What is the national prevalence of hearing loss among older US adults?In this cohort study of a nationally representative sample of 2803 older adults, 65.3%  of those aged 71 years or older had hearing loss, and by age 90 years, 96.2% of adults had hearing loss. However, only 29.2% of those with hearing loss used hearing aids.These findings suggest that hearing loss among the oldest old adults  is more pervasive than previously thought and warrants deeper consideration of discrete severity measures of hearing loss in this population, rather than binary hearing loss terminology. This cohort study estimates the prevalence of hearing loss and hearing aid use by age and demographic covariates in a large, nationally representative sample of US adults aged 71 years and older. National prevalence estimates are needed to guide and benchmark initiatives to address hearing loss. However, current estimates are not based on samples that include representation of the oldest old US individuals , who are most at-risk of having hearing loss.To estimate the prevalence of hearing loss and hearing aid use by age and demographic covariates in a large, nationally representative sample of adults aged 71 years and older.In this cohort study, prevalence estimates of hearing loss by age, gender, race and ethnicity, education, and income were computed using data from the 2021 National Health Aging and Trends Study. Survey weights were applied to produce nationally representative estimates to the US older population. Data were collected from June to November 2021 and were analyzed from November to December 2022.Criterion-standard audiometric measures of hearing loss and self-reported hearing aid use.In this nationally representative sample of 2803 participants  aged 71 years or older, 38.3%  were aged 71 to 74 years, 36.0%  were aged 75 to 79 years, 13.8%  were aged 80 to 84 years, 7.9%  were aged 85 to 89 years, and 4.0%  were aged 90 years or older; 53.5%  were female and 46.5%  were male; and 7.5%  were Black, 6.5%  were Hispanic, and 82.7%  were White. An estimated 65.3% of adults 71 years and older  had at least some degree of hearing loss . The prevalence was higher among White, male, lower-income, and lower education attainment subpopulations and increased with age, such that 96.2%  of adults aged 90 years and older had hearing loss. Among those with hearing loss, only 29.2%  used hearing aids, with lower estimates among Black and Hispanic individuals and low-income individuals.These findings suggest that bilateral hearing loss is nearly ubiquitous among older US individuals, prevalence and severity increase with age, and hearing aid use is low. Deeper consideration of discrete severity measures of hearing loss in this population, rather than binary hearing loss terminology, is warranted. It is associated with cognitive decline,10 Other commonly used national estimates of hearing loss are based on self-reports12 that may lack sensitivity and specificity compared with criterion-standard hearing measures, especially for older adults, who tend to underestimate their level of hearing loss.13However, nationally representative estimates of hearing loss among older adults, particularly those older than 80 years, are limited. National estimates are from the National Health and Nutritional Examination Surveys (NHANES), nearly a decade old, and not proactively sampled to include a representative sample of the oldest old . NHANES data collection methods may also pose barriers for those with travel restrictions or who are homebound, excluding their participation.14 is a large, nationally representative panel study of Medicare beneficiaries aged 65 years and older with robust oversampling of the oldest old. Following a 2019 pilot,15 the 2021 NHATS round 11 protocol added pure-tone audiometry, the criterion standard for quantifying audiometric hearing loss, in addition to already collected self-reported hearing aid use. Using these data, we present the largest and most recent epidemiological estimates of measured hearing loss and hearing aid use among older adults in the US.The National Health and Aging Trends Study (NHATS)14 Although initial sampling was among community-dwelling adults, annual follow-up interviews are completed regardless of residential status . The last sample replenishment occurred in 2015; hence, the current participants are aged 71 years and older. NHATS oversamples by age and race to provide statistical power for analyses of commonly underrepresented populations and uses in-home visits to overcome travel restrictions. NHATS data are publicly available. Data collection protocols were approved by the Johns Hopkins University institutional review board, and all participants provided informed consent. This cohort study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for observational studies.NHATS is a nationally representative longitudinal panel study of Medicare beneficiaries aged 65 years and older in the continental US  begun in 2011.In round 11 of NHATS, 2803 of 3817 surveyed participants completed the hearing measures. Participants without hearing measures were excluded from the final estimates. For estimates stratified by demographic and socioeconomic variables, an additional 30 participants had data missing for race or ethnicity, 26 had data missing for education level, and 2 had income data missing.17 Trained technicians used a software-assisted algorithm that mimics clinical best-practice guidelines to identify the lowest volume, measured in decibels hearing level (dBHL), at which a participant could reliably respond to the presented tones. Data were collected in each ear at 0.5, 1.0, 2.0, 4.0, and 8.0 kHz. Before and during the assessment, the audiometer software continuously monitors the ambient environmental noise to ensure that noise levels do not interfere with validity of the assessment. In addition, the software monitors responses for false-positives and flags implausible or rare results.Pure-tone air conduction audiometry was conducted in participants’ homes using an iPad (Apple)–based portable audiometer (SHOEBOX Ltd) with sound-attenuating Sennheiser DD450 headphones.18 In sensitivity analyses, we categorized pure-tone average using the recently adopted World Health Organization categories .18 Hearing aid use was defined by the question, “In the last month, have you used a hearing aid or other hearing device?” with yes or no as the answer.Consistent with previous population-level definitions of hearing loss, a pure-tone average of the frequencies most important for speech understanding  was derived for the better-hearing ear  and worse-hearing ear . We categorized pure-tone average using the clinical cut points most used in population-level research .Black, Hispanic, and White, respectively), highest education level , and income with respect to the Federal Poverty Level (FPL) for a household with at least 2 people aged 65 years or older .Age was categorized in 5-year bins . Sociodemographic covariates included self-reported gender , race and ethnicity  and Kruskal-Wallis analysis of variance (continuous variables) to understand how well the analytic sample represents the entire study sample and inform interpretation  of findings. Statistical significance was set at 2-sided P < .05. Next, among those with hearing measures, we estimated the prevalence of better-ear hearing loss  among adults aged 71 years and older by demographic and socioeconomic variables by standardizing weighted prevalence estimates by age and gender to the population aged 71 years and older living in the continental US using data from the 2020 Census Bureau, American Community Survey, per NHATS guidelines.19 Overall prevalence estimates were calculated by all 5-year age strata. However, because of limitations in Census estimates and minimum cell size requirements, estimates that were further stratified by other demographic variables, as well as reporting of absolute number (in millions), were restricted to ages 71 to 74, 75 to 79, 80 to 84, and 85 or older years. Subsequent analyses estimated national prevalence of hearing loss by better-hearing and worse-hearing ear by age category and by hearing severity (better-ear) by age category. Finally, we calculated prevalence estimates of hearing aid use for adults aged 71 years and older with hearing loss. Population numbers were calculated from the estimated population of older adults in the continental US extrapolating the estimated prevalence of hearing loss among NHATS participants.We compared differences in demographic and socioeconomic variables between NHATS participants who did and did not complete hearing assessment using χNHATS weights are calculated to account for differential probabilities of selection and nonresponse, and survey design, to make nationally representative parameter estimates. Analyses used round 11 survey weights. Data were collected from June to November 2021 and were analyzed from November to December 2022 using Stata statistical software version 17 (StataCorp).Compared with 2803 participants who completed hearing testing, 1014 participants who did not complete testing were older , more likely to be Black  or Hispanic , and more likely to have lower income levels  . Our anaIn weighted analyses, 65.3% , or approximately 7 in 10, of adults aged 71 years and older had hearing loss  in the better ear , representing an estimated 21.5 million individuals. Prevalence estimates were higher when using a worse-ear  definition . When the newly adopted World Health Organization thresholds were applied, the prevalence of hearing loss increased such that 8 in 10 US individuals aged 71 years and older had hearing loss  compared with female participants  . HoweverWhite participants  had a higher prevalence of hearing loss compared with Black  and Hispanic  participants. Stratified by age, the difference by race and ethnicity narrowed