Biomedical illustration and visualization techniques provide a window into complex molecular worlds that are difficult to capture through experimental means alone. Biomedical illustrators frequently employ color to help tell a molecular story, e.g., to identify key molecules in a signaling pathway. Currently, color use for molecules is largely arbitrary and often chosen based on the client, cultural factors, or personal taste. The study of molecular dynamics is relatively young, and some stakeholders argue that color use guidelines would throttle the growth of the field. Instead, content authors have ample creative freedom to choose an aesthetic that, e.g., supports the story they want to tell. However, such creative freedom comes at a price. The color design process is challenging, particularly for those without a background in color theory. The result is a semantically inconsistent color space that reduces the interpretability and effectiveness of molecular visualizations as a whole. Our contribution in this paper is threefold. We first discuss some of the factors that contribute to this array of color palettes. Second, we provide a brief sampling of color palettes used in both industry and research sectors. Lastly, we suggest considerations for developing best practices around color palettes applied to molecular visualization. One of the key elements of the brief is showing how the drug acts at a molecular level. The visualization you produce must be accurate, as well as beautiful, informative, and memorable: your client wants hospital administrators, doctors, and patients to be interested in, and to opt in to, this drug. In telling this story, color palette will be one of the main and most challenging creative decisions you will have to make in the production process. The brief may include a suggested color palette that aligns with the drug’s branding, or you may be free to choose colors that you feel are most appropriate to tell the story that you want to tell. If you choose poorly, you risk a visualization that is unappealing, ineffective, or incomprehensible. If you are a biomedical illustrator, this is a common scenario that you are faced with. As a researcher who works with molecules, aspects of this scenario may also be quite familiar. Color selection can be overwhelming, particularly for novices, and outside of standard best practices for color there are no general guidelines in place for the coloring of molecules. This paper identifies some of the rationale for the broad use of color in molecular visualizations, provides a set of contemporary color palette examples, and discusses considerations towards best practices to lead to more interpretable, accurate, and consistent molecular visualizations without compromising on aesthetics or overly limiting creative freedom.Focus + context molecules. Molecular visualizations are often structured in a visual hierarchy such that focus molecules are shown prominently and in full detail. Context molecules or structures are de-emphasized and provide an overview of and add visual interest to the scene.Both Molecular reaction(s). Molecules can interact in reactions that fundamentally change their properties, synthesize new molecules, or destroy molecules. A specific and commonly–visualized scenario is ligand binding, where a ligand is defined as any substance that forms a complex with another molecule to serve a biological purpose [binding site. This step initiates (or blocks) a series of reactions that contribute to pathways integral to the life cycle and behavior of a cell, with natural implications in drug development and protein engineering research.  purpose . The speMolecular pathway. A sequence of molecular reactions, often which are initiated by a ligand binding event, describe a molecular pathway. Understanding molecular pathways and their functions is critical to understanding the functioning of higher-order structures such as cells, tissues, organs, organ systems, and even the entire body. An example of this is shown in Visualizations can tell stories that aid in exploration, analysis, and communication of complex molecular phenomena to a range of audiences and user types –3. For tColor plays a vital role in conveying each of these story features. While it can be represented in several formats, such as RGB , CMYK , or HSL  , for themonochromatic, analogous, or complementary, among others. Monochromatic palettes are formed from tints and shades of a single color, as in Analogous palettes comprise colors that are adjacent on the color wheel, as in Complementary palettes are comprised of colors that are opposite each other on the color wheel, as in A color palette is the combination of colors used to design a visualization. A number of color harmony rules aid in creating visually pleasing palettes. Derived from Itten’s seven models of color contrast , harmony1https://phil.cdc.gov/Details.aspx?pid=23311. can everyone still recognize it as the COVID-19 virus? What are the consequences if they can’t?While the artist often has creative license to choose their palette, other factors come into play. Clients from different sectors have different aims. A pharmaceutical company has different requirements than an educational or research institution. The intended audience may have certain cultural sensitivities, visual or cognitive disabilities, or other factors to consider. While on an individual basis these requirements can provide guard rails that limit the color design space, color selection remains broad and inconsistent overall. This leads to diluted semantic meaning of molecular structures, which can impact a visualization’s interpretability and effectiveness on a larger scale. For example, if COVID-19 spike proteins are colored blue, rather than the red used in the well-known version produced by Alissa Eckert and Dan Higgins for the CDC,Many education and research-oriented applications use the CPK coloring convention for atoms  when ato2In this section we briefly discuss works that explore color associations, as well as the use of color in molecular visualizations within the areas of biomedical illustration and scientific visualization.2https://informationisbeautiful.net/visualizations/colours-in-cultures/. In this graphic, we see the color black associating with, e.g., intelligence for Asian cultures and style for Japanese and Hindu cultures. Even in the same culture, a color can take on different meaning in different contexts, suggesting a more subjective and nuanced interpretation of color. Considering black again for Native American cultures in the “Colours in Culture” graphic, we see it associates with both balance and death. As a Western culture example, Wexner’s [despondent, dejected, unhappy, melancholy as well as powerful, strong, masterful. Adams & Osgood [Evaluation (E), Potency (P), and Activity (A). Examples for each factor include: good ↔ bad for E, strong ↔ weak for P, and active ↔ passive for A. They explored the perception of color in general, as well as seven distinct colors: white, grey, black, red, yellow, green, and blue. Among their findings, blue, white, and green were associated across nearly all 23 cultures as good, while black and grey more typically associated with bad. Black and grey furthermore were associated with passive, implying a degree of subjectivity in assessing the mood of these colors. Red is considered a strongly active color, although cultures disagree on its evaluation. Yellow exposes similar cultural disagreements on its evaluation. Filmmakers frequently take advantage of such color–mood associations to define the tone or mood of a film. Wei et al. [Color can elicit different emotional and psychological reactions and associations , 13. AccWexner’s  study of& Osgood  conductei et al.  analyze calm often associates with cool colors with high lightness and low saturation. This strong association was found again in a related study by Kulahciogu & de Melo [playful or exciting do not exhibit such distinct color palette associations, and Bartram et al. [agree, disagree, and neutral. However, color can also have strong semantic associations that postively impact performance. Lin et al. [Color affect is also well-studied in visualization, with studies such as Bartram et al.’s work on the strength of associations between certain color palettes and affective response . For exa de Melo  investigm et al.  note them et al. . In theim et al.  demonstrn et al.  found a n et al.  have sho“completely arbitrary and are chosen solely for aesthetic appeal [Biomedical illustration is a field devoted to illustrating and animating biological and medical topics, often with a human focus. Biomedical illustrators follow perceptual principles in color design of a molecular visualization, but frequently take artistic license regarding the specific colors in a color palette. David Goodsell’s watercolor paintings of molecular machinery are foundational to the practice of illustrating and visualizing molecules, which use color strategically to encode the spatial organization of molecules , 22. Howc appeal .” Biomedc appeal . For exac appeal  perceptuc appeal , and sugcomplementary colors. However, this still offers a staggering array of choices and requires a degree of color expertise to use. A similar tool with additional constraints for color selection could be more useful for content authors creating these assets who lack a background in color theory.A wealth of publications address the use and efficacy of colormaps, such as the controversial rainbow colormap , in visuColor is used to provide structural cues on a molecular surface. These cues are aided