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Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

Utilizing well logging data, outcrop profiles, and previous research, this study analyzes the sedimentary and tectonic evolution of the Yan'an Formation in the Ordos Basin, correlating the resulting sedimentary facies with hydrocarbon reservoirs to establish the necessary connections. The study reveals that: (1) Vertically, the sediment grain size shows a pattern of coarser grains at the bottom and top, with finer grains in the middle. Horizontally, the grain size tends to become finer from the northern, western, and southern parts of the basin toward the central-western region. (2) Tectonic movements during the Yan'an period controlled the sedimentary environment. These tectonic activities, through uplift and subsidence, caused the Yan'an Formation to experience four stages of sedimentary environments: braided river, lake, delta, and meandering river. (3) The Yan'an Formation exhibits four types of reservoir sandbody stacking patterns—continuous superposition, intermittent superposition, interbedded sand-mud, and single sandbody types—with continuous and intermittent stacking being the most common. (4) The hydrocarbons in the Yan'an Formation originated from the Chang 7 Member of the Yanchang Formation and migrated into the Yan'an reservoirs. The oil is characterized by its low density, low viscosity, and low pour point, indicating it is a high-maturity, high-quality crude oil. [ABSTRACT FROM AUTHOR]

The pore-throat structure is a critical factor in the study of unconventional oil and gas reservoirs, drawing particular attention from petroleum geologists, and it is of paramount significance to analyze to enhance oil and gas production. In tight sandstone, which serves as a significant hydrocarbon reservoir, the internal pore-throat structure plays a decisive role in the storage and migration of fluids such as water, gases, and hydrocarbons. This paper employs casting thin section (CTS), field emission scanning electron microscope (FE-SEM), high-pressure mercury injection (HPMI), and low-temperature nitrogen gas adsorption (LT−N2−GA) experimental tests to qualitatively and quantitatively analyze the characteristics of the pore-throat structure in tight sandstone. The results indicate that the pore types in tight sandstone include intergranular residual pores, dissolution pores, intercrystalline pores, and microfractures, while the throat types encompass sheet-shaped, curved-sheet-shaped, and tubular throats. Analysis of the physical and structural parameters from 13 HPMI and 5 LT−N2−GA samples reveals a bimodal distribution of pore-throat radii. The complexity of the pore-throat structure is identified as a primary controlling factor for reservoir permeability. The fractal dimension (D) exhibits an average value of 2.45, displaying a negative correlation with porosity (R2 = 0.22), permeability (R2 = 0.65), the pore-throat diameter (R2 = 0.58), and maximum mercury saturation (R2 = 0.86) and a positive correlation with threshold pressure (R2 = 0.56), median saturation pressure (R2 = 0.49), BET specific surface area (R2 = 0.51), and BJH total pore volume (R2 = 0.14). As D increases, reservoir pores tend to decrease in size, leading to reduced flow and deteriorated physical properties, indicative of a more complex pore-throat structure. [ABSTRACT FROM AUTHOR]

