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## Why describe the components and role of the reproductive system?

Animal reproduction is necessary for the survival of a species. The eggs hatch and the offspring develop in the pouch for several weeks.

### Learning Outcomes

- Describe advantages and disadvantages of asexual and sexual reproduction
- Describe human male and female reproductive anatomies
- Describe the roles of male and female reproductive hormones
- Discuss internal and external methods of fertilization
- Explain how the embryo forms from the zygote
- Describe the process of organogenesis and vertebrate formation
- Explain fetal development during the three trimesters of gestation

## Simple Quantum Mechanics question about the Free particle, (part2)

He goes on and says that psi(x,t) might as well be the following:

because the original equation,

only differs by the *sign* in front of k. He then says let k run negative to cover the case of waves traveling to the left, $k = pm sqrt<(2mE)>/hbar$.

Then after trying to normalize psi(x,t), you find out you can't do it! He then says the following,

"A free particle cannot exist in a stationary state or, to put it another way,

there is no such thing as a free particle with a definite energy."

How did he come to this logical deduction. I don't follow. Can someone please explain Griffith's statement to me?

### Nancy Knowlton

Nancy Knowlton received her undergraduate degree from Harvard University and her PhD from the University of California, Berkeley. She was a professor at Yale University before moving to the Smithsonian Tropical Research Institute in Panama and later joining the Scripps Institute of Oceanography at UC San Diego. Currently, she is the Sant Chair in Marine… Continue Reading

## MATERIALS AND METHODS

### Study species and sites

*Phlox hirsuta* (Polemoniaceae) is an endangered species endemic to serpentine soils in northern California (USFWS, 2000, 2006). *P. hirsuta* is a semi-woody perennial that grows in compact clumps. Individual plants reach 5� cm in height and produce flowers for approximately 7� weeks between early March and early June depending on the weather and elevation of the population (Ferguson *et al.*, 2006). The total number of flowers each plant produces ranges from three to 818 (Ruane *et al.*, 2013) and is positively correlated with plant size (*n* = 374, *R* 2 = 0띄, *P* < 0�). During peak flowering, an average of 64୵ % (*n* = 376, s.e. = 1୳ %) of an individual's flowers are open simultaneously and insects are commonly observed moving between flowers on the same plant. Self pollen grains not only fail to sire seeds themselves, but also interfere with the ability of outcross pollen to sire seeds (Ruane *et al.*, 2013).

Petal colour ranges from white (very rarely) to dark pink. Individual flowers remain open for approximately 8 d, fading to white during the last 2𠄳 d of anthesis (L. Ruane, pers. observation). Although species from ten orders have been observed visiting *P. hirsuta* flowers (Ruane *et al.*, 2013), Diptera, Hymenoptera and Lepidoptera are the most frequent visitors (Ferguson *et al.*, 2006). Following pollination, seed-containing fruits require 35� d to ripen in the field. Although each ovary contains three ovules, rarely does more than one seed per fruit develop for example, 15, 83 and 2 % of fruits resulting from natural pollinations in the field contained zero, one and two seeds, respectively (*n* = 325 L. Ruane, unpubl. res.). Thus, fruit set can be used to estimate total seed set in this species.

This study took place in three of the five known *P. hirsuta* populations near Yreka in Siskiyou County, California, between April and July 2013. The China Hill population (41끄�″N, 122뀶�″W) contains thousands of plants scattered across approximately 19 ha of an open ridge and adjacent slopes the Cracker Gulch population (41끀�″N, 122끃�″W) contains 1003 plants on 5 ha and the Greenhorn population (41끃�″N, 122끁�″W) contains 1315� plants over 8ୱ ha. Greenhorn differs from the other two populations in that it comprises about nine discrete sub-occurrences, which probably resulted from habitat fragmentation due to human activities (USFWS, 2006).