by illumination models, such as Hermosilla et al.’s recent approach  that incanalogous color palette can achieve. Additionally, Waldin et al.’s coloring technique is limited to structure, rather than function. Lastly, the initial molecular color selections made by the user are arbitrary and lack semantic association. Klein et al. [Limited works address color application across spatial scales in molecular visualization. Waldin et al.  present n et al.  apply a 33https://meetings.ami.org/2021/onlinesalon/. and from the 2021 VIZBI Poster Gallery4https://vizbi.org/Posters/. in the Proteins category.The colors used to visualize molecules are dependent upon a number of considerations, such as corporate branding, personal taste, and cultural sensitivities. We demonstrate the breadth of color palette choices with a brief sample of color palettes drawn from the Association of Medical Illustrators 2021 Online Salon3.1Numerous factors influence the choice of a molecular color palette. Pharmaceutical marketing and research are major drivers for the production of molecular visualizations. Marketing videos for a new drug are contracted to specialty biomedical illustration studios every year. In many of these instances, the brief requests color palettes to follow the branding of the drug or the parent pharmaceutical company. In other instances the client may focus less on brand colors but instead request a palette reflective of a particular mood, e.g., comforting or dramatic. Such client–specific color requests can be helpful in constraining a design space that is at times overwhelming, but are partially responsible for the broad range of palettes and the lack of semantic consistency in the coloring of particular molecules. Beyond the pharmaceutical sector, different target areas often play a role in color palette choices. A molecular visualization aimed at academic/educational use often drives the author to make different color choices than a visualization for pharmaceutical use, as the main goal for this sector is often to engage and teach a broad, diverse audience. For example, Drew Berry’s work mainly targets the educational and research sector, with visualizations for colorblind-friendliness and frequent use of yellows, blues, and purples applied to lambert shaders with ambient occlusion . In contanalogous color, into their palette for the comforting, pleasant emotions that often associate with this color [5https://informationisbeautiful.net/visualizations/colours-in-cultures/. Biomedical illustrators based in North America commonly use red to indicate aberrant molecular activity, e.g., constitutive activation, where a molecule is always turned “on.” For East Asian cultures this is not semantically intuitive, and may be taken to mean the opposite for audiences that do not have this exposure to North American conventions.Culture is also a major driver of color selection, given its affective role in visualization. Many molecular visualizations incorporate blue, or a close is color , 35. Varis color , 16, 36,is color , 36, depLikely the most significant element of color choice for molecular visualizations comes down to the author’s tastes and aesthetic preferences. Furthermore, biomedical illustrators and studios often wish to develop a house style that sets them apart from other studios. The decision process for color selection may often be guided by basic perceptual principles, e.g., saturated focus and desaturated context, and color harmony rules. However, the ultimate decision to color a ligand purple, orange, or another color is the author’s decision with little to no semantics attached. Exceptions include the case mentioned previously in a North American context, where red is often used to indicate aberrant activity of a molecule , and the3.2To illustrate the broad use of color in practice for molecular visualizations today, we conducted an informal study where we extracted the color palettes from 20 molecular visualizations that were produced in the last year. This ensures that our sampling captures recent trends in color design. Since such works may be created by biomedical illustrators, bioinformaticians, structural biologists, and visualization researchers , we sampcolor mood, e.g., colorful or muted. We used this semi-automatic feature as a way to minimize subjectivity bias for this phase. Experimentation with each of the five color mood options found that colorful consistently yielded palettes that captured the broadest color range in the image and most often included colors of molecules that may have proportionally occupied fewer pixels but were important to include in terms of the story. Palettes from this mode were the closest to what we, as experienced authors of molecular visualizations, would have generated by eye. However, in some instances the system did not pick up an important story feature. In these instances we manually adjusted the palette, replacing a redundant color, e.g., if the generated palette included two shades of a color, to include this color instead. This manual adjustment to the palette introduces a possible subjective bias in the study design.We generated color palettes from each molecular visualization using the Adobe Color tool , which hAfter a palette is generated from an image, Adobe Color stores the resulting color palette as only a “custom” color harmony rule. Our intervention was required to match this custom palette to the closest-matching harmony rule, which we did by attempting to duplicate the palette by eye for each rule setting. This involved at times changing what the system had identified as the central color, or by making slight adjustments to a palette color to identify the harmony rule that was the closest, if not exact, match for the generated palette. This human subjectivity is another possible source of bias, although we did our best to limit this through consistency of choices and monitor use. We further discuss this possible bias in Split complementary is the most common rule, employed in 11 palettes in both groups, with two additional double split complementary palettes used in the AMI group. Palettes with an analogous rule are the second most common (five), and occur more frequently in the AMI group. The VIZBI group exhibits harmony rules that are closest to a triad (b4) and a square (b10) rule.split/double split complementary rules in their palettes. VIZBI group color rules for palettes are more variable, although split complementary is the dominant rule for this group as well. This latter point could reflect that some of the posters submitted were created in part by biomedical illustrators, e.g., b1 and b2. Cool base colors  are more common (11) relative to warm colors, but this is fairly evenly–balanced between both groups.Generally, the palettes generated from the AMI group tend to be less saturated, show a greater contrast range, and favor analogously. However, the semantics behind the color choices are unclear, except in the case of the COVID-19 spike protein’s red coloring. Emphasizing a visual hierarchy or to focus attention on the main narrative appears to be the primary factor in color selection, with only sporadic consistency in coloring particular types of molecules, e.g., ligands.Although we did not observe consistent color semantics across all 20 visualizations, some patterns emerge. We discuss these in the following, but note the need for a larger, international study before using such findings as the basis for formal and actionable guidelines. VIZBI posters that visualize COVID-19 more consistently use red for the spike proteins , although this is not true for all (b4). The AMI group visualization of COVID-19 (a4), while using a warm color, uses pink instead. Purple is a popular color for receptor proteins for the AMI group , although also used for membrane molecules , ligands (a4), or other elements (a9). VIZBI posters use purple less frequently, and more often for a ligand  rather than for a receptor (b3) or other structural element (b8). In comparison, AMI works favor yellow/orange for a ligand  or other focus molecule . Yellow/orange is used more broadly, and less frequently, in the VIZBI group. These findings show that, while color selection on the whole is largely arbitrary, there is a clear aspect of peer influence in color selection, particularly in the AMI group. Best practices could be formalized from this, particularly if a larger survey with international groups and cultures uncovers a similar and stronger pattern of color application. In both groups on a per-visualization basis, individual molecular coloring is consistent for structural elements, e.g., molecules comprising a cell membrane are all colored the same or 4aesthetics, interpretability, and ultimate effectiveness of a molecular visualization while also simplifying the design process for content authors with limited training in color theory.Molecular visualization remains a young and rapidly growing field. Allowing room for creativity is important for biomedical illustrators, structural biologists, and experts from related fields to innovate on the ways that we visualize molecules. However, framing coloring approaches within a set of best practices can provide a common ground that aids in the Aesthetics. Aesthetics are integral to drawing and guiding attention in a molecular visualization. Best practices can help content authors who lack formal training or intuition in color theory to easily craft more aesthetically–pleasing visualizations. The 60-30-10 rule from interior design is a useful rule of thumb to guide the composition and harmony of a molecular visualization. In this