with increasing age in the overall sample. Among participants aged 71 to 75 years, 55.5% of White  vs 43.3% of Black  participants had hearing loss. However, for participants aged 85 years and older, 93.4% of White participants  vs 84.9% of Black participants  had hearing loss. Prevalence estimates by age were not computed for the Hispanic ethnicity category because there were fewer than 10 observations in some of the age groups. Similar trends were found by education and income levels, whereby a larger proportion of participants with lower education levels and lower income levels had hearing loss in the overall sample, but the gap narrowed with increasing age .Among adults with hearing loss, 29.2%  reported hearing aid use. Use increased with hearing loss severity such that 14.4%  of those with mild, 45.3%  of those with moderate, and 67.9%  of those with severe hearing loss used hearing aids. A higher prevalence of hearing aid use was found among male participants  vs female participants , White  vs Black  or Hispanic  participants, and those with higher education and income levels  compared with previous work in NHANES .9 A possible explanation for the difference, especially among older age groups, is NHATS oversamples and retains underrepresented populations who are more likely to have hearing loss, including the oldest and lower income individuals. In particular, the use of home visits or facility visits for institutionalized participants in NHATS may make assessment and retention more feasible vs use of mobile testing centers in other nationally representative samples.Compared with previous studies in nationally representative data, we report higher prevalence estimates among the oldest old individuals. Estimates from NHANES, which has limited representation over age 80 years, suggested that approximately 66%21 Likewise, researchers should consider moving away from binary measurement in this age group to better understand the magnitude of the effect of differing degrees of hearing loss on poor aging outcomes.Our analyses shed light onto the pervasive nature of hearing loss among older adults because nearly all individuals aged 85 years and older have hearing loss. At such high proportions, severity (vs any) becomes important for characterizing hearing among older adults. Clinicians and public health officials should consider moving away from broad binary descriptions of hearing loss, especially among the oldest age groups, toward more granular categories to improve understanding and perceptions of hearing. This shift will also allow for more targeted public health planning and resource allocation, as different degrees of hearing loss may warrant different interventions and programs.18 When the World Health Organization thresholds were applied, prevalence of hearing loss increased such that 8 in 10 US individuals aged 71 years and older had hearing loss  or informing potential pharmacologic interventions to treat or prevent hearing loss , they have little implication for immediate clinical and public health interventions to address hearing loss among older adults. First, we showed that as age increased, the difference in prevalence of hearing loss among demographic subgroups narrowed to nearly no difference. Second, the negative impacts of hearing loss are similar regardless of demographic groups, and the highly prevalent nature of hearing loss warrants scoping public health approaches.Our study agrees with previous findings8 Our work limited prevalence estimates to those with hearing loss  and found larger proportions of hearing aid use in the strata of those aged 80 years and older: 31.2% for adults aged 80 to 85 years and 36.9% for those aged 85 years and older. Although NHATS’s unique sampling strategy may contribute to differences, the observed increase in proportion of hearing aid owners in this population may also reflect the increasing trend in overall uptake in hearing aids.26 Our findings that race and ethnicity, education, and income levels were associated with lower prevalence of hearing aid use are consistent with previous studies in representative and population-based samples.28 As use of other technologies  increase, future work could consider additional questions to further characterize the use of hearing care in the US.Similar to other studies, hearing aid use was relatively low, at only 29.2% of older adults with hearing loss. Previous estimates using NHANES reported that 17.6% of all adults aged 80 years and older use hearing aids (approximately 22% of those ≥80 years with hearing loss when extrapolating from published tables).7 In addition, addressing hearing loss to reduce dementia has been added to the National Plan to Address Alzheimer Disease,6 and Medicare20 coverage of hearing aids was included in the proposed 2021 Build Back Better bill in the US Congress. Accurate prevalence estimates are essential, because proportional estimates off by just a few percentage points could reflect large differences  when extrapolating to absolute numbers for the US population. In the future, larger samples will increase the precision of hearing estimates among the oldest old, but the current data offer a first step in the direction of more accurate estimates.The presented data have implications for recent hearing-focused policy and initiatives. The 2017 Over-the-Counter Hearing Aid Act became official following the US Food and Drug Administration final regulations that created a regulated over-the-counter category of hearing aids for mild-to-moderate hearing loss.Although there are strengths of NHATS data, including the large sample size and oversampling of older individuals, the analysis has limitations. Groups at the highest risk for hearing loss are more likely to be missing hearing assessment data, which could suggest that the data presented in this analysis underestimate hearing loss prevalence. In addition, current estimates are cross-sectional; nationally representative data on the incidence and timing of hearing loss across the life course are lacking. Future work should provide temporal incidence measures. Moreover, even though NHATS is designed to be nationally representative, there are some limitations in the generalizability. The presented data are only for adults aged 71 years and older because the last replenishment occurred in 2015 . Only the continental US states are sampled. Although the NHATS data oversample Black individuals, other racial and ethnic groups are not oversampled because of the relatively low population totals among older individuals. Therefore, we were not able to calculate prevalence estimates beyond White, Black, and Hispanic individuals. However, additional iterations of NHATS will allow for analyses of other subpopulations and racial and ethnic groups. Notably, in keeping with the changing demographics of the US, NHATS plans to oversample Hispanic individuals in the next replenishment. Hearing aid data are in the context of the use of hearing aids in the past month and do not offer details into total length of use or allow for estimates of overall ownership of hearing aids regardless of use, which may be helpful for policy analyses moving forward. The use of income among older adults is limited because it may not fully encapsulate socioeconomic status among older individuals; many are retired and have various sources of wealth. Although efforts are made to include all sources of income among participants, cautious interpretation is warranted. Furthermore, although NHATS offers several first-in-kind estimates in specific demographic groups , there is still imprecision in the estimates owing to the width of the intervals, and caution should be used when applying the estimates.This study found that approximately two-thirds of adults older than 71 years and nearly all adults older than 85 years have hearing loss, but relatively few use hearing aids. These robust estimates from a nationally representative study that oversamples older adults are higher than previous estimates and provide updated metrics for resource planning related to ongoing and future hearing policy initiatives.


A solid additive-assisted layer-by-layer (SAA-LBL) processing was developed to facilitate the inter-diffusion between polymer donor and acceptor and optimize the morphology of quasi-planar heterojunction for high-performance organic solar cells (OSCs).The pre-phase separation between fatty acid and polymer donor, easily tuned via controlling the cohesive energy, is critical to form the desired vertical phase-separation morphology.The SAA-LBL is generally applicable to various OSC systems, and enables the record efficiency of 19.02% among the binary OSCs.The online version contains supplementary material available at 10.1007/s40820-023-01057-x. Morphology is of great significance to the performance of organic solar cells (OSCs), since appropriate morphology could not only promote the exciton dissociation, but also reduce the charge recombination. In this work, we have developed a solid additive-assisted layer-by-layer (SAA-LBL) processing to fabricate high-efficiency OSCs. By adding the solid additive of fatty acid (FA) into polymer donor PM6 solution, controllable pre-phase separation forms between PM6 and FA. This intermixed morphology facilitates the diffusion of acceptor Y6 into the donor PM6 during the LBL processing, due to the good miscibility and fast-solvation of the FA with chloroform solution dripping. Interestingly, this results in the desired morphology with refined phase-separated domain and vertical phase-separation structure to better balance the charge transport /collection and exciton dissociation. Consequently, the binary single junction OSCs based on PM6:Y6 blend reach champion power conversion efficiency (PCE) of 18.16% with SAA-LBL processing, which can be generally applicable to diverse systems, e.g., the PM6:L8-BO-based