### Data collection

Before quantifying floral display size, local conspecific density, petal colour, florivorous beetle density, pre-dispersal seed predation and fruit set, we tagged a total of 376 plants (125� plants within each of three populations). Tagged plants spanned each population's geographical range. During peak flowering, floral display size was estimated by counting the number of open flowers. A digital picture was taken above each plant (with a scale bar visible) so that plant size could be determined using ImageJ software (Schneider *et al.*, 2012). Local conspecific density was estimated by recording the number of *P. hirsuta* individuals that were flowering within 1- and 2-m radii of each tagged plant. To quantify petal colour, we assigned a numerical value that ranged from 1 (completely white) to 12 (very dark pink) by comparing each plant's petal colour with a gradient of colours on a paint chip (A5, Ace Hardware, Oak Brook, IL, USA). Petal colour could not be determined for 15 tagged plants on China Hill because a heavy rain removed their petals before colour was quantified.

The number of florivorous beetles on each plant was also recorded during peak flowering. Beetle density was calculated by dividing the number of beetles on each plant by the total number of reproductive structures (buds, open flowers, wilted flowers). If the total number of reproductive structures exceeded 25, then bamboo skewers were used to construct a grid on top of each plant to ensure accurate counts. To assess whether differences in beetle densities on a single day were indicative of more long-term differences, we recorded the number of beetles on each plant again 3𠄶 d later and we counted the number of damaged flowers on each plant. Flowers were recorded as damaged if they had bite marks, holes and/or missing petals accompanied by the presence of frass. The number of damaged flowers was divided by the total number of flowers for which florivory could be assessed to calculate the percentage of flowers damaged by beetles on each plant.

Approximately 4 weeks after peak flowering in each population, plants were revisited to quantify fruit set, which is a good indicator of total seed set in this species because the majority of enlarged ovaries contain a single seed. The number of enlarged and non-enlarged ovaries on each plant was counted to calculate percentage fruit set. In some cases (68 of the 376 plants), mammals (i.e. rabbit, hares and/or deer) consumed reproductive structures after peak flowering but before fruits were ripe. Severed stems were used as evidence of granivory by mammals. When plants experienced granivory, we used the total number of reproductive structures (counted during peak flowering) to calculate the percentage of reproductive structures eaten by mammals and percentage fruit set.

To identify the floral organs eaten by florivorous beetles, we examined 180 flowers (36 plants × 5 flowers per plant) that had one or more damaged petals on China Hill. After recording the number of petals eaten, flowers were dissected and the presence/absence of damage to stamens, stigmas and styles was recorded. Stamens were scored as damaged if a portion of an anther was missing. This is probably a conservative estimate of stamen damage, as beetles were seen inserting their heads within corolla tubes (probably eating pollen) without noticeably damaging anthers. Stigmas were scored as damaged if one, two or three of their stigma arms were missing. When all three stigma arms had been removed, style length was compared with other style lengths on the same plant to determine whether it had also been damaged.

### Analyses

Wilcoxon tests were performed to determine how categorical variables (i.e. floral display size, total number of reproductive structures, number of fruits, number of plants within 1 m, number of plants between 1 and 2 m, and petal colour) differed among populations. Similarly, one-way analyses of variances (ANOVAs) were performed to determine how continuous variables (i.e. plant size, beetle density, percentage of flowers damaged, percentage of unripe fruits eaten and percentage fruit set) differed among populations. To determine if differences between population means were statistically significant (*P* < 0뜅), Wilcoxon each pair and Tukey's HSD tests were performed for categorical and continuous variables, respectively. Simple regressions were used to determine if larger plants produced larger floral displays, more reproductive structures and more fruits.

Simple regressions were also used to determine whether the number of beetles on each plant during peak flowering correlated with the percentage of flowers damaged on that plant and to determine if the density of beetles on each plant remained constant over time. Logistic regressions were used to determine whether the presence/absence of stamen, stigma or style damage depended on the number of petals eaten. Stigmas, styles and anthers are positioned inside corolla tubes, and may only be accessible when the majority of the petals are consumed.