rule, 60% of the scene should be a dominant color, 30% a secondary color, and the last 10% an accent color, as demonstrated in split complementary harmony rule that is popular with biomedical illustrators aligns with this practice. Molecular visualizations that follow established color harmony rules and basic perceptual principles can be more aesthetically–pleasing, and furthermore may be easier to interpret in their ability to guide user attention to salient story points [y points , 23, 25.Interpretability. Color is an important driving force in the interpretability, or readability, of a molecular visualization. It can help the audience focus on the intended parts of the story, which ultimately leads to a more effective visualization. For instance, readability guidelines for text-based presentations recommend roughly 80% contrast between focus and context elements [active versus passive color ranges can clearly define focus + context elements and establish a scene hierarchy. Although different cultures or other contexts may assign different semantic meanings, warm, saturated, or light value colors perceptually tend to draw attention, while cool, desaturated, dark value colors often recede visually when positioned with active colors [Complementary colors can then be employed to differentiate the ligand from its receptor. A split complementary palette of yellow (ligand) and purple (receptor), such as in analogously-colored to indicate functional relatedness, as in elements . Additioe colors . This peEffectiveness. Lastly, an effective molecular visualization is read correctly by the intended audience. For example, in a given visualization, can the audience correctly identify a ligand? Coloring best practices can help to create a semantic layer of communication that provides an intuition to a broad audience of certain structural or functional properties of a molecule. This is particularly valuable since molecules are themselves rather abstract, often looking like “partially-melted gummy bears” that are difficult to relate to macroscale-world structures. Just as how a lay audience may not necessarily know exactly what a red blood cell is or what it does at a technical level, through shape and color cues they can recognize its basic properties and relate it to blood on a larger scale. This strategy can be extended to the molecular level by coloring hemoglobin red. Consistent coloring across multiscale may facilitate understanding of properties of other molecules as well.Coloring molecules according to the type of pathway that they are involved in is another consideration. Certain color families and harmony rules could be applied to, e.g., signal transduction pathways, while metabolic or gene expression pathways are assigned a different color family. Further color treatment in the form of different applications of color fresnel or glow effects could then be used if the pathway is showing aberrant activity, as is frequent in cancer. This guideline remains general enough to allow for creative license and follows principles for aesthetic and interpretable visualizations, while adding additional semantics for greater understanding.An alternative consideration could be assigning color families and harmony rules according to structure. This could be related to the structure of a single molecule, e.g., the domains of a protein could be assigned particular shades or tints. This guideline may also be applied to the structure of entire molecular scenes, e.g., membrane structures that are comprised of thousands of phospholipid molecules is assigned to a particular color family, while native internal molecular structures are allocated to a different portion of the color space. Currently, coloring by structure is mostly consistent within a given visualization, but this is not the case when comparing between the majority of visualizations. Coloring according to structure has some natural overlap with coloring according to function. Guidelines for both perspectives could be applied together to give color greater meaning in a molecular visualization.Lastly, cultural associations are important considerations in developing guidelines to ensure the semantics come across as intended. In our globalized world, adoption of best practices that generalize across cultures is preferable to, e.g., palettes biased towards Western sensibilities. Works such as Adams et al.’s  cross-cu5This work presents a discussion and limited study of the space of color choices used in industry and research, and additionally draws on the authors’ own experience in crafting molecular visualizations. While our aim is to summarize and introduce considerations for developing guidelines and best practices for semantically meaningful molecular color palettes, we note some limitations in this paper.The color palettes selected in our study and the rationales we discuss for authors’ color palette selection draw primarily from North American or European venues and motivations. This bias is important to note, and acts as further motivation to conduct larger, international studies and focus groups for the formal development of best practices. We do not comprehensively summarize the various rationales for color palette selection, and rather discuss a subset of the most common rationales. Similarly, we do not aim to comprehensively summarize the space of color usage in molecular visualization. This is an interesting direction and may be useful in the establishment of robust guidelines, but is beyond the scope of this work. Although limited, our considerations are meant to provide a starting point for establishing and discussing the need for best practices in coloring molecular visualizations.A second source of possible subjectivity and bias is the generation of color palettes from images that we sampled in our study. Although Adobe Color is a high-quality tool popular in both research and industry for color palette creation, use of another tool may lead to variations in the color palettes that we generated. Our decision to use a particular color mood and subsequent minor adjustments we made to determine color palettes and their closest harmony rules were based on the subjective opinion of the researcher generating the color palette. Additionally, our criteria for adjusting automatically-generated palettes to include a color important to the story is another source of subjectivity in palette generation. This could have had an effect on the resulting harmony rule. Bias could have been mitigated in these instances by having another researcher generate palettes and compare the results for consistency. However, we did not incorporate this into our pipeline, as this is a preliminary study to explore color use and to motivate future work in this space.chroma, and lightness) [Despite the fact that we frame our discussion of color in this paper around the HSL color space, this may not be the optimal color space. Although spatially uniform and therefore easier to describe, it is not perceptually uniform. For the latter, a color space such as HCL  , 42 may The spatial range that molecular visualization covers is large, spanning from tiny individual molecules up to large scenes of molecules that form even larger structures. An extensive discussion of the multiscale nature of the molecular world falls beyond the scope of this work, but this is a major challenge that best practices for coloring molecular visualizations must account for. Audience tasks may also be different at each scale. For example, CPK coloring rules are useful to identify residues for binding sites, but a different rule is necessary when exploring protein secondary domains, or an entire cell at molecular resolution. Works such as Waldin et al.  provide 6Establishing a robust set of guidelines that retain the flexibility for creative expression and innovation is challenging and requires further research in the form of perceptual user studies. For example, Jenkinson et al.  present Focus groups on color use with experts in biomedical illustration, visualization, structural biology, and bioinformatics are also necessary when developing guidelines to ensure that all stakeholders have a voice in the process. Our prior work has explored stakeholder perspectives from biomedical illustration and animation studios and visualization researchers . This waColors are not interpreted in the same way in all cultures. Guideline development requires numerous, inclusive discussions and perceptual studies with people from different backgrounds. Further, international studies into the use of color and its meaning beyond Western aesthetics are necessary. As previously discussed, CVO workshops  that recThe use of color in the context of glows, as demonstrated in 7Current color palettes for molecular visualizations are largely arbitrary and determined by a wide range of factors. Implementing coloring best practices can lead to more interpretable and effective molecular visualizations that are stylistically and semantically consistent, without overly compromising on creative freedom or aesthetics. Such guidelines can also simplify the color design process. These guidelines could be applicable not only for communication-oriented tasks, but for exploratory and analytically-oriented visualizations as well. Color can be a means to elevate scientific and health literacy at individual and population levels. With molecular visualization becoming more prevalent in mainstream culture, we feel that color best practices can, and should, be implemented to improve public understanding of molecules.Supplementary Material DetailsClick here for additional data file.