devices and thick-film devices. The efficacy of SAA-LBL is confirmed in binary OSCs based on PM6:L8-BO, where record PCEs of 19.02% and 16.44% are realized for devices with 100 and 250 nm active layers, respectively. The work provides a simple but effective way to control the morphology for high-efficiency OSCs and demonstrates the SAA-LBL processing a promising methodology for boosting the industrial manufacturing of OSCs.The online version contains supplementary material available at 10.1007/s40820-023-01057-x. Organic solar cells (OSCs) have drawn worldwide attention because they are colorful, translucent, cost-effective, flexible and solution-processable for large area fabrication –11. ReceTill to now, the best-performed OSCs are based on the BHJ structure –21. HoweNowadays, a p-i-n like quasi-planar heterojunction (QPHJ) structure fabricated with layer-by-layer (LBL) processing has been gradually considered as a more ideal structure than BHJ, for donor enriches on the top and acceptor enriches at the bottom, which could repress charge recombination and shunting between the anode and cathode , 24–29. In this work, we report a convenient and effective strategy of solid additive-assisted LBL (SAA-LBL) processing to construct the QPHJ structure for high-efficiency OSCs. Specifically, the solid additive involves a series of fatty acids (FAs) of different cohesive energies, and the classical polymer donor PM6 and acceptor Y6 are chosen for our research. We find that by regulating the polarity and ratio of FA in PM6 film, the phase separation could be precisely tuned. During the deposition of Y6, the FA will facilitate the inter-diffusion of Y6 into PM6 film, due to their good solubility with chloroform and the Y6 molecules. The pre-formed phase separation between FA:PM6 induces more refined phase-separation structure to promote the exciton diffusion to D:A interface with a short distance. While the electrons and holes could transport to their respective electrodes through more straightforward pathways. Finally, the OSC based on PM6:Y6 via SAA-LBL processing exhibit champion PCE of 18.16%, higher than 17.52% of LBL-type devices and 16.80% of BC-type devices. With this SAA-LBL processing, the PM6:L8-BO-based devices with 95 and 250 nm films reach high PCEs of 19.02% and 16.44%, respectively, which are ones of the highest PCEs in binary OSCs with thin and thick films, validating the advantages of the SAA-LBL processing in fabrication of thick film devices. Our work demonstrates a promising approach for controlling the morphology and fabricating high efficiency OSCs.2 glovebox, the PEDOT:PSS layer was baked in air at 150 °C for 15 min. The different approaches for active layer fabrication are shown in Fig. −1 chloroform solution at 2800 rpm for 30 s, then the Y6 layer was spin coated from 8 mg mL−1 chloroform solution (0.5% v/v CN) at 2500 rpm for 30 s on the top of the donor layer. The total thickness of LBL-type active layer is around 95 nm. For SAA-LBL-type, firstly the PM6 layer was spin coated from 7 mg mL−1 chloroform solution (with different weight ratio of FAs (0 ~ 15%) in the donor mixture) at 2800 rpm for 30 s, then the Y6 layer was spin coated from 8 mg mL−1 chloroform solution (0.5% v/v DIO) at 2500 rpm for 30 s on the top of the donor layer. The total thickness of SAA-LBL-type active layer is around 95 nm. For PM6:L8-BO-based devices, all conditions are the same as PM6:Y6 system except from the solvent additive is DIO with 0.25%. For thick film devices, the concentrations of PM6 and L8-BO are 12 mg/mL and 14.4 mg mL−1, respectively. The other fabrication details are as the same as thin film devices. All devices were treated with thermal annealing at 80 ℃ for 8 min. Then a 5 nm-thick PDINN film was spin coated as the electron transport layer from 1 mg mL−1 methanol solution. Finally, the Ag (100 nm) electrode was deposited by thermal evaporation to complete the device with an active area of 6.00 mm2.Organic solar cells (OSCs) were fabricated on glass substrates commercially being pre-coated with a layer of indium tin oxide (ITO). The conventional structure of ITO/poly polystyrene sulfonate (PEDOT:PSS)/Active Layer/N,N′-Bis{3-[3-(Dimethylamino)propylamino]propyl}perylene-3,4,9,10-tetracarboxylic diimide (PDINN)/Ag was adopted  under AM 1.5G spectra whose intensity was calibrated by the certified standard silicon solar cell  at 100 mV cm−2. A mask with an area of 4.73 mm2 , China) was used to measure the efficiencies. The external quantum efficiency (EQE) data were gained by using the solar-cell spectral-response measurement system .The The chemical structures of PM6, Y6, and three FAs, i.e., the decanoic acid (FA-C9), tridecanoic acid FA-C12) and heptadecanoic acid (FA-C16), are shown in Fig. 2 and hepJ-V curves presented in Fig. Voc) of 0.85 V, a short-circuit current density (Jsc) of 26.66 mA cm−2, a fill factor (FF) of 74.1%, and a PCE of 16.80%. For LBL-type devices, a little better performance is achieved with a similar Voc of 0.85 V, a higher Jsc of 26.81 mA cm−2, and an increased FF of 76.5%, and thus an improved PCE of 17.52%, which is consistent with our previous reports that LBL processing could supply not only BHJ-like interfacial area for exciton dissociation, but also D or A pure phase for charge transport and repressed charge recombination [Jsc and FF occurs simultaneously, i.e., Jsc of 27.74 mA cm−2 and FF of 76.7%, and the PCE reaches 18.16%, which is one of the highest for PM6:Y6 devices. We measured the photostability of BC-type, LBL-type and SAA-LBL-type devices in MPP tracking under 1 sun illumination . It is found that the SAA-LBL-type device exhibits slightly better photostability than the other two devices. To comprehensively compare the effect of polarity and ratio of FA on device photovoltaic performance, we investigated the effect of FA-C9, FA-C12, and FA-C16 on the device performance, as well as their various ratios. It shows that FA-C12 and FA-C16 promote the PCEs of OSCs, especially the Jsc, compared to LBL-type devices without FAs, as shown in Table We first study the effect of SAA-LBL processing on the device performance of OSCs and compare it with LBL and BC processing. From the −2, respectively. The LBL-type devices display slightly higher EQE than BHJ devices especially in the range of 400–500 nm. The SAA-LBL-type devices show obviously higher EQE than the other two devices, specifically in the PM6 absorption range, mainly due to the optimized morphology discussed later. All the integrated current densities agree well with those gained from J-V curves with small deviation of < 5%  spectra are measured to figure out the photocurrent generation, which is the major reason underlying the improved device performance in different structure devices Fig. b. All thVoc and Jsc on light intensity (Plight) are measured. The slope of Voc versus Plight is adopted to examine the type of recombination [−1, respectively. By fitting the Jsc-Plight curves with Eq. S1, the J-V and EQE results. Comparing with BC-type device, the charge transport in LBL-type device is improved, as attributed to the primary vertical phase separation discussed later [–4 and 3.62 × 10–4 cm2 V−1 s−1, respectively. As for LBL-type device, the hole and electron mobility increase to 6.72 × 10–4 and 5.09 × 10–4 cm2 V−1 s−1. With the contribution of FA-C12, mobility ascends to 7.45 × 10–4 and 7.12 × 10–4 cm2 V−1 s−1 for hole and electron, respectively. The results are shown in Fig. Jsc.To reveal the effect of different processing methods on charge recombination. The dependence of bination , as showed later , 36. Witentclass1pt{minimaPdiss) and charge collection probability (Pcoll) of BC-type, LBL-type and SAA-LBL-type devices, the photocurrent density (Jph) versus effective voltage (Veff) characteristics are measured . Herein, the Pdiss of the BC-type, LBL-type and SAA-LBL-type devices are almost the same, approaching or exceeding 0.99, implying all of them have sufficient D:A interface for exciton dissociation. However, the Pcoll is different among these three heterojunctions. The SAA-LBL-type device possesses the highest one, 0.901, for its effective charge transport and reduced recombination, as a result, the FF and Jsc values get elevation simultaneously. The lowest Pcoll of LBL-type devices may be caused by its monomolecular recombination due to the limit distribution of acceptor permeation (see TOF–SIMS characterization). The similar phenomenon was also observed in Peng’s research [To examine the exciton dissociation probability . After spin coating chloroform (CF) solution of Y6 or CF solvent, the carboxyl peak reduces to a negligible level compared to PM6, which proves that the additive FA could be removed by the sequential deposition of Y6. To verify the negligible effect of FA residual to active layer, we directly add FA-C12 into D:A blend solution to cast BC-type devices. From the results shown in Table S2, we could find even if the addition of FA up to 10% (w/w), the effect to PCE is mild. While the ATR-FTIR is an auxiliary evidence, TOF–SIMS could directly prove the vertical phase distribution of films to clarify the role of FA-C12 in device fabrication , Attenuated Total Reflectance-Fourier-Transform Infrared Spectroscopy (ATR-FTIR) and time-of-flight secondary ion mass spectrometry (TOF–SIMS) are measured. From the UV–Vis spectra , the addition of FA-C12 does not have obvious impact on the absorption behaviors of film, which suggests the function of acid is physical not chemical. Besides, spin-coating of pristine chloroform on PM6 layer does not affect much the uniform of the whole film . Though RMS of PM6 film decreases, PM6 still remains fiber network , which is important to charge transfer. The PM6 films containing FA display obvious carboxyl peak at around 2300 cmχD−Add could be used to compare the miscibility of PM6 donor and FA additive, where small