Multiple linear regressions were used to determine the factors that explain variation in percentage fruit set (the continuous response variable) in each population. Beetle density, petal colour, floral display (number of flowers open simultaneously), number of conspecific plants within 1 m, and number of conspecific plants between 1 and 2 m were the fixed effects. Percentage of unripe fruits eaten by mammals was also included as a fixed effect in the Greenhorn population mammal herbivory was absent (or nearly absent) in the other two populations (Table 1 ). Interactions between floral display size and conspecific plant density were also examined. To maximize the independence between local conspecific density measurements, the number of plants within 2 m only included the number of individuals between 1 and 2 m. No strong collinearity was found between independent variables, as all variance inflation factors were less than 1랅.

### Tableਁ.

Summary statistics for three *Phlox hirsuta* populations (sample size is equal to 125 or 126 plants per population means and s.e. are presented)

Character | China Hill | Cracker Gulch | Greenhorn |
---|---|---|---|

Plant size (cm 2 ) | 457뜹 (25띲) a | 137띅 (12) b | 246랉 (24띇) c |

Floral display* | 71띕 (4띹) a | 39띆 (2띹) b | 43띓 (3뜨) b |

No. of reproductive structures † | 183뜲 (11띱) a | 50띸 (4뜉) b | 58뜈 (4띆) b |

No. of fruits | 29랃 (2뜡) a | 5뜆 (0띕) b | 16랆 (1띀) c |

Plant density (1 m) ‡ | 3띀 (0뜣) a | 2띨 (0뜦) b | 1띘 (0뜖) c |

Plant density (2 m) § | 8띥 (0띖) a | 7띃 (0띣) a | 4띐 (0뜴) b |

Petal colour ¶ | 8띇 (0뜗) a | 7띔 (0뜡) b | 8뜇 (0뜗) ab |

Beetle density** | 0 (0) ab | 0 (0) a | 0 (0) b |

% Flowers damaged | 45띴 (2띓) a | 46뜅 (1랒) a | 63랆 (1띳) b |

% Unripe fruits eaten | 0뜀 (0뜀) a | 0뜓 (0뜓) a | 19뜑 (2랅) b |

% Fruit set | 17뜩 (1뜇) a | 12띦 (1뜗) b | 30띹 (1띧) c |

Means sharing letters are not significantly different (*P* > 0뜅).

*Number of flowers open simultaneously during peak flowering.

† Total number of reproductive structures (i.e. buds, open flowers and wilted flowers).

‡ Number of conspecific reproductive plants within 1 m.

§ Number of conspecific reproductive plants between 1 and 2 m.

¶ Petal colour ranged from 1 (completely white) to 12 (very dark pink).

**Number of florivorous beetles per plant divided by the total number of reproductive structures.

Analyses were performed using JMP, version 9୰ (SAS Institute, Cary, NC, USA).

## 5. Current status and future directions

The COVID-19 has been discovered at the end of 2019 and currently, it is a pandemic threat of international concern. Currently, there are no targeted therapies effective against COVID-19. However, many vaccines are undergoing clinical trials and a couple of drugs are going through re-purposing schemes [6]. A phase-3 clinical trial of natural honey for the treatment of COVID-19 has also been started as mentioned by the National Institute of Health [97]. It is already proved that honey plays a potential role against several enveloped viruses. Besides, honey acts as an antagonist of platelet-activating factor (PAF) which is involved in COVID-19 [98,99]. Therefore, we can say that honey may have a protective/beneficial effect on COVID-19. A lot of studies have been done to prove that honey is a potential natural medicine that can fight against several chronic diseases including diabetes, hypertension as well as autophagy. Also, honey can heal wound quickly by repairing damaged tissue, boosting up the immune system, and fight with the virus, bacteria as well as fungus. However, so far without some minor issues, there is no report of the serious harmful effect of honey on the human body. This review will be helpful to rethink the insights of possible potential therapeutic effects of honey in fighting against COVID-19 by strengthening the immune system, autophagy, anti-inflammatory, antioxidative, antimicrobial, antidiabetic, anti-hypertensive as well as cardioprotective effects ( Figureਂ ). However, basic research on the effect of honey on SARS-CoV-2 replication and/or host immune system need to be investigated by *in vitro* and *in vivo* studies.

