Incomplete Sequences Research Articles

Generative AI Models for the Protein Scaffold Filling Problem.

De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches used in the field of mass spectrometry to analyze and sequence proteins. However, these approaches often produce incomplete protein sequences with gaps, namely scaffolds. The protein scaffold filling problem refers to filling the missing amino acids in the gaps of a scaffold to infer the complete protein sequence. In this article, we tackle the protein scaffold filling problem based on generative AI techniques, such as convolutional denoising autoencoder, transformer, and generative pretrained transformer (GPT) models, to complete the protein sequences and compare our results with recently developed convolutional long short-term memory-based sequence model. We evaluate the model performance both on a real dataset and generated datasets. All proposed models show outstanding prediction accuracy. Notably, the GPT-2 model achieves 100% gap-filling accuracy and 100% full sequence accuracy on the MabCampth protein scaffold, which outperforms the other models.

Timing of the subduction initiation and ophiolite emplacement of the inner Tauride Ocean: Insight from the Pınarbaşı ophiolite in Central Türkiye

Late Cretaceous ophiolites are widespread in southern Türkiye, including the Tauride Belt Ophiolites and the Central Anatolian Ophiolites. Here, we report new geological, geochemical, geochronological, and zircon isotopic data from ophiolitic rocks of the Pınarbaşı ophiolite (Eastern Tauride Belt). The Pınarbaşı ophiolite forms incomplete sequences, with its mantle section dominated by serpentinized harzburgite and chromitites, while the crustal section comprises ultramafic and mafic cumulates. Whole-rock and mineral chemistry data suggest a supra-subduction zone origin. LA-ICP-MS zircon U-Pb age of 88.2 ± 0.4 Ma (MSWD = 0.84) was obtained from a plagiogranite dyke cutting serpentinized peridotite of the Pınarbaşı ophiolite. Another, U-Pb zircon crystallization age of 96.8 ± 0.6 Ma (MSWD = 1.53) was obtained from a plagiogranite dyke within the mélange of the Pınarbaşı ophiolite. Hafnium isotope compositions of these zircons (εHf(t) = +26.4 to +3.1) suggest a depleted to slightly enriched mantle source. The formation ages of the crustal rocks (gabbro, plagiogranite, and dolerite) of the Cretaceous ophiolites in the Tauride Belt from this and previous ophiolite occurrences, suggest that supra-subduction zone ophiolites in the Inner Tauride Ocean formed during the period ∼101–87 Ma. On the other hand, the peak metamorphism of the metamorphic sole rocks is reported to have occurred around ∼104 Ma. According to all available petrogenetic and geochronological data, it is considered that the supra-subduction zone crust developed shortly (∼ 3–4 Myr) after the initiation of intra-oceanic subduction and this lasted ∼10–15 Myr.

Iterative Time Series Imputation by Maintaining Dependency Consistency

Data imputation is crucial in the analysis of incomplete time series, such as forecasting and classification, which involves learning dependencies among the observed values to infer missing ones. As there are no ground truths for missing values, the challenge of time series imputation lies in preventing the model from overfitting to spurious correlations. In this paper, we believe that ensuring dependency consistency between observed and imputed values in a sequence is paramount for data imputation. Based on this idea, we propose a model called IR 2 -Net 1 , which combines an incomplete representation mechanism ( IRM ) with an iterative reconstruction framework ( IRF ) to establish a closed-loop learning-validation imputation paradigm. Firstly, IRM facilitates the representation of dependencies in incomplete sequences while preserving their distributions and semantics, effectively preventing the model from capturing spurious correlations. Secondly, IRF enables the model to reconstruct identical complete sequences separately based on imputed and observed values, ensuring that the dependencies of imputed values remain consistent with those of the observed ones. We conduct experiments on four datasets and compare IR 2 -Net with seven state-of-the-art imputation models. The experiment results show that IR 2 -Net outperforms all the baselines by 4.1%-23.4% in terms of accuracy. Moreover, IRF and IRM are two general modules that can be easily integrated into two existing models, significantly enhancing their performance by 18.3%-42.0%.

