Are there any correlations among different potential domains of quality among adult liver transplant programs?In this cohort study of 114 transplant programs that performed a total of 44 554 transplants, there was no correlation among measures reflecting recipient outcomes , program aggressiveness , or waiting list management . Furthermore, only 2 adult liver transplant programs were found to be in the best performing quartile for all 3 domains concurrently.These findings suggest that institutional-, regional-, or policy-level interventions to improve transplant program performance in one domain of quality may not be associated with deleterious outcomes in improvement in other potentially related domains. This cohort study examines correlations among different potential domains of quality for US adult liver transplant programs. US liver transplant programs have traditionally been evaluated on 1-year patient and graft survival. However, there is concern that a narrow focus on recipient outcomes may not incentivize programs to improve in other ways that would benefit patients with end-stage liver disease.To determine the correlation among different potential domains of quality for adult liver transplant programs.This retrospective cohort study was conducted from 2014 to 2019 among adult liver transplant programs included in the United Network for Organ Sharing and Scientific Registry of Transplant Recipients program-specific reports. Liver transplant programs in the United States completing at least 10 liver transplants per year were included. Data were analyzed from March 2 to August 13, 2020.The potential domains of quality examined included recipient outcomes , aggressiveness , and waiting list management . The correlation among measures, aggregated at the center level, was evaluated using linear regression to control for mean Model for End Stage Liver Disease–Sodium score at organ allocation. The extent to which programs were able to achieve high quality across multiple domains was also evaluated.P = .19) or marginal graft use  after correcting for mean allocation Model for End Stage Liver Disease–Sodium scores. There were 2 transplant programs (1.8%) that performed in the top quartile on all 3 measures, while 4 transplant programs (3.6%) performed in the bottom quartile on all 3 measures.Among 114 transplant programs that performed a total of 44 554 transplants, the mean (SD) 1-year graft and patient survival was 90.3% (3.0%) with a total range of 75.9% to 96.6%. The mean (SD) waiting list mortality rate was 16.7 (6.1) deaths per 100 person-years, with a total range of 6.3 to 53.0 deaths per 100 person years. The mean (SD) marginal graft use rate was 15.8 (8.8) donors per 100 transplants, with a total range of 0 to 49.3 donors. There was no correlation between 1-year graft and patient survival and waiting list mortality (β = −0.053; This cohort study found that among US liver transplant programs, there were no correlations among 1-year recipient outcomes, measures of program aggressiveness, or waiting list management. These findings suggest that a program’s performance in one domain may be independent and unrelated to its performance on others and that the understanding of factors contributing to these domains is incomplete. For example, since the start of public reporting, 1-year graft and patient survival rates for liver transplant programs improved from approximately 82% in 1992 to 92% in 2020.2Since 1991 the United Network for Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipients (SRTR) have been required to publish regular reports on program-specific survival rates for all solid organ transplants performed in the United States.2 This may make it difficult to identify the best and worst performing programs and limit how much can be learned from each for the purpose of quality improvement. Second, the narrow focus on recipient outcomes may detract from opportunities to improve in other domains of quality. For example, there may be value in programs’ use of marginal organs, termed aggressiveness, to maximize the number of patients receiving transplants. There may also be value in optimizing outcomes for patients on the waiting list, which reflects how the broader population of patients with liver disease are treated.4 However, changes in practice to improve quality in one domain may adversely impact performance in the others. At present, little is known about the correlation between potential domains of quality among liver transplant programs.Despite improvements in outcomes, there are growing concerns over the singular focus on program-specific recipient survival rates. First, 1-year graft and patient survival rates do not vary much, with 95% of programs rating within 7% of the mean in 2020, a statistic that has not changed substantially over the last 30 years.To address this, we examined the correlations among 1-year graft and patient survival and 2 proxy measures, waiting list management and program aggressiveness, after deceased-donor liver transplantation using aggregated data from UNOS and SRTR. We hypothesized that aggressiveness will be inversely correlated with waiting list mortality and that aggressiveness will be inversely correlated with recipient outcomes, specifically 1-year graft and patient survival. In this study, we evaluated whether performance within each domain was individually associated with performance in the other 2 domains and sought to determine what proportion of US transplant programs excelled in 1, 2, or all 3 domains.STROBE) reporting guideline for cohort studies.This cohort study was deemed exempt from approval and informed consent by the institutional review board at the University of Michigan, Ann Arbor, owing to the use of retrospective deidentified data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  greater than 40. These data were merged with SRTR National Center-Level Summary data regarding 1-year graft and patient survival for deceased donor liver transplants, mean Model for End Stage Liver Disease–Sodium (MELD-Na) allocation scores, and program-level aggregated data regarding waiting list mortality extracted from program-specific reports, which are also publicly available on the SRTR website.All 146 liver transplant programs in the US with data available were included, except for those that met the following exclusion criteria: fewer than 10 liver transplants per year (19 programs) or pediatric-only programs (13 programs). This resulted in a final cohort of 114 liver transplant programs with data available for analysis.5 After the creation of these proxy measures of quality, our primary outcomes of interest were the correlation coefficients among these 3 domains of quality. An additional analysis was undertaken to assess the role that donor service area (DSA) competition had in associations among domains of quality. We split the included transplant centers into low competition (contained within a DSA with ≤3 included transplant centers) and high competition (contained within a DSA with >3 included transplant centers) and repeated our analysis assessing the associations with performance across the 3 domains of quality.We measured recipient outcomes using 1-year patient and graft survival. We generated proxy measures for waiting list management quality and program aggressiveness. For waiting list management, we used program-specific 1-year waiting list mortality rates, which are available in the program-specific reports. For program aggressiveness, we calculated the rate of marginal graft use per 100 transplant episodes, which included the use of any graft from a donor of age older than 65 years, with BMI greater than 40, or who was deceased by cardiac death. While no universal definition of aggressive graft use exists, a variety of measures, including age, Centers for Disease Control and Prevention (CDC) classification of increased infectious risk, elevated liver function test results, donation after cardiac death, and elevated BMI. The criteria chosen for this analysis were chosen a priori, because they represent the most common categories of marginal grafts that have been published, with the exception of CDC increased infectious risk classification, which has not been associated with worse outcomes and which recent data suggest is not associated with differential organ use rates.Data were extracted and analyzed between March 2 and August 13, 2020. The purpose of this analysis was to evaluate the association between measures of transplant program quality. The 3 domains included recipient outcomes using mean 1-year graft and patient survival, waiting list management using mean unadjusted waiting list mortality rates, and program aggressiveness using proportion of donors meeting the criteria for marginal graft. Since our data sources report these outcomes yearly, along with the number of patients both on each program’s waiting list and who received transplants during the study period, these data were aggregated over the study period to allow for the calculation of program-specific mean 1-year graft and patient survival, allocation MELD-Na, and waiting list mortality rates for the entire study period.P = .05 significance level for all hypothesis testing.We report descriptive statistics and completed univariable testing with the Kruskal-Wallis test for nonparametric variables and the analysis of variance test for parametric variables. We assessed correlations between the combinations of each of these 3 domains using linear regression models. All models included mean allocation MELD-Na for the transplant center over the study period as a covariate. STATA statistical software, version 16.1/MP, (StataCorp) was used to perform all statistical analyses. We used a 2-sided approach at the A total of 114 transplant programs were included, completing 44 554 liver transplants over the study period. The median (interquartile range [IQR]) annual deceased-donor liver transplant volume was 69 (38-117) donors and the mean (SD) allocation MELD-Na was 29.1 (3.0). Among all programs, mean (SD) 1-year graft and patient survival was 90.3% (3.0%) with a total range of 75.9% to 96.6%. The mean (SD) waiting list mortality rate was 16.7 (6.1) deaths per 100 person-years, with a total range of 6.3 to 53.0 deaths per 100 person years. The mean (SD) marginal graft use rate was 15.8 (8.8) donors per 100 transplants, with a total range of 0 to 49.3 donors. A summary of demographic differences among programs stratified by performance in the domain of recipient outcomes is reported in A summary of differences in the outcomes assessed among programs in each category of quality, stratified by performance, is reported in P = .83) or its waiting list mortality rate . Similarly, no correlation was found between each program’s waiting list mortality rate and its marginal graft use rate . We found a statistically significant unadjusted correlation between a program’s mean annual transplant volume and its marginal graft use rate .To investigate the correlation among each of these domains with the others, we generated scatterplots of the unadjusted rates for each of the 3 domains with each of the other domains . We founThe results of the additional analysis split by high- and low-competition DSA’s were similar to those found within the primary analysis. We found no significant correlations among a program’s 1-year graft and patient survival rate, marginal graft use rate, or its waiting list mortality rate, regardless of the transplant center being geographically located within a high- or low-competition DSA , using lower-quality grafts may result in a worsening of 1-year graft and patient survival.9 In an effort to improve survival rates, programs may elect not to place patients who are particularly ill on the waiting list in favor of recipients with increased likelihood of survival or may elect to instead pass on marginal grafts that they believe may have increased probability of failure compared with standard-risk organs.12 These strategies may worsen existing disparities in end-stage liver disease outcomes by limiting access to waiting list registration for patients receiving care in locations with poor performance on traditional survival measures. However, contrary to our hypotheses, we found that a program’s performance in any domain of quality was uncorrelated with its performance in the other domains. This has several important implications. First, the lack of correlation between program aggressiveness and 1-year graft and patient survival raises concerns about the principles guiding our decision-making around marginal grafts and the quality or usability