## 4 Answers 4

**Update [2011-09-20]:** I expanded the paragraph about $eta$-expansion and extensionality. Thanks to Anton Salikhmetov for pointing out a good reference.

$eta$-conversion $(lambda x . f x) = f$ is a special case of $eta$- conversion *only* in the special case when $f$ is itself an abstraction, e.g., if $f = lambda y . y y$ then $(lambda x . f x) = (lambda x . (lambda y . y y) x) =_eta (lambda x . x x) =_alpha f.$ But what if $f$ is a variable, or an application which does not reduce to an abstraction?

In a way $eta$-rule is like a special kind of extensionality, but we have to be a bit careful about how that is stated. We can state extensionality as:

- for all $lambda$-terms $M$ and $N$, if $M x = N x$ then $M = N$, or
- for all $f, g$ if $forall x . f x = g x$ then $f = g$.

The first one is a meta-statement about the terms of the $lambda$-calculus. In it $x$ appears as a formal variable, i.e., it is part of the $lambda$-calculus. It can be proved from $etaeta$-rules, see for example Theorem 2.1.29 in *"Lambda Calculus: its Syntax and Semantics"* by Barendregt (1985). It can be understood as a statement about all the *definable* functions, i.e., those which are denotations of $lambda$-terms.

The second statement is how mathematicians usually understand mathematical statements. The theory of $lambda$-calculus describes a certain kind of structures, let us call them "*$lambda$-models*". A $lambda$-model might be uncountable, so there is no guarantee that every element of it corresponds to a $lambda$-term (just like there are more real numbers than there are expressions describing reals). Extensionality then says: if we take any two things $f$ and $g$ in a $lambda$-model, if $f x = g x$ for all $x$ in the model, then $f = g$. Now even if the model satisfies the $eta$-rule, it need not satisfy extensionality in this sense. (Reference needed here, and I think we need to be careful how equality is interpreted.)

There are several ways in which we can motivate $eta$- and $eta$-conversions. I will randomly pick the category-theoretic one, disguised as $lambda$-calculus, and someone else can explain other reasons.

Let us consider the typed $lambda$-calculus (because it is less confusing, but more or less the same reasoning works for the untyped $lambda$-calculus). One of the basic laws that should holds is the exponential law $C^ cong (C^B)^A.$ (I am using notations $A o B$ and $B^A$ interchangably, picking whichever seems to look better.) What do the isomorphisms $i : C^ o (C^B)^A$ and $j : (C^B)^A o C^$ look like, written in $lambda$-calculus? Presumably they would be $i = lambda f : C^ . lambda a : A . lambda b : B . f langle a, b angle$ and $j = lambda g : (C^B)^A . lambda p : A imes B . g (pi_1 p) (pi_2 p).$ A short calculation with a couple of $eta$-reductions (including the $eta$-reductions $pi_1 langle a, b angle = a$ and $pi_2 langle a, b angle = b$ for products) tells us that, for every $g : (C^B)^A$ we have $i (j g) = lambda a : A . lambda b : B . g a b.$ Since $i$ and $j$ are inverses of each other, we expect $i (j g) = g$, but to actually prove this we need to use $eta$-reduction twice: $i(j g) = (lambda a : A . lambda b : B . g a b) =_eta (lambda a : A . g a) =_eta g.$ So this is one reason for having $eta$-reductions. Exercise: which $eta$-rule is needed to show that $j (i f) = f$?

## Connecting with colleagues: creating a reproductive health network

In the UK today there are six million employees suffering fertility challenges. With 88% of those who feel unsupported at work at risk of quitting their jobs, it is vital that employers tackle the stigma and help employees access much-needed support.

It can be a difficult decision for employees to share their personal fertility struggles with their employer, but having someone to speak to within the workplace can go a long way to easing their anxieties. That someone doesn’t need to be their line manager or HR – it can be a colleague, who has experienced something similar and can share tips and advice, or just be a sounding board.

