# Capillary imbibition in lubricant coated channels

Sergi G. Leyva  Departament de Física de la Matèria Condensada, Universitat de
Barcelona, 08028 Spain  Universitat de Barcelona Institute of Complex Systems
(UBICS), Universitat de Barcelona, 08028 Spain  Ignacio Pagonabarraga
Departament de Física de la Matèria Condensada, Universitat de Barcelona,
08028 Spain  Universitat de Barcelona Institute of Complex Systems (UBICS),
Universitat de Barcelona, 08028 Spain  Aurora Hernández-Machado  Departament
de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Spain
Institut de Nanociència i Nanotecnologia, Universitat de Barcelona, 08028
Spain  Rodrigo Ledesma-Aguilar  School of Engineering, University of
Edinburgh, The King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, UK

###### Abstract

Capillary imbibition underpins many processes of fundamental and applied
relevance in fluid mechanics. A limitation to the flow is the coupling to the
confining solid, which induces friction forces. Our work proposes a general
theoretical framework for the modeling of the transport of liquids in
lubricant impregnated surfaces. We show that for sufficiently small lubricant
viscosity, dissipation entirely occurs in the lubricant layer, resulting in a
linear growth of the advancing front. As a result, an external force gives
rise to an exponential front growth. This new capacity to control multiphase
flows sets new experimental challenges that can be determinant for micro and
nanofluidic devices.

Spontaneous imbibition, also known as capillary filling, occurs when one fluid
displaces a second one from a solid porous medium due to its preferential
affinity to wet the internal surfaces of the solid. Applications can be found
in nanofluidics [1], where elastocapillary forces support the self-assembly of
arrays of carbon nanotubes [2]; biophysics, where capillary forces are known
to influence protein folding [3, 4, 5]; and medical devices, where lateral
flows, an example of capillary driven flows, are widely used to detect the
presence of a target substance, and set the basis for antigen detection [6,
7].

Classical imbibition corresponds to a viscous fluid displacing a gas in a
uniform porous medium, where the front position, $l(t)$, follows the “slowing-
down” growth of Washburn’s law, $l(t)\propto t^{\alpha}$, with $\alpha=1/2$
[8]. Understanding and controlling the exponent $\alpha$, is therefore of both
fundamental and practical interest. Pradas et al. [9, 10] and Queralt et al.
[11] showed that the exponent can be lowered to $\alpha<1/2$ by introducing
disorder in the channel topography. On the other hand, Primkulov et al. [12]
reported a larger exponent $\alpha=1$, but lower imbibition speed, by capping
the front with a slug of a viscous oil.

What happens if the solid walls of a porous medium are replaced by a liquid
surface? Experimental realisations of lubricant impregnated surfaces, like
Liquid-Infused Porous Surfaces (SLIPS) or Lubricant-Impregnated Surfaces (LIS)
[13, 14, 15, 16, 17] have gained much attention in the recent years. These
materials have outstanding properties for droplet manipulation due to their
low friction, resistance to extreme conditions, and self-healing properties,
as well as their ability to induce drag reduction in contact with a single
liquid phase [18, 19, 20]. Here we address the fundamental question of how
spontaneously invades a porous medium coated with a lubricant.

We show that the lubricant viscosity plays a determinant role to trigger a
qualitative change in the dissipation mechanism where the liquid front
advances at a constant rate, instead of slowing down, as would occur if the
fluids were in direct contact with the solid. We also identify the high
sensitivity of the liquid front to external perturbations, which opens an
avenue to new modes of liquid manipulation in the microscale.

To elucidate the front dynamics, we have carried out 2D lattice-Boltzmann (LB)
numerical simulations of the imbibition into a solid channel coated with a
liquid lubricant layer. We couple the LB method to a ternary free-energy model
of three immiscible fluids, which we solve by using the Cahn-Hilliard
equation. The ternary free-energy model [21], allows to independently choose
the surface tensions of the liquids, and thus the Neumann angles at their
intersection as well as the wettability of the solid. In our simulations, Fig.
1a, two reservoirs hold liquids of equal density but different viscosities,
$\eta_{1}$ and $\eta_{2}$. The reservoirs are connected by a solid channel of
length $L$ and width $H$, whose internal surfaces are coated by a thin film of
a third liquid of viscosity $\eta_{s}$ (the lubricant). The lubricant is kept
in place by two small pillars located at the edges of the channel. This
geometry mimics SLIPS, where the lubricant is locked into the surface by
roughness [22]. Further details on the simulation methodology and choice of
parameters are provided in the supplementary information [23].

As shown in the simulation snapshot of Fig. 1a, an appropriate choice of the
surface tensions leads to the spontaneous imbibition of liquid 1 into the
channel, displacing liquid 2. The displacing and displaced liquids form an
advancing meniscus with a well-defined apparent angle relative to the solid,
$\theta$, suggesting a driving capillary force $F_{c}\propto\gamma\cos\theta$,
where $\gamma$ is the surface tension of the interface between liquids 1 and
2. Despite this similarity, the meniscus does not touch the solid, but moves
on top of the lubricant layer.

Figure 1:  Spontaneous imbibition in a lubricant coated channel. (a) Lattice-
Boltzmann simulation snapshot. The liquid on the left, of width $\bar{H}_{1}$,
preferentially wets the surface of a thin lubricant layer of width
$\bar{h}_{1}$, and displaces a resident fluid in a channel of width
$H=\bar{H}_{1}+2h_{1}$, and length $L$. A meniscus, of apparent angle $\theta$
and position $l(t)$, advances within the channel. (b) Effect of the viscosity
of the lubricant, $\eta_{s}$, on the imbibtion curves at fixed viscosity
contrast between the displaced and displacing liquids,
$\eta_{2}/\eta_{1}=1\times 10^{-2}$. (c) Velocity profile in the displacing
fluid and in the lubricant layer, $v(y)$, for
$\eta_{1}/\eta_{s}=5\times{10}^{-1}$ (squares) and
$\eta_{1}/\eta_{s}=5\times{10}^{3}$ (circles). The solid lines shows the
theoretical prediction (see text). The velocity is made dimensionless by the
velocity at the center of the channel, $v(H/2)$, while the $y$ coordinate is
normalised by the channel width, $H$.

We focus on the familiar case of a viscous liquid displacing a much less
viscous fluid, $\eta_{2}/\eta_{1}=10^{-2}$, and analyse the motion of the
meniscus at different lubricant viscosities, $\eta_{s}$. Fig. 1b shows the
corresponding $l$ vs $t$ curves, where the penetration length is normalised by
the length of the tube, and time is normalised using the filling time
predicted by Washburn’s law, $T={3\eta_{1}{L}^{2}}/{H\gamma\cos{\theta}}$
[24]. For very large lubricant viscosity, the front advances following the
scaling of Washburn’s law, $l(t)\sim t^{1/2}$, indicating that the viscous
force, $F_{v}$, increases with increasing $l$. Decreasing the lubricant
viscosity leads to an unexpected result: The front grows linearly, $l(t)\sim
t$, thus suggesting that $F_{v}$ is independent of $l$. In addition, the
filling time is significantly shorter than that predicted by Washburn’s law.
We shall show that these effects are not a transient due to inertia or
dynamic-angle effects [25, 26, 27, 24]. They correspond to a new long-time
regime entirely dominated by the viscous dissipation in the lubricant film.