Environmental DNA metabarcoding in the Cape Fold aquatic ecoregion: Opportunities and challenges for eDNA uptake in an endemism hotspot

Abstract Environmental DNA (eDNA) metabarcoding has the potential to significantly improve surveys of biodiversity in freshwater systems. However, this methodology is still infrequently used in global hotspots of endemic species, in part because of two major barriers to the success of eDNA metabarcoding: (1) insufficient regional taxonomic representation in public reference sequence databases; and (2) inconsistent species incidence and abundance estimates when compared to conventional surveys. We sampled eDNA and conducted visual surveys in the headwaters of two rivers in the Cape Fold aquatic ecoregion, South Africa. A reference sequence database was generated for the regional diversity of fishes to improve taxonomic classification of endemic species. We also compared the consistency of incidence data and relative abundance estimates of fishes from eDNA metabarcoding sequencing results and visual surveys (snorkel and underwater cameras) at each site sampled. Only 1% of eDNA metabarcoding reads could be classified to fish species without the supplementation of reference sequence databases for local endemic species. Once regional reference sequences were added, a total of nine species were detected and >99% reads classified. A strong positive relationship (Φ = 0.75) was observed between the patterns of detections using eDNA and visual approaches. However, eDNA metabarcoding detected more species than visual methods. In addition, the relative read frequency and abundance observed in visual surveys was significantly correlated (R2 = 0.85–0.88, p < 0.001) in the majority of the small pools surveyed. Incomplete public reference sequence databases hinder the use of eDNA metabarcoding in regions of high endemicity. Local reference sequence libraries can overcome these challenges. When appropriately implemented, eDNA metabarcoding shows promise, producing results consistent or better than the more frequently used and labour‐intensive visual surveys. Efficient survey methods like eDNA metabarcoding are urgently needed to improve aquatic monitoring efforts globally. Understanding how eDNA metabarcoding sequencing results relate to conventional survey methods is a key step in its implementation in ecoregions with high endemicity.

Telomere-to-telomere, gap-free assembly of the Rosa rugosa reference genome

Rosa, a genus esteemed worldwide for its ornamental plants, has encountered barriers in functional genomic studies and further genetic enhancement due to incomplete sequences and floating regions in previously sequenced genomes. Our groundbreaking study introduced a meticulously assembled, continuous, and fully bridged reference genome for Rosa rugosa, constructed through a sophisticated combination of PacBio High-Fidelity, ONT ultra-long reads, and Hi-C data. This robust assembly spanned 444.55 Mb and encompassed 34 109 protein-coding genes. We have uniquely assembled each chromosome into single, gap-free structures, successfully identifying all 14 telomeres and seven centromeres, a feat not achieved previously. The centromeric regions were distinguished by tandem repeats, primarily composed of centromere-specific 159-bp monomers, and a significant enrichment of ATHILA/Gypsy long terminal repeat retrotransposons in proximal regions. Our research highlighted recent tandem duplications as instrumental in bolstering R. rugosa's stress tolerance, environmental adaptability, and enhanced anthocyanin synthesis. Furthermore, our study ventured into uncharted territory by predicting transcription factors potentially regulating anthocyanin biosynthesis through the employment of gene co-expression networks, providing new avenues for research. This comprehensive reference genome not only serves as a cornerstone for in-depth exploration of genomic architecture and functionalities in R. rugosa but also acts as a catalyst for innovative breeding strategies and genetic refinement within the genus.

Normative Measurements of the Infraorbital Nerve by Magnetic Resonance Imaging in an Australia Cohort.

To report the normative dimensions of the infraorbital nerve on fat-suppressed gadolinium-enhanced MRI and correlate with patient demographics in an Australian cohort. A retrospective review of patients who underwent coronal fat-suppressed gadolinium T1-weighted MRI from September 2021 to December 2023. One hundred sixty-eight orbits were included. The maximum diameter of the infraorbital nerve and the optic nerve sheath was measured. Orbits were excluded if there was unilateral or bilateral pathology of the infraorbital nerve or optic nerve sheath, incomplete MRI sequences, poor image quality, or indiscernible infraorbital nerve on radiological examination. The mean age of participants was 58 ± 16 years, and 50% were females (n = 42). The mean normative measurements (mean ± standard deviation) on coronal T1-weighted imaging: optic nerve sheath, 5.08 ± 0.67 mm. On coronal fat-suppressed gadolinium T1-weighted imaging: infraorbital nerve, 0.89 ± 0.22mm. No significant differences were found between male or female participants in both the infraorbital nerve (p = 0.757) or optic nerve sheath (p = 0.646). There was no significant correlation between age and mean diameter of the infraorbital nerve (r = 0.077, p = 0.320) or optic nerve sheath (r = 0.075, p = 0.336). Additionally, no significant difference was identified between the mean diameter of the infraorbital nerve (p = 0.079) and optic nerve sheath (p = 0.120) across age groups. The mean infraorbital nerve to optic nerve sheath ratio was 0.18 ± 0.00. Normative dimensions of the infraorbital nerve may be used to identify enlargement in conditions such as IgG4-related ophthalmic disease and reactive lymphoid hyperplasia.