However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application . The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 haThe online version contains supplementary material available at 10.1007/s11119-022-09887-2. It is estimated that globally 9–14 million tonnes of phosphorus (P) leaches from fields into watercourses  aims to produce sufficient crops sustainably for society's needs while minimising costs to the producer and harm to the environment. This involves the management of spatial and temporal variation within fields; it requires intense information. Growers who adopt PA must consider the need, value, costs and possible other sources of information to identify whether they can use it to improve their efficiency and reduce environmental impact. The associated uncertainty of the information they acquire for this purpose affects both its value and consequently their view of this approach to management. Current decision-making on variable rate application (VRA) of fertiliser is based primarily on yield responses as functions of management inputs in agronomic trials  to predict how these soil variables change within well-managed fields and so determine the effect of prediction errors on the expected loss. It was hypothesised that, once uncertainty in predictions has been accounted for, soil spectroscopy could adequately predict the spatial variability to justify variable rate application of P and K fertiliser. For this, soil samples from four fields in the Cambridgeshire Fens of the UK were considered. Measurements of available P and K estimated from soil spectra were used to predict how the concentrations vary across the fields and to compute the associated error variances from interpolation. The expected losses associated with varying applications of fertiliser given the error variance of the predictions are computed. The expected losses are compared with the losses that would have accrued if estimates from wet chemistry had been used to determine a single application rate per field. General conclusions are drawn about the effect of uncertainty in nutrient status on economic and environmental losses and practical considerations are given with regard to implementing the loss function.Data were obtained in sample surveys of four fields in the Fen district of Cambridgeshire, England, in 2018 and 2019. The region was originally dominated by peat, much of which has oxidized since the land was drained in the seventeenth century. The land surface is now 1 to 2 m lower than it was except for the natural sinuous drainage channels containing mineral sediment. These former channels, known locally as ‘rodhams’, have become elevated features in the landscape  extract and Inductively Coupled Optical-Emission Spectroscopy . The soil samples were dried in air and milled, and sub-samples were pressed into small wells (6 mm across and approximately 1 mm deep) and placed in a Tensor II spectrometer . The absorbance spectrum in the range 9998–3999 cm−1, i.e. the near-infrared (NIR), of each sub-sample was measured with a resolution of 1 cm. Each sub-sample's mid-infrared (MIR) spectrum in the range 4000–600 cm−1 was recorded on the same instrument with a resolution of 2 cm−1. A DP-6000 Delta Premium portable X-ray fluorescence (pXRF)  was used to scan the soil samples. The pXRF features a Rh X-ray tube operated at 10–40 keV with a high resolution  were removed from the NIR spectra  regression with the kernel algorithm on the derivative spectra. Separate models were used for Fields 3 and 4, whereas a single model was used on pooled data for Fields 1 and 2 because they were close to one another. The number of components to be included in the model were selected as follows. First, the mean squared error (MSE) between the known values and the predictions was computed by leave-one-out cross-validation (LOO-CV). The standard deviation of the LOO-CV residuals was also computed. To minimise over-fitting, models were computed for a maximum of 15 components. Subsequently, the model was chosen that included the fewest components, yet lay within the MSE's standard deviation of the model that had the smallest error overall  multiple regression, known as the Granger–Ramanathan averaging method   or maximization of the likelihood of the residuals given the data (reml). Lark et al. . If Preliminary investigations  suggested that the estimated soil properties, s in Eq.  were re- and Eq.  was solvThe linear mixed models were cross-validated by LOO-CV. The linear mixed models were re-estimated for each iteration to diminish bias in parameter values  and −1). It was assumed that the yield is given by the dose–response equation:−1), −1) in the soil for every 1 kg ha−1 fertiliser applied, and The loss function, By definition, the loss given by Eq.  is zero Given the probability distribution, This also minimises the expected loss function, 0 in Eq. , is appl−3 for bulk density of this peat soil was taken from Milne et al.  was assessed based on kriged maps by computing the total expected loss Eq.  across etidyverse  Fig. .The optimal P fertiliser application varied substantially in all fields. The variation was less pronounced for K, particularly in Field 4. Across Field 1, most kriged estimates of K fall on the asymptote of the dose–response curve, hence oss Fig. . Consequ−1) and for K fertiliser across three fields (ranging from £6–£81 ha−1). That is, for available K the difference between −1 compared with uniform application. Most K fertiliser would be used on a field-basis in all cases under VRA  Table . There wFor this study it was assumed that the error was homogeneous across the sample locations that were predicted by spectroscopy. This allowed for the more generic assumption that the prediction library already existed . If, however, predictions were obtained from a single sampling campaign, the error variance of locations with associated wet chemistry data and those without could be separated in a subsequent geostatistical analysis, as Delhomme  suggesteError propagation is rarely taken into account in soil surveys based on spectroscopy. Ramirez-Lopez et al. , listed One merit of kriging is that in addition to providing unbiased predictions it minimises the squared-errors of those predictions, which are known. The predictions are best in that sense. Kriging, like other forms of regression, smooths: unobserved small values are over-estimated, and large ones under-estimated  in the model. Kriging within se Lark, .The loss function requires certain requirements of, or assumptions about, data. For example, the soil's bulk density was estimated from general knowledge in the area  allows one to make statements with confidence, it can also identify where the effort of reducing uncertainty will result in the largest gains. The loss function has enabled scientists and managers to decide how much field-work and analysis is required to answer specific questions in environmental monitoring. For example, it is used to optimise the size of samples for survey; see Yates , FaechneR2 and investigate whether the accuracy is “sufficient” to address relevant questions. In this particular study to test the hypothesis whether soil spectroscopy could adequately predict the spatial variability to justify variable rate application of P and K fertiliser.The loss function framework provides a method to assess the quality of predictions from spectroscopy beyond specific metrics such as −1 for P and £47 ha−1 for K. Although these costs fall within the range of cost savings of VRA compared with UA (£7–121 ha−1), they are larger than the savings for Field 2 specifically. However, this study investigated a best-case scenario for spectroscopy and did not optimize the data-collection for cost-effectiveness. One could reduce these costs by measuring the reflectance spectra of the soil surface on the run in the field. So far, however, trials to estimate P and K in the soil from visible–NIR in the field by Cozzolino et al.  was not strictly accompanied with a large increase in profit (Field 1). Hence, in order to account for the environmental benefits of precise fertiliser application, the results suggest that costs of P leaching (e.g. remediation) need to be included in sustainable phosphorus management strategies aimed at precise fertiliser application. These costs of P leaching can be substantial. For example, if the excess fertiliser finds its way into water bodies, the cost to water companies has been estimated to range between 75 and 114 million pounds sterling per year for England and Wales Below is the link to the electronic supplementary material.


Here, a green synthesis strategy has been used to develop a self-assembled gum kondagogu-sodium alginate bioconjugate sponge adorned with silver nanoparticles, for the first time. The properties of the nanocomposite sponge were then analyzed using FTIR, TGA, SEM, and MicroCT. The ensued biobased sponge exhibited hierarchical microstructure, open cellular pores, good shape memory, and mechanical properties. It merges the attributes of an open cellular porous structure with metal nanoparticles and are envisaged to be deployed as a sustainable catalytic system for reducing contaminants in the aqueous environment. This nanocomposite sponge showed enhanced catalytic effectiveness ( The release of a huge quantity of organic contaminants from the agricultural field, pharmaceutical, and chemical industries creates severe environmental pollution that directly causes a significant threat to the health of human beings and all other living organisms ,2. The tMetal nanoparticles have garnered tremendous attention from researchers owing to their excellent selectivity and outstanding reactivity for catalytic destruction of contaminants . Among tCochlospermum Gossypium (Bixaceae family) . Sponges (KS and Ag@KS) were cut into a thickness of 3 mm and diameter of 5 mm, and placed onto the agar plates covered with bacteria. All the plates were inoculated for 24 h at 37 °C. The antibacterial activity was expressed in terms of zone of inhibition, which was determined as the total diameter (mm) of each sample plus the halo zone where bacterial growth