Fig. 1c shows profiles of the tangential velocity of the displacing phase and
the lubricant far upstream of the meniscus. The expected parabolic flow
profile of a forced fluid [28] is approached for large $\eta_{s}$. The
velocity in the lubricant layer becomes vanishingly small, which effectively
behaves like a solid. In contrast, for small $\eta_{s}$, the flow profile
resembles a plug flow in the displacing and displaced phases, while there is
strong variation of the velocity in the lubricant layer, where shear stresses
are sustained.

Figure 2:  (a) Schematics of the lubricant coated channel. The rectangular
sections represent the bulk of the displacing and displaced liquids, and of
the lubricant layer, which are separated by the meniscus region. (b)
Theoretical imbibition curves (solid lines) vs simulation results (symbols)
for different values of the parameter $\lambda$. For $\lambda\rightarrow 1$,
corresponding to small lubricant viscosity, the imbibition curves approach the
asymptotic limit $\hat{l}=\hat{t}$ (red dashed line). The values of $\Sigma$
with decreasing lubricant viscosity correspond to $0.03,0.01,0.2,0.9,1.7,3.2$
and $24$, respectively.

Building on these observations, we propose a simplified model of the flow in
each fluid phase where we neglect the dynamics close to the meniscus and
describe the flow profile in the four “bulk” flow regions depicted in Fig. 2a.
The tangential velocity profiles in the displacing and displaced phases,
$v_{1}(y)$ and $v_{2}(y)$, and in the adjacent regions of the lubricant layer,
$v_{s1}(y)$ and $v_{s2}(y)$ are obtained from lubrication theory, assuming
that the pressure profiles in each region, namely $p_{1}$, $p_{2}$, $p_{s1}$
and $p_{s2}$, only vary along the longitudinal coordinate $x$. Accordingly,
the flow profiles obey the Stokes equations. Four out of the eight integration
constants are found by imposing continuity of the velocity and tangential
stress at the interface with the lubricant layer. The remaining constants are
determined by fixing the average velocities of the fluids and the lubricant,
$\frac{1}{H/2-h}\int_{h}^{H/2}v_{i}dy=u$ and
$\frac{1}{h}\int_{0}^{h}v_{s_{i}}dy=u_{s}$ [23].

In general, $u$ and $u_{s}$ are independent free parameters; however, for the
imbibition geometry the lubricant layer responds to the capillary driving
force that acts on the meniscus. Therefore, we expect that if no external
forces in the lubricant are present, the average velocity of the lubricant
obeys $u_{s}=\alpha u$, with $0\leq\alpha\leq 1$. This relationship allows us
to eliminate the pressure gradient terms from the Stokes equations, and
instead characterize the flow through $u$ and $\alpha$. Figure 1c shows the
excellent agreement of the theoretical prediction with the simulation velocity
profiles, where $u_{s}$ and $u$ are fixed to the measured liquid flow. The
theoretical results show that a vanishing average lubricant velocity $u_{s}$
leads to a negative derivative of the velocity profile close to the solid,
indicating a recirculating flow in the lubricant layer, as reported in
SLIPS/LIS simulations [19] (see analytical solution in the supplementary
material [23] ).

The model can be used to determine the viscous friction force (per unit
length) exerted by the lubricant layer on the moving fluids,
$F_{v}=2l\tau_{1}+2(L-l)\tau_{2}$, where
$\tau_{i}\equiv\>\eta_{s}dv_{s_{i}}(h)/dy$ is the shear stress. From the
velocity profiles we obtain [23]

$F_{v}=\frac{4(2-3\alpha)u\eta_{s}}{h}\left(\frac{l}{1+\frac{2\bar{H}}{3\delta_{1}{h}}}+\frac{L-l}{1+\frac{2\bar{H}}{3\delta_{2}h}}\right).$
(1)

Here, $\bar{H}_{i}\equiv H-2h=\bar{H}$ is the width of either liquid and
$\delta_{i}\equiv\eta_{i}/\eta_{s},i=1,2$ is the viscosity ratio between the
displacing/displaced ($i=1$/$i=2$) liquid and the lubricant. Letting
$\delta_{i}h/\bar{H}\rightarrow{0}$, $\eta_{2}/\eta_{1}\rightarrow 0$ and
setting $\alpha=0$, this expression reduces to the classical result of the
viscous force acting on a single liquid in contact with a solid channel i.e.,
$F_{v}={12\eta_{1}ul}/{H}$. On the contrary, the limit of small lubricant
viscosity is achieved by letting $\delta_{i}h/\bar{H}\rightarrow\infty$, where
the friction force reduces to $F_{v}={4(2-3\alpha)\eta_{s}uL}/{h}.$ In this
regime the viscous force is dominated by the lubricant layer, despite being
the less viscous phase, and the force does not depend on the position of the
front; rather, its magnitude scales with the entire length of the channel,
$L$. Comparing the energy dissipation rate in the bulk of the displacing and
displaced liquids
$\dot{E}_{b}=\int_{0}^{l}\int_{h}^{H/2}\eta_{1}|{\nabla}v_{1}|^{2}\>dydx+\int_{l}^{L}\int_{h}^{H/2}\eta_{2}|{\nabla}v_{2}|^{2}\>dydx,$
to that of the lubricant
$\dot{E}_{s}=\int_{0}^{l}\int_{0}^{h}\eta_{s}|{\nabla}v_{s_{1}}|^{2}\>dydx+\int_{l}^{L}\int_{0}^{h}\eta_{s}|{\nabla}v_{s_{2}}|^{2}\>dydx,$
for $\delta_{i}h/\bar{H}\rightarrow\infty$, we find
${\dot{E}_{b}}/{\dot{E}_{s}}\rightarrow 0$ [23]. Therefore, in this limit the
energy dissipation occurs in the lubricant, and not in the bulk of the
displacing and displaced phases.

The imbibition growth law, $\dot{l}(t)$, is derived from the force balance,
$F_{c}=F_{v}+F_{m}$, between the capillary ($F_{c}$), viscous ($F_{v}$) and
contact-line friction ($F_{m}$) forces per unit length. The simulations show
that the contact angle settles to a constant value, $\theta\approx\theta_{e}$
after a short transient. Accordingly, $F_{c}=2\bar{H}\gamma\cos\theta_{e}/H$.
The unbalanced interfacial stress close to the triple line is given by
$\gamma\cos\theta(u)$. Since the contact angle is finite, for small
velocities, one can expand this term
$\gamma\cos\theta(u)\simeq\gamma\cos\theta(0)+ku$, resulting in $F_{m}=ku$,
where $k$ is a friction coefficient [24]. Independently, it has been reported
that such a linear scaling holds in the limit $\eta_{s}/\eta_{1}\rightarrow 0$
[29].