of currently discarded grafts. Second, the lack of correlation between marginal graft use rate and waiting list mortality rate suggests that measurement of outcomes within the broad population of patients with liver disease is likely complex and at the very least our understanding of the factors affecting these measures are incomplete. For these reasons, performance in any 1 of or even several of these quality domains should be deemphasized in favor of a broader few of transplant center quality. Specifically, transplant centers should be incentivized to make certain their candidate pool reflects societal need, and, importantly, the demographic characteristics of the communities they serve. Transplant payer networks, including Centers for Medicare & Medicaid Services, should be asked to ensure that access to transplant is not overly cumbersome for marginalized populations, such as non–English speaking patients, members of racial/ethnic minority groups, and patients without financial resources. While payers have more influence over access to transplantation, center-level efforts to increase transplant access  should be incentivized and measured as factors associated with transplant center quality. Some measure of performance in the treatment of the broader population of liver disease seems particularly salient to our patients and the expansion of our measures of aggressiveness and waiting list outcomes to include components that more accurately reflect population-level quality may more closely approximate the ideal of true population-based quality measures.Liver transplant programs may make changes to their practices under the assumption that a relationship exists among performance in the domains of waiting list management, aggressiveness, and recipient outcomes.The results of this study should be interpreted within the context of certain limitations. Our data represent program-level aggregated rates rather than rates calculated using individual patient data owing to restrictions from the SRTR on the use of center-identified patient-level data. While there was little variation in unadjusted rates of 1-year graft and patient survival, future studies using individual patient data allowing for adjustment for patient characteristics may reveal an association not seen with our analyses. Additionally, we used mean rates over a 5-year study period. It is possible that significant changes occurred over the 5 years of data included in this study such that a correlation may be present year-over-year that would be obscured by using longer time interval mean rates. That said, low annual volumes for many of the programs included could make shorter time scales less reliable as true measures of transplant program quality and more influenced by random variations in measure performance. This is further related to the cross-sectional nature of the study, limiting our ability to investigate how changes in performance in one domain of quality may be related to changes in another domain for a particular program. Additionally, our data do not contain information that may influence complexity of care  and therefore limit our ability to account for the influence of these factors on program quality. Similarly, a variety of unmeasured center, region, organ-procurement organization, or donor service area factors may influence the association between any of the measured discussed herein. While it seems unlikely that these characteristics would differentially affect quality measure associations across centers, we cannot assess this directly in this study. We corrected for mean allocation MELD-Na in our models to adjust for known program-level differences in patient risk as well as a crude measure of donor availability, which may impact the quality measures assessed here, but further adjustment for clinical characteristics may reveal an association not elucidated in this study.This cohort study found that there was no correlation among adult liver transplant programs’ performance with respect to unadjusted 1-year graft and patient survival rates and performance on 2 alternative measures: waiting list mortality rates and marginal graft use rates. We also found that only 1.8% of programs were in the top quartile for all 3 measures, with the plurality of programs not represented in the top quartile for any of the 3 measures. These findings suggest that institutional-, regional-, or policy-level interventions to improve program performance on one measure may not have deleterious effects on performance on the other measures. However, further studies using individual patient-level data allowing for robust adjustment of clinical characteristics are needed.


Fagus sylvatica) rot holes in Windsor, Savernake, and Epping Forests (United Kingdom). For each rot hole, physical and environmental conditions were measured, including tree diameter, rot hole dimensions, rot hole height, substrate density, water content, and water potential. These parameters were used to assess the influence of environmental conditions and habitat characteristics on invertebrate communities. Rot hole invertebrate communities were extremely diverse, containing both woodland generalist and saproxylic specialist taxa. Large variation in community structure was observed between rot holes and across woodlands; all sites supported threatened and endangered taxa. Environmental conditions in rot holes were highly variable within and between woodland sites, and communities were predominantly structured by these environmental conditions. In particular, turnover between invertebrate communities was linked to high β-diversity. The linked heterogeneity of environmental conditions and invertebrate communities in rot holes suggests that management of deadwood habitats in woodlands should strive to generate environmental heterogeneity to promote invertebrate diversity. Additional research is required to define how management and conservation activities can further promote enhanced biodiversity across rot holes. Hollows of veteran trees  provide habitat for many rare and threatened saproxylic invertebrates. Rot holes are highly heterogeneous, particularly in terms of substrate and microclimate conditions. There is, however, a dearth of information regarding the differences in biological communities inhabiting rot holes with different environmental conditions. Invertebrates were sampled from European beech ( Rot holes, or tree hollows and cavities, can provide stable and long-lasting habitats for many organisms , includiPrevious intensive woodland management has led to a significant loss of large old trees and their associated microhabitats in many woodland systems . As a reUsing family-level analyses of whole invertebrate communities, this study aims to investigate rot hole invertebrate community structure and its relationship with a range of microhabitat characteristics. It was hypothesized that rot holes contain diverse invertebrate communities that are: (i) significantly different from one another within and between woodland sites; (ii) related to physical habitat characteristics such as tree diameter and rot hole height; and (iii) different from one another across gradients of microhabitat conditions .Fagus sylvatica L. ). Sample size was determined by the frequency, volume, accessibility, and suitability of rot hole habitats from which material could be collected . Visual searches were conducted in pre-selected areas of each woodland determined by availability of fallen trees for extraction of cross-sections for a linked mycological project. Trees within a ~25 m radius of these areas were investigated, as well as those found on walking routes guided by site managers.Samples were collected between February and July 2016 from three ancient woodlands in southern England, United Kingdom: Epping Forest , Savernake Forest , and Windsor Forest . All woodlands are Sites of Special Scientific Interest (SSSI) notified under Section 28 of the Wildlife and Countryside Act 1981  and supp3 of rot was removed by gloved hand and placed into a plastic zip-lock bag; never more than 50% of the available rot was collected . The height of rot holes from the base of trees, tree diameter at breast height (1.3 m above ground), and rot hole opening dimensions were measured . Between 500 and 2,000 cmOn breaking the substrate apart, any invertebrates found were placed in tubes containing 70% ethanol . All remaining material was processed through Tullgren funnels  and left for 72 h with vials containing 70% ethanol used for collection. Invertebrates were identified under an Olympus SZX7 stereomicroscope using morphological keys. While identifications were made to species level when appropriate specimens and morphological keys were available, analyses were conducted at the family level to avoid bias by ensuring even representation of diversity across taxa. The feasibility of species identification was limited by a combination of the lack of appropriate species-level keys , the majority of specimens being immature and thus unidentifiable , and the poor condition of some collected specimens. While appreciating that species-level identification provides the greatest resolution, the taxonomic breadth covered in this study was considered sufficient to allow informative and valuable analysis.3), water content (%), and water potential (MPa). Density (d) and water content (u) were determined by weighing, oven-drying (at ~60°C for 72 h) and further weighing, prior to calculating using:Each sample was characterized by density (g/cmv is the fresh volume (cm3), mw is the mass of water (g), mwet is the wet mass (g), and mdry is the oven-dry mass (g).where Water potential was measured using a WP4C Dew Point PotentiaMeter , calibrated using 0.5 M KCl standard solution  to identify either positive or negative associations between families  was then related to rot hole environmental characteristics. We first summarized the environmental conditions measured in the rot hole habitats, using principal component analysis . FollowiA total of 9,577 individuals from 66 invertebrate families were collected . Communi2 = 0.13, df = 2, P = 0.94; Kruskal–Wallis rank sum test), and communities generally exhibited high α-diversity (0.68 ± 0.17 SE). The diversity of rot holes, at the site (β-diversity) and landscape (γ-diversity) scales, was also high . Differences between diversity across sites, β-diversity (0.96), were predominantly driven by turnover (0.88) rather than nestedness (0.08). BINMATNEST results also showed limited nestedness across rot holes .To test the first hypothesis, we investigated the diversity of rot hole invertebrate communities across woodlands, which was similar . Co-occurrence was infrequent across rot holes, with 71.91% of taxa combinations occurring once or less . In the subset of co-occurrences (n = 621), only 7.7% (n = 48) were nonrandom, with most (n = 42) of these combinations being positive . Communities were different from one another along both PC1  and PC2 ; the most distinct pattern was lower richness and abundance present in the wettest and least dense rot hole habitats. The nestedness of communities was related to neither PC .To address the second and third hypotheses, we compared invertebrate communities against the characteristics of the rot holes they inhabited. Invertebrate community composition was significantly related to PCs , between sites (β), and across the landscape (γ). Variation in community structure was driven by a range of environmental conditions, including water content and substrate density, as well as rot hole dimensions and tree diameter. Heterogeneity of rot holes is thus an important consideration in the management and conservation of these woodland microhabitats, even at the family level.The rot hole communities were very diverse, comprising a variety of families involved directly and indirectly in the decomposition process . The difCommunity diversity was primarily driven by intersite variation and environmental gradients both between sites and across rot holes. Climatological and geographical differences along a gradient from west to east , as well as differences in the regional invertebrate families, are likely to be responsible for generating systematic differences in community structure between woodlands  (Abiotic conditions directly affect invertebrate community composition due to the specific habitat requirements of many taxa . Both mobaeidae) . These abaeidae) .Although the conservation of veteran trees has been identified as important for woodland management , an undeieab071_suppl_Supplementary_MaterialsClick here for additional data file.