χD−Add means fine miscibility, otherwise, the phase separation between donor and additive will happen. Contact angle measurements are implied to calculate the χD−Add in the light of Owens and Wendt’s theory and Young Equation [−1, respectively. As a result, the χD−Add of PM6 with three FAs is 0.0073, 0.31 and 0.82 for FA-C9, FA-C12 and FA-C16, respectively. Figure χD−Add of FA-C16 and PM6 contributes to incomplete phase separation, leading to slightly inadequate area for exciton dissociation. Excitingly, FA-C12 has suitable miscibility with PM6 which could not only form enough interfacial area for exciton dissociation, but also apt size of phase separation to restrain charge recombination.Since the polymer domain could be maintained after spin coating acceptor for the solvent CF solely swells the polymer fibril network instead of dissolving , 40, theEquation . Water aχD−Add and dependence of Voc and Jsc on Plight , it is convinced that the FA-C12 seems a suitable solid additive for LBL processing, and the morphology induced by FA-C12-assisted LBL processing seems better to reduce charge recombination and improve charge transport compared to the others. Figure S11 demonstrates the Pdiss of three structures, comparing the GISAXS results, FA-C12 conducts to appropriate domain size to repress charge recombination. On the contrary, FA-C16 and FA-C9 could not provide fluent charge transport as effective as FA-C12. More than that, profited from the tunability of FAs, the polarity of FA could be easily adjusted by change the length of carbon chain and the amount of carboxyl. Hence, the miscibility of donor and FA could be well controlled so as to explore the most proper domain size.The grazing incidence small angle X-ray scattering (GISAXS) are also measured to study the effect of FAs to domain size . The linecut and fitting curves could be seen in Fig. S9. The domain size of BC-type film is 19 nm, and the LBL-type is 21 nm, slightly larger than BC-type due to the feature of LBL processing. For film processed with FA-C9-assisted-LBL processing, the domain size is 17 nm, which is the smallest value among these films. The domain sizes of film fabricated with FA-C12 and FA-C16-assisted LBL-processing are similar, both are 22 nm. Generally, the large domain size would facilitate to form well-connected phase domains and thus excellent charge transport pathway . CombineJ-V and EQE test  on the evolution of PM6 phase separation is also investigated by the AFM  diffraction peak locates at q = 1.69 Å−1, so the lamellar stacking distance and π-π stacking distance could be calculated as 22.4 and 3.72 Å, respectively. For PM6/FA-C12 blend film, the lamellar distance is 22.4 Å and the π-π stacking peak almost disappear, implying the addition of FA-C12 weakens the π-π intermolecular interaction. As an impurity, FA-C12 could hinder the packing of PM6 fiber, which makes PM6 film become easier to be swollen and Y6 easier to diffuse and intercalate with donor PM6. The crystal coherent lengths (CCL) for the lamellar stacking (CCL100) and π-π stacking (CCL010) all decrease after adding FA-C12, which indicates the addition of FA-C12 will slightly reduce the lamellar stacking of PM6, but obviously affect the π-π stacking of PM6. As for blend films shown in Fig. q = 1.73 Å−1 in the out-of-plane (OOP) direction with π-π stacking distance of 3.63 Å, while SAA-LBL-type film has a mildly larger qz than the others, thus closer π-π stacking. In the in-plane (IP) direction, the lamellar stacking peaks of all three structures appear at around 0.29 Å−1, corresponding to a lamellar distance of 21.7 Å. The CCL100 of BC-type, LBL-type and SAA-LBL-type are 28.4, 31.4 and 34.7 Å, respectively. The increased CCL100 in LBL-type and SAA-LBL-type compared to pristine film implies the CF will swell a fraction of PM6 during deposition of Y6, which makes the PM6 chains reorganize and thus contributing to the more order stacking of PM6. The larger CCL100 sizes of LBL-type and SAA-LBL-type than BC-type accord well with increased EQE value of PM6 range  is adopted to investigate the effect of solid additive on film morphology, especially the molecular crystallinity. Figure Jsc of SAA-LBL-type devices, time-resolved absorption measurements are carried out. 800 nm is used as excitation wavelength to selectively excite the acceptors in D:A blend films. The results shown in Fig. To investigate the effect of SAA-LBL-type morphology on charge generation process and explain higher excitons –45. It iJsc and FF are originated from the optimized morphology.Photoluminescence (PL) spectra could study the charge transfer from one phase to another. The PL results of pristine PM6 and Y6 films as well as three blend films processed with different methods are shown in Fig. Jsc reaches a record value of 27.30 mA/cm2 among PM6:L8-BO based devices. The serious decrease of FF caused by low mobility of charges may be responsible for the falling PCE of thick film devices.Since the SAA-LBL-type structure enables great successfulness in PM6:Y6 devices, it is applied to high efficiency PM6:L8-BO blend to verify its generality Table . ExcitinJsc of 27.74 mA cm−2 and FF of 76.7%. The achieved champion efficiency is also one of the highest among PM6:Y6-based binary OSCs. Moreover, the generality of SAA-LBL processing is confirmed by applying it to more diverse OSC systems. Particularly, with SAA-LBL, the OSCs based on PM6:L8-BO reach high PCEs of 19.02% and 16.44%, which are the best results among binary OSCs with optimal and thick films, respectively. Our work demonstrates a simple but efficacious method to fabricate high-performance OSCs for future commercialization.In summary, we have developed a solid additive-assisted layer-by-layer processing to fine-tune the morphology and optimize the device performance of organic solar cells. By modifying the miscibility of PM6 and the solid additives, i.e., the fatty acids (FAs), the appropriate phase separation is induced with tridecanoic acid being solid additive mixed with precursor donor solution, and this facilitates the inter-diffusion between donor and acceptor and induces a more ideal vertical phase separation, leading to better balance of charge generation, transport and collection. Consequently, the PCE of OSCs based on PM6:Y6 increases from 17.52% (LBL-type) to 18.16% (SAA-LBL-type) with the simultaneous enhancement of Supplementary file1 (PDF 1631 KB)Below is the link to the electronic supplementary material.


However, statistical significance was lost when considering only en bloc resections with positive horizontal margins (p = 0.068). In conclusion, a higher rate of residual lesion was found after a piecemeal ESD resection, but not after an en bloc resection with positive horizontal margins.Endoscopic submucosal dissection (ESD) in colorectal lesions is demanding, and a significant rate of non-curative procedures is expected. We aimed to assess the rate of residual lesion after a piecemeal ESD resection, or after an en bloc resection but with positive horizontal margins , for colorectal benign neoplasia. A retrospective multicenter analysis of consecutive colorectal ESDs was performed. Patients with LocRR ESDs for the treatment of benign colorectal lesions with at least one follow-up endoscopy were included. A cohort of en bloc resected lesions, with negative margins, was used as the control. A total of 2255 colorectal ESDs were reviewed; 352 of the ESDs were “non-curative”. Among them, 209 were LocRR: 133 high-grade dysplasia and 76 low-grade dysplasia. Ten cases were excluded due to missing data. A total of 146 consecutive curative resections were retrieved for comparison. Compared to the “curative group”, LocRRs were observed in lengthier procedures, with larger lesions, and in non-granular LSTs. Recurrence was higher in the LocRR group (16/199, 8% vs. 1/146, 0.7%;  Colorectal cancer is one of the most prevalent malignancies worldwide. In order to decrease the burden of colorectal cancer, efforts should be made to screen the population for early detection of the cancer and, preferably, to detect premalignant lesions. It is expected, due to the adenoma–adenocarcinoma sequence that is responsible for the majority of colorectal cancers, that malignancy incidence should decrease upon the resection of precursor lesions. There are two main endoscopic techniques for the resection of early gastrointestinal cancers and premalignant lesions. Endoscopic submucosal dissection (ESD) is now one of the mainstay endoscopic treatments for premalignant lesions of the digestive tract. This technique allows en bloc resection regardless of the lesion size and morphology, which is crucial for an accurate pathological evaluation and for a lower recurrence rate, as compared to a piecemeal resection by endoscopic mucosal resection (EMR). The selection of colorectal lesions for ESD is based on their size, morphology, and location and by a careful evaluation of the target lesions concerning their microvasculature and surface pattern, using classification systems such as Kudo classification, NICE, or JNET. Benign lesions or malignant lesions confined to superficial submucosa are usually candidates for endoscopic resection. Overall, very good clinical outcomes have been described using ESD in colorectal lesions ,2,3. HowUpon the diagnosis of a non-curative resection, clinicians and patients need to decide whether further therapeutic approaches should be applied, or if an endoscopic follow-up strategy would be enough, by a careful consideration of the risks and benefits of both strategies. The European Society of Gastrointestinal Endoscopy (ESGE) recently renamed “curative resections” as “very low risk resections” (VLRRs) or “low risk resections” (LRRs). Non-curative ESD (NC-ESD) are “local risk resections” (LocRR) or “high risk resections” (HRR) . For colThe best strategy after an ESD defined as LocRR is not clear, since