Using the dimensionless variables $\hat{l}\equiv l/L$, $\hat{t}\equiv
t\gamma\cos\theta\bar{H}/LH[4\eta_{s}L(2-3\alpha)/h+k]$ and $\hat{u}\equiv
d\hat{l}/d\hat{t}$, we can integrate the equation of the front motion and
obtain

$\frac{\hat{l}^{2}}{2}\frac{(\lambda-1)}{1+\frac{2\bar{H}}{3h\delta_{1}}}+\hat{l}\left(\frac{1}{1+\frac{2\bar{H}}{3h\delta_{1}}}+\lambda{\Sigma}\right)=\lambda\left(1+{\Sigma}\right)\hat{t},$
(2)

where

$\lambda\equiv\frac{\eta_{1}}{\eta_{2}}\frac{3\delta_{2}h+2\bar{H}}{3\delta_{1}h+2\bar{H}}\quad\textrm{~{}and~{}}\quad{\Sigma}\equiv\frac{kh}{4\eta_{s}{L}(2-3\alpha)}.$
(3)

The parameter $\lambda$ contains the relative effect of the viscosities of the
three fluids together with the fraction of the channel occupied by the
lubricant, and $\Sigma$ quantifies the strength of the friction of the
meniscus relative to the lubricant layer.

As shown in Fig. 2a, Eq. 2 agrees well with the simulations, with the contact-
line friction coefficient, $k$, used as the only fitting parameter 111The
value of $k=34$ simulation units, is computed once, which indicates it has a
weak dependence on the lubricant properties and is controlled by the
viscosities in the displacing and displaced fluids. The classical, diffusive-
like growth regime of Wahsburn’s Law is recovered by imposing $\Sigma=0$ and
letting $\delta_{i}h/\bar{H}\rightarrow 0$. This eliminates the effect of the
meniscus and reduces $\lambda$ to the familiar viscosity contrast, i.e.,
$\lambda\rightarrow\eta_{1}/\eta_{2}$. Then, taking $\lambda\gg 1$ gives
$\hat{l}\rightarrow\sqrt{4\bar{H}\eta_{s}\hat{t}/3h\eta_{1}}$.

On the other hand, for $\delta_{i}h/\bar{H}\gg 1$ and $\lambda\rightarrow 1$
equation (2) yields the linear growth law $\hat{l}\rightarrow\hat{t}$. After
recovering dimensions we find

$l(t)=\frac{\bar{H}h\gamma\cos\theta}{H[4\eta_{s}{L}(2-3\alpha)+kh]}t.$ (4)

Remarkably, the velocity of the front depends only on the viscosity and
thickness of the lubricant layer and on the channel length.

The linear growth regime identified in this letter occurs for
$\delta_{i}h/\bar{H}\gg 1$, where energy dissipation occurs primarily in the
lubricant layer. For intermediate regimes the asymptotic growth of the front
will conform to Washburn’s law. A cross-over length $l_{c}$ can be estimated
by comparing the magnitudes of quadratic and linear contributions in Eq. 2. We
obtain $l_{c}\sim 2L(1+\lambda\Sigma[1+2\bar{H}/3h\delta_{1}])/(\lambda-1)$,
which implies $l_{c}>L$ if $\lambda<3/(1-2\Sigma[1+2\bar{H}/3h\delta_{1}])$.
If the friction associated to the contact line is negligible compared to the
lubricant dissipation $\Sigma=0$, a crossover length $l_{c}>L$ requires
$\lambda<3$. However, increasing the contact line friction increases the
crossover length, and when ${\Sigma}>1/2$ Washburn’s law will never be
observed as the simulation results show in Fig. 2b.

Here we have focused on the case of spontaneous imbibition. However, a low-
viscosity lubricant layer has a strong impact in the sensitivity of the front
to perturbations, and leads to a significant modification of the front
dynamics when the fluids are subject to external forces. Let us consider a
uniform external force acting on the displacing liquid, $F_{e}=f\bar{H}l$, for
small $\eta_{s}$. Fig. 3 shows a speed-up of the front as it advances in the
channel, in sharp contrast to the classical result of forced imbibition, where
the motion of the front is linear. This effect is also captured by the
theoretical model upon adding an external force, which leads to the solid
curves shown in Fig. 3 after numerical integration. An approximate expression
of the growth law can be obtained in the regime $\lambda\rightarrow 1$ and
$\delta_{1}\rightarrow\infty$, which gives

$\hat{l}=\frac{e^{\psi\hat{t}}-1}{\psi}=\frac{e^{t/t_{i}}-1}{\psi},$ (5)

where $\psi=LHf/2\gamma\cos\theta$ is the forcing coefficient. Equation (5)
predicts an exponential invasion of the channel in agreement with the
simulation results (dashed curved in Fig. 3), with a characteristic time scale
$t_{i}=\eta_{s}L/Hhf$ determined by the competition between viscous forces in
the lubricant layer and the external forcing.

Our work can be used to design experimental setups that optimize imbibition in
SLIPS/LIS channels. For example, using liquids with viscosities
$\eta_{1}=1700$ mPas, $\eta_{2}=17$ mPas and $\eta_{s}=10$ mPas [31], and a
typical lubricant thickness of 1 $\mu$m in a channel of $H\simeq{20}~{}\mu$m
would result in $\lambda\simeq{8}$, thus making the linear regime reported
here accessible in experiments.

Figure 3: Forced imbibition dynamics in a lubricant-coated channel. External
forcing leads to an exponential growth of the position of the advancing front.
The strength of the external force is quantified the forcing coefficient
$\psi$. The simulation parameters correspond to $\Sigma=24$, $\lambda=1.2$ and
$\delta_{1}=5000$. The symbols correspond to simulations, solid lines
correspond to the numerical solution of the differencial equation including
the forcing term, and the dashed grey lines to the approximation in Eq. 5.

The analytical framework, validated with simulations, provides a starting
point to characterise further effects that might be relevant in spontaneous
imbibition processes in SLIPS and LIS, such as the precise role of the
dissipation in the ridge, or the effect of the varying width of the lubricant
in the channel.

Altogether, the ideas reported in this work will help rationalise the effect
of a lubricant layer in naturally-occurring situations as well as inspire
solutions to technological challenges. For example, in pitcher plants, which
inspired SLIPS originally [13], the textured surface that supports the
lubricant layer has corrugations which form semi-open channels, and such
structures could sustain the capillary flows reported in this paper [32].
Binary liquid capillary bridges, which spontaneously move in confinement and
can therefore be used for droplet transport applications, have been reported
to spontaneously leave a thin film of liquid adhered to a channel wall [33].
In antifouling applications [34, 35, 36, 37, 29], some studies suggest that
bacteria can accumulate in the lubricant layer, limiting its medical
applications [38]. Controlling spontaneous flows could be a key solution to
enhance LIS properties and make it a suitable material for medical
applications [39].