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

Turbidites of the Early Cretaceous Lingshandao Formation on Lingshan Island, eastern China, show numerous soft-sediment deformation structures. The turbidites accumulated in a rift basin plain on the distal part of a low-energy fan, together with autochthonous fine-grained sediments that settled from suspension. Occasionally, sets of both types of sediments slumped down the basin slope. The turbidites were studied in two sections. Paleocurrent directions, measured from sole marks at the base of the sandy turbidites, indicate a slope from SE to NW; this is consistent with the dip direction of the axial planes of the major slump folds. Among the numerous types of soft-sediment deformation structures that are present, two types are of special interest because of their specific genesis, viz. rip-up clasts and dome structures. It appears that rapid sedimentation must be considered as the main trigger behind these deformations: the rapid accumulation of particularly the sandy turbidites resulted in overloading of the underlying beds and then consequently in an increase in their pore-water pressure that induced liquefaction and fluidization, which triggered the formation of the rip-up clasts and dome structures. The downslope movement of freshly deposited turbidites after slope failure over the inclined sedimentary surface induced shear stresses, which resulted in the complication of the rip-up clasts and the preferred orientation of some dome structures. Both the dip directions of the axial planes of some of the dome structures and microfolds within the rip-up clasts are consistent with the paleocurrent direction indicated by the sole marks and the slump folds. Les turbidites de la Formation de Lingshandao d'âge crétacé précoce dans l'île Lingshan, en Chine orientale, présentent de nombreuses structures de déformation de sédiments meubles. Les turbidites se sont accumulées dans une plaine de bassin d'effondrement dans la partie distale d'un cône de faible énergie, accompagnées de sédiments a grains fins autochtones déposés à partir de la suspension. De temps à autre, des ensembles des deux types de sédiments ont glissé le long du talus du bassin. Les turbidites ont été étudiées dans deux coupes. Les directions de paléocourant, mesurées à la lumière d'hypoglyphes à la base des turbidites sableuses, indiquent une pente allant du S-E vers le N-O, ce qui concorde avec la direction de pendage des plans axiaux des grands plis de glissement. Parmi les nombreux types de structures de déformation de sédiments meubles présents, deux types sont particulièrement intéressants en raison de leur genèse, à savoir des clastes arrachés et des dômes. Il semble qu'une sédimentation rapide doive être considérée comme étant le principal déclencheur de ces déformations; l'accumulation rapide des turbidites sableuses en particulier s'est traduite par une surcharge des lits sous-jacents et, conséquemment, une augmentation de leur pression interstitielle qui en a induit la liquéfaction et la fluidisation, cette dernière déclenchant la formation des clastes arrachés et des structures en dôme. Le mouvement vers le bas du talus de turbidites fraîchement déposées après la rupture sur la surface sédimentaire inclinée a induit des contraintes de cisaillement, qui se sont traduites par la complication des clastes arrachés et l'orientation préférentielle de certains dômes. Les directions de pendage des plans axiaux de certains dômes et de microplis dans les clastes arrachés s'accordent avec la direction de paléocourant indiquée par les figures basales et les plis de glissement. [Traduit par la Rédaction], Introduction Soft-sediment deformation structures (SSDS; this acronym is used in the present contribution for both singular and plural) include a wide range of sediment deformations with various morphologies; they are [...]

Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformers, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures, "hallucinated" structures and diffusion models. Results suggest that low complexity regions in the sequences designed by deep learning, for generated structures in particular, remain to be improved, when compared to the native sequences. Author summary: We proposed SPIN-CGNN, a deep learning-based method for fixed backbone protein design. Our studies showed that introducing contact map-based graph construction, second-order edge updates, and selective kernels cumulatively improved the performance over existing methods in native and generated structures according to multiple measures, including amino-acid compositions, amino-acid substitutions, low complexity regions, and conservations of hydrophobic/hydrophilic positions for specific evaluation of fix backbone protein design methods. [ABSTRACT FROM AUTHOR]

The article discusses the recent advancements in protein structure prediction, with a focus on the achievements of AlphaFold. It explains how AlphaFold has made significant progress in accurately predicting 3D protein structures by dividing the problem into subproblems. However, the article acknowledges that there is still work to be done in mapping a single sequence to its real-world structure and dynamics. It compares AlphaFold 2 to previous methods and mentions other methods that have been developed in response. The article concludes by stating that the success of AlphaFold 2 has opened up new possibilities for structural biology research. [Extracted from the article]

Determining intrinsically disordered regions of proteins is essential for elucidating protein biological functions and the mechanisms of their associated diseases. As the gap between the number of experimentally determined protein structures and the number of protein sequences continues to grow exponentially, there is a need for developing an accurate and computationally efficient disorder predictor. However, current single-sequence-based methods are of low accuracy, while evolutionary profile-based methods are computationally intensive. Here, we proposed a fast and accurate protein disorder predictor LMDisorder that employed embedding generated by unsupervised pretrained language models as features. We showed that LMDisorder performs best in all single-sequence-based methods and is comparable or better than another language-model-based technique in four independent test sets, respectively. Furthermore, LMDisorder showed equivalent or even better performance than the state-of-the-art profile-based technique SPOT-Disorder2. In addition, the high computation efficiency of LMDisorder enabled proteome-scale analysis of human, showing that proteins with high predicted disorder content were associated with specific biological functions. The datasets, the source codes, and the trained model are available at https://github.com/biomed-AI/LMDisorder. [ABSTRACT FROM AUTHOR]