Similar Features Research Articles

Unveiling the interplay of Mollow physics and perturbed free induction decay by nonlinear optical signals of a dynamically driven two-level system

Nonlinear optical signals in optically driven quantum systems can reveal coherences and thereby open up the possibility for manipulation of quantum states. While the limiting cases of ultrafast and continuous-wave excitation have been extensively studied, the time dynamics of finite pulses bear interesting phenomena. In this paper, we explore the nonlinear optical probe signals of a two-level system excited with a laser pulse of finite duration. In addition to the prominent Mollow peaks, the probe spectra feature several smaller peaks for certain time delays. Similar features have been recently observed for resonance fluorescence signals [K. Boos , ]. We discuss that the emergent phenomena can be explained by a combination of Mollow triplet physics and perturbed free induction decay effects, providing an insightful understanding of the underlying physics. Published by the American Physical Society 2024

Challenges in Distinguishing Rectal Leiomyosarcoma from Ovarian Malignancy: A Diagnostic Conundrum

Introduction: Rectal leiomyosarcoma is a rare malignant mesenchymal tumor accounting for <0.1% of all malignant colorectal neoplasms and carries a grave prognosis. Case Presentation: A 50-year-old postmenopausal, P3L3 woman presented with complaints of abdominal pain for two months, dark-colored loose stools, 10-15 episodes per day for 2-3 months, and gradual weight loss for 4-5 months. On ultrasonography and MRI of the pelvis, a large irregular lobulated ill-defined solid lesion was seen arising from the right side of the pelvis measuring 14x8x14cm. The right ovary was not observed to be separate from the mass. There was omental thickening and peritoneal deposits with ascites. The final impression suggested a right-sided malignant ovarian mass with peritoneal dissemination (ORADS-5) with a differential leiomyosarcoma. Her tumor markers (CA- 125; CA19-9, and CEA) were raised. Given the strong dilemma between ovarian malignancy and leiomyosarcoma and the advanced stage of the disease, the patient was planned for an ultrasound-guided biopsy of the lesion. The histopathological report revealed atypical spindle cell proliferation with myoid features with the presence of multiple mitotic figures. The diagnosis of rectal leiomyosarcoma was further confirmed with immunohistochemical tests, which were positive for actin and desmin and negative for CD 117 and S100. The patient was referred to oncosurgeon for further management and was planned for surgical resection followed by chemotherapy with intravenous doxorubicin. Conclusion: Rectal leiomyosarcoma is commonly misdiagnosed as ovarian malignancy because of its exophytic growth and similar clinical features at the time of the presentation. Hence, clinicians must be aware of these differentials for the successful management of cases.

Royalty, Racism, and Risk

Abstract W. E. B. Du Bois provides a thesis on Black masculinity formation that includes primary traits of this social identity and dynamics that can engender or stymie its development. Yet his framework does not directly reference sexual minorities. This study considers whether and how Du Bois’s framework on masculinity is germane to the experiences of young Black people with diverse sexual identities by assessing whether they recount similar tropes and features. The analysis is theoretically informed by a New Millennium Du Boisian Mode of Inquiry and a qualitative analysis for 168 young Black persons who reside in the South. Three themes emerge that adopt, amplify, and adapt dimensions of Du Bois’s thesis and demonstrate that key aspects of his framework resonate with Black persons excluded from his original work. Despite nuanced sexual identities, it was common for individuals to espouse Du Bosian tenets associated with Black masculinity such as a protector/provider trope, respectability, racial pride, educational attainment, economic mobility, and self-help as well as concerns about racism. These findings inform research on expectations about masculinity into which many men are generally socialized as well as possible hierarchies among intersecting social identities.

Long-term polystyrene nanoparticles exposure reduces electroretinal responses and exacerbates retinal degeneration induced by light exposure

The impact of plastic pollution on living organisms have gained significant research attention. However, the effects of nanoplastics (NPs) on retina remain unclear. This study aimed to investigate the effect of long-term polystyrene nanoparticles (PS-NPs) exposure on mouse retina. Eight weeks old C57BL/6 J mice were exposed to PS-NPs at the diameter of 100 nm and concentration of 10 mg/L in drinking water for 3 months. PS-NPs were able to penetrate the blood-retina barrier, accumulated at retinal tissue, caused increased oxidative stress level and reduced scotopic electroretinal responses without remarkable structural damage. PS-NPs exposure caused cytotoxicity and reactive oxygen species accumulation in cultured photoreceptor cell. PS-NPs exposure increased oxidative stress level in retinal pigment epithelial (RPE) cells, leading to changes of gene and protein expression indicative of compromised phagocytic activity and cell junction formation. Long-term PS-NPs exposure also aggravated light-induced photoreceptor cell degeneration and retinal inflammation. The transcriptomic profile of PS-NPs-exposed, light-challenged retinal tissue shared similar features with those of age-related macular degeneration (AMD) patients in the activation of complement-mediated phagocytic and proinflammatory responses. Collectively, these findings demonstrated the oxidative stress- and inflammation-mediated detrimental effect of PS-NPs on retinal function, suggested that long-term PS-NPs exposure could be an environmental risk factor contributing to retinal degeneration.