Corn starch serves as food, feed, and a raw material for industrial use. Starch makes up most of the biomass of the corn hybrid and is the most important and main yield component in corn breeding programs. Starch is composed of two polymers, branched amylopectin and linear amylose, which normally constitute about 75% and 25% of the corn starch, respectively. Breeding for corn starch quality has become economically beneficial because of the development of niche markets for specialty grains. In addition, due to the increased demands of biofuel production, corn ethanol production is receiving more attention. Consequently, improving starch quantity has become one of the most important breeding objectives. This review will summarize the use of corn starch, and the genetics and breeding of grain quality and quantity for industrial applications. Corn is the third most important grain crop in the world after wheat and rice. It serves as a primary energy source in livestock feed. Corn is processed into a multitude of food and industrial products, including starch, sweeteners, corn oil, beverages and industrial alcohol, and biofuel . In the https://www.nass.usda.gov/ accessed on 27 November 2021). Due to the premiums offered by end-users, specialty starch corns are receiving more attention as potentially profitable alternatives to conventional field corn. Traditionally, corn has been treated and marketed as a bulk commodity. Corn grain users and processors have become more interested in the quality characteristics of corn grain. Corn that meets specific user needs is called value-added corn. For example, waxy corn for wet milling is usually grown under contract for wet-corn millers with a premium of USD 0.55 per bushel (US Grains Council: https://grains.org/ accessed on 27 November 2021). In another example, the Enogen corn variety provides alpha amylase enzymes to ethanol plants, and ethanol producers are willing to reward farmers with a premium of up to USD 0.40 per bushel (US Grains Council: https://grains.org/ accessed on 27 November 2021).Starch is a major component of corn and makes up approximately 75% of its dry matter, which accumulates in the endosperm tissue ,2,3. StaPlant breeders have modified the starch of corn to better meet the needs of the livestock feeder, food industry, and industrial users ,2,3,4. Thttps://www.ncga.com/ accessed on 27 November 2021). In wet milling, the grain is soaked or steeped in water and dilute sulfurous acid for 24 to 48 h. This steeping facilitates separation of the grain into many of its component parts. After steeping, the corn slurry is processed to separate the corn germ. The corn oil from the germ is extracted, and the remaining starch, gluten, and fiber components are further segregated. The starch can then be processed in one of three ways: fermented into ethanol, dried, and sold as dried or modified corn starch, or processed into corn syrup , which, according to the US Environmental Protection Agency (https://www.epa.gov/ accessed on 27 November 2021), is one of the six most common air pollutants and results in negative effects on the environment. A few advanced wet-milling technologies have been introduced, such as enzymatic wet milling to shorten steeping time and reduce SO2 application, saving 5% of operation costs, and ultrasound-assisted wet milling, which increases starch yield by 10% with the same input resources . Si. Siae) gae) is located on chromosome 5, which encodes the starch-branching enzyme II (SBEIIb) (ae locus has been sequenced (Gene ID: 542238), and the gene has 16,939 bp composed of 22 exons. Unlike waxy corn, ae mutant alleles do not confer 100% amylose starch, but rather a range of amylose proportions depending on genetic modifiers (multi-enzyme reaction) and the genetic background and environment (cooler temperature results in higher amylose content) [ae) has been identified to produce high-amylose corn lines (AE11 line) since 1948 [ae) allele, result in an increase in starch amylose content higher than 70%.The single recessive gene mutant amylose-extender ((SBEIIb)  [14,22,2nce 1948 . Howevernce 1948  releasedSBEIIb gene containing 84 bp decreased the total activities of SBEs to about 71% and increased the amylose proportion of corn kernels up to 60%. The high amylose proportion suggested that this germplasm line was derived from the same resource of the high amylose inbred line, AE11. In general, there is a negative correlation between amylose proportion and starch content that was found in most of the high amylose inbred germplasm lines. However, this study showed that the kernel weight per ear of GEMS0067 was only 8.3% lower than its wild type. Therefore, GEMS0067 was recommended as a high potential elite germplasm line for corn breeding of a high amylose proportion. Wu et al.  studied SBEIIa and SBEIIb play a key role in amylopectin synthesis. In kernels of the SBEIIa mutant, the degree of starch branching and composition are unaffected [SBEIIb mutant (the ae mutant), kernels contain elongated starch granules, exhibit drastically increased amylose content, and the structure of amylopectin is distinctly altered, exhibiting a lower branching degree [ZmSBEIIa and ZmSBEIIb genes by hairpin SBEIIRNAi constructs into the elite inbred corn line Chang7–2. These SBEIIRNAi transgenes led to the down-regulation of ZmSBEIIa and ZmSBEIIb expression and SBE activity to various degrees and altered the morphology of starch granules. Transgenic corn lines with amylose content of up to 55.9% were produced, and these avoided the significant decreases in starch content and grain yield that occur in high-amylose ae mutants.SBEs have multi-isoforms in corn; affected ,23,25,26g degree ,25,30. Zg degree  reportedCurrently, the fastest growing use of corn is ethanol fuel production. Ethanol fuel production is making a significant contribution to creating renewable fuels and reducing carbon emissions for a cleaner environment ,33. One Bt2) gene and the Shrunken2 (Sh2) gene  transcription factor expressed during kernel development. Qi et al. [ZmDof3 is associated with a starch-synthesis-related gene, such as Su2 (encodes SSIIa). ZmDof3 knockdown prohibited starch accumulation, resulting in reduced starch content.There are various researchers attempting to enhance total starch accumulation and a consequent increase in starch quantity. One example involves engineering ADP-glucose phosphorylase (AGPase) activity in endosperm. AGPase is composed of two subunits, encoded by the Brittle2 (h2) gene . Li et ai et al.  discoverBt2, Sh2, Sh1 and GBSSIIa (to enhance the activity of sucrose synthase (SS), AGPase, and granule-bound starch synthase (GBSS)), with the suppression of SBEI and SBEIIb (to reduce the activity of starch branching enzyme) using RNAi technology. Transgenic corn plants expressed all six genes, which increased endosperm starch content from 2.8% up to 7.7%, and there was a 37.8–43.7% increase in the proportion of amylose compared to untransformed control plants. Additional agronomic trait improvements were found, such as a 20.1–34.7% increase in 100-grain weight, and a 13.9–19% increase in ear weight. These promising results that unlock the multigene engineering approach can be applied to improve starch quantity and quality as well as starch-dependent agronomic traits simultaneously.As previously addressed, starch biosynthesis is a complex trait controlled by many genes and modifiers; thus, there are multiple potential breeding targets for total starch improvement. Jiang et al.  demonstrIn addition to the genetic engineering approaches addressed above, conventional breeding approaches with DNA markers have also been performed. With advanced genotyping technology and computational biology, the genome-wide association study (GWAS) is becoming a popular method to characterize the causal relationship between genetic polymorphisms (gene) and biological traits (phenotype). By conducting a GWAS in a set of 263 inbred lines, Liu et al.  identifiWe understand the key steps in starch metabolism, but how they are connected requires further clarification. The molecular mechanism of network and regulation involved in overall starch biosynthesis and accumulation remains unclear. From a breeding perspective, single-gene mutants are used to give stepwise improvements in these traits. As addressed in this review, there are many genes and modifiers that contribute to starch biosynthesis. We continue to face challenges in starch breeding of corn .Throughout the past decades, there have been revolutionary advancements in various omics and sequencing technologies, as well as computational biology. Proteomics datasets have been produced to study the characterization of the coordinated accumulations of numerous proteins responsible for starch biosynthesis in corn . DNA maMoreover, other challenges have begun to surface with large-scale and multi-dimensional datasets produced by advanced high-throughput genomic, transcriptomic, proteomic data, and phenotype and environmental data. Regarding these, machine learning (ML) plays a pivotal role in data mining and analysis, providing relevant information that can be used for efficient interpretation of results and decision making toward characterizing the gene network and achieving breeding targets ,45. ML h


Recent advances in experimental techniques have enabled sequencing of TCRs and their antigenic targets (epitopes), allowing to research the missing link between TCR sequence and epitope binding specificity. Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance. Here, we establish a By encoding epitopes at the atomic level with SMILES sequences, we leverage transfer learning and data augmentation to enrich the input data space and boost performance. TITAN achieves high performance in the prediction of specificity of unseen TCRs (ROC-AUC 0.87 in 10-fold CV) and surpasses the results of the current state-of-the-art (ImRex) by a large margin. Notably, our Levenshtein-based K-NN classifier also exhibits competitive performance on unseen TCRs. While the generalization to unseen epitopes remains challenging, we report two major breakthroughs. First, by dissecting the attention heatmaps, we demonstrate that the sparsity of available epitope data favors an implicit treatment of epitopes as classes. This may be a general problem that limits unseen epitope performance for sufficiently complex models. Second, we show that TITAN nevertheless exhibits significantly improved performance on unseen epitopes and is capable of focusing attention on chemically meaningful molecular structures.https://github.com/PaccMann/TITAN.The code as well as the dataset used in this study is publicly available at Bioinformatics online. A large repertoire of T-cell receptors with different specificity is needed to provide protection against a wide range of pathogens. This repertoire is generated using stochastic gene recombination and can theoretically produce diversities of 10eceptors  in an inRecent advances in high-throughput sequencing techniques have led to the generation of an increasing amount of datasets linking TCR sequences to the epitopes they bind. However, the available data is still extremely sparse compared to the