the guidelines mostly reflect an extrapolation of data from piecemeal or incomplete resection by EMR. However, positive horizontal margins after a complete endoscopic ESD may have a distinctive clinical significance due to the inherent differences in both techniques, namely, the ability to clearly identify the lateral margin of the lesion during the mucosal incision ,12. The aim of this study was to evaluate all the consecutive NC-ESDs performed in reference centers in the Western setting, assessing the rate of residual lesion during the endoscopic follow-up in benign lesions, and making comparisons to a control group of curative lesions, in order to better guide patients after LocRR.A retrospective, multicentric, multinational analysis of prospective registries of all the patients submitted to colorectal ESD between November 2009 and June 2021 was performed. Investigators with experience of more than 100 ESDs at the time of data collection were invited to participate with data from non-curative resections of benign colorectal lesions . Fifteen centers from Portugal, Spain, France, Belgium, Italy, Austria, and Australia participated and contributed with these LocRR cases. The general indication for colorectal ESD was the presence of a colorectal neoplastic lesion without endoscopic features of deeply invasive (>SM1) adenocarcinoma ,14. For In parallel, consecutive ESDs performed in the coordinating center, Centro Hospitalar S. João, Porto, Portugal, were analyzed in order to retrieve all the curative resections of benign colorectal lesions —VLRRs—for comparison (control group).Patients gave their written informed consent before the procedure, and the Ethics Board of the coordinating center approved the study (255/2020), with clinicaltrials.gov identifier NCT04484311.Resection was considered en bloc whenever the target lesion was removed in a single piece, or it was considered piecemeal if the lesion was recovered in more than one fragment. Piecemeal ESD was considered whenever the lesion was removed in several pieces using only the ESD technique and devices. A knife-assisted resection (KAR)/hybrid technique was defined if a snare was used to complete the resection, after partial resection by the ESD technique. If pathological evaluation showed free margins in an en bloc resected specimen, we called it R0 resection; horizontal margins were only evaluated in en bloc resected specimens. For the purpose of statistical analysis, resections classified as HMx or VMx  were considered HM1 and VM1, respectively.Colorectal curative resections (VLRRs) were those that were R0, with low- or high-grade dysplasia. All the other resections that were included  were considered LocRRs . Due to the retrospective design of the study, the histological report could not be standardized and centrally analyzed. However, international standards were used and applied by expert pathologists in each center. The major outcomes were the rate of residual dysplasia in the ESD scar, detected in follow-up endoscopies , after LocRR. Additionally, a comparison of the follow-up results between VLRRs and LocRRs was performed.t test or the Mann–Whitney test, while the chi-square test or Fisher’s exact test was used for categorical variables, as appropriate. Normality was verified using the Kolmogorov–Smirnov test to ensure correct test selection. Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) v.25.An electronic database was created and filled by the investigators of the different centers. Absolute (n) and relative frequencies (%) were used to describe categorical variables. Continuous variables were described using means and standard deviations or medians and interquartile ranges (IQR), according to the normality of the distribution. Continuous variables were compared using Student’s A total of 2255 consecutive colorectal ESDs were reviewed. From them, 352 were considered “non-curative” ESDs, performed in either benign or malignant epithelial colorectal lesions that had at least one follow-up endoscopy. For the purpose of this study, 209 were LocRR and considered for evaluation: 133 HGD and 76 LGD. Of these, 10 were excluded due to missing data; therefore, a total of 199 benign colorectal lesions with LocRR were further included in the analysis.The prospective registry of the consecutive ESDs performed in the coordinating center was also analyzed, and a total of 146 curative resections (VLRRs), collected in the same time period, were used as a control group for comparison .Considering all the analyzed patients , 200 (58%) were male. The median lesion size was 40 mm (IQR 30–55 mm). In all, 141 (41%) lesions were located in the colon, and 204 (59%) were located in the rectum. As expected, most of the lesions were lateral spreading tumors (LSTs) of the granular, mixed-type morphology.Procedural time, size, and other factors predicting a more complex procedure, such as the presence of the non-lifting sign and colonic location , were higher in the non-curative group. An analysis comparing baseline data between VLRR and LocRR can be found in p = 0.002). However, statistical significance was lost when considering en bloc resections only (p = 0.068), contrary to what was observed in piecemeal-resected lesions  for the resection of benign colorectal lesions. In fact, this is the largest Western study concerning non-curative (LocRR) ESDs (n = 199) performed for benign colorectal lesions. We showed that piecemeal resection, but not positive horizontal margins, is associated with a higher rate of residual lesions in follow-up endoscopies. This result could have implications in the way we interpret pathological reports showing a positive horizontal margin in an endoscopically complete resection.It is now widely accepted that ESD is very valuable for the treatment of colorectal neoplasia, and good outcomes have been described in Eastern and Western Countries ,12,13. EWe analyzed a large multinational case series of NC-ESD procedures with a median endoscopic follow-up time of almost 2 years. According to a large metanalysis, 98% of residual lesions in the scar will be detected in the first year . Hence, Previous reports have shown that HM+ is the main cause of non-curative resection after an ESD for colorectal lesions ,17, but ESD is a technique that allows the clear visualization of the lateral margin during the mucosal incision. Therefore, the presence of adenoma in the lateral margin in the ESD specimen should be unlikely. One explanation for the presence of a high rate of HM+ could be related to the post-ESD handling of the specimen. Since the margins of the lesions are easily identifiable, the endoscopist could perform the mucosal incision closer to the neoplastic tissue as compared to other organs. The small amount of normal tissue around the lesion may lead to an injury of the lateral margin by the pins when fixing it in the cork or rubber plate, causing the pathologist to falsely diagnose a positive horizontal margin. This may explain the low rate of recurrence and, therefore, the absence of clinical relevance of HM+ after a complete endoscopic resection by ESD. Widening the margins would lower the reported HM+ rates, but with doubtful clinical benefit, since outcomes between HM+ and HM0 are similar.Data on colorectal ESD piecemeal-resected lesions are scarce, but this procedure seems to be a risk factor for recurrence in large lesions . BesidesA previous meta-analysis showed a recurrence rate of 4% after an ESD, without considering the lateral margin status or whether the lesion was en bloc or piecemeal resected . We founAs expected, LocRRs were obtained in lengthier procedures, with larger lesions, with a much larger proportion of non-granular LSTs, with lesions that were already submitted to previous treatments and with the non-lifting sign, and with procedures that needed complementary techniques. Most of these are known risk factors for difficult and non-curative colorectal ESDs ,27,28,29Our study has some limitations. Its major strength is the very large number of Western centers that participated, allowing us to gather the largest Western case series on local-risk resections after a colorectal ESD. The main limitation is its retrospective nature, relying on prospective collected data, which could limit the interpretation of some data . However, all the participating centers had a prospective ESD registry, which minimizes the risk of selection and information biases associated with observational and retrospective studies. Nevertheless, we must be aware that the fact that the VLRR lesion cases came from only one center may have resulted in selection bias, and the generalization of the results to other Western institutions is not straightforward since all the lesions in this study came from high-volume centers. Prospective multinational studies are now warranted to confirm these results and, eventually, to create predictive scores including not only non-curative criteria  but also size and other morphological characteristics of the target lesion, as well as technical aspects of the ESD procedure, in order to better predict recurrence or residual disease and, consequently, to apply more accurate follow-up and therapeutic strategies.In conclusion, we found a higher rate of residual lesions after piecemeal resection ESDs, but not after en bloc resection with a “positive horizontal margin”. Considering the absence of clinical consequences in our results, in agreement with those previously published, we think that there is enough evidence not to recommend a widening of the lateral margin in a colorectal ESD in order to have a higher rate of negative margins—which could lead to a higher rate of adverse events—nor to recommend stricter surveillance after en bloc resection with “positive horizontal margins”, provided that the endoscopist has the clear notion of a complete and radical resection during the ESD.