Sedimentology and stratigraphy of lower Cretaceous fluvial to shallow marine deposits on the central Atlantic passive margin: The Aaiun-Tarfaya Basin, Morocco

This paper presents the first integrated regional outcrop-based sedimentological study of the northern Aaiun-Tarfaya Basin located in Morocco (NW Africa). The Lower Cretaceous Tan-Tan Formation has been subdivided into six new members and placed within a sequence stratigraphic framework that includes two incomplete depositional sequences. Strong thickness variations of individual lithostratigraphic units from north to south suggest differential subsidence during sedimentation and/or the existence of major topography on the basal unconformity that the succession onlaps. The results provide valuable insights into the timing of local tectonics in the Western Anti-Atlas and the control on the evolution of the sedimentary system. Deposition of each of these six units is interpreted to be the result of a complex interplay between an overall eustatic sea-level rise during the early Cretaceous, sediment delivery controlled by tectonic movements in the Western Anti-Atlas and Reguibat Shield and periods of differential subsidence in the basin. The results document the style of evolution of a back-stepping wave-dominated system feeding into the Central Atlantic during the passive margin phase. The improved facies and depositional models together with improved understanding of the evolution of the delta have significant implication for exploring the deep-water equivalents offshore.

The Stylo Cysteine-Rich Peptide SgSnakin1 Is Involved in Aluminum Tolerance through Enhancing Reactive Oxygen Species Scavenging.

Stylo (Stylosanthes spp.) is an important pasture legume with strong aluminum (Al) resistance. However, the molecular mechanisms underlying its Al tolerance remain fragmentary. Due to the incomplete genome sequence information of stylo, we first conducted full-length transcriptome sequencing for stylo root tips treated with and without Al and identified three Snakin/GASA genes, namely, SgSnakin1, SgSnakin2, and SgSnakin3. Through quantitative RT-PCR, we found that only SgSnakin1 was significantly upregulated by Al treatments in stylo root tips. Histochemical localization assays further verified the Al-enhanced expression of SgSnakin1 in stylo root tips. Subcellular localization in both tobacco and onion epidermis cells showed that SgSnakin1 localized to the cell wall. Overexpression of SgSnakin1 conferred Al tolerance in transgenic Arabidopsis, as reflected by higher relative root growth and cell vitality, as well as lower Al concentration in the roots of transgenic plants. Additionally, overexpression of SgSnakin1 increased the activities of SOD and POD and decreased the levels of O2·- and H2O2 in transgenic Arabidopsis in response to Al stress. These findings indicate that SgSnakin1 may function in Al resistance by enhancing the scavenging of reactive oxygen species through the regulation of antioxidant enzyme activities.

The estimation of b-value of the frequency–magnitude distribution and of its 1σ intervals from binned magnitude data

SUMMARY The estimation of the slope (b-value) of the frequency–magnitude distribution of earthquakes is based on a formula derived by Aki decades ago, assuming a continuous exponential distribution. However, as the magnitude is usually provided with a limited resolution, its distribution is not continuous but discrete. In the literature, this problem was initially solved by an empirical correction (due to Utsu) to the minimum magnitude, and later by providing an exact formula such as that by Tinti and Mulargia, based on the geometric distribution theory. A recent paper by van der Elst showed that the b-value can be estimated also by considering the magnitude differences (which are proven to follow an exponential discrete Laplace distribution) and that in this case the estimator is more resilient to the incompleteness of the magnitude data set. In this work, we provide the complete theoretical formulation including (i) the derivation of the means and standard deviations of the discrete exponential and Laplace distributions; (ii) the estimators of the decay parameter of the discrete exponential and trimmed Laplace distributions and (iii) the corresponding formulas for the parameter b. We deduce (iv) the standard 1σ intervals for the estimated b. Moreover, we are able (v) to quantify the error associated with the Utsu minimum-magnitude correction. Furthermore, we have discussed the formulas to produce statistically independent magnitude differences. We tested extensively the b-value estimators on simulated synthetic data sets including complete catalogues as well as catalogues affected by a strong incompleteness degree such as aftershock sequences where the incompleteness is made to vary from one event to the next. We have also analysed the real aftershock sequence of the 30/10/2016 Norcia (central Italy) to integrate the finding of the simulations. To judge the performance of the various estimators we have introduced an index p that can be seen as a non-parametric extension of the Student's t index. The main outcomes of this paper are that (1) the b-value estimators devised for continuous magnitude data are not adequate for binned magnitudes, (2) for complete data sets, estimators based on magnitudes and on magnitude differences provide substantially equivalent results, (3) for incomplete magnitude data sets, estimators based on magnitude differences provide better results and (4) for incomplete aftershock sequences there is no evidence that methods based on positive magnitude differences are superior than other methods using differences. This conclusion is further confirmed by our analysis of the above-mentioned Norcia seismic sequence. This last finding contrasts with the van der Elst's claim that the so called ${{b}_ + }$ method is the most adequate to treat real aftershock sequences.