I. P. acknowledges support from Ministerio de Ciencia, Innovación y
Universidades (Grant No. PID2021-126570NB-100 AEI/FEDER-EU), from Generalitat
de Catalunya under Program Icrea Acadèmia and project 2021SGR-673DURSI (Grant
No. 2017 SGR 884). A.H.-M. acknowledges support from Ministerio de Ciencia,
Innovación y Universidades (Grant No. PID2019-106063GB-100).

## References

  * Rauscher and Dietrich [2008] M. Rauscher and S. Dietrich, Wetting phenomena in nanofluidics, Annual Review of Materials Research 38, 143 (2008), https://doi.org/10.1146/annurev.matsci.38.060407.132451 .
  * De Volder  _et al._ [2010] M. De Volder, S. J. Park, S. Tawfick, D. Vidaud, and A. J. Hart, Fabrication and electrical integration of robust carbon nanotube micropillars by self-directed elastocapillary densification, Journal of Micromechanics and Microengineering 21 (2010).
  * Bico  _et al._ [2018] J. Bico, E. Reyssat, and B. Roman, Elastocapillarity: When surface tension deforms elastic solids, Annual Review of Fluid Mechanics 50, 629 (2018), https://doi.org/10.1146/annurev-fluid-122316-050130 .
  * Snir and Kamien [2005] Y. Snir and R. D. Kamien, Entropically driven helix formation, Science 307, 1067 (2005), https://www.science.org/doi/pdf/10.1126/science.1106243 .
  * Hansen-Goos  _et al._ [2007] H. Hansen-Goos, R. Roth, K. Mecke, and S. Dietrich, Solvation of proteins: Linking thermodynamics to geometry, Phys. Rev. Lett. 99, 128101 (2007).
  * Li  _et al._ [2012] X. Li, D. R. Ballerini, and W. Shen, A perspective on paper-based microfluidics: Current status and future trends, Biomicrofluidics 6, 10.1063/1.3687398 (2012), 011301, https://pubs.aip.org/aip/bmf/article-pdf/doi/10.1063/1.3687398/13819907/011301_1_online.pdf .
  * Estrela  _et al._ [2016] P. Estrela, K. Koczula, and A. Gallotta, Lateral flow assays, Essays in Biochemistry 60, 111 (2016), https://portlandpress.com/essaysbiochem/article-pdf/60/1/111/481162/bse0600111.pdf .
  * Washburn [1921] E. W. Washburn, The dynamics of capillary flow, Phys. Rev. 17, 273 (1921).
  * Pradas and Hernández-Machado [2006] M. Pradas and A. Hernández-Machado, Intrinsic versus superrough anomalous scaling in spontaneous imbibition, Phys. Rev. E 74, 041608 (2006).
  * Pradas  _et al._ [2008] M. Pradas, A. Hernández-Machado, and M. A. Rodríguez, Dynamical scaling of imbibition in columnar geometries, Phys. Rev. E 77, 056305 (2008).
  * Queralt-Martín  _et al._ [2011] M. Queralt-Martín, M. Pradas, R. Rodríguez-Trujillo, M. Arundell, E. Corvera Poiré, and A. Hernández-Machado, Pinning and avalanches in hydrophobic microchannels, Phys. Rev. Lett. 106, 194501 (2011).
  * Primkulov  _et al._ [2020] B. K. Primkulov, J. Y. Y. Chui, A. A. Pahlavan, C. W. MacMinn, and R. Juanes, Characterizing dissipation in fluid-fluid displacement using constant-rate spontaneous imbibition, Phys. Rev. Lett. 125, 174503 (2020).
  * Wong  _et al._ [2011] T.-S. Wong, S. H. Kang, S. K. Y. Tang, E. J. Smythe, B. D. Hatton, A. Grinthal, and J. Aizenberg, Bioinspired self-repairing slippery surfaces with pressure-stable omniphobicity, Nature 477, 443 (2011).
  * Smith  _et al._ [2013] J. D. Smith, R. Dhiman, S. Anand, E. Reza-Garduno, R. E. Cohen, G. H. McKinley, and K. K. Varanasi, Droplet mobility on lubricant-impregnated surfaces, Soft Matter 9, 1772 (2013).
  * Miranda  _et al._ [2014] D. F. Miranda, C. Urata, B. Masheder, G. J. Dunderdale, M. Yagihashi, and A. Hozumi, Physically and chemically stable ionic liquid-infused textured surfaces showing excellent dynamic omniphobicity, APL Materials 2, 056108 (2014), https://doi.org/10.1063/1.4876636 .
  * Urata  _et al._ [2015] C. Urata, G. J. Dunderdale, M. W. England, and A. Hozumi, Self-lubricating organogels (slugs) with exceptional syneresis-induced anti-sticking properties against viscous emulsions and ices, J. Mater. Chem. A 3, 12626 (2015).
  * Cui  _et al._ [2015] J. Cui, D. Daniel, A. Grinthal, K. Lin, and J. Aizenberg, Dynamic polymer systems with self-regulated secretion for the control of surface properties and material healing, Nature materials 14 (2015).
  * Kim  _et al._ [2020] S. Kim, H. N. Kim, and S. Lee, A lubricant-infused slip surface for drag reduction, Physics of Fluids 32, 091901 (2020).
  * Vega-Sánchez  _et al._ [2022] C. Vega-Sánchez, S. Peppou-Chapman, L. Zhu, and C. Neto, Nanobubbles explain the large slip observed on lubricant-infused surfaces, Nature Communications 13, 351 (2022).
  * Tsai [2013] P. A. Tsai, Slippery interfaces for drag reduction, Journal of Fluid Mechanics 736, 1–4 (2013).
  * Semprebon  _et al._ [2016] C. Semprebon, T. Krüger, and H. Kusumaatmaja, Ternary free-energy lattice boltzmann model with tunable surface tensions and contact angles, Phys. Rev. E 93, 033305 (2016).
  * Geraldi  _et al._ [2019] N. Geraldi, J. Guan, L. Dodd, P. Maiello, B. Xu, D. Wood, M. Newton, G. Wells, and G. Mchale, Double-sided slippery liquid-infused porous materials using conformable mesh, Scientific Reports 9 (2019).
  * [23] See EPAPS Document No.xxxx which includes further information on the model.
  * Ruiz-Gutiérrez  _et al._ [2022] E. Ruiz-Gutiérrez, S. Armstrong, S. Lévêque, C. Michel, I. Pagonabarraga, G. G. Wells, A. Hernández-Machado, and R. Ledesma-Aguilar, The long cross-over dynamics of capillary imbibition, Journal of Fluid Mechanics 939, A39 (2022).
  * Joos  _et al._ [1990] P. Joos, P. Van Remoortere, and M. Bracke, The kinetics of wetting in a capillary, Journal of Colloid and Interface Science 136, 189 (1990).
  * Martic  _et al._ [2003] G. Martic, J. De Coninck, and T. Blake, Influence of the dynamic contact angle on the characterization of porous media, Journal of colloid and interface science 263, 213—216 (2003).
  * Quéré [2007] D. Quéré, Inertial capillarity, EPL (Europhysics Letters) 39, 533 (2007).
  * Bruus [2008] H. Bruus,  _Theoretical Microfluidics_, Oxford Master Series in Physics (OUP Oxford, 2008).
  * Keiser  _et al._ [2017] A. Keiser, L. Keiser, C. Clanet, and D. Quéré, Drop friction on liquid-infused materials, Soft Matter 13, 6981 (2017).
  * Note [1] The value of $k=34$ simulation units, is computed once, which indicates it has a weak dependence on the lubricant properties and is controlled by the viscosities in the displacing and displaced fluids.
  * Peppou-Chapman  _et al._ [2020] S. Peppou-Chapman, J. K. Hong, A. Waterhouse, and C. Neto, Life and death of liquid-infused surfaces: a review on the choice, analysis and fate of the infused liquid layer, Chem. Soc. Rev. 49, 3688 (2020).
  * Bohn and Federle [2004] H. F. Bohn and W. Federle, Insect aquaplaning: Nepenthes pitcher plants capture prey with the peristome, a fully wettable water-lubricated anisotropic surface, Proceedings of the National Academy of Sciences 101, 14138 (2004), https://www.pnas.org/doi/pdf/10.1073/pnas.0405885101 .
  * Bico and Quéré [2002] J. Bico and D. Quéré, Self-propelling slugs, Journal of Fluid Mechanics 467, 101 (2002).
  * Kratochvil  _et al._ [2016] M. Kratochvil, M. Welsh, U. Manna, B. Ortiz, H. Blackwell, and D. Lynn, Slippery liquid-infused porous surfaces that prevent bacterial surface fouling and inhibit virulence phenotypes in surrounding planktonic cells, ACS Infectious Diseases 2 (2016).
  * Lee  _et al._ [2023] M.-S. Lee, H. Hussein, S.-W. Chang, C.-Y. Chang, Y.-Y. Lin, Y. Chien, Y.-P. Yang, C.-Y. Chen, S.-H. Chiou, R. Jih, R. Hwu, V. Charushin, S. Bachurin, M.-S. Lee, H. Hussein, S.-W. Chang, C.-Y. Chang, Y.-Y. Lin, and C.-C. Chang, International journal of molecular sciences nature-inspired surface structures design for antimicrobial applications, International Journal of Molecular Sciences 24, 1348 (2023).
  * Yang  _et al._ [2022] Y. Yang, Q. Zhu, L. Xu, and X. Zhang, Bioinspired liquid-infused surface for biomedical and biosensing applications, Frontiers in Bioengineering and Biotechnology 10 (2022).
  * Xie  _et al._ [2019] Y. Xie, J. Li, D. Bu, X. Xie, X. He, L. Wang, and Z. Zhou, Nepenthes-inspired multifunctional nanoblades with mechanical bactericidal, self-cleaning and insect anti-adhesive characteristics, RSC Adv. 9, 27904 (2019).
  * Jeong  _et al._ [2022] J.-U. Jeong, Y.-G. Heo, J.-A. Cho, Y. Roh, S. J. Kim, C.-H. Lee, and J.-W. Lee, Nanostructure-based wettability modification of tial6v4 alloy surface for modulating biofilm production: Superhydrophilic, superhydrophobic, and slippery surfaces, Journal of Alloys and Compounds 923, 166492 (2022).
  * Zhu  _et al._ [2022] Y. Zhu, G. Mchale, J. Dawson, S. Armstrong, G. Wells, R. Han, H. Liu, W. Vollmer, P. Stoodley, N. Jakubovics, and J. Chen, Slippery liquid-like solid surfaces with promising antibiofilm performance under both static and flow conditions, ACS Applied Materials & Interfaces 14 (2022).