Automated multiconformer modeling holds significant promise for interpreting experimental structural biology data and for generating novel hypotheses linking macromolecular conformational dynamics to function.With the advent of AlphaFold, protein structure prediction has attained remarkable accuracy. These achievements resulted from a focus on single static structures. The next frontier in this field involves enhancing our ability to model conformational ensembles, not just the ground states of proteins. Notably, deposited structures result from interpretation of density maps, which are derived from either X-ray crystallography or cryogenic electron microscopy (cryo-EM). These maps represent ensemble averages, reflecting molecules in multiple conformations. Here, we present the latest developments in qFit, an automated computational approach to model protein conformational heterogeneity into density maps. We present algorithmic advancements to qFit, validated by improved R However, careful modeling of high-resolution X-ray crystallography and cryo-EM experiments can reveal widespread conformational heterogeneity, particularly for protein side chains and local backbone regions3. Discrete conformational heterogeneity of such small magnitude is significant for many biological functions, including macromolecular binding, catalysis, and allostery6.Macromolecular X-ray crystallography and single-particle electron microscopy (cryo-EM) can provide valuable information on macromolecular conformational ensembles. These experiments cannot capture all conformations present in solution, as many would disrupt the ability to obtain crystals or align classifiable particles7, ignoring weak but potentially biologically rich signals encoding alternative conformations sampled by distinct copies of the protein in the experiment.While the underlying data from X-ray diffraction or cryo-EM experiments contain information on temporal and spatial averages of tens of thousands to billions of protein copies, conventional structural modeling and refinement procedures fail to capture much of this valuable information. Most depositions in the Protein Data Bank reflect only an averaged, single ground state set of atomic coordinates8. The two ways to model the conformational heterogeneity present in the sample are to create ensembles or to use alternative conformations (multiconformers)9. The PDB “ensemble” format encodes multiple complete copies of the entire system in different models within a single file. Ensemble refinement approaches are implemented in phenix.ensemble_refinement and Vagabond11. In contrast, multiconformers extend the conventional single structure model by encoding each individual conformation using a distinct “alternative location indicator ” within a single model. Altlocs are assigned distinct letters and can range from single atoms to a large number of connected or non-connected residues. Refinement and validation programs treat atoms sharing the same altloc as having the ability to interact with each other and with atoms lacking an altloc. In contrast, atoms with different altlocs cannot interact. By representing the underlying heterogeneity through discrete conformations with labeled altlocs, multiconformer models encode the distribution of states that contribute to the density map. Further, when compared to ensemble methods that yield multiple complete copies of a protein13, multiconformer models are more interpretable and easier to modify in interactive modeling software such as Coot14.Ideally, we would accurately model the complete ensemble of protein conformations reflected in experimental data17 in X-ray crystallography, and errors in particle alignment and classification, poor modeling of beam induced motion, and imperfect detector Detector Quantum Efficiency (DQE) in cryo-EM18. These factors make visually distinguishing signals in Coot14 or other visualization software very difficult, especially when genuine low-occupancy signals overlap. Additionally, in X-ray crystallography, this process is iterative. Each time a new alternative conformation is placed, it can impact the entire electron density map, often requiring adjustments to previously modeled regions. The difficulty of this process can lead to burnout and human bias, where parts of the protein are carefully modeled as multiconformers, whereas other regions remain modeled as single conformers. Despite these complications multiconformer modeling can be implemented manually or using software such as FLEXR19 or qFit, as described below.However, many factors make manually creating multiconformer models difficult and time-consuming. Interpreting weak density is complicated by noise arising from many sources, including crystal imperfections, radiation damage, and poor modeling22. This program leverages the ensemble-rich experimental data from density maps that are better than 2.0 angstroms (Å) resolution to automatically generate parsimonious multiconformer models21. As input, qFit takes a refined single-conformer structure and either a high-resolution X-ray or cryo-EM map as input, and then leverages powerful optimization algorithms to identify alternative protein21 or ligand23 conformations.To enable more routine and impartial multiconformer modeling, we have previously developed qFitfree over single-conformer X-ray structures derived from high-resolution (better than 2.0 Å) data and improve model geometry metrics across a diverse test set. We further demonstrate that qFit can identify alternative side-chain conformations in high-resolution cryo-EM datasets. With the improvements in model quality outlined here, qFit can now be increasingly used for finalizing high-resolution models, in both individual cases and large structural bioinformatics projects5, to derive ensemble-function insights.Here, we present updates to qFit including algorithmic improvements to protein conformation selection based on Bayesian information criteria (BIC), B-factor sampling, and updated cryo-EM scoring. Collectively, these advances enable the unsupervised generation of multiconformer models that routinely improve Rfree below 20%). For X-ray maps, we recommend using a composite omit map as input to minimize model bias24. For cryo-EM modeling applications, equivalent metrics of map and model quality are still developing, rendering the use of qFit for cryo-EM more experimental.qFit protein is a tool that automatically identifies alternative conformations based on a high-resolution map  and a well-refined single-conformer structure  and computationally (improving the efficiency and reliability of the code).https://github.com/ExcitedStates/qfit-3.0). The version of qFit associated with this paper is 2023.1.All code and associated documentation can be found in the qFit GitHub repository  and Bayesian information criterion (BIC), we select a parsimonious multiconformer for each residue . The detA.1 -The qFit process begins with sampling backbone conformations . For eacA.2 -For aromatic residues , qFit takes the conformations from the backbone step (above) and builds part of the side chain out to Cγ (prior to the aromatic ring) based on the input model coordinates . Then, wA.3 -χ dihedral angle for every residue (χ1), the input is the backbone conformations (or for aromatic residues the backbone and “aromatic angle” conformers described above). We exhaustively sample around the χ1 dihedral angle by enumerating a conformation every 10° between rotamers. For proline, we sample the exo and endo conformations of the pyrrolidine ring, by +/− 60° in steps of 10°. We then eliminate conformations that clash with other parts of the same sampled conformation (based on hard spheres) or are redundant .The following steps occur for each  residue . For theThese sampled conformations are then subjected to a quadratic programming (QP) optimization, which identifies the set of conformations whose weighted calculated density best fits the experimental electron density. The output of QP typically yields 5 to 15 conformations that best explain the density.Next, qFit samples the B-factors of the conformers. The input atomic B-factors are multiplied by a factor ranging from 0.5 to 1.5 in increments of 0.2. The resulting 50 to 150 conformation/B-factor combinations are subjected to a mixed-integer quadratic programming (MIQP) optimization. The MIQP algorithm incorporates two additional constraints relative to QP: a cardinality term, which limits the maximum number of conformations to five, and a threshold term, which stipulates that no individual conformation can have an occupancy weight below 0.2. In qFit, MIQP then outputs up to 5 conformations.χ(n-1) serve as the starting conformers for the sampling χ(n) angle. For residues with only one dihedral angle , we proceed directly to scoring χ1.For residues with subsequent dihedral angles, the conformations selected by the MIQP procedure at A.4 -Upon reaching the terminal dihedral angle, we perform the optimization steps outlined above (QP/MIQP), but instead of relying only on the optimization algorithm to decide on the number of conformations to output, we also consider the model complexity . qFit ruqFit then outputs the set of conformations with the lowest BIC value, concluding the qFit residue routine.B.After the sampling and scoring of each individual residue, qFit considers the entire protein together. First, we use MIQP and BIC to select the best fitting conformations among connected residues, ensuring that neighboring backbone conformations have the same occupancy. Second, we label the alternative conformers while being aware of clashes. This labeling step is not parallelized.B.1 -After identifying the optimal conformations for each residue in parallel, qFit reconnects the backbone atoms . Moving B.2 -14. Running ‘qFit segment only’ will adjust the occupancy of the remaining conformations and correct the labeling of alternative conformations.Next, qFit determines the correct altloc labeling  of coupled alternative conformers using Monte Carlo optimization with a simple steric model to prevent spatially adjacent conformers from sterically clashing . There iB.3 -25, where we iteratively refine the occupancy, coordinates, and B-factors, removing conformations with occupancies under 10%. Once the model is stable (has no conformations with occupancies less than 10%), we perform a final round of refinement which optimizes the placements of ordered water molecules  should then be refined. We provide scripts for a refinement procedure with Phenixolecules . This re26. We clustered these structures at a sequence identity threshold of 30% and selected the highest-resolution structure. Finally, we ensured that the datasets ran without error through the qFit pipeline, including refinement with Phenix, resulting in 144 diverse structures  protein X-ray crystallography structures from the PDBructures .phenix.refine  Rfree value for 73% (105/144) of structures , which is in line with theoretical expectations for the increase in model complexity created by qFit28. Rfree is an valuable metric for monitoring overfitting, which is an important concern when increasing model parameters as is done in multiconformer modeling. An additional check on overfitting comes from monitoring R-gap, calculated as the difference between Rwork and Rfree. qFit models have similar R-gap values compared to deposited models (mean: 3.0% for both models). Collectively, these results indicate that qFit improves the quality of most models without overfitting . The majority of these structures had a deposited model Rfree of over 20%. These high Rfree values are notable because our re-refinement procedure generally improved Rfree relative to the originally deposited model 29. We focused on the residues adjacent to the RNA binding motif. Among these residues, qFit identified well-justified alternative conformations for residues Leu58, Phe69, and Met175, including new rotamers for Leu58 and Met175, that were not present in the deposited model . In contrast, 40.7%  of residues in the qFit models were multiconformers . The vas30. This category is essentially imperceptible to visual inspection, as the atom centers are nearly superimposable, but is important to avoid outlier bond geometry because of adjacent residues having larger displacements.Alternative conformations created by qFit come in a few main varieties. First and most obvious are alternative conformations that represent drastic changes in coordinates, most commonly in the form of rotameric changes. Most alternative conformations found in deposited models fall into this category. Second are more subtle changes in side-chain and backbone coordinates to represent heterogeneity within a rotameric state. This behavior is exemplified by the Tyr residue in phenix.rotalyze, which outputs the rotamer state for each conformer as a string  in qFit model”, represents 2.38% of residues . In contrast, only two residues (0.06% of the dataset) are classified in the converse category, “Additional Rotamer(s) in deposited model”.