high dimensionality of the search space created by the TCR theoretical diversity. Moreover, current experimental settings typically link many TCRs to a single epitope, which leads to datasets that contain information about tens of thousands of TCRs, but only a few hundred different epitopes.Nevertheless, several studies have attempted the prediction of TCR specificity from sequence using machine learning  exploits a bimodal neural network architecture to explicitly encode both TCR and epitope sequences. More specifically, TITAN uses convolutions to aggregate local information and fuses the modalities, using an interpretable attention mechanism from which binding probabilities are predicted.Since interpretability is paramount in healthcare applications of machine learning, the use of context attention is central to our model, as it allows us to explain the choices of the algorithm and to analyze which amino acids or even atoms the model focuses on. These highlighted entities can be interpreted in the context of the underlying biochemical processes. We explore different encoding strategies for the epitopes such as SMILES , a strinβ chain sequences.In order to assemble a larger and more diverse dataset, we combine data collected in the VDJ database  with a rβ sequences downloaded from the VDJ database , i.e. 40 438 TCR sequences assigned to 191 peptides. Since this dataset is highly imbalanced, we exclude epitopes with less than 15 associated TCR sequences and downsample to a limit of 400 TCRs per epitope. After these preprocessing steps, we are left with a dataset of 10 599 examples on 87 epitopes. We refer to this dataset as VDJ.We use all human TCRThe COVID-19 dataset  originally contained 154 320 examples associated with 269 different epitopes or groups of epitopes. To avoid ambiguity, we keep only samples associated with a single unique epitope and exclude unproductive sequences. Then we apply the same preprocessing steps as for the VDJ dataset, downsampling to 400 TCRs/epitope and excluding epitopes with less than 15 associated TCRs to arrive at a dataset of 12 996 examples.VDJ+COVID-19.We refer to the combined dataset with samples from VDJ and the COVID-19 dataset as Since these primary datasets contain only positive examples, we need to generate negative data. This can be achieved by shuffling the sequences, thereby associating TCRs with epitopes that they have not been shown to bind. Due to the low probability of a randomly drawn TCR binding a specific epitope, this manner of generating negative samples is established in the field . It has To ensure a fair comparison to the state of the art model ImRex, we also downloaded the publicly available dataset that ImRex was trained on. This dataset is based on the VDJ database and contains 13 404 samples for 118 different epitopes, with 50% negative samples. We use it to train all models for the final comparison (see Section 3.4). To evaluate the performances, we use an independent test set generated from the McPAS database  (downloaWe are interested in learning a mapping k-nearest-neighbor (K-NN) classifier. As a distance metric between samples, we utilize the sum of the length-normalized Levenshtein distance of the respective epitope and TCR protein primary sequences.Our baseline model for the presented TCR-epitope binding prediction is constituted by a ie and it to be epitope and TCR sequences, respectively (it is the CDR3 region). Let ja, we first retrieve the subset of training data k nearest neighbors using the distance measurek (to avoid ties), s.t. k leading to the best ROC-AUC score for comparisons. Note that this model is non-parametric.More formally, for the training data distance , i.e. a The TCR sequences are encoded with the BLOSUM62 matrix , which iiα are computed as:The remaining architecture is inspired by reference input, where T is the sequence length and H is the number of convolutional filters. Further, context input, where U and K are sequence length and number of convolutional filters in the other modality, respectively. A = 16 and then summed up, which enables the layer to take the context into account for determining feature relevance. iα, which are used to filter the inputs. Finally, both TCR and peptide information gets passed to two dense layers with 368 and 184 nodes, respectively, which output the binding probability.We call A = 256, using four convolutional channels with kernel sizes 3, 7, 9 and 13 for epitopes and 3, 7, 13, 19 for TCR and using three final dense layers with 2048, 1024 and 512 nodes.By using SMILES encodings of the epitopes, predicting epitope receptor binding affinity can be seen as a CPI prediction task. We utilize BindingDB , as of ATCR split. Additionally, we generate a strict split, where we ensure that each TCR and each epitope is restricted to a single fold, ensuring that neither TCRs nor epitopes contained in the validation dataset were shown during training. To ensure the separation of TCRs and epitopes in their folds, we generate negative data by shuffling within each fold. A UMap  and a learning rate of 0.0001. In the convolutional and dense layers, we employed dropout (P = 0.5) and ReLU activation. All models were trained with a batch size of 512 on a cluster equipped with POWER8 processors and a single NVIDIA Tesla P100. The learning rate was tuned using the VDJ dataset and the remaining hyperparameters were chosen carefully based on previous experience.All described architectures were implemented in PyTorch 1.4 and used the pytoda package for data handling and preprocessing. The models optimized binary cross entropy loss with Adam  , we can represent them in a fine-grained, atom-wise manner using SMILES strings. We can see in k = 13, the K-NN baseline achieves the best results with a ROC-AUC of 0.78 ± 0.007 and a balanced accuracy of 0.71 ± 0.008 —over a number of exemplary CDR3 sequences. We see that while there is some preference to focus on certain positions, the model adapts the attention to the different sequences. The mean inter-TCR variance per token is at We can investigate the decision processes of TITAN using the information contained in the attention layers. AA CDR3 setting) on several exemplary epitopes. We can clearly see that the model chooses to focus heavily on the same positions on each epitope. The preferred positions are independent of the sequence of both the input epitope and the context TCR. Comparing the attention scores across epitopes, we find that both the inter-epitope and the intra-epitope variance of the attention are extremely small, at pretrained semifrozen augmented setting. The chosen epitope CINGVCWTV was not shown during training. The attention scores therefore reflect the transferable knowledge the model has gained during pretraining. We see that the attention patterns align well with our expectations. The attention is high on many of the oxygens, as well as on the two thiol groups of the cysteines. Nevertheless, we see that while the attention layers may extract some chemically relevant structures, they fail to capture others. Specifically, large and hydrophobic amino acid sidechains tend to get low attention scores, although they may be of high importance for molecule interactions.Pretraining the models on the compound interaction task greatly increased the diversity of sequences that were seen by the epitope channel of the model. While most of the ligands in BindingDB are not peptides, and may therefore exhibit structures and chemical properties that differ strongly from the epitopes, the model may nevertheless infer general rules of chemical interaction from them. This enables the model to learn more meaningful attention weights, which may be the reason for the performance boost we observe for pretrained models. In k = 25  and one where all samples including epitopes contained in the ImRex training dataset were removed (unseen epitopes test set). ROC-AUC scores are compared in Moris ed ImRex , an imagWe find that all of our model settings outperform ImRex on both independent test sets by a large margin. Moreover, our K-NN baseline model clearly outperforms ImRex on seen epitopes.AA CDR3, AA full, SMI CDR3, SMI full) all perform better on the independent seen epitopes test data than during 10-fold crossvalidation, while for the pretrained model settings, performance is comparable to the crossvalidation. For the unseen epitopes test set, we observe a similar trend as above in the strict split cross-validation. All models show performances clearly better than chance, with the pretrained semifrozen model with augmentation even achieving a ROC-AUC of 0.78. However, one needs to keep in mind, that the sample size for the unseen epitopes test is at only 1500, making especially high (or low) scores likely to be statistical outliers.Moreover, we see that the base models  instead of amino acid sequences improves the predictive power of TITAN. This reformulation not only enables data augmentation—by exploiting the non-uniqueness of SMILES —to enricTo assess model performance in a more rigorous fashion, we designed a K-NN classifier based on the Levenshtein distance as a baseline model. Surprisingly, this simple model achieves a mean ROC-AUC score of 0.79 ± 0.007 on the TCR split crossvalidation. This model surpassed the performance of complex neural networks from previous publications  and is oAs a final assessment of model performance, we compare TITAN to the current state of the art for general TCR specificity prediction, ImRex. To ensure fair comparison, we train our models on the ImRex training data and evaluate the performance on an independent test set derived from a different database. We demonstrate that both pretrained and base TITAN models, as well as the K-NN baseline, outperform ImRex by a large margin. The best result is again achieved by the pretrained semifrozen augmented TITAN model, with a ROC-AUC of 0.87 on epitopes included in the training data.Finally, the main challenge for generic TCR–epitope interaction prediction remains the generalization to unseen epitopes. Here, we report two major breakthroughs. First, we demonstrate that TITAN exhibits moderate performance on unseen epitopes, where the best TITAN model achieves a ROC-AUC of 0.62 ± 0.05 on a strict 10-fold crossvalidation split, and ROC-AUC of 0.78 on an independent test set of unseen epitopes. Second, by dissecting the attention heatmaps we were able to identify a possible explanation for the observed poor unseen epitope generalization capabilities. We demonstrate that TITAN reduces the general TCR–epitope prediction task to the simpler task of TCR classification, by internally treating the epitopes as classes instead of focusing on their properties. This shortcut could present a general problem for sufficiently complex models, as long as the extreme sparsity of sampling of the epitope sequence space is not remedied. Until then, our future endeavors might include using an enriched training dataset consisting of TCR-epitope pairs as well as compound-protein interaction pairs from BindingDB, or further improving the amino acid and SMILES embeddings by training on diverse databases UniProt . In genebtab294_Supplementary_DataClick here for additional data file.