# Background Knowledge Enhancement for Readable, Relevant, and Factual
Biomedical Lay Summaries

Domenic Rosati
scite.ai / Brooklyn, N.Y.
Dalhousie University / Halifax N.S

###### Abstract

Communication of scientific findings to the public is vital for keeping non-
experts informed of developments such as life-saving medical treatments. To
assist with this, there is an effort to develop methods for automatically
generating readable lay summaries from scientific documents. Currently these
summaries suffer from critical factual errors. One popular intervention for
improving factuality is using additional external knowledge to provide factual
grounding. However, it is unclear how these grounding sources should be
retrieved, selected, or integrated. Also, in a lay summarization setting it is
unclear how supplementary grounding documents might effect readability or
relevance of the generated summaries. We develop a simple method for selecting
grounding sources and integrating them with source documents and use the
BioLaySum summarization dataset to evaluate the effects of different grounding
sources on factuality, relevance, and readability scores. We found that
grounding source documents improves the relevance and readability of lay
summaries but does not markedly improve factuality of lay summaries. This
continues to be true in zero shot summarization settings where we hypothesized
that grounding might be even more important for factual lay summaries.

##  1 Introduction

ToDo: \- [ ] Fix tables and figures and captions \- [ ] Redo results \- [ ]
Rewrite to simplifiy and make much smaller

Automatic lay summarization of biomedical research is a promising approach to
help inform non-experts of vital scientific and clinical discoveries that
might be relevant to their lives. However, known issues with factuality in
automatic summarization systems (gabriel_go_2021; maynez_faithfulness_2020)
are still a barrier that prevents their safe deployment.

Some interventions to control for factuality ensure that summaries are more
extractive drawing from the source text as much as possible (ladhak-
etal-2022-faithful). In lay summarization though, the source text is difficult
to understand for non-experts so making summaries more extractive is not an
option. Another intervention suggested to mitigate gaps in factuality is using
grounding sources with retrieval augmentation (shuster_retrieval_2021). This
has been show to help maintain factuality in lay summaries of biomedical
abstracts (guo_cells_2022) without harming readability or relevance of the
summaries. Further, (guo_cells_2022) argues that retrieval augmentation makes
sense for the lay summarization task because lay summaries need to provide
necessary background knowledge such as definitions which are not often found
in the source text.

In this paper we wanted to understand (i) how we might develop a retrieval
augmentation solution for lay summarization when using whole scientific papers
for models with limited input context length and (ii) what is the effect of
different grounding sources that contain different types of background
knowledge on readability, relevancy, and factuality.

We develop a simple method for selecting grounding sources, a two stage
approach to retrieve and re-rank semantically relevant passages, from a
variety of retrieval sources to enhance source documents with. We assess this
method using the BioLaySum (goldsack_making_2022) biomedical paper lay
summarization dataset.

Lay Summary: [Messenger RNAs carry the instructions necessary to synthesize
proteins that do work for the cell]backgroundknowledge… In this work , we
surveyed mRNA ends from ∼10 , 000 genes in immune cells from genetically
distinct human individuals.  Simple Wiki: messenger RNA […] carries a genetic
message from the DNA to the protein making machinery of the cell. UMLS: RNA is
unique among biological macromolecules in that it can encode genetic
information. Wikipedia: An mRNA molecule is transcribed from the DNA sequence
and is later translated into protein. Abstracts: Virtually all messenger RNAs
(mRNAs) in eukaryotes are cleaved and polyadenylated at their 3′ ends.

