expectation that wind-pollinated taxa may be more prone to synchronous fluctuations in yield at larger spatial scales than insect-pollinated taxa . Here, we use global crop production data for plants known to be alternate bearing at an individual level to evaluate patterns of seed production at the national level. Specifically, we assess whether these crops are alternate bearing at national scales and whether patterns differ across pollination syndromes and are consistent with findings in masting systems. For this analysis, we use data from the Food and Agriculture Organization of the United Nations. The global, long-term nature of the FAO data offers a unique opportunity to study reproductive patterns in perennial plants, but also poses some limitations that could mask a signal of alternate bearing even if one exists. First, the FAO reports data at the national level, and while the total area under production is included, it is not spatially explicit and thus cannot serve as a proxy for the extent of cultivation. As such, we cannot directly test for synchrony among individuals or populations using these data. While a signal of alternate bearing in national-scale data would require synchrony at smaller scales, a lack of a signal does not preclude synchrony at the farm or regional level. Studies of synchrony in mast-seeding species suggest that we might expect signals of alternate bearing to be weaker in data at national compared to local scales. In that sense, our analysis is likely to underestimate the magnitude of alternate bearing at more local scales and thus their potential impact on farmer livelihoods. Furthermore, multiple crops are sometimes grouped together into a single FAO crop category , which could mask a species-specific signal. Finally, there is no information on the genetic variety or cultivation practices employed in each country that could influence the tendency toward alternate bearing within taxa. These features make any observed patterns in these data particularly salient. The presence of alternate bearing at national scales would highlight its ecological and socio-ecological importance.