Background: Deaths due to traffic accidents are preventable and the access to health care is an important determinant of traffic accident case fatality. This study aimed to assess the relation between mortality due to traffic accidents and health care resources (HCR), at the population level, in 66 sub-regions of Poland. Methods: An area-based HCR index was delivered from the rates of physicians, nurses, and hospital beds. Associations between mortality from traffic accidents and the HCR index were tested using multivariate Poisson regression models. Results: In the sub-regions studied, the average mortality from traffic accidents was 11.7 in 2010 and 9.3/100.000 in 2015. After adjusting for sex, age and over time trends in mortality, out-of-hospital deaths were more frequently compared to hospitalized fatal cases (incidence rate ratio (IRR) = 1.68, 95% CI 1.45–1.93). Compared to sub-regions with high HCR, mortality from traffic accidents was higher in sub-regions with low and moderate HCR , which reflected the differences in out-of-hospital mortality most pronounced in car accidents. Conclusions: Poor HCR is an important factor that explains the territorial differentiation of mortality due to traffic accidents in Poland. The high percentage of out-of-hospital deaths indicates the importance of preventive measures and the need for improvement in access to health care to reduce mortality due to traffic accidents. Injuries from traffic accidents due to the dangerous behaviors of pedestrians and drivers are one of the main causes of death in most countries. Traffic accident-related mortality is unevenly distributed geographically, with Western European countries having much lower rates than Central and Eastern European countries ,2,3. FatIn addition to poor safety standards and drunkenness influencing the behavior of road users, a significant factor that increases the risk of a fatal traffic accident is the incompliance of vehicle speeds with road traffic rules ,7,8. HigAlthough there has been a significant reduction in traffic accident mortality in the last three decades, due to improvements in road systems, education, and prevention campaigns , the cauThe purpose of the study was to assess the relation between mortality due to traffic accidents and health care resources (HCR), at the population level, in 66 sub-regions of Poland.This is an ecological-type study, in which annual data on HCR and deaths due to transport accidents collected from 66 administrative areas of Poland were analyzed. These areas were defined as sub-regions in the Nomenclature des Unités Territoriales Statistiques (NUTS-3) in 2006 . In geneThe study analyzed the data on deaths due to transport accidents. The individual categories of transport accidents have been coded by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), as transport accidents in total (codes V01–V99), including accidents with participation of pedestrian (V01–V09), pedal cyclist (V10–V19), motorcycle rider and three-wheeled motor vehicle (V20–V39), car (V40–V49), and heavy transport vehicle (V50–V97), and other and unspecified transport accidents (V98–V99) .The study analyzed the age-standardized mortality due to transport accidents. A direct standardization method that considers the demographic structure in 5-year age groups was used for separately analyzing the data for each of the 66 sub-regions of Poland. As a standard population, we assumed the population of Poland in 2010, for which standardized mortality rates were determined . In addiBased on the place of death, deaths due to traffic accidents were classified into (1) out-of-hospital deaths; and (2) fatal hospitalized cases, which included those that occurred in a hospital or long-term care institutions .Changes in mortality rates during the period 2010–2015 were described with the deaths prevented or postponed (DPP) index . The valHealth care was assessed in each sub-region using the HCR index, which was created on the basis of all human and infrastructural resources potentially affecting access to health care. Three factors were included in this index: number of physicians per 10,000 population, number of nurses per 10,000 population, and number of hospital beds per 10,000 population. To calculate the HCR index, its components—variables—were normalized by a linear transformation in such a way that their expected value was equal to 0 and standard deviation was equal to 1. Finally, the arithmetic mean of the transformed components was taken as the index value . The HCRTo determine the relationship between the traffic accident-related mortality and the HCR index, we used the Poisson regression model for count data. Calculations were realized using generalized linear models, with logarithm as a link function and Poisson as a probability distribution, including the number of people in sub-regions as the offset.Due to repeated measurements in the same statistical units , the generalized estimating equations (GEE) framework was used for obtaining generalized linear models for correlated data . An exchβ). Two types of multivariate models were evaluated. The first model included the index of HCR at the sub-region level, variables describing demographic category , and the year observation as independent variables. Depending on the place of death, different categories of deaths were analyzed in separate models as follows: out-of-hospital deaths, hospitalized fatal cases, and in total. In the second model, all traffic accident deaths were included with the index of HCR as the independent variable and an additional variable describing the place of death. This model was adjusted for gender, age, and the observation year. The results are presented as IRR with 95% CIs and p-values of corresponding Wald’s test.The Poisson models were presented using the regression coefficients that were exponentially transformed, so they can be interpreted as the expected relative change of the dependent variable  calculated for an increase of the independent variable by 1 unit  = e® SPSS® Statistics for Windows, version 20.0 statistical package . The significance level was assumed as α = 0.05 in statistical tests.Statistical calculations were performed using IBMIn the period 2010–2015, a total of 24,910 deaths due to transport accidents were recorded in Poland, which included 19,367 deaths among men (77.7%). Accidents in the age groups 25–44 (29.6%) and 45–64 (31.3%) were found to be dominant. Accidents involving cars (36.4%) and pedestrian (31.1%) were the most frequent. A high percentage of deaths due to transport accidents occurred out of hospital (65.2%) and the remaining occurred in hospital (34.1%) or long-term care institutions (0.7%).2 in the sub-regions with low HCR while it was over sevenfold higher (923 people/km2) in the sub-regions with high HCR, which indicates that high HCR index are mainly linked to highly urbanized areas.In the sub-regions studied, the range for low, moderate and high HCR index, assigned by the terciles for the whole sample, were: −0.50 to −0.14, −0.13 to 0.22 and 0.23 to 1.52, respectively (according to the average for years 2010–2015). The territorial distribution of sub-regions by HCR is presented in In the group of sub-regions with high HCR, there were about two times more hospital beds and physicians compared to sub-regions with low HCR, but only 30% more nurses. The highest mortality rate due to traffic accidents  was noted in the sub-regions with low HCR  and moderate HCR , and the deaths were mostly out-of-hospital ones . Among the analyzed accident categories, the highest burden was caused by those involving cars, and high mortality rates occurred in the sub-regions with low and moderate HCR . In sub-regions with high HCR, accidents involving pedestrians were dominant , whereas the lowest mortality rates were related to accidents involving pedal cyclists. Generally, mortality in sub-regions with high HCR was lower compared to sub-regions with low HCR, with the exception of other and unspecified transport accidents, which were the lowest in these sub-regions  .The mean percentage of out-of-hospital deaths due to transport accidents for the period 2010–2015 ranged from 23.7% in the Kraków sub-region to 96.3% in the Szczeciński sub-region .The total standardized mortality rate due to transport accidents was the highest in the sub-regions with the lowest values for the HCR index, whereas it was lowest in the sub-regions with the highest values of the index. The age standardized rate of out-of-hospital deaths was the highest in the sub-regions with low HCR . The value of both these rates was about threefold higher than hospitalized fatal cases in the sub-regions with low HCR. Likewise, in the sub-regions with moderate HCR, the disproportion of deaths was twofold, while in the sub-regions with high HCR the out-of-hospital deaths and hospitalized fatal cases were at a similar level. A comparison of hospitalized fatal cases between the groups of sub-regions showed that the number of these cases was 1.5-fold higher in the sub-regions with high HCR than those with low HCR .p = 0.080). Similarly, no significant differences were found for out-of-hospital deaths (p = 0.140) and hospitalized fatal cases (p = 0.182). Between years 2010 and 2015, the total mortality due to transport accidents decreased by 21.4%, but the changes in hospitalized fatal cases were higher (27.7%) in comparison to out-of-hospital deaths (17.5%)  .p ≤ 0.01). The effect was slightly lower for the sub-regions with moderate HCR compared to those with high HCR . Similarly significant relationships were found in cases involving pedal cyclists and cars, and partially in cases involving pedestrians (only low HCR vs. high HCR) and heavy transport vehicles (only moderate HCR vs. high HCR). The accident mortality was higher among men than women , and a significant gender relationship was found to persist in each accident category. Age was also associated with mortality in traffic accidents, with the highest increase in deaths (516%) noted in the oldest age group  compared to the youngest age group  . Similar relationships for pedestrians and pedal cyclists were observed in the older age group. In case of accidents involving motorcycle riders and three-wheeled motor vehicles, cars, and heavy transport vehicles, and other and unspecified transport accidents, compared to those in the 0–14 age group, the highest proportion of mortality occurred among young adults aged 15–24 years.A significant relationship was found between the HCR index and total mortality due to transport accidents . In the p ≤ 0.001), by 14% in sub-regions with moderate HCR compared with high HCR , as well as among accidents involving pedestrians (by 20%), and other and unspecified transport accidents (by 41%). The mortality related to HCR among pedal cyclists was higher by 25% (in sub-regions with low HCR vs. those with high HCR) and 34% (in sub-regions with moderate HCR vs. those with high HCR), as well as for accidents involving heavy transport vehicles (by 33% in sub-regions with moderate HCR vs. those with high HCR).The pattern of the increased proportion of total mortality due to traffic accidents was reflected in the out-of-hospital