Figure 1: Dummy

Contributions: Our findings were consistent with (guo_cells_2022) that
grounding doesn’t generally worsen factuality when compared to a baseline
without grounding. However, this also means that we did not observe gains in
factuality with grounding which we discuss the implications of below as we
hypothesized. We find that grounding primarily has a large effect on
readability where in the zero shot summarization setting definitional
background knowledge from Unified Medical Language System (UMLS) and
simplified encyclopedic background knowledge from Simple Wikipedia provide
better readability scores.

DCRS FKGL bartscore summac bertscore rouge1 rouge2 rougeL LED 12.36 15.51
-2.21 21.37 86.22 45.01 15.44 24.15 All 12.29 15.42 -2.28 21.36 86.26 45.31
15.75 24.47 Wikipedia 12.27 15.58 -2.24 21.17 86.15 44.70 15.01 23.81
Abstracts 12.30 15.62 -2.29 21.30 86.18 44.67 15.29 24.04 UMLS 12.32 15.61
-2.23 21.22 86.14 44.67 14.94 23.80 Wiki Simple 12.31 15.58 -2.22 21.24 86.14
44.51 14.87 23.74

Table 1:  Readability, factuality, and relevancy scores for our methods
measured against a LED baseline trained with 8k and 16k tokens of the paper to
be summarized respectively. Lower is better for DCRS and FKGL and higher is
better for the other scores.

##  2 Method

Summarizing scientific papers requires more input tokens than a large language
model can typically support due to memory constraints. The average token count
for articles in BioLaySum are 8,963 tokens for PLOS and 13,942 tokens for
eLife with articles up to 45,563 tokens. Since we want to explore the effect
of grounding articles with additional retrieved sources there is an even
greater need for large token input support. Because of these factors, the base
model we use in our experiments is the Longformer Encoder-Decoder (LED)
(beltagy_longformer_2020) which supports an input token length of 16,384
tokens (see Appendix B for additional training and inference details).

Our method was designed to test the effect of different grounding sources on
downstream summarization. guo_cells_2022 suggested that retrieval sources
could be important for lay summarization because of the background knowledge
required to produce a lay summary. In addition to definitional background
knowledge from UMLS and encyclopedic background knowledge from Wikipedia which
were used in guo_cells_2022, we introduced two other retrieval sources, Simple
Wikipedia for access to encyclopedic background knowledge in simpler terms and
Scientific Abstracts for access to further contextual background knowledge
that might be emulate the additional supplementary knowledge an expert has
when crafting a lay summary. In all, we tested the following four grounding
sources (1) UMLS named entity definitions (2) Scientific Abstracts (from
Crossref) (3) Wikipedia (English) (4) Simple Wikipedia (English). See Appendix
E for a full description of these grounding sources and how they were used.

Our retrieval augmentation consisted of two steps: (i) retrieving and (ii) re-
ranking documents, First we took each sentence in the first 1,024 tokens of
the article (roughly corresponding to the abstract) and searched them using
BM25 on indexes constructed for each grounding source (except UMLS which uses
another method discussed below and Scientific Abstracts which used a search
API). Indexes were constructed using Pyserini (Lin_etal_SIGIR2021_Pyserini).
The top 1 most relevant passage is selected and then added to a pool of
candidate passages. In the case of Scientific Abstracts, we skip the abstract
of the document we are enhancing. In the case of UMLS, we follow
guo_cells_2022 by using the SciSpacy entity linker (neumann_scispacy_2019)
over the first 1,024 tokens and provide definitions for all of UMLS named
entities in the pool of candidate passages.

The above procedure results in too many results to fit within context length.
In order to resolve this, we rank the pool of candidates passages against the
first 1,024 tokens of the using a crossencoder (reimers_sentence-bert_2019)
(see Appendix B for details). Finally we construct our inputs to be summarized
by selecting the first 8,192 tokens of the original document and the top
relevant grounding passages up to 8,192 tokens. A <|SEARCH|> token is inserted
between the original document and the grounding passage and global attention
is placed on the <|SEARCH|> token in order to assist with attention over the
grounding passages.

We also supplement all grounding sources with a bibliographic reference string
containing the title, authors, and year of the paper being summarised. This
was motivated by seeing many ground truth summaries which cited the source
document by first authors names as is common in scientific literature (For
example: "Parks et al . analyzed data on US deaths between 1980 and 2016 ."
which is the first sample in the eLife training subset of BioLaySum).

DCRS FKGL bartscore summac bertscore rouge1 rouge2 rougeL All 11.31 14.04
-3.35 20.30 85.78 39.09 11.26 21.50 None 11.23 14.15 -3.08 20.30 86.02 40.66
11.98 21.97 Abstracts 11.45 14.48 -3.39 20.26 85.72 39.41 11.42 21.52 UMLS
10.80 13.42 -3.37 20.25 85.56 38.40 10.00 20.69 Wikipedia 11.35 14.11 -3.49
20.26 85.49 39.05 10.48 20.61 Wik Simple 10.88 13.43 -3.75 20.21 84.96 36.40
9.03 19.20

Table 2:  Zero shot summarization setting exploring readability, factuality,
and relevancy scores for each grounding source using GPT 3.5 Turbo.

##  3 Experiments

###  3.1 BioLaySum

We experiment with the method above using the BioLaySum lay summarization
dataset which contains 29,119 training samples (24,773 from PLOS and 4,346
from eLife) and 1,617 validation samples (1,376 from PLOS and 241 from eLife).
See Appendix A for more details. This dataset is used to evaluate the ability
for models to provide factual, readable, and relevant lay summaries of
biomedical research articles from research papers in PLOS and eLife which are
paired with human written lay summaries. Since the test set is withheld all
results reported below are on the validation test set. All of the methods use
a LED base model trained for 4 epochs evaluated every 5,000 steps, like
(goldsack_making_2022) the checkpoint with the best ROUGE-2 is selected (see
Appendix B for more details).

Relevancy was measured based on BERTScore, Rouge1, Rouge2, and RougeL.
Factuality was measured using a BARTScore (yuan_bartscore_2021) measure for
eLife and PLOS documents which were trained on the BioLaySum dataset as well
as an unsupervised metric SummaC (laban_summac_2022). Readability scores used
Dale-Chall Readability Score (DCRS) and Flesch-Kincaid Grade Level (FKGL)
measures.

Table LABEL:tab:our_method compares the following methods (i) LED, a baseline
model that is only trained on the original documents to generate the output
lay summaries (8k and 16k indicate the number of tokens the model is trained
on) and (ii) Grounded, the model is trained on the original document which is
supplemented with passages from the retrieval sources as mentioned earlier.
While Grounded has access to the full 16,384, it is important to note that
only half of that is used for original document, the rest is occupied by the
retrieval sources.