was inhibited. The samples were tested in triplicates, and the average values are presented.The Gram-positive fW is the initial weight (g), while dW represents the residual weight of the sponge (g).The biodegradation of the KS and Ag@KS sponges in aqueous solutions under aerobic circumstances was evaluated using the technique described elsewhere . The exp+ ions to AgNPs and the fabrication of Ag@KS sponge is schematically represented in + precursor solution. These groups have high affinity and binding capability towards Ag+ ions through electrostatic interaction, resulting in Ag+ ions being reduced to AgNPs. The large number of active functional groups distributed along the backbone of the conjugate serve a dual role as a reducer and stabilizer for AgNPs.A green pathway for converting Ag+ ions to AgNPs. The functional groups of the conjugate firmly bind and stabilize the nanoparticles, as observed from the highly colloidal and homogeneous solution after the formation of AgNPs. The color of the solution intensified with increasing AgNO3 concentration, indicating the generation of more AgNPs and their deposition on the surface of the conjugate at higher Ag precursor concentrations; similar findings have been reported previously [3 concentrations are shown in 3 concentrations resulted in enhanced absorption peak strength at 420 nm. At 10 mM AgNO3 concentration, 90 ± 2% of Ag+ content in the precursor solution was retained in the Ag@KS sponge, as per the ICP-MS analysis  values  determined from the XRD spectrum correlate well with the standard values [The predominantly spherical AgNPs in the size range of 1.5–6 nm are well disseminated in the conjugate solution, as shown by TEM imaging d. The pad values ,30. The d values ,32.3) and 6.2 wt% silver content as AgNPs. The digital photographs of KS and Ag@KS sponges on a dandelion flower, exposing their low density, are represented in The Ag@KS sponge developed by green synthesis has a low density  with pore size ranging from 50–300 μm, as shown in the micro CT image and histogram  are 0.041, 0.058, and 0.116 min−1 for 1.5, 1.9, and 3.1 mg of sponge, respectively. An increasing tendency could be observed in the kapp values as the catalyst amount increased  [km parameter was compared with other catalysts reported in the literature ). As shown in ncreased c. For co(3.1 mg) ,41. The terature , which rWhen KS sponge was utilised, no catalytic reduction of MB to leucomethylene blue (LMB) was detected (data not shown), indicating the potential of AgNPs as a catalyst. The reusability of catalyst is of important concern in the environmental and economic perspectives and, therefore, the reusability of Ag@KS sponge for MB degradation was investigated. The results showed retention of catalytic activity over five cycles, though a slight reduction (78% degradation from 98%) was observed e. As pre−1 for 1.0, 1.8, and 3.6 mg of sponge, respectively. Here, the kapp values continued to rise as the catalyst amount increased  with that of other documented sponges in the literature  oxidation with a release of electrons, resulting in a negatively charged layer on the sponge’s surface [4− to MB/4-NP, leading to the disruption of pollutant’s chromophore structure. The 4-NP is reduced to 4-Aminophenol and MB to leucomethylene blue. surface . When thEscherichia coli and Staphylococcus aureus by measuring the zone of inhibition of bacterial growth around discs placed on agar medium  and Escherichia coli (Gram-negative), thus widening their applications in the environmental field [The antibacterial activity of KS and Ag@KS sponges were evaluated against r medium . Figure activity b,d. AgNPal field .2 L−1, while that for the Ag@KS sponge is 535 ± 35 mg O2 L−1. By comparing the weight loss, 90% of KS and 65% of Ag@KS were degraded in 28 days. The rapid deterioration of the KS sponge implies that the microorganisms present in the sludge could act on it faster, but the presence of AgNPs on the Ag@KS sponge slows down the activity of microorganisms in the material and thereby reduces the rate of degradation.The biodegradability of the sponges was tested under aerobic conditions with an inoculum taken from WWTP. The bio-based nanocomposite sponges have gained popularity as a greener choice for reducing waste generation and lowering production costs. The results presented here suggest that the green synthesized sponges can be biologically degraded by the microbes, thus minimizing the secondary contamination of the environment.2+ ions. The structural stability, surface morphology, and compressive strength can be tailored both by the decoration of AgNPs on the sponge and ionic crosslinking. As the NPs are evenly distributed along the surface of the Ag@KS sponge, the oxidation, aggregation, and leach out of AgNPs are effectively minimized, leading to enhanced efficiency towards the catalytic reduction of MB and 4-NP from aqueous media. When compared to previously published catalyst systems, the nanocomposite sponge used in this study demonstrated impressive catalytic activity. The shape recovery property of the sponges in water is of much significance since it enables the repeated usage of the material without compromising structural integrity. The reusability study of the sponge showed retention of catalytic activity of 78% and 75% for MB and 4-NP, respectively, even after five cycles. Additionally, it effectively reacted with both Gram-positive and Gram-negative bacteria during the antibacterial study. Finally, the biodegradability of the sponge (65% within 28 days) affirms its eco-friendliness and at the same time offers numerous opportunities for environmental remediation applications.In this study, we have developed a conjugate of gum kondagogu and sodium alginate that overwhelms the components individually in terms of hydrophilicity and mechanical strength. In addition to being an affordable and renewable biopolymer source, tree gum conjugate simultaneously aids the generation and stabilization of AgNPs with greater than 90% retention of NPs. The AgNPs immobilized conjugate sponge with low density and high porosity are then crosslinked with Ca3) allows the effective mass transfer of the contaminants to the catalytic sites (AgNPs). The observed negligible leaching of Ag nanoparticles (<0.2 wt %) from Ag@KS sponge supports its use for long-term water remediation operations. The novelty of this model sponge catalyst is the exploitation of greener synthesis and biodegradability aspects in the design of newer sustainable materials. Furthermore, they display excellent antibacterial properties against Escherichia coli and Staphylococcus aureus. The catalysis was achieved with an activity parameter of 37 min−1 g−1 and 44 min−1 g−1 for MB and 4-NP, respectively. The prepared Ag@KS sponge shows excellent reusability even after five cycles of usage. These findings will pave the way to exploit the enormous potential of tree gum-based sponge systems in a vast variety of applications in environmental, industrial, and medicinal fields.In conclusion, AgNPs immobilized on tree gum based sustainable sponges represent a new class of catalyst carrier systems. The preparation of Ag@KS sponges is facile with the deployment of renewable and earth-abundant materials and can therefore be converted to many other green synthesized nanoparticle-based catalyst systems with excellent catalytic efficiency. The combination of high macroporosity (92%) and low density (22 ± 0.5 mg/cm


The COVID-19 pandemic has affected the lives of people globally for over 2 years. Changes in lifestyles due to the pandemic may cause psychosocial stressors for individuals and could lead to mental health problems. To provide high-quality mental health support, health care organizations need to identify COVID-19–specific stressors and monitor the trends in the prevalence of those stressors.This study aims to apply natural language processing (NLP) techniques to social media data to identify the psychosocial stressors during the COVID-19 pandemic and to analyze the trend in the prevalence of these stressors at different stages of the pandemic.We obtained a data set of 9266 Reddit posts from the subreddit \rCOVID19_support, from February 14, 2020, to July 19, 2021. We used the latent Dirichlet allocation (LDA) topic model to identify the topics that were mentioned on the subreddit and analyzed the trends in the prevalence of the topics. Lexicons were created for each of the topics and were used to identify the topics of each post. The prevalences of topics identified by the LDA and lexicon approaches were compared.The LDA model identified 6 topics from the data set: (1) “fear of coronavirus,” (2) “problems related to social relationships,” (3) “mental health symptoms,” (4) “family problems,” (5) “educational and occupational problems,” and (6) “uncertainty on the development of pandemic.” According to the results, there was a significant decline in the number of posts about the “fear of coronavirus” after vaccine distribution started. This suggests that the distribution of vaccines may have reduced the perceived risks of coronavirus. The prevalence of discussions on the uncertainty about the pandemic did not decline with the increase in the vaccinated population. In April 2021, when the Delta variant became prevalent in the United States, there was a significant increase in the number of posts about the uncertainty of pandemic development but no obvious effects on the topic of fear of the coronavirus.We created a dashboard to visualize the trend in the prevalence of topics about COVID-19–related stressors being discussed on a social media platform (Reddit). Our results provide insights into the prevalence of pandemic-related stressors during different stages of the COVID-19 pandemic. The NLP techniques leveraged in this study could also be applied to analyze event-specific stressors in the future. The COVID-19 pandemic has affected the lives of people globally for over 2 years. To mitigate infection, safety measures such as social distancing, lockdowns, and school closures have been implemented. The mental health of many has been impacted due to stress, anxiety, loneliness, and