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Timely and accurate DNA replication is critical for safeguarding genome integrity and ensuring cell viability. Yet, this process is challenged by DNA damage blocking the progression of the replication machinery. To counteract replication fork stalling, evolutionary conserved DNA damage tolerance (DDT) mechanisms promote DNA damage bypass and fork movement. One of these mechanisms involves "skipping" DNA damage through repriming downstream of the lesion, leaving single-stranded DNA (ssDNA) gaps behind the advancing forks (also known as post-replicative gaps). In vertebrates, repriming in damaged leading templates is proposed to be mainly promoted by the primase and polymerase PRIMPOL. In this review, we discuss recent advances towards our understanding of the physiological and pathological conditions leading to repriming activation in human models, revealing a regulatory network of PRIMPOL activity. Upon repriming by PRIMPOL, post-replicative gaps formed can be filled-in by the DDT mechanisms translesion synthesis and template switching. We discuss novel findings on how these mechanisms are regulated and coordinated in time to promote gap filling. Finally, we discuss how defective gap filling and aberrant gap expansion by nucleases underlie the cytotoxicity associated with post-replicative gap accumulation. Our increasing knowledge of this repriming mechanism - from gap formation to gap filling - is revealing that targeting the last step of this pathway is a promising approach to exploit post-replicative gaps in anti-cancer therapeutic strategies.

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BACKGROUND: The purpose of the present study was to analyze osteotomy gap filling of multiple time points until 2 years post-medial opening wedge high tibial osteotomy (MOWHTO). The absolute value of gap filling and postoperative changes relative to the preoperative void osteotomy gap were evaluated using computed tomography (CT) at each time point. METHODS: Data of thirty patients who underwent MOWHTO between September 2019 and July 2021 were retrospectively analyzed. Surgical procedures without bone grafts were performed; a standardized rehabilitation protocol was implemented. The osteotomy gap filling rate was measured using CT scans at the immediate postoperative period and at 6, 12, and 24 months after surgery. Statistical analysis was performed to assess changes over time. RESULTS: The osteotomy gap filling rate showed a significant continuous increase after MOWHTO, reaching 45.2% at 6 months and 66.7 and 84.4% at 1 and 2 years postoperatively, respectively. The most substantial increase occurred within the initial 6 months, thus indicating a critical period for bone healing. CONCLUSION: The osteotomy gap filling rate showed a significant and gradual increase from immediately after surgery to 2 years after MOWTHO without bone grafting, the greatest of which was achieved in the initial 6-month period. Therefore, this study may be helpful for planning postoperative rehabilitation, including the extent of weight-bearing load and the period of crutch use.

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With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we still face a series of challenges, including repetitive fragments, inverted repeats, low sequencing coverage, and the limitations of sequencing technologies. These challenges lead to unknown gaps in small genomes, hindering complete genome assembly. Although there are many existing assembly software options, they do not fully utilize the potential of artificial intelligence technologies, resulting in limited improvement in gap filling. Here, we propose a novel method, DLGapCloser, based on deep learning, aimed at assisting traditional tools in further filling gaps in small genomes. Firstly, we created four datasets based on the original genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla. To further extract effective information from the gene sequences, we also added homologous genomes to enrich the datasets. Secondly, we proposed the DGCNet model, which effectively extracts features and learns context from sequences flanking gaps. Addressing issues with early pruning and high memory usage in the Beam Search algorithm, we developed a new prediction algorithm, Wave-Beam Search. This algorithm alternates between expansion and contraction phases, enhancing efficiency and accuracy. Experimental results showed that the Wave-Beam Search algorithm improved the gap-filling performance of assembly tools by 7.35%, 28.57%, 42.85%, and 8.33% on the original results. Finally, we established new gap-filling standards and created and implemented a novel evaluation method. Validation on the genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla showed that DLGapCloser increased the number of filled gaps by 8.05%, 15.3%, 1.4%, and 7% compared to traditional assembly tools.