deaths, especially in the category of accidents involving cars. On the other hand, for hospitalized fatal cases, a significant reduction of total mortality due to traffic accidents by 25% related to HCR occurred in sub-regions with low HCR compared to those with high HCR , such as in out-of-hospital deaths  and hospitalized fatal cases . Reduced mortality trends were noted for traffic accidents involving motorcycle rider and three-wheeled motor vehicle as well as heavy transport vehicles . No significant changes were observed in mortality from accidents involving pedal cyclist and other and unspecified transport accidents .p ≤ 0.001). The mortality in sub-regions with low HCR was 25% higher compared to those with high HCR  and 19% higher in sub-regions with moderate HCR compared to those with high HCR . Similar relationships were found for accidents involving pedestrians, motorcycle riders and three-wheeled motor vehicles, and cars. Among the cases involving pedal cyclists, the opposite relationship was found: mortality was 28% higher in sub-regions with moderate HCR compared to those with high HCR and 24% higher in sub-regions with low HCR compared to those with high HCR. Moreover, among cases involving pedal cyclists, the increase in out-of-hospital deaths of 10% was lower compared to the other accident categories. In the case of heavy transport vehicles, there was dependence only with the out-of-hospital deaths and the level of HCR was negligible. For other and unspecified transport accidents, however, a 32% reduction in mortality  was found in sub-regions with low HCR compared to those with high HCR.In the 66 sub-regions of Poland, large geographical variation was observed in mortality due to traffic accidents and in HCR. A low HCR was associated with an increased mortality due to traffic accidents, especially in the case of out-of-hospital mortality. There were more fatal hospitalized cases in the areas with high HCR. The relationship between traffic accident mortality and HCR was significant after controlling for gender, age, and changes in mortality over time. For accidents involving motorcycle riders, three-wheeled motor vehicles, cars, and heavy transport vehicles and other or unspecified transport accidents, the highest out-of-hospital mortality was found in adolescents. For accidents involving pedestrians and pedal cyclists, the highest mortality was observed in the elderly.The results obtained are similar to those reported by studies conducted in other countries, despite the use of different methods for estimation ,12,13,20The higher out-of-hospital mortality rate, especially in the case of car accidents, shows that the severity of these accidents is determined by the specific infrastructure conditions of the accident site. These accidents often occur in less urbanized areas where drivers develop high speeds. The results of studies by other authors  indicateIn model 2, the increase in the rate of out-of-hospital deaths due to traffic accidents was lower compared to the results obtained from the care index for the category of pedal cyclist accidents, although in the other accident categories the increase in deaths was higher in relation to low and moderate HCR. It is likely that in the case of cyclist deaths, other features may play a greater role in increasing mortality when the level of HCR is low or moderate. However, it should be noted that areas with low levels of HCR are also less populated and have an imperfect infrastructure of bicycle paths. The higher mortality of cyclists in the areas with low- and moderate HCR may be related to these factors, which suggests that the HCR index may also be a marker of other factors connected with mortality due to traffic accidents .Our results showed gender-related health inequalities, with significantly higher mortality rates in men, as confirmed by other studies ,30. RiskPatterns of mortality due to traffic accidents differed territorially, with the highest mortality rates recorded in sub-regions with low levels of HCR, which were located in the northern part of Poland. In these areas, more out-of-hospital deaths occurred, due to health care disparities. In the sub-regions of high HCR (large urban agglomerations), out-of-hospital mortality was lower compared to the low HCR sub-regions. This was due not only to better access to care, but also to the possibility of implementing preventative measures, more frequent traffic safety checks, and better road infrastructure, including traffic flow separators, better street lighting, and stepped bends ,34,35,36The results of this study showed a significant reduction in mortality due to traffic accidents between 2010 and 2015. These positive changes may have resulted from improvements in the provision of medical equipment and apparatus to emergency services and from shorter waiting times for assistance due to the spread of mobile telephony. In addition, actions concerning the enforcement of traffic regulations and imposition of fines as well as the implementation of regulations on the technical conditions of vehicles played an important role . HoweverAccording to our knowledge, this study is the first to assess the relationship between mortality due to traffic accidents and HCR in Poland using the area-level index. The information provided by the area-based HCR index may prove useful in identifying areas in need of health interventions. In addition, only a few studies have been conducted assessing avoidable deaths (DPP), most of which take place out-of-hospital . Our anaThe following limitations should be taken into account in interpreting the results of this study. First of all, this ecological study was carried out at the population level, and cannot determine the cause, mechanism and manner of death. Secondly, the data for analysis were collected from only one source  and the study did not include information on the type of injuries or medical interventions performed. As research by other authors showed, the duration of the pre-hospital phase varies by the type of injury, which particularly applies to victims of penetrating injuries ,42,43. BIn addition, we had no information on how many accident victims received delayed medical assistance on the scene eventually died in inpatient care due to hemodynamic instability. This suggests that the results obtained may be underestimated. Moreover, the relationship between the out-of-hospital mortality and total accident mortality may be influenced by unknown factors, such as road conditions and speed of travel, weather, use of seat belts and helmets, and the impact of alcohol, which determine the severity of accidents ,7,8. NevPoor HCR is an important factor that explains the territorial differentiation of mortality due to traffic accidents in Poland. The high percentage of out-of-hospital deaths indicates the importance of preventive measures and the need for improvement in access to emergency medical services and intensive care units to reduce mortality due to traffic accidents.


Chinese Journal of Lung Cancer (Zhongguo Fei Ai Za Zhi) recently confirmed that the manuscript written by Zhu ZT, et al, published in the Sep. 2010 issue of Chinese Journal of Lung Cancer, was a duplicate publication of their another paper in Zhong Liu Fang Zhi Yan Jiu.The editorial team of 10.3779/j.issn.1009-3419.2010.09.01Zhu Z, Yu Y, Wang K, Li E, Liu Y, Liu Y. Effect of Bufalin on Proliferation and Apoptosis of Human Non-small Cell Lung Cancer A549 Cell. Zhongguo Fei Ai Za Zhi, 2010, 13(9): 841-845. DOI: 10.3971/j.issn.1000-8578.2010.09.007Yu Y, Liu Y, Wang K, Zhu Z, Xing D, Ha M. Effect of Bufalin on Inducing Cell Apoptosis and Its Mechanisms in Human Lung Adenoma Cells. Zhong Liu Fang Zhi Yan Jiu, 2010, 37(9): 1000-1003. DOI: Chinese Journal of Lung Cancer.These facts are apparent evidences of “duplicate publication” and the Editorial team decided to retract the paper from Chinese Journal of Lung CancerThe Editorial Office of Oct 19, 2010《中国肺癌杂志》编辑部决定对一文作撤稿处理。题目：蟾蜍灵对人非小细胞肺癌A549细胞增殖与凋亡的影响* 王锴 李恩泽 刘阳阳 刘云鹏作者：朱志图 郁云龙单位：121001 锦州，辽宁医学院附属第一医院肿瘤科*, 邢永达, 哈敏文. 肿瘤防治研究, 2010, 37(9): 1000-1003.]中大部分数据重复刊发，属于变相“重复发表”行为。原因：此文与《肿瘤防治研究》 2010年37卷第9期通讯作者为朱志图的文章[郁云龙, 刘云鹏, 王锴, 朱志图《中国肺癌杂志》编辑部2010年10月19日https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969378


Processing results in the transformation of pulses’ structural architecture. Consequently, digestion is anticipated to emerge from the combined effect of intrinsic (matrix-dependent) and extrinsic (processed-induced) factors. In this work, we aimed to investigate the interrelated effect of intrinsic and extrinsic factors on pulses’ structural architecture and resulting digestive consequences. Three commercially relevant pulses  were selected based on reported differences in macronutrient and cell wall composition. Starch and protein digestion kinetics of hydrothermally processed whole pulses were assessed along with microstructural and physicochemical characteristics and compared to the digestion behavior of individual cotyledon cells isolated thereof. Despite different rates of hardness decay upon hydrothermal processing, the pulses reached similar residual hardness values (40 N). Aligning the pulses at the level of this macrostructural property translated into similar microstructural characteristics after mechanical disintegration (isolated cotyledon cells) with comparable yields of cotyledon cells for all pulses (41–62%). We observed that processing to equivalent microstructural properties resulted in similar starch and protein digestion kinetics, regardless of the pulse type and (prolonged) processing times. This demonstrated the capacity of  hardness as a food structuring parameter in pulses. Furthermore, we illustrated that the digestive behavior of isolated cotyledon cells was representative of the digestion behavior of corresponding whole pulses, opening up perspectives for the incorporation of complete hydrothermally processed pulses as food ingredients. Pulses are a sustainable and nutrient-rich food matrix being composed of different ratios of complex carbohydrates 60–65%), protein 21–25%), lipids (2–6%), and micronutrients encapsulated within (intact) cotyledon cell walls (CW) –25%, lip. Various%, proteiin vitro macronutrient digestion [in vivo benefits, such as low glycaemic response [The reductionist approach of studying digestion kinetics of the ICC fraction revealed that the preservation of cells during processing and consumption results in the CW and the starch-surrounding starch-protein matrix as physical barriers for enzymatic digestion retarding igestion ,16,17,18response ,20. HoweStrong evidence exists that the process-induced levels of pectin thermosolubilization determine the permeability of the CW for digestive enzymes ,22,23, aRecent research in our laboratory has shown that hardness can be used as an indicator for related microstructural properties  by monitoring the decrease of cellular adhesion of pulse cotyledon cells . We hypoThe emerging interest in pulses has resulted in a major body of evidence demonstrating similarities in terms of process-induced softening (microstructure) and retarded digestion kinetics of pulses as a group ,32,33,34To understand the effects of food cellular