In this webinar, Workplace Fertility Community committee members, Hortense Thorpe (Founder, Centrica’s Fertility Group Network) and Helen Burgess (Employment Law Partner, Shoosmiths) and guest panellist, Paul Breach (Co-Chair, Natwest Group’s Fertility & Loss Network), will share tried and tested approaches for creating employee support systems in organisations of various sizes, as well as practicalities to consider when implementing a support network.

This is quite a broad question and it indeed is quite hard to pinpoint *why exactly* Fourier transforms are important in signal processing. The simplest, hand waving answer one can provide is that it is an *extremely* powerful mathematical tool that allows you to view your signals in a different domain, inside which several difficult problems become very simple to analyze.

Its ubiquity in nearly every field of engineering and physical sciences, all for different reasons, makes it all the more harder to narrow down a reason. I hope that looking at some of its properties which led to its widespread adoption along with some practical examples and a dash of history might help one to understand its importance.

### History:

To understand the importance of the Fourier transform, it is important to step back a little and appreciate the power of the Fourier series put forth by Joseph Fourier. In a nut-shell, any periodic function $g(x)$ integrable on the domain $mathcal

where $e^*This idea that a function could be broken down into its constituent frequencies (i.e., into sines and cosines of all frequencies) was a powerful one and forms the backbone of the Fourier transform.*

### The Fourier transform:

The Fourier transform can be viewed as an extension of the above Fourier series to non-periodic functions. For completeness and for clarity, I'll define the Fourier transform here. If $x(t)$ is a continuous, integrable signal, then its Fourier transform, $X(f)$ is given by

and the inverse transform is given by

### Importance in signal processing:

First and foremost, a Fourier transform of a signal tells you **what frequencies are present in your signal and in what proportions**.

Example:Have you ever noticed that each of your phone's number buttons sounds different when you press during a call and that it sounds the same for every phone model? That's because they're each composed of two different sinusoids which can be used to uniquely identify the button. When you use your phone to punch in combinations to navigate a menu, the way that the other party knows what keys you pressed is by doing a Fourier transform of the input and looking at the frequencies present.

Apart from some very useful elementary properties which make the mathematics involved simple, some of the other reasons why it has such a widespread importance in signal processing are:

- The magnitude square of the Fourier transform, $vert X(f)vert^2$ instantly tells us how much power the signal $x(t)$ has at a particular frequency $f$.
- From Parseval's theorem (more generally Plancherel's theorem), we have $int_mathbb
vert x(t)vert^2 dt = int_mathbb vert X(f)vert^2 df$ which means that *the total energy in a signal across all time is equal to the total energy in the transform across all frequencies*. Thus, the transform is energy preserving.

Convolutions in the time domain are equivalent to multiplications in the frequency domain, i.e., given two signals $x(t)$ and $y(t)$, then if

$z(t)=x(t)star y(t)$ where $star$ denotes convolution, then the Fourier transform of $z(t)$ is merely

For discrete signals, with the development of efficient FFT algorithms, almost always, it is faster to implement a convolution operation in the frequency domain than in the time domain.

By being able to split signals into their constituent frequencies, one can easily block out certain frequencies selectively by nullifying their contributions.

Example:If you're a football (soccer) fan, you might've been annoyed at the constant drone of the vuvuzelas that pretty much drowned all the commentary during the 2010 world cup in South Africa. However, the vuvuzela has a constant pitch of

235Hz which made it easy for broadcasters to implement a notch filter to cut-off the offending noise. [1]

A shifted (delayed) signal in the time domain manifests as a phase change in the frequency domain. While this falls under the elementary property category, this is a widely used property in practice, especially in imaging and tomography applications,

Example:When a wave travels through a heterogenous medium, it slows down and speeds up according to changes in the speed of wave propagation in the medium. So by observing a change in phase from what's expected and what's measured, one can infer the excess time delay which in turn tells you how much the wave speed has changed in the medium. This is of course, a very simplified layman explanation, but forms the basis for tomography.