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High-aspect-ratio patterns are required for next-generation three-dimensional (3D) semiconductor devices. However, it is challenging to eliminate voids and seams during gap-filling of these high-aspect-ratio patterns, such as deep trenches, especially for nanoscale high-aspect-ratio patterns. In this study, a SiO2 plasma-enhanced atomic layer deposition process incorporated with ion collision using bias power to the substrate was used for bottom-up trench gap-filling. The effect of bias power frequency on SiO2 trench gap-filling was then investigated. Results showed that changes in bias power frequency did not significantly change the process rate, such as SiO2 growth per cycle. At relatively low bias power frequencies, high-energy ions formed an overhang at the entrance of the high-aspect-ratio trench pattern through sputter etching and redeposition, blocking the pattern entrance. However, at relatively high-frequency bias power, overhang formation due to sputtering did not occur. In the trench interior, due to a scattering effect of ions, deposition was thicker at the bottom of the trench than that at the top, achieving bottom-up gap-filling and void-free gap-filling.

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Different tests measure text comprehension, including the cloze gap-filling test, often used for language learning. Different studies hypothesized cognitive strategies in this type of test and their relationship with working memory and performance. However, no study investigated the cloze test, working memory, and possible cognitive strategies, while performing the test. Therefore, this study aimed to identify cognitive visual strategies in the cloze test by applying an unsupervised algorithm and to analyze the relationship between these strategies with working memory and performance in the cloze test. Our sample consisted of 51 university students, the largest sample in studies of cognitive strategies with cloze tests. Participants answered an 11-item cloze test in a computer with eye-tracking, a verbal working memory test, and a visuospatial working memory test. Our analysis of participants' scanpath identified two main strategies: one with fewer toggles between text and word bank and fewer fixations than the other one, indicating the existence of a global strategy. Furthermore, a model predicting the efficiency of participants in the cloze test found that item complexity, using a global strategy, and higher scores of working memory were the most significant predictors. These results confirm the hypothesis of a global strategy being related to successfully achieving higher-order reading processes.

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The availability of the complete genome of an organism plays a crucial role in the comprehensive analysis of the entire biological entity. Despite the rapid advancements in sequencing technologies, the inherent complexities of genomes inevitably lead to gaps during genome assembly. To obviate this, numerous genome gap-filling tools utilizing long reads have emerged. However, a comprehensive evaluation of these tools is currently lacking. In this study, we evaluated seven software under various ploidy levels and different data generation methods, and assessing them using QUAST and two additional criteria such as accuracy and completeness. Our findings revealed that the performance of the different tools varied across diverse ploidy levels. Based on accuracy and completeness, FGAP emerged as the top-performing tool, excelling in both haploid and tetraploid scenarios. This evaluation of commonly used genome gap-filling tools aims to provide users with valuable insights for tool selection, assisting them in choosing the most suitable genome gap-filling tool for their specific needs.

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The utilization rate of active sites in cathode materials for Zn-based batteries is a key factor determining the reversible capacities. However, a long-neglected issue of the strong electrostatic repulsions among divalent Zn2+ in hosts inevitably causes the squander of some active sites (i.e., gap sites). Herein, we address this conundrum by unraveling the "gap-filling" mechanism of multiple charge carriers in aqueous Zn-MoS2 batteries. The tailored MoS2 /(reduced graphene quantum dots) hybrid features an ultra-large interlayer spacing (2.34 nm), superior electrical conductivity/hydrophilicity, and robust layered structure, demonstrating highly reversible NH4 + /Zn2+ /H+ co-insertion/extraction chemistry in the 1 M ZnSO4 +0.5 M (NH4 )2 SO4 aqueous electrolyte. The NH4 + and H+ ions can act as gap fillers to fully utilize the active sites and screen electrostatic interactions to accelerate the Zn2+ diffusion. Thus, unprecedentedly high rate capability (439.5 and 104.3 mAh g-1 at 0.1 and 30 A g-1 , respectively) and ultra-long cycling life (8000 cycles) are achieved.