Improved Catalytic Activity of Spherical Nucleic Acid Enzymes by Hybridization Chain Reaction and Its Application for Sensitive Analysis of Aflatoxin B1.

Conventional spherical nucleic acid enzymes (SNAzymes), made with gold nanoparticle (AuNPs) cores and DNA shells, are widely applied in bioanalysis owing to their excellent physicochemical properties. Albeit important, the crowded catalytic units (such as G-quadruplex, G4) on the limited AuNPs surface inevitably influence their catalytic activities. Herin, a hybridization chain reaction (HCR) is employed as a means to expand the quantity and spaces of G4 enzymes for their catalytic ability enhancement. Through systematic investigations, we found that when an incomplete G4 sequence was linked at the sticky ends of the hairpins with split modes (3:1 and 2:2), this would significantly decrease the HCR hybridization capability due to increased steric hindrance. In contrast, the HCR hybridization capability was remarkably enhanced after the complete G4 sequence was directly modified at the non-sticky end of the hairpins, ascribed to the steric hindrance avoided. Accordingly, the improved SNAzymes using HCR were applied for the determination of AFB1 in food samples as a proof-of-concept, which exhibited outstanding performance (detection limit, 0.08 ng/mL). Importantly, our strategy provided a new insight for the catalytic activity improvement in SNAzymes using G4 as a signaling molecule.

Molecular insights into FucR transcription factor to control the metabolism of L-fucose in Bifidobacterium longum subsp. infantis

Bifidobacterium longum subsp. infantis commonly colonizes the human gut and is capable of metabolizing L-fucose, which is abundant in the gut. Multiple studies have focused on the mechanisms of L-fucose utilization by B. longum subsp. infantis, but the regulatory pathways governing the expression of these catabolic processes are still unclear. In this study, we have conducted a structural and functional analysis of L-fucose metabolism transcription factor FucR derived from B. longum subsp. infantis Bi-26. Our results indicated that FucR is a L-fucose-sensitive repressor with more α-helices, fewer β-sheets, and β-turns. Transcriptional analysis revealed that FucR displays weak negative self-regulation, which is counteracted in the presence of L-fucose. Isothermal titration calorimetry indicated that FucR has a 2:1 stoichiometry with L-fucose. The key amino acid residues for FucR binding L-fucose are Asp280 and Arg331, with mutation of Asp280 to Ala resulting in a decrease in the affinity between FucR and L-fucose with the Kd value from 2.58 to 11.68 μM, and mutation of Arg331 to Ala abolishes the binding ability of FucR towards L-fucose. FucR specifically recognized and bound to a 20-bp incomplete palindrome sequence (5’-ACCCCAATTACGAAAATTTTT-3’), and the affinity of the L-fucose-loaded FucR for the DNA fragment was lower than apo-FucR. The results provided new insights into the regulating L-fucose metabolism by B. longum subsp. infantis.

MIT-FRNet: Modality-invariant temporal representation learning-based feature reconstruction network for missing modalities

The investigation of missing modalities aims to extract valuable feature information from missing multi-modal data, it is a focal point in multi-modal learning. Existing missing modalities processing methods primarily focused on multi-modal fusion schemes to achieve optimal performance, but they faced the following two key challenges: (1) how to improve the robustness of incomplete multi-modal sequence representation, (2) how to effectively learn modality-invariant representations to mitigate heterogeneity between modalities. In this paper, we propose a modality-invariant temporal representation learning-based feature reconstruction network called MIT-FRNet for the missing modalities to tackle these challenges. In the MIT-FRNet, we first extract the latent features for each modality considering intra-modality and inter-modality, and then introduce an encoder-decoder framework to address the first challenge, it reconstructs missing element features via taking the incomplete modal sequences as input and implementing the inter-modal and cross-modal attention mechanisms for feature extraction. And then, through treating each timestamp as a single Gaussian distribution, we design a fine-grained similarity constraint based on distribution-level modality-invariant representations to learn effective modality-invariant representations, thereby addressing the second challenge. Finally, the efficiency of proposed model is validated through the classification results after multi-modal fusion that involves using a gate encoder to pass it and followed by a vector fusion to fuse it. Extensive experiments on public benchmark datasets demonstrate that the proposed MIT-FRNet method achieves promising results under varying missing rates while exhibiting good convergence.

Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters.•From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised.•Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.•Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.

Small apes adjust rhythms to facilitate song coordination

Song coordination is a universal characteristic of human music. Many animals also produce well-coordinated duets or choruses that resemble human music. However, the mechanism and evolution of song coordination have only recently been studied in animals. Here, we studied the mechanism of song coordination in three closely related species of wild Nomascus gibbons that live in polygynous groups. In each species, song bouts were dominated by male solo sequences (referred to hereafter as male sequence), and females contributed stereotyped great calls to coordinate with males. Considering the function of rhythm in facilitating song coordination in human music and animal vocalizations, we predicted that adult males adjust their song rhythm to facilitate song coordination with females. In support of this prediction, we found that adult males produced significantly more isochronous rhythms with a faster tempo in male sequences that were followed by successful female great calls (a complete sequence with "introductory" and "wa" notes). The difference in isochrony and tempos between successful great call sequences and male sequences was smaller in N.concolor compared with the other two species, which may make it difficult for females to predict a male's precise temporal pattern. Consequently, adult females of N.concolor produced more failed great call (an incomplete sequence with only introductory notes) sequences. We propose that the high degree of rhythm change functions as an unambiguous signal that can be easily perceived by receivers. In this regard, gibbon vocalizations offer an instructive model to understand the origins and evolution of human music.

Conditional generative learning for medical image imputation

Image imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application is considered. It is derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys: given an incomplete sequence of three CECT images, we are required to impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the “best guess” of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown that the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task.

Matching visually observed 3D model to original reference object

In the development of a visual environment simulator or a thermal imager simulator, one of the challenges encountered when assessing the external appearance of engineering structures is the need to ensure the correspondence of the developed 3D model of a reference object to the original 3D object. Since creating a complete model is currently impractical, the criterion for assessment is based on the conformity of forming specified components of the cognitive model of a human observer necessary in their professional activities. Research has shown that to address this challenge, it is advisable to choose one of the artificial intelligence methods to entrust it with the task of evaluating the developed 3D model and minimize the subjectivity in the decision-making process of “whether the external appearance of the 3D model meets the client's requirements.” The article proposes using deductive reasoning in ChatGPT to assess the conformity of the external appearance of a 3D model to a specified 3D object by representing the model as incomplete sequences and evaluating its ability to complete them in accordance with conclusions made by humans.The suggested approach involves solving the problem based on scenarios where humans formulate logical conclusions from provided information, followed by the application of ChatGPT in processing the presented sequences of conclusions. A comparative analysis is presented to determine the extent to which ChatGPT demonstrates deductive reasoning and how closely it aligns with human deductive models. This allows for the creation of a model with deductive reasoning and contributes to discussions on the competencies of artificial intelligence.

SSRL: Self-Supervised Spatial-Temporal Representation Learning for 3D Action Recognition

For 3D action recognition, the main challenge is to extract long-range semantic information in both temporal and spatial dimensions. In this paper, in order to better excavate long-range semantic information from large number of unlabelled skeleton sequences, we propose Self-supervised Spatial-temporal Representation Learning (SSRL), a contrastive learning framework to learn skeleton representation. SSRL consists of two novel inference tasks that enable the network to learn global semantic information in the temporal and spatial dimensions, respectively. The temporal inference task learns the temporal persistence of human actions through temporally incomplete skeleton sequences. And the spatial inference task learns the spatially coordinated nature of human action through spatially partially skeleton sequence. We design two transformation modules to efficiently realize these two tasks while fitting the encoder network. To avoid the difficulty of constructing and maintaining high-quality negative samples, our proposed framework learns by maintaining consistency among positive samples without the need of any negative sample. Experiments demonstrate that our proposed method can achieve better results in comparison with state-of-the-art methods under a variety of evaluation protocols on NTU RGB+D 60, PKU-MMD and NTU RGB+D 120 datasets.