The results in table LABEL:tab:our_method show a few trends, first that
Grounded improves relevancy and readability over the LED 8k setting which has
access to the same amount of tokens of the original document. In some cases
Grounded also improves over the LED 16k despite seeing only half of the
original document 111Interested readers should reference koh_how_2022 which
explores the effect of truncation on summary quality. However, these gains are
not very large which indicates that the grounding method that we explored is
only marginally helpful. Additionally, despite our hypothesis that factuality
would be improved with grounding, consistent with guo_cells_2022 factuality is
not noticeably different from baseline models.

###  3.2 Analysis of Grounding Methods

To assess the impact of various retrieval sources on summarization quality, we
trained a model for each retrieval corpus that exclusively used that corpus to
select grounding passages. In Table 3 we see that there are generally not very
large differences when using a grounding source. The most noticeable
difference is that articles grounding in passages from Scientific Abstracts
have the highest relevancy scores including a 1.6 increase in Rouge 1 from the
second best retrieval source (Wikipedia). It is possible that this is because
this source maintains scientific language that is still used in the lay
summaries, while other grounding sources have different genre roles such as
definitions in the case of UMLS or non-expert reference literature in the case
of Wikipedia. Interestingly, grounding in Simple Wikipedia does not generally
lead to a more readable summary. In general, we speculated that the lack of
larger differences between methods could be due to the model learning not to
pay attention to the grounding sources during training.

###  3.3 Zero-shot Summarization

The lack of large differences in performance between grounding sources
inspired us to consider settings where the a model might be more likely to use
grounding sources. We designed an experiment using GPT 3.5 for zero-shot
summarization using these grounding sources as in-context supplements to the
original article. We hypothesized that a large frozen model would not learn to
ignore the grounding context and could provide more information on the utility
of each grounding source on downstream summarization quality. It is important
to note that a key confounding factor in this experiment is that the lay
summaries may already exist in GPT 3.5’s training dataset.

The experiment consisted of prompting GPT 3.5 to provide a lay summary of
summary of the article. In the Baseline case, we just selected the leading
4,096 tokens of the article to fit in context to summarize. For the other
cases, we select the first 2,048 tokens of the article to fit in context and
the other 2,048 are used for the grounding sources selected using the methods
described in section 2. To fit within budget, a random sample of three hundred
summaries from the PLOS train dataset was selected. Summaries were generated
with a temperature of 0 to make the generations as deterministic as possible.
Appendix C has the full details on our experiment including the prompts used.

The results in table 2 show that grounding sources make much more of a
difference for zero-shot summaries than trained summaries. The Baseline input
has the highest Interestingly, UMLS and Wikipedia Simple encourage more
readable summaries than other methods which is what we would expect to find
since UMLS provides definitions which are vital to assisting non-experts with
engaging scientific findings and since Wikipedia Simple provides plain
language encyclopedic knowledge. As we saw above, Scientific Abstracts as a
grounding source allows us to construct more relevant summaries, perhaps due
to Scientific Abstracts preserving the scientific language and context that is
still vital for lay summaries.

##  4 Discussion

Grounding the original document in retrieval results helped most with
readability, marginally with relevance and not at all with factuality. We
believe this gives credence to the idea that grounding for lay summarization
is primarily helpful for providing the model with background information that
helps explain concepts, define terms, and otherwise situate the reader with
the necessary information to be able to understand a scientific finding. We
saw that the UMLS and Wikipedia Simple sources provided best effect on
improving readability in the zero shot summarization setting which is
intuitive in that they provide clear definitions and encyclopedic background
knowledge in simple terms. Scientific Abstracts had the best effect of all
sources on relevancy which is also intuitive because related abstracts such as
ones with similar findings might help construct a more robust summary. These
results indicate that we should continue to investigate the role of background
information on improving readability and relevance in lay summarization. In
particular, our retrieval method is quite simple more sophisticated methods of
retrieval such as dense passage retrieval (izacard2022unsupervised) could be
used to enhance the relevancy of grounding documents.

The lack of improvement in factuality is an important aspect that future work
should investigate. One explanation is that grounding sources could introduce
factual or relevancy errors if the retrieved documents are irrelevant or
incorrect and they end up being used in the generated summary. However,
factuality metrics only measure the summary against original document, this
means that statements that cannot be grounded in the original document may be
penalized. This is an issue in lay summarization where there can be statements
that are factual and provide necessary background knowledge but cannot be
found in the original document. Future work should investigate methods of
measuring factuality that are able to incorporate the necessary background
knowledge when measuring the factuality of a lay summary.

##  5 Related Work

There are a number of works looking at automatic lay summarization or text
simplification in the biomedical domain goldsack_making_2022; guo_cells_2022;
luo_readability_2022; devaraj_paragraph-level_2021. One central issue is
understanding the effect of text simplification on various aspects of summary
quality such as relevancy. (devaraj_evaluating_2022) explores the effect of
text simplification on factuality by introducing a taxonomy of different error
types that allows them to observe that while factual errors of missing
information are a common error across generated and gold summaries, errors of
substitution such as mixing up entities is a common occurrence in text
simplification summarization models.

Supplementing source documents with external knowledge has been one of the
main interventions discussed for mitigating factual errors and hallucinations
in natural language generation systems (shuster_retrieval_2021;
lewis_retrieval-augmented_2021). With the idea that having access to grounding
sources allows models to draw on those sources when generating text rather
than being forced to rely on parametric knowledge which may be flawed
(thoppilan2022lamda; mallen2022trust). In summarization, researchers have used
factual knowledge from external sources to improve factuality by encoding
external knowledge in models during training (zhu_enhancing_2021; mao_fact-
driven_2022) or to correct already generated summaries (lee_factual_2022).

(guo_cells_2022) evaluates retrieval augmentation as a method for enhancing
abstracts with background knowledge. They use definitions from UMLS and
Wikipedia as different retrieval corpora and find their method improves both
the readability and relevancy of summaries while maintaining a similar level
of factuality as models not using grounding.

## Limitations

Retrieval augmentation adds complexity to natural language generation
requiring a separate retrieval module and for the retrieval step to complete
before the text generation step can begin. Additionally, retrieval
augmentation introduces more input text than the original input which is
problematic for many neural network architectures with limited input space
especially in the case of summarizing entire scientific papers. While we
discuss ways of managing the selection of text for placing in the input, our
method maximizes the usage of a models input space which leads to higher cost
and inference time to produce summaries. Finally, retrieval augmentation
itself could introduce factual or relevancy errors if the retrieved documents
are irrelevant or incorrect and they end up being used in the generated
summary.

##  Appendix A BioLaySum

The BioLaySum (goldsack_making_2022) dataset has two splits, one for articles
and lay summaries from PLOS and the other for articles and summaries from
eLife. There are 29,119 training samples (24,773 from PLOS and 4,346 from
eLife) and 1,617 validation samples (1,376 from PLOS and 241 from eLife).
There is an additional test set but this was not used as the reference
summaries are not publically available. All results are presented using the
validation set. Our zero shot summarization experiment uses a random sample of
300 summaries from the PLOS validation sample set.