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The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

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Translesion synthesis (TLS) DNA polymerases were originally described as error-prone enzymes involved in the bypass of DNA lesions. However, extensive research over the past few decades has revealed that these enzymes play pivotal roles not only in lesion bypass, but also in a myriad of other cellular processes. Such processes include DNA replication, DNA repair, epigenetics, immune signaling, and even viral infection. This review discusses the wide range of functions exhibited by TLS polymerases, including their underlying biochemical mechanisms and associated mutagenicity. Given their multitasking ability to alleviate replication stress, TLS polymerases represent a cellular dependency and a critical vulnerability of cancer cells. Hence, this review also highlights current and emerging strategies for targeting TLS polymerases in cancer therapy.

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Agriculture is the main contributor to anthropogenic nitrous oxide (N2O) and methane (CH4) emissions. Therefore, mitigation options are urgently needed. In contrast to carbon dioxide, eddy covariance measurements of N2O and CH4 fluxes are still scarce, and thus little is known how environmental and biotic drivers as well as management affect the net N2O and CH4 exchange in grasslands. Thus, we investigated the most important drivers of net ecosystem N2O and CH4 fluxes in a temperate grassland, and continued a N2O mitigation experiment (increased clover proportion vs. fertilization with slurry). Random forest gap-filling models were able to capture intermittent emission peaks, performing better for half-hourly N2O than for CH4 fluxes. The unfertilized clover parcel (parcel B) continued to show lower N2O emissions (4.4 and 2.7 kg N2O-N ha-1 yr-1) compared to the fertilized parcel (parcel A; 6.9 and 5.9 kg N2O-N ha-1 yr-1) for 2019 and 2020, respectively. Tier 1 nitrogen (N) emission factors of 2.6 % and 1.9 % were observed at the fertilized parcel during the study period. Lower soil N concentrations indicated a lower N leaching risk at the clover than at the fertilized parcel. Annual CH4 emissions (including periods with sheep grazing) were similar from both parcels, and ranged from 25 to 38.5 kg CH4-C ha-1. The most important drivers of both N2O and CH4 fluxes were lagged precipitation and water filled pore space, but also management (for N2O from parcel B; CH4 from parcel A). Biotic variables such as vegetation height and leaf area index were important predictors for the N2O exchange, while grazing temporarily increased CH4 emissions. Overall, reducing N fertilization and increasing the legume proportion were effective N2O reduction measures. In particular, adjusting N fertilization to plant N demands can help to avoid high N2O emissions from grasslands.

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BACKGROUND: Local assembly with short and long reads has proven to be very useful in many applications: reconstruction of the sequence of a locus of interest, gap-filling in draft assemblies, as well as alternative allele reconstruction of large Structural Variants. Whereas linked-read technologies have a great potential to assemble specific loci as they provide long-range information while maintaining the power and accuracy of short-read sequencing, there is a lack of local assembly tools for linked-read data. RESULTS: We present MTG-Link, a novel local assembly tool dedicated to linked-reads. The originality of the method lies in its read subsampling step which takes advantage of the barcode information contained in linked-reads mapped in flanking regions. We validated our approach on several datasets from different linked-read technologies. We show that MTG-Link is able to assemble successfully large sequences, up to dozens of Kb. We also demonstrate that the read subsampling step of MTG-Link considerably improves the local assembly of specific loci compared to other existing short-read local assembly tools. Furthermore, MTG-Link was able to fully characterize large insertion variants and deletion breakpoints in a human genome and to reconstruct dark regions in clinically-relevant human genes. It also improved the contiguity of a 1.3 Mb locus of biological interest in several individual genomes of the mimetic butterfly Heliconius numata. CONCLUSIONS: MTG-Link is an efficient local assembly tool designed for different linked-read sequencing technologies. MTG-Link source code is available at https://github.com/anne-gcd/MTG-Link and as a Bioconda package.