A feature matching and compensation method based on importance weighting for occluded human pose estimation

Human pose estimation is a hot research in the field of computer vision. Existing algorithms perform well in normal scenarios but struggle in occlusion environments. In order to address occlusion problems, we firstly design a multi-scale feature reinforcement backbone (MFRI) to obtain accurate features, which contains three sub-modules. The first sub-module reinforces detailed keypoint features and eliminates redundant background, The second sub-module assigns different weights to different scale information based on importance. The fourth sub-module integrates different scale feature information using a cascaded method. In addition, we design an occlusion handling method. To begin with, we design a multi-scale feature extractor (MSFE) to construct human topological feature, and propose a feature similarity matching method between keypoint detail features and topological features to identify occluded keypoints and eliminate obstacle features. Based on similarity, we design a feature compensation method to extract common attributes among all keypoints and unique features of each keypoint from multiple aspects to compensate for occluded features. Finally, we propose an adjacency matrix improvement method to enhance its ability for describing node relationships. We conduct comparative experiments on the COCO2017 dataset, COCO-Wholebody dataset, and CrowdPose dataset, achieving accuracy of 78.8%, 66.3%, and 77.8%, respectively. Additionally, we design a series of ablation experiments and visualizations. The results show that our method has better performance in handling occlusions.

Formation mechanism of micro-seismicity difference between rockbursts in deep-seated parallel tunnels

Deep-seated parallel tunnels are highly susceptible to rockbursts owing to the geological environment and engineering disturbances caused by adjacent tunnel excavations. However, accurately predicting these is challenging. The temporal and spatial characteristics of microseismic (MS) events and rockbursts in parallel tunnels can be jointly analyzed owing to their similar occurrence environments. However, although the left and right tunnels exhibited similar rockburst features during the construction of the Gaoloushan highway tunnel, noticeable differences in the MS events were observed. To clarify the cause of this phenomenon, the mechanisms of the influences of the buried depth, rock mass structure, hydrogeological conditions, construction parameters, blasting disturbance, and other factors on the MS activities of the left and right tunnels were investigated. Our findings suggested that the original geological occurrence environments, including the buried depth, rock mass structure, and hydrogeological conditions, were the primary reasons for the MS differences between the left and right tunnels. There was no obvious correspondence between the tunneling speed and evolution of the MS events. The influences of the section change and blasting on the MS anomaly were restricted by the geological environment and surrounding rocks. The results provide valuable insights into the effects of geological conditions and construction parameters on the formation mechanisms of rockbursts and MS events and may assist in developing an effective early warning strategy, as well as in preventing and controlling rockbursts in parallel tunnels or tunnel groups.

Synthesis and in vitro antitumor evaluation of new thieno[2,3-d]pyrimidine derivatives as EGFR and DHFR inhibitors

New thienopyrimidine derivatives 2–16 have been synthesized and their in vitro cytotoxicity was evaluated against five different human cancer cell lines HCT-116, Hela, MDA-MB-231, MCF7 and PC3. Compounds 6e, 7a, 7b, 7d, 10c and 10e displayed the highest antitumor activity against all tested cell lines compared to Doxorubicin. Enzyme inhibition assay revealed that compounds 6e and 10e showed high inhibitory activity against EGFR-TK, with IC50 values of 0.133 and 0.151 µM, compared to Olmutinib (IC50 = 0.028 µM); while the highest DHFR inhibitory activity was shown by compounds 7d and 10e with IC50 values of 0.462 and 0.541 µM, compared to Methotrexate (IC50 = 0.117 µM). Cell cycle analysis following a flow cytometric study using colorectal HCT-116 cancer cell line proved that compound 6e induced cell cycle arrest in G0-G1 phase, while compound 10e arrested the cell cycle at both G0-G1 and S phases. Additionally, both compounds (6e and 10e) were potently able to induce apoptosis in HCT-116 cell line. Docking results of compounds 6e and 10e into the pocket of EGFR active site showed their similar main binding features with Olmutinib, while compounds 7d and 10e showed only moderate fitting into DHFR compared to methotrexate. In silico studies revealed that most of the tested compounds obeyed Lipinski’s RO5 and showed positive drug likeness scores.

EMC+GD_C: circle-based enhanced motion consistency and guided diffusion feature matching for 3D reconstruction

AbstractRobust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms.