feelings of uncertainty about the pandemic . The majSocial media platforms, such as Twitter and Reddit, are commonly used as the data source for obtaining insights regarding mental health status. As people share their feelings or experiences on the platforms, the content may reflect users’ emotions. The changes in emotions of the population could be reflected by their behavior on social media. Many researchers have utilized NLP techniques and social media data to analyze the mental health status of the population. De Choudhury et al  introducIn the field of public health and informatics, some researchers have utilized NLP topic modeling to summarize COVID-19–related discussions on social media. Medford et al  observedAlthough some prior NLP research aimed at devising methods to utilize social media data to measure the population’s mental health status, few studies focused on identifying psychosocial stressors. According to the American Psychological Association , a psychIn this paper, we utilized the latent Dirichlet allocation (LDA) topic model to identify pandemic-related distress, by identifying the topics being discussed on the subreddit \rCOVID19_support. After applying the LDA model, we visualized the trends in the prevalence of topics at different stages of the pandemic. Several existing works leveraged NLP to analyze the population’s mental health status during the pandemic; however, existing studies did not explore the possibility of analyzing COVID-19–related stressors. This study focused on monitoring the stressors during the pandemic. Although most existing research focused on the mental health impacts at the beginning of the outbreak of the pandemic, the data set extracted in this study covered the posts on the subreddit starting from February 2020, the outbreak of the pandemic, up to July 2021. This allowed us to visualize the changes in the prevalence of stressors at different stages of the pandemic. Observing the trends can provide insights into the latest predominant stressors, and the findings could also be useful for mental health support providers and policy makers. We believe applying NLP to summarize text can help us obtain insights into mental health status and stressors during the pandemic.In this study, Reddit was selected as the data source for applying machine learning models to obtain insights into the prevalence of stressors during COVID-19. There are several advantages of using Reddit in this study. De Choudhury and De  found thThere are different subreddits related to COVID-19. We selected \rCOVID19_support for studying COVID-19-related stressors. Users on the platform \rCOVID19_support ask questions about COVID-19 and share their experience during hard times in the pandemic. The discussions on other subreddits, such as \rCOVID-19, \rCoronavirus, and \rCoronaVirus2019nCov focused on news and information instead of sharing experiences during the pandemic. As the topics discussed on \rCOVID19_support are more related to our research topic, we selected this subreddit as our data source.Reddit posts were extracted from the subreddit \rCOVID19_support using the Pushshift API . The datOn Reddit, some of the posts are tagged by “flairs,” which describe the content or the nature of the posts. The flairs in the data set included: “Support,” “Questions,” “Discussions,” “Trigger Warning,” “Good News,” “Firsthand Account,” “Resources,” “Vaccines are SAFE,” “News,” “Biosafety Request,” “The answer is NO,” “Misinformation-debunked,” and “Desperate mod.”Posts that are tagged with flairs such as “Resources” and “News” have a low tendency to include content related to psychosocial stressors. With the use of flairs tagged to the posts, we filtered posts with a low chance of including content related to our research topic. On the subreddit, some of the posts included content related to sharing information such as the latest news about COVID-19, potential adverse effects of vaccines, and tips about infection prevention. Some users asked questions such as which vaccines are safe, whether the adverse effects of vaccines are normal, and whether it is safe to visit their grandparents during the pandemic. The posts labelled with flairs “Support” and “Trigger Warning” have a high tendency to include content about users’ personal experiences, stressors, or feelings during the pandemic. Posts with flairs “News” and “Questions” tend to not include content about stressors. As we were focused on understanding stressors in this study, we extracted a subset to include posts that were labelled with the flairs that have a high tendency to include relevant content.In the data set, 4654 posts were labelled by flairs, and 4612 posts were not. The number of posts tagged by each of the flairs is shown in In this study, we used the LDA topic model to identify the COVID-19–related distress that was mentioned on Reddit. The topic model is an unsupervised machine learning model that can be applied to different research topics such as computational social science and understanding scientific publications . LDA is In this study, the LDA model in the Python scikit-learn package was used to identify the topics in the data set. The model was trained by TF-IDF features that were created from the data set. For each of the data points, the topic model outputs the proportions of contents belonging to each of the topics. The dominant topic for each of the posts was then identified by finding the topic with the highest value in the LDA output. The number of topics is the major hyperparameter of the LDA model, which affects the interpretability of topics. There are different ways to determine a suitable number of topics to achieve high interpretability of topics. In the study by Jelodar et al , the numThe output of topic modeling is highly dependent on the feature vector . For the first trial, the feature vector used to train the LDA model included TF-IDF with max_feature 300. This means the feature vector includes 300 columns of tokens, which are the terms consisting of one or more words that have the highest TF-IDF values in the given corpus. Then, the model was evaluated by the aforementioned methods, namely selecting sample posts from each of the topics and evaluating the topic coherence manually. In addition to evaluating LDA topic coherence, features were manually evaluated to determine whether they were likely to cause the LDA topic model to cluster posts in desired ways. For example, the topic model may group posts with tokens “suggestion,” “anyone,” and “thank you” into the same topic because those words tend to appear together when authors were asking for suggestions at the end of posts. However, clustering this topic does not help with understanding the stressors and may act as noise. To avoid this, those tokens were removed from the feature vectors. Besides removing those tokens, we also identified some tokens that were useful for identifying the topics. For example, the tokens “grocery shopping,” “maskless,” and “no mask” commonly appeared when users were expressing their fears of getting infected. We hypothesized that including those tokens in the feature vector would help the LDA topic model identify topics related to fear of coronavirus. The feature vector was then updated by selecting these tokens. Then, the LDA was trained by the new feature vector, leading the output of the model to be closer to the desired result. The iteration process to improve the topic model is illustrated in A psychosocial stressor lexicon is a list of keywords for each of the stressors. After the topic groups were defined, each of the topic groups was evaluated to search keywords that directly indicate the existence of topics in the text. For example, if the posts include education-related words such as “college” and “online learning,” it can be concluded that the authors have mentioned educational problems in the post. Lexicons were created for some of the topics. If a post included any of the words listed in the lexicons, it was assumed that the post included content related to the corresponding topics. Each of the posts could contain more than one topic. With the use of lexicons, each of the posts was annotated by whether it included content of each of the topics.In LDA, topics are defined as a mixture of terms with different probability distributions; this means a word could belong to more than one topic and it could cause inaccuracy in the prediction. In contrast, the lexical approach has higher interpretability on topic classification, but it requires careful selection of keywords to avoid including terms belonging to more than one topic. In this study, we assumed that we do not have prior knowledge of how Reddit users expressed their feelings on the subreddit. To obtain insights into what topics existed in the subreddit and the keywords for each of the topics, we applied the LDA model before applying the lexical approach. In this study, the lexical approach was created for 2 purposes: first, to further analyze the subtopics within the topic group. Some of the topics may include some common words and have to be grouped into the same topic group in the LDA model. The topics in the same topic group could be separated by choosing unique keywords. The second reason for creating the lexicon was to verify the result from the LDA.In previous steps, each of the data points was annotated with the LDA output, which represents the proportion of the content belonging to each of the topic groups, and the results of the lexical approach, which represent whether the post includes words that were listed in lexicons. Then, the monthly sum of the LDA model output for each topic groups was computed. The trend of topics in the data set can then be visualized and compared with the development of the pandemic. Regarding the pandemic development, the numbers of total cases, new cases per day, and vaccinated population were obtained from Our World Data . In thisThis study uses public datasets derived from social media and does not involve any human participants. As such it does not require ethics board approval.After grouping LDA topics, 6 topics were identified: (1) fear of