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The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000-2020+ Landsat data, 2017-2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. Sentinel-2 and Landsat reflectance values were aggregated into four quarterly averages approximating the four seasons common in Europe (winter, spring, summer and autumn), as well as the 25th and 75th percentile, in order to retain intra-seasonal variance. Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. An accuracy assessment shows TMWM performs relatively better in Southern Europe and lower in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. We quantify the usability of the different component data sets for spatiotemporal machine learning tasks with a series of land cover classification experiments, which show that models utilizing the full feature space (30 m DTM, 30 m Landsat, 30 m and 10 m Sentinel-2) yield the highest land cover classification accuracy, with different data sets improving the results for different land cover classes. The data sets presented in the article are part of the EcoDataCube platform, which also hosts open vegetation, soil, and land use/land cover (LULC) maps created. All data sets are available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through SpatioTemporal Asset Catalog (STAC) and the EcoDataCube data portal.

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In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.

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Thin film encapsulation (TFE) is an essential component to ensure reliable operation of environmentally susceptible organic light-emitting diode-based display. In order to integrate defect-free TFE on display with complex surface structures, additional planarization layer is imperative to planarize the surface topography. The thickness of conventional planarization layer is as high as tens of µm, but the thickness must be reduced substantially to minimize the light leakage in smaller devices such as micro light-emitting diodes. In this study, a thin-less than 2 µm-planarization is achieved via solvent-free process, initiated chemical vapor deposition (iCVD). By adapting copolymer from two soft, but curable monomers, glycidyl acrylate (GA) and 2-(dimethylamino)ethyl methacrylate, excellent planarization performance is achieved on various nano-grating patterns. With only 1.5 µm-thick iCVD planarization layer, a 600 nm-deep trench polyurethane acrylate pattern is flattened completely. The TFE fabricated on planarized pattern exhibits excellent barrier property as fabricated on flat glass substrate, which strongly suggests that iCVD planarization layer can serve as a promising planarization layer to fabricate TFE on various types of complicated device surfaces.

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Eddy covariance method has become a key technique to measure CH4 flux continuously in lakes. A large number of CH4 flux data was missing due to variable reasons. In order to reconstruct a complete time series of CH4 flux, it is necessary to find an appropriate gap-filling method to insert the CH4 flux data gap. Based on the routine meteorological data and CH4 flux data measured at Bifenggang site in the eastern part of the Taihu eddy flux network during 2014 to 2017, we analyzed the control factors of CH4 flux at the half-hour scale and daily scale. With those data, we tested that whether nonlinear regression method and two machine learning methods, random forest algorithm and error back propagation algorithm, could fill the CH4 flux gap at the half-hour scale and daily scale. The results showed that CH4 flux at the half-hour scale was mainly influenced by sediment temperature, friction velocity, air temperature, relative humidity, latent heat flux and water temperature at 20 cm in the growing season, and was mainly affected by relative humidity, latent heat flux, wind speed, sensible heat flux and sediment temperature in non-growing season. The CH4 flux at the daily scale was mainly affected by latent heat flux and relative humidity. Random forest model was the best in CH4 flux data gap filling at both time scales. The random forest model with the input variables of day of year, solar elevation angle, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, and wind speed was more suitable for filling the CH4 flux data gap at the half-hour scale. The random forest model with the input variables of day of year, sediment temperature, friction velocity, air temperature, water temperature at 20 cm, relative humidity, air pressure, wind speed, and downward shortwave radiation was more suitable for filling CH4 flux data gap at the day scale. The interpolation models could fill the data gap better at daily scale than that at the half-hour scale.

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Advances in medicine and biotechnology rely on a deep understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant knowledge gaps remain, with current approaches to identify and curate missing annotations being limited to a set of already known reactions. Here, we introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a workflow to identify and curate nonannotated metabolic functions in genomes using the ATLAS of Biochemistry and genome-scale metabolic models (GEMs). To resolve gaps in GEMs, NICEgame provides alternative sets of known and hypothetical reactions, assesses their thermodynamic feasibility, and suggests candidate genes to catalyze these reactions. We identified metabolic gaps and applied NICEgame in the latest GEM of Escherichia coli, iML1515, and enhanced the E. coli genome annotation by resolving 47% of these gaps. NICEgame, applicable to any GEM and functioning from open-source software, should thus enhance all GEM-based predictions and subsequent biotechnological and biomedical applications.