χ angles leads to distinct rotameric assignments. This category, “Consistent & Different Rotamers”, represents 0.82% of residues (n=373). The final category, “Different”, covers both multiconformer and single-conformer residues where there are no shared rotamer states between the two models. One reason this category occurs is for similar reasons as the “Consistent & Different” category: differences in terminal χ angles in weak density lead to distinct rotamer assignments. Another contributor to this category is single conformers, generally in the deposited model, modeled into density that qFit interprets as multiconformer. Often the rotamer modeled by the single conformer fits an “average” rather than the two distinct minima fit by the multiconformer model. “Different” rotamer assignments represent 3.04% of residues . While the analyses above include all residues, focusing on residues that were modeled in as multiconformers in the deposited models (n=970) reveals a large increase in the “Different” and “Consistent & Different Rotamers” categories, to 14.88% (n=144) and 27.68% (n=268) of residues, respectively. This increase highlights the sensitivity of the rotamer assignments and motivates benchmarking qFit on “true positive” synthetic data in addition to deposited multiconformers.The final two categories cover disagreements in rotamer assignments. There are many cases where we observe only partial agreement between alternative conformers modeled in both the deposited and qFit models. These multiconformer residues share at least one common rotamer, but also populate alternative rotamers that are distinct between the two models. This behavior generally occurs in longer residues where subtle differences at higher χ angles. When considered with the improvements in Rfree, these results indicate that qFit is detecting more of the true underlying conformational heterogeneity that exists in crystallographic data.Collectively, these analyses revealed that qFit identifies the majority of deposited alternative conformations and discovers new ones. Discrepancies between manually modeled and qFit alternative conformations predominantly result from weak density at terminal 33.Although qFit improves the agreement of model to data by the addition of alternative conformations, we questioned whether this improvement comes at the cost of degrading model geometry. On one hand, the absence of geometric constraints in qFit backbone residue sampling and the connections made during qFit segment may result in worse geometry. On the other hand, placing additional alternative conformers may alleviate strain in the model that can result from fitting a single conformer into density that should be supported by multiple conformers34. Component scores that examine all atoms  or side-chain atoms (rotamers) account for all alternative conformers. In contrast, scores that evaluate the backbone  are reported for single-conformer residues or using only altloc A for multiconformer residues. Therefore, the overall MolProbity score includes some of the contributions of alternative conformations, but also misses the potential impact on some other aspects. In the future, we aim to explore updated metrics that consider all alternative conformations.To validate geometry, we used MolProbity to evaluate the deposited and qFit models. MolProbity compares input models with idealized values and then provides component scores for various geometric and steric features that are summarized in an overall “MolProbity score”34. qFit adds an additional conformation, both explaining previously unmodeled density and bringing the SD-CE bond lengths to 1.790 Å  and 1.794 Å  for the two conformations much closer to the expected values  of the SARS-CoV-2 Nsp3 macrodomain (PDB: 7KR0)35. This model had a high proportion of residues (47%) manually modeled as alternative conformations and did not employ qFit, making it an ideal comparison structure. We refer to this structure as the “ground truth 7KR0 model”, and evaluated how well its alternative conformations were recapitulated by qFit as resolution was artificially worsened across synthetic datasets.In the previous sections, we established that qFit has the potential to improve RTo create the dataset for resolution dependence, we used the ground truth 7KR0 model, including all alternative conformations, and generated artificial structure factors from 0.8 to 3.0 Å resolution (in increments of 0.1 Å). We then added random noise to the structure factors that increased as resolution worsened  and 5B. To evaluate the fidelity of qFit in recapitulating the ground truth 7KR0 model, we categorized each residue across two axes: positive/negative and true/false. First, we define the positive/negative axis, with positive being modeled as multiconformer and negative being modeled as single-conformer in the outputted qFit model. Next, we define the true/false axis as agreement of the outputted qFit model with the ground truth 7KR0 model in terms of multiconformer/single-conformer status . Due to many residues in both the ground truth and qFit models having alternative conformations that nearly overlap each other, we categorize residues as multiconformer only if they possess at least two alternative conformers with a side-chain heavy-atom root-mean-square deviation (RMSD) greater than 0.5 Å. From this cutoff, 50 out of the 169 residues (30%) in the ground truth model are classified as multiconformers.We used a similar 0.5 Å cutoff for defining agreement between multiconformer residues in categorizing a residue as a true positive, false positive, false negative, or true negative . A “trueWe observed that qFit is consistently strong at capturing single-conformer residues (true negatives) across resolutions; however, the power to detect alternative conformations (true positives) is limited beyond resolutions of ~1.8–2.0 Å . This befree values over the deposited values from the previous sections (n=110). Although this dataset is more diverse, it has a notable weakness relative to the 7KR0 dataset test: the 7KR0 alternative conformations were modeled manually, whereas the larger dataset has alternative conformations modeled by qFit. Therefore, this second synthetic dataset assesses convergence of the qFit models across resolution.Next, we tested the ability of qFit to detect alternative conformations over a larger, more diverse dataset. We generated artificial structure factors for the qFit models with improved Rfree and the number of correctly modeled conformers, but it is difficult to tell whether our synthetic noise procedures properly capture the dependence of qFit performance on input model/data agreement  (As single-particle cryo-EM is increasingly producing high-resolution (better than 2 Å) reconstructions where alternative conformers can be detectedinality) .39. Some of the original 22 structures did not have FSC curves in EMDB (n=6) due a lack of data, and others had an EMDB calculated resolution worse than 2 Å (n=8)  at 0.143). However, only 8 of these structures have a resolution better than 2 Å (FSC at 0.143) when calculated by the Electron Microscopy Data Bank (EMDB) Å (n=8) . The absphenix.autosharpen, we sharpened all maps and re-refined each structure  against its sharpened map. qFit was run with the ‘EM’ flag and the output model was refined using the qFit real space refinement script  of residues in the deposited model had at least two alternative conformers in the deposited structure, compared with 39.6% (n=266) in the qFit model. To determine if qFit could recapitulate the modeling of alternative conformers from deposited structures, we compared the high-resolution apoferritin deposited model  with the qFit model using the same criteria outlined in the resolution dependence section above (RMSD within 0.5 Å). qFit correctly models 78% of residues in the first asymmetric unit. This includes Arg22, which has two alternative conformations in the deposited model. qFit was able to recapitulate both alternative conformations , highligphenix.real_space_refine  the MolProbity clashscore of most structures, primarily due to increased clashes with waters, likely because their positions are not reset in e_refine .40, including information on masking, handling of bulk solvent, and local resolution. Our approach depends on sampling and scoring based on resolution. While there is an accepted formula for calculating resolution (FSC at 0.143), the maps to calculate these can vary, leading to differences in resolution as we observed between the deposited versus EMDB calculated resolution. Further, resolution can vary across a single model, and metrics for such local resolutions are not always widely available. Additionally, the handling of background bulk solvent values vary widely, from masking to flattening these values. The EM community will be able to benefit from improved ensemble modeling as efforts to standardize the storage of raw, meta, and processed data continue to improve.While we have made significant progress in modeling alternative conformations in cryo-EM data, the lack of consistent map handling, validation, and metrics with cryo-EM structures and maps is a major impediment to further development. Even among this select group of structures, there were varying levels of experimental and computational map details on EMDB and in manuscriptsfree values. Further, we determine that qFit can reliably pick up on alternative conformers that were modeled manually, highlighting that qFit could be used as a tool to significantly speed up modeling of high-resolution structures.Structural biology plays a vital role in understanding the complex connection between protein structure and function. However, since proteins exist as ensembles, structural biology modeling approaches need to adapt accordingly. X-ray crystallography and cryo-EM data hold significant information on these ensembles that are often ignored. qFit offers a solution by leveraging powerful optimization algorithms to transform well-modeled single or static models into multiconformer models. Here we demonstrate that qFit can uncover widespread conformational heterogeneity that better represents the true underlying conformational ensemble data as demonstrated by lower R45. With the ability to capture different conformations, there is a growing demand for methods that can detect protein alternative conformers to extract as much biological information as possible. This is highlighted in massive ligand-soaking campaigns48, where there are often hundreds of structures with different ligands to parse. qFit provides a key tool to help extract the most out of these structures by improving the models and providing a better jumping-off point to determine how ligand binding impacts the protein. However, our data here shows that not only does qFit need a high-resolution map to be able to detect signal from noise, it also requires a very well-modeled structure as input.This automation in modeling is needed especially in light of advances in data collection automation and fast detectors. These tools have revolutionized the field of X-ray crystallography, enabling high-temperature datasets, time-resolved experiments, and high-throughput data collection40. Current classification approaches do not allow sorting based on signals as small as alternative side-chain conformations, necessitating approaches like qFit for modeling51. We see great potential in combining qFit with classification approaches to understand conformational heterogeneity at different scales. In the future, qFit can likely be applied more widely to EM maps in regions with high local resolution52. In addition, we will also incorporate modeling of nucleic acids, with an emphasis on automating refinement of alternative base positions in high-resolution ribosome structures in future work54. However, we encountered many difficulties in applying qFit to EM data relative to the more established X-ray data. In particular, there