structure on small intestinal digestion, the current study aimed to investigate the effect of hydrothermal treatment on the digestion kinetics of three different pulse types: chickpea (CP), pea (PE), and black bean (BB). The pulse types were selected based on described differences in their digestive structural barriers. The first objective was to investigate the use of hardness as a food structuring parameter to align the structural and digestion properties of the pulses. Secondly, as thermal processing times have demonstrated the capability to modulate starch digestion behavior of pulses to different extents, we aimed to investigate the effect of processing times on macronutrient digestion in ICC. So far, most insights on the engineering of pulses nutritional functionalities have been investigated on the level of ICC, being the structural characteristic with the highest level of nutrient encapsulation. Still, the majority of pulse consumption involves whole cooked pulse seed material (WSM) . Therefore, the third objective of the present work focused on the evaluation of the ICC and the respective WSM of CP, PE, and BB to elucidate to what extent the digestion behavior of WSM is characterized by its characteristic microstructure.Cicer arietinum, Argentina), dry black turtle beans (BB) , and dry green peas (PE)  were harvested in 2018/2019 and obtained from Greenyard Prepared  and Casibeans©  immediately after harvest. Pulse seed material was used in two ways in this work: (i) to be studied as whole seed material (WSM); and (ii) to generate individual cotyledon cells (ICC). Plant material was sorted, cleaned from defect seeds and foreign material, and kept at −40 °C until further use [2(H2O)6, NaOH, sodium potassium tartrate  NaHCO3, NaCl, KH2PO4 , and the Total Starch Kit .Kabuli chickpeas (CP) (ther use . Storagether use . Pepsin w/w). Disintegrated WSM was collected, frozen, and kept at −40 °C. Seed coat%/cotyledon% ratios were determined gravimetrically as a quantitative measure for the weight distribution of the (raw) cotyledon to the whole seed material on a dry weight basis by dividing the weight of seed coat material by the weight of the cotyledon determined by weighing.Dry pulse seeds were soaked overnight  followed by hydrothermal treatment at 95 °C for 30, 60, and 90 min  and 60, 90, and 120 min (BB) to obtain hardness-aligned samples . CookingTo evaluate the digestion and structural properties of the ICC of CP, PE, and BB, ICC were isolated from the corresponding thermally treated WSM as described previously, with slight modifications ,16. In sDry matter content of raw and thermally treated WSM, as well as of ICC, was determined by oven drying method for 24 h at 105 °C  to a constant weight in triplicate . SubsequTo describe the differences between the three pulses in the level of starch bioencapsulation, starch%/protein% and starch%/FRR% ratios were calculated. Starch%/protein% ratios were calculated by dividing starch by protein present as a quantitative measure for differences at the level of protein as a barrier for starch digestion. Additionally, starch%/FRR% ratios were used as a quantitative measure for the structure-determining compounds .® photo-analyzing software. Starch gelatinization was assessed using the birefringence observable in polarized light mode. To evaluate microstructural changes induced upon digestion, pellets were recovered from digests and assessed under a light microscope.Samples were suspended in demineralized water, placed on a microscope slide, and viewed with an Olympus BX-51 light microscope . Representative light micrographs were taken, selecting an objective of 40× magnification and cellSense Standardv/v) at 4 °C until analysis. Sections were washed in demineralized water to remove cytoplasmic material, stained in 0.1% calcofluor for 1 min, and subsequently washed. To examine CW thickness, stained sections were mounted onto a slide, and CW were visualized using fluorescence microscopy. The distance between 300 adjunct cells was measured at multiple spots (n = 1000) using cellSense® software. Measured distances were halved and averaged to determine the CW thickness of one cotyledon cell. To reduce errors induced by the non-uniformity and inclination of CW, measurements were made close to the middle area of CW, and CW vertical to the cutting surface was visually chosen.CW thickness was determined following a microscopic imaging approach on raw cotyledon cryosections. Therefore, raw, dehulled pulse cotyledons were cryosectioned (20 µm) using a cryomicrotome . Sample sectioning was done for 30 cotyledon halves of different pulse seeds, and sections were stored in 70% ethanol (n = 25) was plotted against processing time to generate a hardness evolution profile. Kinetic modeling of the hardness profile  of raw, WSM, and ICC samples were assessed using an LS 13 320 particle size analyzer  equipped with a Universal Liquid Module as described elsewhere  in triplw/w) in high volume DSC pans , hermetically sealed and equilibrated overnight at 25 °C. Sample and reference pans  were equilibrated at 25 °C for 2 min followed by a temperature ramp (5 °C/min) from 25 to 120 °C. Data were evaluated using Version 4.5A of TA Universal Analysis 2000 Software, (TA Instruments-Waters LLC) in duplicate.Thermal properties of the samples were evaluated using Differential Scanning Calorimetry (DSC)  as a quantitative measurement of enthalpy and transition temperature of thermal events to estimate gelatinization parameters according to Chigwedere et al.  with minin vitro gastrointestinal digestion protocol [g for 5 min. Both, digestive supernatant and pellet were collected, frozen, and stored at −40 °C until further analysis.Hardness-aligned and mechanically disintegrated WSM and their respective ICC were digested following the standardized static protocol , with saprotocol ,42. To sp < 0.05; data not shown).From a statistical point of view, each of the individual, single digestion reactors can be seen as a repetition . MoreoveStarch and protein digestion kinetics of the pulse samples were studied quantitatively by assessment of the released metabolites in the digestive supernatant  and qualDigested starch (%) was determined by measuring reducing sugars present in the digestive supernatant by mixing 2 mL diluted supernatant with 1 mL dinitrosalicylic color reagent for each digestion time . After i2 groups) using a spectrophotometric method [As protein digestion results in a mixture of heterogeneously sized peptides, different protein digestion evaluation approaches were used as detailed explained in our previous publication . Proteinc method ,46.To determine the ‘readily bioaccessible’ fraction, trichloroacetic acid (TCA)  was added to the digestive supernatant precipitating larger proteins and peptides. In this way, centrifugation separates larger fractions from amino acids and small oligopeptides, which represent the potentially readily bioaccessible fraction. To describe the digested protein by its monomeric constituents (amino acids), the digestive supernatant was hydrolyzed by HCl (6 N) at 110 °C for 16 h followed by rotary evaporation of the acid as explained in detail elsewhere (‘digested soluble protein’) . This al2 groups in the protein fractions were determined by OPA spectrophotometric assay. To determine the total α-amino groups in the undigested sample ) and the final digested starch or protein concentration or final hardness (fC) were estimated:−1). The fit of the model was evaluated by calculating R2adjusted and visual analysis of the residual and parity plots. Initial reaction rates were calculated by determining the slope of the tangent line to the fractional conversion model at the start of the reaction (t = 0 min) as earlier described by Pallares Pallares et al. [Kinetic modeling of experimental data was carried out using SAS version 9.4 . Hardness evaluation, starch, and protein digestion kinetics in the small intestinal phase were modeled using a fractional conversion model (Equation (4)), previously used to describe hardness degradation ,21,25,42n pulses ,31. Usins et al. . Signifit, Additionally, after a model discrimination procedure, starch digestion of black beans during the small intestinal phase was modeled using a logistic model (Equation (6)) suitable to describe the characteristic initial lag phase, recently used to describe starch digestion kinetics in common bean and Bambara groundnut ICC ,24,47.(p ≤ 0.05) using JMP Pro 15 .Data from proximate analysis, thermographs, PSD, and DSC were statistically analyzed byTukey HSD post hoc test , and the reaction rate constant (k) for softening. Overall, significant differences in the reaction rate of softening, k, for all three pulse types were observed. k was significantly lower for BB (0.046 ± 0.002 min−1) as compared to CP and PE implying longer processing times required to reach softening. Whereas CP and PE required 30 min to soften completely, BB softening required twice as long. Irrespective of the different softening rates, the three pulses reached similar final hardness levels, which corresponded to 70 ± 2% hardness reduction upon hydrothermal treatment in all pulses.Kinetic modeling using a fractional conversion model was employed  to quantTo explore the process-induced microstructural properties of cooked seeds, the microstructure of mechanically disintegrated pulses (WSM) was assessed qualitatively (microscope) and quantitatively (PSD) as a function of processing time  to estabBy controlling the passage of digestive enzymes and hydrolysis products, pulses’ structural features  determine the digestive responses . To be aProximate characterization: Analysis of starch (33–61%), protein (17–21%), lipid (2–7%), and ash (2–4%) confirmed previously reported differences in chemical composition between pulses for both sample types    [15,32,4SM; ICC) . Figure Structural characterization: We employed hydrothermal processing to residual hardness to obtain pulses with similar microstructural characteristics. The microstructural properties of processed pulse samples were similar, which became apparent when looking at the PSD , non-starch polysaccharides, and to a lesser extent, oligosaccharides . ICC samlations. . The chalations. .fstarch, %) and reaction rate constant  as previously described for other pulses . All three ICC fractions reached similar extents of starch digestion (81–91%). Progression of starch digestion was quantitatively evaluated by kinetic modeling using a fractional conversion model (Equation (4)) to estimate final levels of starch digestion  A2,B2,C2 Digested soluble protein: At the beginning of the small intestinal digestion, about 25–37% of protein was already solubilized . By the end of the small intestinal digestion, that fraction increased to 52–60%.Many scientists have applied a reductionist approach focusing on the ICC fraction when studying digestion barriers in pulses ,32,33,50Hydrothermal processing times have been described to affect starch and protein digestion in pulses to different extents . ICC isoMoisture, starch, and protein contents for ICC of BB and CP were similar for the three different processing times . Figure The starch digestion kinetics