Derivatives of signals (n th derivatives too) can be easily calculated(see 106) using Fourier transforms.

### Digital signal processing (DSP) vs. Analog signal processing (ASP)

The theory of Fourier transforms is applicable irrespective of whether the signal is continuous or discrete, as long as it is "nice" and absolutely integrable. So yes, ASP uses Fourier transforms as long as the signals satisfy this criterion. However, it is perhaps more common to talk about Laplace transforms, which is a generalized Fourier transform, in ASP. The Laplace transform is defined as

The advantage is that one is not necessarily confined to "nice signals" as in the Fourier transform, but the transform is valid only within a certain region of convergence. It is widely used in studying/analyzing/designing LC/RC/LCR circuits, which in turn are used in radios/electric guitars, wah-wah pedals, etc.

This is pretty much all I could think of right now, but do note that *no amount* of writing/explanation can fully capture the true importance of Fourier transforms in signal processing and in science/engineering

Lorem Ipsum's great answer misses one thing: The Fourier transform decomposes signals into constituent complex exponentials:

and complex exponentials are the **eigenfunctions** for linear, time invariant systems.

Put simply, if a system, $H$ is linear and time-invariant, then its response to a complex exponential will be a complex exponential of the same frequency but (possibly) different phase, $phi$, and amplitude, $A$, --- and the amplitude may be zero:

So the Fourier transform is a useful tool for analyzing linear, time-invariant systems.

It's **fast** (e.g. useful for convolution), due to its linearithmic time complexity (specifically, that of the FFT).

I would argue that, if this were not the case, we would probably be doing a lot more in the time domain, and a lot less in the Fourier domain.

### Edit: Since people asked me to write why the FFT is fast.

It's because it cleverly avoids doing extra work.

To give an concrete example of how it works, suppose you were multiplying two polynomials, $a_0 x^0 + a_1 x^1 + ldots + a_nx^n$ and $b_0 x^0 + b_1 x^1 + ldots + b_nx^n$ .

If you were to do this naively (using the FOIL method), you would need approximately $n^2$ arithmetic operations (give or take a constant factor).

However, we can make a seemingly mundane observation: in order to multiply two polynomials, *we don't need to FOIL the coefficients*. Instead, we can simply *evaluate* the polynomials at a (sufficient) number of points, do a *pointwise* multiplication of the evaluated values, and then *interpolate* to get back the result.

Why is that useful? After all, each polynomial has $n$ terms, and if we were to evaluate each one at $2n$ points, that would still result in $approx n^2$ operations, so it doesn't seem to help.

But it does, if we do it correctly! Evaluating a single polynomial at *many* points at once is *faster* than evaluating it at those points individually, *if we evaluate at the "right" points*. What are the "right" points?

It turns out those are the roots of unity (i.e. all complex numbers $z$ such that $z^n = 1$ ). If we choose to evaluate the polynomial at the roots of unity, then a lot of expressions will turn out the same (because a lot of monomials will turn out to be the same). This means we can do their arithmetic *once*, and re-use it thereafter for evaluating the polynomial at all the other points.

We can do a very similar process for interpolating through the points to get back the polynomial coefficients of the result, just by using the *inverse* roots of unity.

There's obviously a lot of math I'm skipping here, but effectively, the FFT is basically the algorithm I just described, to evaluate and interpolate the polynomials.

One of its uses, as I showed, was to multiply polynomials in a lot less time than normal. It turns out that this saves a tremendous amount of work, bringing down the running time to being proportional to $n log n$ (i.e. linearithmic) instead of $n^2$ (quadratic).

Thus the ability to use the FFT to perform a typical operation (such as polynomial multiplication) much faster is what makes it useful, and that is also why people are now excited by MIT's new discovery of the Sparse FFT algorithm.