coronavirus, (2) educational and occupational problems, (3) family problems, (4) problems related to social relationships, (5) mental health symptoms, and (6) uncertainty about the development of the pandemic. We grouped the posts according to the topics and created word clouds for each of the 6 subsets. We used t-distributed Stochastic Neighbor Embedding (t-SNE), which can be used to visualize high-dimensional data, to visualize the coherence of topic groups.The major topic in the data set was “fear of coronavirus.” The words frequently appearing in the topic of “fear of coronavirus” are shown in The LDA model identified the topic of “educational and occupational problems.” Some students shared their feelings about the online learning experience, and some felt upset because they missed the social engagement from school activities. Some people expressed worries about finding jobs during the pandemic. Other posts mentioned the situation of current university students having online classes for the final year and worried about finding a job after graduation. Due to the existence of this type of content and tokens related to education and work such as “online learning,” “graduation,” “university,” and “find jobs” appearing together, we grouped the educational and occupational LDA topics into the same group.The topic “family problems” included content about several subtopics: the worry of elderly parents getting infected, worry about parents who were already infected, and the anger of having opposing views with family members due to different acceptance of social distancing measures. The topic “mental health symptoms” included mentioning symptoms such as insomnia, obsessive compulsive disorder, and feeling depressed. Some of the users described their feelings and mental health symptoms on Reddit and asked for mental health support but did not explain the stressors. The topic “problems related to social relationships” included content about loneliness and uncertainty about the balance between social activities and pandemic safety measures. The topic “uncertainty on development of pandemic” refers to the content about the worry of the pandemic lasting forever. For this topic, some posts included discussions about whether vaccination would help get life back to normal.On March 11, 2020, the World Health Organization declared COVID-19 a “pandemic” [The trends in the prevalence of topics have been plotted separately in As shown in The prevalence of the topics “family problems” and “educational and occupational problems” dropped in February 2021. For the topic of uncertainty, its prevalence declined in February 2021 but increased in April 2021, when the new variant was becoming prevalent in the United States. The most common topic in December 2020 and January 2021 was “uncertainty about development of pandemic.” Starting from May 2020, the prevalence had a high correlation with the number of cases.To determine the distribution of topics in each month, we calculated the percentage of posts belonging to each topic in each month, and the corresponding trend was plotted .According to The keywords for each of the topics were selected by evaluating the sample posts from the topic groups in the LDA model. As we assumed we had no prior knowledge of which mental health issues were expressed on the platform and the commonly used keywords for each of the topics, the LDA model was applied before using the lexical approach. Once the keywords and topics were obtained, we could use the lexical approach to label the topics mentioned in each of the posts and visualize the trends. In some cases, the posts may describe the topics without using the keywords. For example, in the topic “mental health symptoms” in the LDA model, the text in a post may include the words “feel,” “anxious,” “depressed,” and “tired”; however, those words are also common in other topics. Therefore, it may be unsuitable to use those keywords to identify the topic “mental health symptoms” using the lexical approach. For this case, the LDA model is needed to identify that topic. In this study, both methods were used, showing similar results for the topics “Fear of coronavirus” and “Pandemic development” (measured using the correlation coefficient).We also plotted the trends in the number of posts for each topic separately in To understand which stressor was the most prevalent at different stages of the pandemic, we measured the percentage of posts containing the topics in each month and plotted the trend.According to In this study, with the use of the topic model on Reddit data (subreddit \rCOVID19_support), we identified 6 topics related to the pandemic, which were “fear of coronavirus,” “educational and occupational problems,” “family problems,” “problems related to social relationships,” “mental health symptoms,” and “uncertainty about development of pandemic.”According to the result of our study, the prevalence of discussions on “fear of coronavirus” dropped significantly after the start of vaccination. One of the possible explanations is that the increase in the vaccinated population may have reduced the perceived risk of COVID-19. Perez-Arce et al  suggesteThe number of posts about “uncertainty about pandemic development” did not have a notable drop while the vaccinated population was growing, but the trend shows a correlation with the number of cases. People may have been uncertain about the length of lockdown and how long social distancing requirements would last. This could be correlated with the number of cases instead of the perceived risks of infections. Briscese et al  suggesteIn our study, the LDA models identified some of the COVID-19–related stressors proposed in prior studies. Taylor et al  devised The trend observed in our study is consistent with those of prior studies. Yarrington et al  studied In this study, the prevalence of stressors was only compared with the number of cases and the vaccinated population and not compared with specific safety measures in specific cities. This was due to the anonymity on Reddit. The demographic information of users was unknown, and the data set obtained in this study included posts written by users in different countries. Every city has implemented social distancing measures and lockdowns at different times, depending on the number of cases and the hospitalization rates. Due to this situation, we could not analyze the relationship between mental health status and the safety measures.Similar to other studies that utilized social media data, the data extracted could only represent the population who would share their experience and feelings on social media. During the pandemic, children and older adults were strongly impacted by the changes, but it is unlikely that they shared their experiences on Reddit or other social media. To understand the needs of people with different demographics, questionnaires or interview-based studies are still required.According to the results of this study, the stressors that were caused by perceived risks were alleviated since the beginning of the vaccine distribution. However, the stressors related to the frustration of uncertainty on the length of social distancing measures then became the major stressors. Lockdown and social distancing policies may depend on the hospitalization rate, transmissivity, and severity of the virus. With a high proportion of the population vaccinated and more experience with handling COVID-19 patients, lockdowns such as those that occurred in the first 2 waves of COVID-19 are not likely to be required. In terms of alleviating mental distress, the government may consider explaining to the public that the health care system is prepared to handle new outbreaks of coronavirus and we are in the process of getting back to normal life.With the use of the lexicons created in this study, we obtained the posts or the sentences that described the worries of getting infected and the uncertainty about whether the pandemic is never-ending. This could be used as a data set for training machine learning classifier models to detect tweets that describe the stressors. Unlike Reddit posts, geographic information is available for tweets. By specifying the users’ location of tweets, we could analyze the mental distress impacts of specific social distancing policies. We could also establish time series models to quantify and predict the expected effects on stressors. This could help policy makers to estimate the impacts on mental distress before implementing a policy.In 2022, a new variant, Omicron, became prevalent, and there were updates to social distancing measures. In the future, we can use similar methods in this study to compare the trends in stressors at the time of the Delta variant and Omicron variant.In this study, we applied topic modeling to a data set that contained Reddit posts in \rCOVID19_support to identify the COVID-19–related psychosocial stressors and to visualize the trend in the prevalence of the stressors. Compared with existing research, which utilized NLP techniques on social media data to study the mental health impacts of the pandemic, our study focused on stressors instead of mental health status. The data set used in this study included posts that were created in a time period of more than 1 year during the pandemic. This allowed us to compare the difference in the prevalence of stressors before and after vaccines were distributed. The proposed topic model also allowed for monitoring the dominant stressors, which enables mental health support providers to notice changes in stressors at different stages of the pandemic. This study demonstrated the potential of using topic modeling on social media discussions to identify event-specific stressors and create a dashboard to analyze and monitor the trends. We hope the findings in this study will provide insights for health care providers and social workers to address the needs of COVID-19–related mental health support. Furthermore, we hope the NLP techniques used in this study will be applied to analyze psychosocial stressors and create corresponding lexicons of future events such as pandemics, protests, or financial crises.