The Kuching Formation: A deep marine equivalent of the Sadong Formation, and its implications for the Early Mesozoic tectonic evolution of western and southern Borneo

The pre-Cretaceous history of Borneo remains relatively poorly studied. Limited exposures of Palaeozoic and lower Mesozoic rocks are located in NW Kalimantan and in West Sarawak, an area interpreted as the West Borneo basement. Lower Mesozoic sedimentary rocks in West Sarawak were analysed to study their depositional environments and implications for the tectonic evolution. Upper Triassic turbidites in West Sarawak, exposed in the northern part of Kuching city, informally named the Kuching Formation, are the deep marine equivalent to the more widespread, shallow marine Sadong Formation. The Kuching Formation comprises thinly-bedded stacked turbidites, consisting of incomplete Bouma sequences, with multiple, erosive channel sandstone bodies deposited under upper flow regime waning flows. Thin debrites with abundant coaly-material are interbedded with the channel sandstones. The Kuching and Sadong formations both contain volcaniclastic detritus that was derived from the westward-subducting Palaeo-Pacific plate, forming a Triassic Andean-type arc which extended from West Borneo in the south to southern China, Taiwan and Japan in the north. Palaeoproterozoic to Archean detrital zircons in the Kuching and Sadong formations reveal a Cathaysian basement source, providing insights into the nature of the West Borneo basement. Quartz-mica schists (Kerait Schist, Tuang Formation) in fault-contact with the two sedimentary successions may have formed during accretion.

Contrastive-Learning-Based Time-Series Feature Representation for Parcel-Based Crop Mapping Using Incomplete Sentinel-2 Image Sequences

Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-series features to identify crop types from incomplete optical image sequences is a significant challenge. This study developed a contrastive-learning-based framework for time-series feature representation to improve crop classification using incomplete Sentinel-2 image sequences. Central to this method was the combined use of inherent time-series feature representation and machine-learning-based classifications. First, preprocessed multi-temporal Sentinel-2 satellite images were overlaid onto precise farmland parcel maps to generate raw time-series spectral features (with missing values) for each parcel. Second, an enhanced contrastive learning model was established to map the raw time-series spectral features to their inherent feature representation (without missing values). Thirdly, eXtreme Gradient-Boosting-based and Long Short-Term Memory-based classifiers were applied to feature representation to produce crop classification maps. The proposed method is further discussed and validated through parcel-based time-series crop classifications in two study areas (one in Dijon of France and the other in Zhaosu of China) with multi-temporal Sentinel-2 images in comparison to the existing methods. The classification results, demonstrating significant improvements greater than 3% in overall accuracy and 0.04 in F1 scores over comparison methods, indicate the effectiveness of the proposed contrastive-learning-based time-series feature representation for parcel-based crop classification utilizing incomplete Sentinel-2 image sequences.

Expression of a recombinant endolysin from bacteriophage CAP 10-3 with lytic activity against Cutibacterium acnes

The bacteriophage CAP 10-3 forming plaques against Cutibacterium acnes which causes skin acne was previously isolated from human skin acne lesion. Incomplete whole genome sequence (WGS) of the bacteriophage CAP 10-3 was obtained and it had 29,643 bp long nucleotide with 53.86% GC content. The sequence was similar to C. acnes phage PAP 1-1 with a nucleotide sequence identity of 89.63% and the bacteriophage belonged to Pahexavirus. Bioinformatic analysis of the WGS predicted 147 ORFs and functions of 40 CDSs were identified. The predicted endolysin gene of bacteriophage CAP 10-3 was 858 bp long which was deduced as 285 amino acids (~ 31 kDa). The protein had the highest similarity with amino acid sequence of the endolysin from Propionibacterium phage PHL071N05 with 97.20% identity. The CAP 10-3 endolysin gene was amplified by PCR with primer pairs based on the gene sequence, cloned into an expression vector pET-15b and transformed into Escherichia coli BL21(DE3) strain. The predicted protein band (~ 33 kDa) for the recombinant endolysin was detected in an SDS-PAGE gel and western blot assay. The concentrated supernatant of cell lysate from E. coli BL21(DE3) (pET-15b_CAP10-3 end) and a partially purified recombinant CAP 10-3 endolysin showed antibacterial activity against C. acnes KCTC 3314 in a dose-dependent manner. In conclusion, the recombinant CAP 10-3 endolysin was successfully produced in E. coli strain and it can be considered as a therapeutic agent candidate for treatment of human skin acne.