## Abstract

From the enediyne class of antitumor antibiotics, uncialamycin is among the rarest and most potent, yet one of the structurally simpler, making it attractive for chemical synthesis and potential applications in biology and medicine. In this article we describe a streamlined and practical enantioselective total synthesis of uncialamycin that is amenable to the synthesis of novel analogues and renders the natural product readily available for biological and drug development studies. Starting from hydroxy- or methoxyisatin, the synthesis features a Noyori enantioselective reduction, a Yamaguchi acetylide-pyridinium coupling, a stereoselective acetylide-aldehyde cyclization, and a newly developed annulation reaction that allows efficient coupling of a cyanophthalide and a *p*-methoxy semiquinone aminal to forge the anthraquinone moiety of the molecule. Overall, the developed streamlined synthesis proceeds in 22 linear steps (14 chromatographic separations) and 11% overall yield. The developed synthetic strategies and technologies were applied to the synthesis of a series of designed uncialamycin analogues equipped with suitable functional groups for conjugation to antibodies and other delivery systems. Biological evaluation of a select number of these analogues led to the identification of compounds with low picomolar potencies against certain cancer cell lines. These compounds and others like them may serve as powerful payloads for the development of antibody drug conjugates (ADCs) intended for personalized targeted cancer therapy.

## Discussion

The COVID-19 pandemic represents an unprecedented infectious disease threat to people around the world. The BIS is a collection of psychological characteristics thought to serve a disease-avoidance function. The primary goal of the current study was to determine the extent to which individual differences in the BIS were uniquely associated with COVID-19 concern and COVID-19-related preventative health behavior. When demographic, health, social, personality, and BIS variables were considered simultaneously, greater germ aversion and pathogen disgust sensitivity were most consistently associated with COVID-19 concern and preventative behaviors. These findings are consistent with BIS theory and suggest potential targets for health promotion interventions.

Both germ aversion and pathogen disgust were uniquely associated with greater concern for COVID-19. Additionally, greater germ aversion was connected with greater engagement in all preventative health behaviors, except wearing a facemask, and greater pathogen disgust sensitivity was associated with more social distancing, handwashing, and cleaning/disinfecting. Notably, the effect sizes of the associations between germ aversion and preventative health behaviors were comparable to the effect sizes of the relations between COVID-19 concern and preventative health behaviors. These findings are consistent with recent work linking BIS indicators with COVID-19 related behavioral intentions and policy attitudes [14, 15]. In general, these findings support theory suggesting the protective function of psychological disease-avoidance mechanisms during a time of real disease threat [29].

The pattern of findings were relatively consistent across preventative health behaviors, except for wearing an antiviral facemask. Recommendations regarding facemasks have varied during the pandemic. At the time of data collection, the CDC only recommended wearing a facemask if individuals thought they had COVID-19, and people were dissuaded from wearing antiviral (e.g., N95) facemasks in order to prevent shortages of such personal protective equipment at hospitals and for healthcare workers [23]. On April 3, 2020 (after data collection), the CDC recommended that everyone should wear a cloth facemask in public or when around others [3]. The lack of significant associations between facemask wearing and BIS indicators or COVID-19 concern, as well as significant negative associations with other variables (e.g., conscientiousness, age), may reflect that wearing facemasks, especially antiviral facemasks, was not recommended at the time.

The present study also provided a comprehensive test of the extent to which a large range of demographic, health, and psychosocial variables were related to COVID-19 responses. Older age was associated with more concern about COVID-19, which is understandable as older adults are at higher risk of complications from COVID-19 [28]. However, this concern did not translate into greater engagement in preventative health behaviors. In fact, older age was associated with less cleaning/disinfecting behavior and utilization of antiviral facemasks. Potentially, older adults were self-isolating more and thus had less need to engage in these behaviors. Greater income was associated with more social distancing and cleaning/disinfecting behavior. Greater financial resources may facilitate the ability to engage in these practices, such as working from home and having access to disinfecting supplies. Recent illness and general perceived health were also associated with many preventative health behaviors. Well-being may be more salient for these individuals, but the underlying motivations may differ. For the former, engaging in preventative health behaviors may be motivated by wanting to protect others from getting sick. Indeed, those who had been ill recently were less concerned about COVID-19, but recent illness was the variable most strongly associated with wearing antiviral facemasks. Individuals who perceive themselves as having generally good health may be motivated to maintain their health and engage in preventative health behaviors to reduce the likelihood of getting sick themselves.