##  Appendix B Model Training and Inference

In our experiments, we use the Longformer Encoder-Decoder (LED)
(beltagy_longformer_2020). We use the allenai/led-base-16384 checkpoint on
huggingface. All models are trained using 16,384 input tokens and an output
length of 512 tokens except for LED 8K which is trained using 8,192 input
tokens. All models are trained on 4 v100 GPUs. Models are trained for 4 epochs
and evaluated every 5,000 steps. The checkpoint with the best Rouge2 is
selected as our best model. We set global attention to the first token and on
a <|SEARCH|> token the model if the source document was supplemented with
retrieval results. For inference, we sample text from the model using the
following parameters with greedy decoding: number of beams 4, min length 100,
length penalty 2.0, early stopping True, no repeat ngram size 3.

The crossencoder we used for re-ranking passages according to semantic
similarity with the first 1,024 tokens of the original document was cross-
encoder/ms-marco-MiniLM-L-6-v2 using the sentence transformers library
(reimers_sentence-bert_2019).

DCRS FKGL bartscore summac bertscore rouge1 rouge2 rougeL LED 8k 12.36 15.51
-2.21 21.37 86.22 45.01 15.44 24.15 LED 16k 12.24 15.32 -2.22 21.34 86.26
45.35 15.58 24.35 All 12.29 15.42 -2.28 21.36 86.26 45.31 15.75 24.47

Table 3:  Readability, factuality, and relevancy scores for our methods
measured against a LED baseline trained with 8k and 16k tokens of the paper to
be summarized respectively. Lower is better for DCRS and FKGL and higher is
better for the other scores.

##  Appendix C Zeroshot summarization

For our zero shot summarization setting we use GPT-3.5-turbo with a
temperature of 0. We prompt the model with the following prompts:

System Prompt: You are a document summarizing agent focusing on summarizing
documents to make them readable for a lay audience. Summarize the documents
presented by the user in as simple terms as possible. Prompt: Summarize this
document for a lay audience: {document} Below are a set of search results that
ground the above document. {search results}

##  Appendix D Sample Summaries

journal.pgen.1002882

Original: Messenger RNAs carry the instructions necessary to synthesize
proteins that do work for the cell . Extending beyond the protein-coding
sequence of a given mRNA is an additional stretch of sequence , harboring
signals that govern how much protein is made and how long the mRNA remains in
the cell before it is broken down . The incorporation of this end region into
mature mRNA is itself subject to change; for the vast majority of human genes
, how and why cells use different mRNA ends remains largely unknown . In this
work , we surveyed mRNA ends from ∼10 , 000 genes in immune cells from
genetically distinct human individuals . We found that mRNA end positions were
not randomly distributed , but rather preferentially flanked the locations of
regulatory signals that govern mRNA fate . The usage of these mRNA length
forms and regulatory elements varied across individuals and could be dissected
molecularly . Our results uncover key mechanisms and regulatory effects of
transcript end processing , particularly as these are perturbed by genetic
differences between humans .

Grounded: Gene expression varies widely between individuals of a population,
and regulatory change can underlie phenotypes of evolutionary and biomedical
relevance. A key question in the field is how DNA sequence variants impact
gene expression, with most mechanistic studies to date focused on the effects
of genetic change on regulatory regions upstream of protein-coding sequence.
By contrast, the role of RNA 3′-end processing in regulatory variation remains
largely unknown, owing in part to the challenge of identifying functional
elements in 3′ untranslated regions. In this work, we conducted a genomic
survey of transcript ends in lymphoblastoid cells derived from genetically
distinct human individuals. Our analysis mapped the cis-regulatory
architecture of 3′ gene ends, finding that transcript end positions did not
fall randomly in untranslation regions, but rather preferentially flanked the
locations of regulatory elements, including miRNA sites. The usage of these
transcript length forms and motifs varied across human individuals, and
polymorphisms in polyadenylation signals and other 3′ motifs were significant
predictors of expression levels of the genes in which they lay. Our findings
suggest that the strategy of integrating observed 3′ transcript ends with
inferred 3′ regulatory motifs will prove to be a critical tool in continued
efforts to interpret human genome variation.

Baseline: Gene expression varies widely between individuals of a population,
and regulatory change can underlie phenotypes of evolutionary and biomedical
interest. A key question in the field is how DNA sequence variants impact gene
expression, with most mechanistic studies to date focused on the effects of
genetic change on regulatory regions upstream of protein-coding sequence. By
contrast, the role of RNA 3′-end processing in regulatory variation remains
largely unknown, owing in part to the challenge of identifying functional
elements in 3′ untranslated regions. In this work, we conducted a genomic
survey of transcript ends in lymphoblastoid cells from genetically distinct
human individuals. Our analysis mapped the cis-regulatory architecture of 3′
gene ends, finding that transcript end positions did not fall randomly in
untranstranslated regions, but rather preferentially flanked the locations of
regulatory elements, including miRNA sites. The usage of these transcript
length forms and motifs varied across human individuals, and polymorphisms in
polyadenylation signals and other 3′ motifs were significant predictors of
expression levels of the genes in which they lay. Our results underscore the
importance of 2 mechanisms at play in the genetics of gene expression: the
usage of distinct 3′ end processing signals and the effects on 3′ sequence
elements that determine transcript fate. Our findings suggest that the
strategy of integrating observed transcript ends with inferred 3′ regulatory
motifs will prove to be a key component in the systems-level modeling of
regulatory networks and their variation.

Zero shot UMLS: This document talks about how genetic differences between
individuals can affect how genes are expressed, which can have important
implications for evolution and medicine. Specifically, the document focuses on
how differences in the way RNA molecules are processed at their ends can
impact gene expression. The researchers used a technique called 3’-end RNA-seq
to study this process in human cells, and found that variations in the
processing of RNA ends can be influenced by genetic differences between
individuals. The study sheds light on the regulatory elements that control
gene expression and suggests that understanding these elements will be
important for interpreting human genome variation.

##  Appendix E Grounding Sources

APA reference strings were constructed using
Crossref222https://www.crossref.org using the first author name, title, and
year from Crossref metadata matched on the BioLaySum document IDs. The UMLS
definitions were from the sciscapcy entity linker (neumann_scispacy_2019). The
Wikipedia index was a prebuilt index enwiki-paragraphs from Pyserini. We
constructed the Simple Wikipedia index using the march 1st dump
333https://dumps.wikimedia.org/simplewiki/20230301/. The index was constructed
extracting the plain text from each article and chunking articles into 6
sentence chunks. The 6 sentence chunks were indexed with Pyserini
(Lin_etal_SIGIR2021_Pyserini). The Scientific Abstracts index was an
Elasticsearch index of Crossref abstracts, it was accessed through scite.ai
search api 444https://api.scite.ai/docs#tag/Search.

Source Mean STD Abstracts 8.81 3.0 Wikipedia 6.66 2.4 UMLS 2.56 2.1 Wiki
Simple 0.71 0.8

Table 4:  Distribution of grounding sources selected by our method (section
2). Scientific Abstracts and Wikipedia are selected far more often than other
sources.