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Numerous published genomes contain gaps or unknown sequences. Gap filling is a critical final step in de novo genome assembly, particularly for large genomes. While certain computational approaches partially address the problem, others have shortcomings regarding the draft genome's dependability and correctness (high rates of mis-assembly at gap-closing sites and high error rates). While it is well established that genomic repeats result in gaps, many sequence reads originating from repeat-related gaps are typically missed by existing approaches. A fast and reliable statistical algorithm for closing gaps in a draft genome is presented in this paper. It utilizes the alignment statistics between scaffolds, contigs, and paired-end reads to generate a Markov chain that appropriately assigns contigs or long reads to scaffold gap regions (only corrects candidate regions), resulting in accurate and efficient gap closure. To reconstruct the missing component between the two ends of the same insert, the RFfiller meticulously searches for valid overlaps (in repeat regions) and generates transition tables for similar reads, allowing it to make a statistical guess at the missing sequence. Finally, in our experiments, we show that the RFfiller's gap-closing accuracy is better than that of other publicly available tools when sequence data from various organisms are used. Assembly benchmarks were used to validate RFfiller. Our findings show that RFfiller efficiently fills gaps and that it is especially effective when the gap length is longer. We also show that the RFfiller outperforms other gap closing tools currently on the market.

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This study aimed to establish and validate a novel evaluation method using digital tomosynthesis to quantify bone formation in the gap after opening wedge high tibial osteotomy (OW-HTO). We retrospectively analyzed bone formation in the gap in 22 patients who underwent OW-HTO using digital tomosynthesis at 1, 2, 3, 6, 9, and 12 months postoperatively. Bone formation was semi-quantitatively assessed using the modified van Hemert's score and density measurements on digital tomosynthesis images. The gap filling value (GFV) was calculated as the ratio of the intensities of the opening gap and the tibial shaft. In addition, the relationship between the modified van Hemert's score and GFV was evaluated. The reproducibility of GFV had an interclass correlation coefficient (ICC [1,2]) of 0.958 for intraobserver reliability and an ICC (2,1) of 0.975 for interobserver reliability. The GFV increased in a time-dependent manner and was moderately correlated with the modified van Hemert's score (r = 0.630, p < 0.001). The GFV plateaued at 6 months postoperatively. In addition, the GFV was higher in patients with a modified van Hemert's score of 2 than in patients with a modified van Hemert's score of 3 (p = 0.008). The GFVs obtained using digital tomosynthesis can be used to assess postoperative bone formation in the opening gap after OW-HTO with high accuracy and reproducibility.

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We provide evidence that children with Developmental Language Disorder (DLD) are impaired in predictive syntactic processing. In the current study, children listened passively to auditorily-presented sentences, where the critical condition included an unexpected "filled gap" in the direct object position of the relative clause verb. A filled gap is illustrated by the underlined phrase in "The zebra that the hippo kissed the camel on the nose…", rather than the expected "the zebra that the hippo kissed [e] on the nose", where [e] denotes the gap. Brain responses to the filled gap were compared to a control condition using adverb-relative clauses with identical substrings: "The weekend that the hippo kissed the camel on the nose [e]…". Here, the same noun phrase is not unexpected because the adverb gap occurs later in the structure. We hypothesized that a filled gap would elicit a prediction error brain signal in the form of an early anterior negativity, as we have previously observed in adults. We found an early (bilateral) anterior negativity to the filled gap in a control group of children with Typical Development (TD), but the children with DLD exhibited no brain response to the filled gap during the same early time window. This suggests that children with DLD fail to predict that a relativized object should correspond to an empty position after the relative clause verb, suggesting an impairment in predictive processing. We discuss how this lack of a prediction error signal can interact with language acquisition and result in DLD.

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Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The "abcd" model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.

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