Pygospio and Scoloplos). None of the other taxons, however, showed such a relationship. We discuss how quantification of abundance and biomass using molecular approaches are hampered by the ecology of DNA i.e. all the processes that determine the amount of DNA in the environment, including the ecology of the benthic species as well as the compositional nature of sequencing data.DNA metabarcoding methods have been implemented in studies aimed at detecting and quantifying marine benthic biodiversity. In such surveys, universal barcodes are amplified and sequenced from environmental DNA. To quantify biodiversity with DNA metabarcoding, a relation between the number of DNA sequences of a species and its biomass and/or the abundance is required. However, this relationship is complicated by many factors, and it is often unknown. In this study, we validate estimates of biomass and abundance from molecular approaches with those from the traditional morphological approach. Abundance and biomass were quantified from 126 samples of benthic intertidal mudflat using traditional morphological approaches and compared with frequency of occurrence and relative read abundance estimates from a molecular approach. A relationship between biomass and relative read abundance was found for two widely dispersed annelid taxa ( The two cores were sampled directly adjacent to each other and were taken simultaneously.Divided over seven sampling events between June 2016 and July 2017, a total of 126 locations were sampled on tidal flats north‐east of the isle of Texel in the western part of the Dutch Wadden Sea (N53°06' E4°54'). Each sampling event comprised 18 stations within a 500 m spatial range . Species were sorted by hand and identified. Taxonomic identification was based on the NIOZ reference collection of local benthic macrofaunal species conform WoRMS Editorial Board . Individ2.32HPO4; 0.12 M; pH ≈ 8). During this process, the sediments were submerged in the buffer for 5 min to release the extracellular DNA from the sediments  following the manufacturer's instructions. DNA from all extractions, as well as four identical mock samples (Table S1) were used as template for amplification in triplicate. A 450 base pair (bp) part of the nuclear small ribosomal subunit (18S) was amplified using the oligonucleotides F04 and R22mod as primer pair . Quality filtered reads were demultiplexed using the split_libraries.py script in QIIME  using blastn; the OTUs which returned a match with a percent identity >99% at the genus level were assigned accordingly. Raw Illumina sequences were deposited in the European Nucleotide Archive (ENA accession number: PRJEB46793).Raw sequences with a quality score ≤30 over 75% of the nucleotide positions were discarded using the fastq_quality_filter script in the FASTX‐Toolkit (2.5r(124) = –.36, p = .72) Therefore relative abundancies were chosen over rarefaction to conserverare OTUs  curve. The ROC curve shows the sensitivity (true positive rate) of the logistic regression as a function of the nonspecificity  and was built using R package ROCR. The reliability of the relative read abundance approach was tested using a linear regression. This linear regression was based on the square root of the biomass data for the morphological method and the relative read abundance data for the molecular method.To test the reliability of the frequency of occurrence approach, the assumption that a higher occurrence in the morphological method corresponds to a higher detection probability in the molecular method was tested. The square root of the abundance data as derived from the morphological method was compared to the occurrence in the molecular method based on a logistic regression using the popbio package in R. The strength of the logistic regression was assessed using Wald's‐χ33.1Gammarus was only identified by the morphological approach but not with the molecular approach. The annelid taxa exclusively detected by the molecular approach mostly include smaller specimens such as Echiura, Eumida, Spio, Streblospio and Polydora. Moreover, the genus Polydora is known to include parasitic species and might therefore have been present concealed in its host species. Figure t test, t125 = 13.1, p < .001) for the molecular approach (mean = 10.0 ± 2.2) than for the morphological approach (mean = 6.6 ± 2.1).In total, 23 and 35 macrofauna taxa (genus‐level) could be identified from the morphological and molecular samples, respectively Table  of whichUrothoe was detected in 64% of the samples for the morphological approach and only in 3% of the samples in the molecular approach. Mollusc species were overall detected at higher rates in the molecular samples. Five mollusc taxa which were detected in the molecular samples  were never detected in the morphological samples. Detection rates for the annelids were higher for both approaches compared to the detection rates of the other phyla. Except for Eteone and Marenzelleria, detection rates for the annelid taxa were higher in the molecular approach compared to the detection rates in the morphological approach.The detection rates  for the 21 most abundant species are visualized in Figure 3.2Pygospio, as derived from the morphological approach, was a significant predictor for the detection of this taxon within the molecular method  = 3.27, p = .001). Most of the other annelid taxa did, however, show a positive relationship between the abundance in the morphological approach and occurrence in the molecular inventory. The only exception was Arenicola for which the odd‐ratio of occurrence in the molecular approach declined with increasing abundance.To test the reliability of the frequency of occurrence approach, the abundance data as derived from the morphological method were modelled using a logistic regression against the occurrence in the molecular data set. Only the abundance data for d Figure , Table 2Pygsopio was able to predict the presence of this taxon in the molecular approach at a 77% rate based on the abundance data in the morphological approach. Scoloplos had a predictability of roughly 60%, with a χ2 of 1.52; however, for all other species, predictability was lower. The lowest predictability was found for Heteromastus and equalled 52%, which is close to a random guess.The sensitivity and specificity for each taxon as based on the logistic regression model is shown in Figure Pygospio, followed by Scoloplos and Tharyx. The slope found for Pygospio and Scoloplos were significant . Capitella and Heteromastus both showed no relationship between relative read abundance and biomass, while Arenicola displayed a (nonsignificant) negative slope.The relative read abundance as derived from the molecular method was modelled in a linear regression against the biomass estimates from the morphological method for six annelid taxa Figure , Table 2Pygospio for both approaches. The relative read abundance approach also found a positive relationship for Scoloplos. Both approaches showed negative relationships for Arenicola.The frequency of occurrence and the relative read abundance approaches both showed roughly the same results Table . A stron4The present study offers a insight in the quantitative abilities of DNA metabarcoding methods. We compared quantitative measurements for abundance and biomass for benthic macrofauna in the intertidal Dutch Wadden Sea for DNA metabarcoding approaches with traditional morphological approaches. Although some widely dispersed annelid taxa showed positive relationships between the outcome of both methods, most taxa did not show such a relationship.4.1Cerastoderma, Magallana and Mytilus) detected by the molecular method were never detected by the morphological method. Explanations for the striking differences in output between the two methods for the different phyla could potentially be sought both in a methodological and an ecological context.The combined use of a morphological as well as a molecular method to quantify the same benthic community allowed us to examine whether detection rates for benthic macrofauna taxa are comparable between the two methods. Although the overlap between species found with both methods is high, 23 and 35 taxa, respectively were found from which 22 taxa in both methods, the detection rates between methods within a taxon showed differences. The detection rates for most arthropod and mollusc taxa showed deviating results between the two methods, but the detection rates for the annelid taxa were comparable. The detection rate of arthropod taxa was higher in the morphological compared to the molecular method while the opposite was true for most molluscs. Additionally, several mollusc taxa  rather than the organism itself (Taberlet, Coissac, et al., 4.2Pygospio using the frequency of occurrence approach and for Pygospio and Scoloplos for the relative read abundance approach and not for any of the other species. Comparable results have been found by Bijleveld et al., (arenicola, which has holobenthic development, has relatively low dispersal capacity, resulting in an aggregated distribution pattern (Günther, Arenicola showed negative relationships in the frequency of occurrence approach, whereas Pygospio, which is distributed more evenly across mud flats (Gudmundsson, The second aim of this study was to test the reliability of the frequency of occurrence and the relative read abundance approaches for quantifying abundance and biomass of marine benthic taxa with molecular methods. For this, we tested the underlying assumptions that a higher abundance or biomass in the morphological data set leads to a higher detection rate or relative read abundance in the molecular data set for the frequency of occurrence and relative read abundance approach, respectively (Deagle et al.,  et al.,  in whichGünther, . These APygospio. Moreover, relative read abundance approach also showed a significant positive relationship for Scoloplos between the relative read abundance and biomass.Compared to the frequency of occurrence approach, the relative read abundance is thought to be more influenced by methodological issues than by ecological issues (Deagle et al., Pygospio and Scoloplos, showed positive relationship for both approaches whereas bigger and more aggregated species such as mollusc species and Arenicola showed no relationship. For this study, a molecular sampling method was chosen in which only small sediment samples were collected from which DNA was extracted directly. Specimens of the smaller taxa might be physically present in the sample while larger metazoan taxa are sampled via their eDNA traces (Taberlet, Prud’homme, et al., The comparable results between the frequency of occurrence and relative read abundance methods might imply that both methods were subject to the same factors or biases in this study. We discussed that the ecological and biological features might be an important factor in this. The sampling strategy used in this study possibly induced biases in the quantitative measurements as well. Smaller species and species which are more evenly distributed such as 4.3Metabarcoding methods have undergone considerable developments in the past decade and the methodology is still advancing rapidly. These advancements and new insights possibly harbour solutions to the current challenges and limitations in quantifying species abundancies from eDNA studies. It is outside the scope of this study to discuss them all, but we would like to highlight some. First, in this study we tested the conventional frequency of occurrence and relative read abundance approaches to quantify macrobenthic abundancies from eDNA studies. We showed that both approaches are limited in their quantitative abilities towards this community. In recent years, approaches have been developed that better incorporate the compositional conditions of eDNA data for microbial communities (Gloor et al., The authors declare no conflict of interest.Lise Klunder, Allert I. Bijleveld, Henk W. van der Veer and Pieternella C. Luttikhuizen conceived the study; Lise Klunder, Loran Kleine Schaars and Pieternella C. Luttikhuizen performed the laboratory and fieldwork; Lise Klunder analysed the data and wrote the first draft of the manuscript; Lise Klunder, Allert I. Bijleveld, Pieternella C. Luttikhuizen, Henk W. van der Veer, Loran Kleine Schaars and Judith D.L. van Bleijswijk edited the manuscript.Appendix S1Click here for additional data file.