Unlike previous work [19], political orientation was not reliably associated with preventative health behavior in this nationally-representative sample. Although those who were more liberal endorsed greater concern about COVID-19, we did not find evidence that political ideology was independently associated with greater engagement in preventative health behaviors, other than greater avoidance of touching one’s face. In our study, we considered a broader range of demographic, health, and psychosocial factors than Kushner Gadarian et al. [19], which may account for the differences in results. Greater religiosity was also associated with engaging in all of the preventative health behaviors, except for handwashing. Although the empirical evidence is mixed, religiosity has been proposed to encourage prosocial behaviors [30], so these results could reflect prosociality if engaging in preventative health behaviors is for the benefit of others. Religiosity has also been associated with greater social conformity [31]. Thus, these findings may be due to religious individuals’ greater sensitivity to social norms. Consistent with the existing literature, conscientiousness and neuroticism were associated with greater concern about COVID-19. However, personality traits were not consistently related to engaging in preventative health behaviors when accounting for demographics and other psychosocial variables. In times of infectious disease threat and the unique situation of a pandemic, traditional personality traits may be a lesser determinant of behavior. Rather, personality traits may be more strongly associated with regular daily activities and lifestyle choices (e.g., physical activity, alcohol consumption).

### Implications for theory and practice

The behavioral immune system is theorized to promote the avoidance of pathogen threats. Some cross-cultural research indicates group differences in BIS reactivity due to differences in historic and contemporary parasite or disease prevalence rates associated with geographic locations [32, 33]. However, most of the BIS research has been conducted in lab-based settings using artificial tasks designed to prompt disease threat [22]. The current study extends this body of evidence and further supports BIS theory by demonstrating that individual differences in germ aversion and pathogen disgust sensitivity were associated with responses to a real-world, contemporary pathogen threat. Such an extension provides important ecological validity to lab-based and historical research.

Findings from this study have implications for policy and public response to future epidemics or pandemics. Our results identify a variety of demographic and psychosocial characteristics that may place individuals at-risk for contracting and spreading disease during pandemics. Designing efficacious messaging that targets these populations may help optimize alterations in human behavior necessary to prevent the spread of infectious diseases. For instance, in addition to focusing on the severity of infection, it may be beneficial for messages about COVID-19 to emphasize aspects of the virus and disease that activate the psychological disease-avoidance processes. For example, messages could incorporate images or language that induce feelings of disgust or make salient the presence of germs or contamination. Although individuals reliably differ in these psychological traits, there are malleable components to the disease-avoidance processes that can be activated, leading to behavior change [29]. Thus, it may be important for future research to examine whether temporarily increasing disgust or germ aversion promotes pandemic-related health behaviors.

### Limitations

Results from this study should be taken in light of certain limitations. The data are cross-sectional preventing causal claims however, the wide range of demographic and psychosocial variables considered reduces the possibility of third variables. Of course, other variables not assessed in the current study may still account for the findings. Theoretically, germ aversion and pathogen disgust sensitivity motivate concern about infectious disease and engagement in preventative health behaviors. Experimental evidence indicates that inducing disgust results in greater avoidance behavior, reducing potential contact with pathogens [34]. However, we cannot rule out the opposite causal relation with the current data. Greater concern about COVID-19 may lead to increased germ aversion and pathogen disgust sensitivity. Experimental or longitudinal data are necessary to determine the causal direction. All variables were assessed with self-report measures, which raises concerns of social desirability or biased responding. At the time of data collection, there were no publically available measures of COVID-19 concern, so we developed our own measure. Although reliable and the items were deemed to be face valid by the research group, the COVID-19 concern measure was not validated. Also, data were collected online. Although a nationally representative sample was recruited, only individuals with reliable internet access, computer, or smartphone were able to complete the study, thus limiting generalizability.