are still disparities in how maps are sharpened and how masks are used to exclude noise or lower experimental signals, such as solvent56, making it very challenging to evaluate whether models, especially multiconformer or ensemble models, have improved fit to the data. We suggest strengthening guidelines for reporting computational processing, and improving validation tools to gauge agreement between models and cryo-EM maps55.While both throughput and resolution are currently lower for cryo-EM, recent high-resolution maps have observable conformational heterogeneity28. To accurately account for water molecules in multiconformer models, partially occupied water molecules must be identified and labeled in connection with protein atoms. Automated detection and refinement of partial-occupancy waters should help further reduce Rfree15 and provide additional insights into hydrogen-bond patterns and the influence of solvent on alternative conformations.We envision many other future improvements that will further enhance the quality and accuracy of multiconformer models for both X-ray crystallography and cryo-EM. Simulations have demonstrated that subpar modeling of the macromolecule(s) and surrounding solvent is a major potential avenue to further reduce R-factors20. Such improvements are important because splitting the backbone, where appropriate, can result in detection of biologically important alternative conformations63. Notably, the recently described FLEXR approach, which leverages Ringer to detect density peaks contributed alternative conformations and Coot to model alternative side chains into density peaks, illustrates that many gains can be made with side chain focused modeling alone19. However, further improvements to backbone modeling, including larger-scale motions such as alternative loop conformations64 or coordinated larger-scale shifts of secondary-structural elements66 will likely yield even higher quality multiconformer models.Additionally, while qFit models have improved geometry in some respects relative to single-conformer models, we still have room for improvement for fixing rotamer outliers and backbone metrics (Ramachandran and Cβ deviations). The geometry improvements are likely mostly due to single-conformer models having strained conformations that fit the “mean” conformation rather than multiple partially overlapping conformations. Further gains in both accuracy and geometry quality will emerge with better sampling of backbone conformations51. But the current data format for structural models  does not allow for more complex representation of ensembles. To appropriately capture the many aspects of ensembles, we would ideally like to have multiple nested ensembles representing both larger and local conformational changes, or to be able to show how two different backbone conformations can each be “parents” to different side-chain conformations. Currently, neither the PDB nor CIF format allows for this type of representation69.Lastly, the experimental and computational advancements in structural biology have increased the focus on ensemble-based models71. Additionally, qFit can provide key data to bridge to the next frontier of structure prediction. While AlphaFold has achieved stunning success in predicting protein structure by training against single-conformation models72, future improvements to structure prediction might be gained by more accurately modeling the extent of conformational heterogeneity72.In summary, qFit drastically reduces the time and effort required to create multiconformer models from X-ray and cryo-EM data, thereby lowering the barrier to generating new hypotheses about the relationship between conformational ensembles and biological function31, the resultant entries represented 24 space groups and 72 folds  X-ray crystallography structures deposited in the PDB . These w72 folds . All thephenix.refine with the following parameters.All structures from the PDB were re-refined using refinement.refine.strategy=*individual_sites *individual_adp *occupanciesrefinement.output.serial=5refinement.main.number_of_macro_cycles=5refinement.main.nqh_flips=Falserefinement.output.write_maps=Falserefinement.hydrogens.refine=ridingrefinement.main.ordered_solvent=Truerefinement.target_weights.optimize_xyz_weight=truerefinement.target_weights.optimize_adp_weight=trueThe re-refined models were used as the input for subsequent qFit models.For this analysis qFit was run using the following command from qFit version 2023.1.qfit_protein composite_omit_map.mtz -l 2FOFCWT,PH2FOFCWT rerefine_pdb.pdbqfit_protein sharpened_map.ccp4 rerefine_cryo-EM.pdb -r <resolution> -em -n 10 -s 5BIC was implemented in the final selection of residue and segment conformations. BIC is defined as the real space residual correlation coefficient penalized by the number of parameters (k):In qFit residue, k is defined as:In qFit segment, k is defined as:BIC is calculated for each candidate cardinality (1–5). We then choose the set of conformations with the lowest BIC as the final conformations for the residue or segment under consideration.To sample B-factors along with atomic coordinates at each step of qFit residue, we first perform one round of quadratic programming to reduce the number of conformations. For all remaining conformations, the input B-factor of each atom in the residue is multiplied by 0.5–1.5 in increments of 0.2. All conformations with sampled B-factors and coordinates are inputs for mixed integer quadratic programming.Due to our exhaustive sampling, there are times when the MIQP optimization algorithm fails to find a non-convex solution. To address this limitation, we have implemented a procedure that iteratively removes solutions one-by-one based on the two solutions with the closest root-mean-square deviation (RMSD) until MIQP identifies a solution.Often, cryo-EM maps are very large and reach memory limits using Python multiprocessing. Multiprocessing is used to model multiple residues independently in parallel. We have now implemented a new scheme to divide the density map into portions centered around each residue of interest and feed those portions of the map into our parallelization.To help refine segments  during X-ray refinement, we now output a restraint file at the end of the qFit protein run for X-ray refinement. This restraint file will enables “group occupancy refinement” for residues in a segment with the same alternative conformation. In group occupancy refinement all residues within the group are refined to the same occupancy, reducing the free parameters to fit.We iteratively run 5 macrocycles of refinement followed by a script that removes any conformations with occupancy less than 0.1. This script also renormalizes the occupancies of any remaining conformations in that segment, ensuring that the occupancy sums to 1. This procedure ends when no conformations have a refined occupancy of less than 0.1 or after 50 total rounds of refinement (whichever comes first). After, we do one final refinement where we release the occupancy constraints on the segments, turn on automated solvent picking, and optimize B-factors (specified as ADP parameters in Phenix) and coordinate weights.To improve the detection of alternative conformations in cryo-EM structures, we made some key updates to part of the qFit algorithm. All of these updates to the algorithm will turn on with the -em flag. First, we now use electron scattering factors when calculating the modeled electron density. Second, we have removed bulk solvent electron density values (set at 0.3 in X-ray qFit protein). We also restricted the cardinality to be 0.3 (compared to 0.2 in X-ray qFit protein) to reduce misplaced conformations.https://github.com/gregdp/mapq). qFit takes in a text file of Q-scores by using the –qscore option in qFit_protein. By default, all residues with a Q-score of less than 0.7 are not modeled as multiconformers, but are considered in qFit segment. Users can also adjust this level by using the –qscore_cutoff option in qFit protein.We implemented the option for users to use Q-scores to determine if qFit should be run on a residue or not. This option is off by default. To use this option, generate Q-scores (mapq.py) available as part of the Q-score command-line interface  to facilitate manual model adjustment after running qFit. This procedure generates connected backbones with consistent occupancies for coupled neighboring conformers.qFit can be used as a tool along with iterative model building and refinement. If a user manually removes or adds additional conformations using CootFor example, suppose residue N has four alternative backbone conformations  and residue N+1 has two alternative conformations . In that case, this procedure will create C and D conformers for residue N+1 by duplicating its A and B conformers. This duplication continues until we reach the end of a segment so that all backbones have the same number of alternative conformations  and are, therefore, properly connected. Subsequent crystallographic refinement of this model (see “Post-qFit refinement script” above) will cause the duplicated conformations to diverge slightly, and will behave as expected without introducing geometry errors.https://github.com/ExcitedStates/qfit-3.0). Our scripts for running qFit protein on an SGE-based server and all scripts for figures can be found here: https://github.com/fraser-lab/qFit_biological_testset/tree/main.Scripts for all metrics can be found in the scripts folder in the qFit GitHub repository (deposited_rerefine.sh) and for the qFit models after the iterative refinement script .R-values were obtained after the final round of refinement for the re-refined deposited models (RMSF.py).RMSF was measured for each residue based on all side-chain heavy atoms. First, the center of all conformations is identified. We then calculate the square root of the mean of the squared distances between the center of each individual conformations to the center of all conformations. Each distance is then weighted by occupancy . We obtain the average heavy atom B-factor for every conformation and multiply it by the occupancy.phenix.rotalyze34 while manually relaxing the outlier criteria to 0.1%. Each alternative conformation is given its rotamer. Rotamers were compared on a residue-by-residue basis. To compare rotamers, we only consider the first two χ dihedral angles. Each residue was classified into four categories: same, additional rotamer in qFit model, additional rotamer in the deposited model, or different.The rotamer name for each alternative conformation was determined by 2 for every 0.1 Å reduction in resolution and placed the models in a P1 box. We randomly shook the coordinates using the shake argument in phenix.pdbtools with root-mean-square error of shaking given as 0.2 * desired resolution of synthetic data. We generated structure factors . We then added noise to the structure factors as follows:To generate artificial electron density data at increasingly poorer resolutions, we first increased the B-factors of all atoms of the ground truth model by 1 Åsftools command in CCP4. Then, the ground truth model with adjusted B-factors was stripped of alternative conformations (if any) at every residue position. The resulting single-conformer model was refined with the The scaling factors of 0.2 and 0.5 for shake RMSD and noise addition was determined by trying out different values and finalizing on the values which gave the most reasonable R-factors over the resolution range after refining the model against the generated structure factors. The addition of noise to  factors .phenix.refine as explained in the post-qFit refinement script section. Since there is some randomness involved in simulating noise in the synthetic datasets, we repeated the synthetic data generation ten times at each resolution and all the steps following that including qFit modeling and post-qFit refinement for each of these noisy synthetic maps. The same steps of data synthesis were followed for the larger qFit test dataset containing 110 models, except that one set of structure factors was generated for each model at each resolution instead of ten as in the 7KR0 dataset.The final refined model was given as input to qFit and the composite omit map was obtained for the True positive residues were those with at least two alternative conformations and an RMSD of less than 0.5 Å between the ground truth and qFit model conformations  . A falseSupplement 1Supplement 2Supplement 3