of ICC from BB and CP obtained after different processing times are presented in No significant differences between the estimated initial lag phases (λ) of the differently treated BB samples at the 95% confidence interval were found . NonetheIn contrast, starch digestion of the CP samples was characterized by an immediate highly dynamic phase (absence of lag phase). After 90 min, hydrolysis products stabilized and reached final starch digestion levels as previously described . In contSimilar to BB starch digestion kinetics, a trend of increasing estimated reaction rate constants and initial reaction rates with prolonged processing times was observed in BB . The difEqually, studying the protein digestion of CP samples revealed an increasing trend of reaction rate constants and initial reaction rates with hydrothermal processing times, although non-significant. Nonetheless, statistical analysis and modeling demonstrated that similar final protein digestion extents were obtained no matter the processing times.A growing body of evidence exists that demonstrates the barrier effect of protein (digestion) to starch digestion ,24,31,51Correlations between the estimated, normalized levels of digested starch and protein were evaluated as a function of digestion time for ICC of CP, PE, and BB . For protein, however, a clear trend of increased protein digestion with increasing processing time was observed for both pulse types as elaborated above . Overallin vitro digestion behavior of both starch and protein in three pulses with distinct proximate and structural characteristics. We studied both the behavior of the WSM as well as ICC isolated thereof to establish process–structure–function relationships of hydrothermally treated pulses. To compare them on a more equivalent basis, hardness and hydrothermal processing were used as a tool to align microstructural characteristics.In the present study, we intended to widen our investigation to the complementary analysis of the k) of BB as compared to CP and PE  to compare digestive properties among different pulse types. Therefore, hardness–microstructure relations as a function of cooking times were established . HardnesP and PE . We hypoP and PE .Additionally, the different evaluations of particular CW properties  hint at a more pronounced CW barrier effect for digestion in BB compared to CP- and PE-related samples. Although pectin solubilization has been identified as the rate-limiting step for pulse tissue softening, starch gelatinization might partly play a role in fostering cell separation. In the case of sufficient pectin solubilization, it can be expected that a higher starch content, linked to a more pronounced starch swelling and gelatinization phenomena, might result in increased intracellular packing. This, together with the reduced strength of the CW as a result of pectin solubilization, potentially fostered cell separation in CP and PE. Therefore, in addition to the lower FRR fraction and CW thickness, the higher starch to FRR ratios  observedOther studies have shown that hardness profiles are affected by intrinsic factors, as well as extrinsic factors, among others by storage-induced changes  and cookQuantitative evaluation of the yield of ICC as a function of processing time highlighted the increase of ICC along with the decrease of hardness until reaching almost constant and maximal ICC yields . Within In pulses, microstructural characteristics play a key role during starch and protein gastrointestinal digestion. In this work,  hardness was employed as a parameter to assess and subsequently align the microstructural properties of CP, PE, and BB using hydrothermal treatment. As elaborated before , the aliin vitro starch digestion, which is characteristic for pulses and results from the macronutrient encapsulation, was observed for the three pulses as described previously by multiple authors [in vitro starch digestion kinetics of ICC of CP, PE, and BB were not influenced by pulse-specific factors after hardness-alignment. Hence, the structural alignment of the three pulse types resulted in similar starch digestion kinetics of ICC with minor differences during the initial phase of starch digestion (lag phase). With this, our results indicate that hydrothermal processing can be used to structurally align pulses and by this starch digestion kinetics. The retarded time-dependent behavior of  authors ,18,21,31in vitro kinetic parameters  and the physiological relevance of those [Apart from the overall observed similarities in the digestion kinetics, distinct but minor pulse-specific digestion behavior can be observed when zooming in into the initial digestion phase. Given the strong relationship of of those , understof those ) as wellof those . While tof those ,56 and iof those ,57, and of those ,21,22,58of those  and cellof those , have beWhen looking at the implications of structure alignment on protein digestion, comparable protein digestion patterns were found for CP, PE, and BB ICC. As observed for starch digestion, differences in the initial minutes of the small intestinal digestion phase were observed. The reported ‘readily bioaccessible’ and ‘digested soluble’ protein extents are in line with previously reported ones for different legumes ,25,31,46λ = 10 min; makx = 1.3 starch%/min; fC = 84%; Protein: DSP: k= 0.076 min−1; Cf= 86%; BP: k= 0.043 min−1; Cf= 57%) [λ = 16 min; makx = 1.1 starch%/min; fC = 79%) [Extending the comparison of hardness-aligned pulses to previous literature on pulses digestive behavior of similar hardness levels (40 N), similar microstructural properties, reaction rate constants and final starch and digested soluble protein levels could be observed in ICC of Bambara groundnuts treated for 120 min (Starch: Cf= 57%) . LookingCf= 57%) , similarf = 79%)  can be oOur observations highlight that samples sharing similar structural properties show similar digestibility, despite differences in processing history and pulse type. With this, our results show that pulse digestion kinetics strongly depend on microstructural properties.Current literature suggests that protein and starch digestion kinetics both depend on pulse type and processing method and/or intensity . Next toInterestingly, all samples exhibited similar final starch digestion extents . For CP,The role of the protein barrier for starch digestion in pulses is well established . Prolongin vitro starch and protein digestion. We hypothesize that processing increased pectin solubilization even in the hardness plateau resulting in CW changes. However, this was not the case for CP samples, where CW and/or cytoplasmic matrix constitute a barrier after thermal treatment, which was little further affected by prolonged processing.Several structural factors can play a role in increasing protein digestion upon longer hydrothermal treatment in BB as recently reviewed by Duijsens et al. . In thisNonetheless, the authors would like to note that despite the elaborated reasoning the process-induced effects for ICC from BB seem minor, especially when compared to previously reported effects ,24. Our in vitro starch and protein digestion kinetics. We believe these observations suggest that digestive insights generated on ICC or WSM might be transferable. This offers the possibility for increased knowledge transfer of the current research. More strikingly, these insights offer great industrial/application possibilities for the generation of innovative, whole pulse-based flours. The incorporation of the seed coat fraction into the pulses and thus food products might result in decreased waste streams.Insights on the digestive response of pulses are mainly generated using a reductionist approach investigating the ICC of pulses exclusively after dehulling and wet-sieving . Besidesin vitro starch and protein digestion have revealed the presence of undigested material in pulse cotyledon cells after simulated small intestinal conditions. Thus, the investigation of the remaining pellets during colonic fermentation in the large intestine would be a significant next step to take, along with the assessment of soluble and insoluble dietary fiber fractions. Not only the potential of these microstructures as delivering systems for the microbiota, but the role of the residual cellular microstructure on the actual starch degradation as well as on the degradation of other nutrients by the microbiota would be insightful.On a different note, the qualitative and quantitative analyses of in vitro starch and protein hydrolysis for three pulse types. Our findings have illustrated that although pulses responded with distinct hardness degradation kinetics to hydrothermal processing, similar residual hardness levels were reached upon prolonged processing times. Individual cotyledon cells are the characteristic microstructure after mechanical disintegration of whole pulses processed to residual hardness. Reaching these plateau-hardness levels indicates pronounced levels of pectin solubilization resulting in cell separation and can thus be linked to high abundances of ICC. In addition, the evaluation of ICC from chickpeas, pea, and black beans processed to equivalent residual hardness levels demonstrated that digestion kinetics strongly depend on microstructural properties governed by processing sensitivity. Furthermore, prolonged processing times applied to hardness-aligned chickpea and black beans samples did not significantly affect digestion kinetics. Our results highlight the suitability of hardness as a material property to align structural properties and by this digestion kinetics between pulses. Comparing the digestive behavior of the characteristic microstructure (ICC) to its respective whole pulse material did not reveal pronounced differences. These insights offer the possibility of using whole pulses rather than ICC for future innovative food design. The use of the whole, hydrothermally pre-treated pulse flours would offer the incorporation of dietary fiber, minerals, and potential phytochemicals while at the same time avoiding the production of by-products.Hydrothermal processing to equivalent hardness levels was proven to be an effective tool to generate (micro)structures with similar susceptibility to Nonetheless, minor pulse-specific behavior was still observed when looking at the rate of hardness decay as well as the initial phase of starch digestion behavior, especially pronounced in beans. The different sensitivity to hydrothermal treatment may originate from differences in CW composition and process-induced permeability. Additional research focusing on the characterization of CW properties of pulses to unravel what makes beans different from other pulses is needed.To conclude, this study expanded the knowledge on the effect of hydrothermal processing on pulses’ digestive responses. These insights can be used in the context of targeted nutrition and the optimization of processing procedures. We suggest taking the next step in the use of pulse-based ingredients/flours by using whole fractions of thermally treated pulse seeds characterized by high levels of digestive barriers, rather than using ICC fractions isolated thereof or conventionally processed flours with broken cells.