Tuberculosis Detection and Air Purifier Suggestion

Tuberculosis (TB) is a disease that can be fatal if not promptly treated. Ensemble deep learning methods have shown promise in aiding the early detection of TB. Previous research typically trained ensemble classifiers using images with similar features, but for optimal performance, an ensemble requires a range of errors, achievable through diverse classification techniques or feature sets. This study focuses on the latter approach, presenting TB detection using deep learning alongside contrast-enhanced canny edge detected (CEED-Canny) X-ray images. CEED-Canny was employed to generate edge-detected lung X-ray images. Two sets of features were derived: one from the enhanced X- ray images and the other from the edge-detected images. By introducing this variation in features, the diversity of errors among the base classifiers was increased, resulting in improved TB detection. The proposed ensemble method achieved a comparable accuracy of 93.59%, sensitivity of 92.31%, and specificity of 94.87% compared to prior research.

Differentiation of hemispheric white matter lesions in migraine and multiple sclerosis with similar radiological features using advanced MRI.

White matter hyperintensities (WMHs), presented on T2-weighted or fluid-attenuated inversion recovery magnetic resonance imaging (MRI) sequences, are lesions in the human brain that can be observed in both migraine and multiple sclerosis (MS). Seventeen migraine patients and 15 patients with relapsing-remitting multiple sclerosis with WMHs, and 17 healthy subjects age-and sex-matched to the migraine group were prospectively enrolled and underwent conventional and advanced MRI studies with diffusion-and perfusion-weighted imaging and single voxel proton magnetic resonance spectroscopy. In both disease groups, elevated T2 relaxation time, apparent diffusion coefficient (ADC) values, and decreased N-acetyl-aspartate levels were found in the intralesional white matter compared to the contralateral normal-appearing white matter (NAWM), while there was no difference between the hemispheres of the control subjects. Migraine patients had the lowest intralesional creatine + phosphocreatine and myo-inositol (mI) values among the three groups, while patients with MS showed the highest intralesional T1 and T2 relaxation times, ADC, and mI values. In the contralateral NAWM, the same trend with mI changes was observed in migraineurs and MS patients. No differences in perfusion variables were observed in any groups. Our multimodal study showed that tissue damage is detectable in both diseases. Despite the differences in various advanced MRI measures, with more severe injury detected in MS lesions, we could not clearly differentiate the two white matter lesion types.

RANSAC-Based Planar Point Cloud Segmentation Enhanced by Normal Vector and Maximum Principal Curvature Clustering

Abstract. Planar feature segmentation is an essential task for 3D point cloud processing, finding many applications in various fields such as robotics and computer vision. The Random Sample Consensus (RANSAC) is one of the most common algorithms for the segmentation, but its performance, as given by the original form, is usually limited due to the use of a single threshold and interruption of similar planar features presented close to each other. To address these issues, we present a novel point cloud processing workflow which aims at developing an initial segmentation stage before the basic RANSAC is performed. Initially, normal vectors and maximum principal curvatures for each point of a given point cloud are analyzed and integrated. Subsequently, a subset of normal vectors and curvature is utilized to cluster planes with similar geometry based on the region growing algorithm, serving as a coarse but fast segmentation process. The segmentation is therefore refined with the RANSAC algorithm which can be now performed with higher accuracy and speed due to the reduced interference. After the RANSAC process is applied, resultant planar point clouds are built from the sparse ones via a point aggregation process based on geometric constraints. Four datasets (three real and one simulated) were used to verify the method. Compared to the classic segmentation method, our method achieves higher accuracy, with an RMSE from fitting equal to 0.0521 m, along with a higher recall of 93.31% and a higher F1-score of 95.38%.

Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

ABSTRACT The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

CRANIOMETRICAL CHARACTERISTICS OF THE HUMAN FETAL SKULL

The purpose of the study – to ascertain the regularities of the age-related dynamics ofmorphometric changes of the skull in the fetal period of human ontogenesis.Material and methods. 59 specimens and 12 computed tomography (CT) scans of theskulls of human fetuses aged from 4 to 10 months of intrauterine development (IUD)were studied. A complex of morphological research methods was applied, which includedanthropometry, morphometry, three- dimensional reconstruction and statistical analysis.The results. It has been established that the longitudinal and transverse dimensions, thecircumference of the cerebral part of the skull grow almost uniformly before birth, but bythe 5th month of the IUD, a more accelerated growth is observed, due to which the scatterdiagram has the appearance of a logarithmic dependence. A somewhat uneven growth ofthese morphometric indicators is also observed in the age-related changes in the width andupper height of the face. The growth of the facial angle during the fetal period was revealed,which can be explained by the development of the mandible («pushing» it forward). At the same time, the basilar angle slightly decreases during the fetal period of development,which can be explained by the increase in the brain volume. The basilar angle increaseswith the age of the fetus, but somewhat unevenly in leptins, i. e., until the beginning of the6th month, it almost does not change, during the 6th-7th months it increases intensively,and then slows down again until the end of the fetal period of IUD. In euryons, there isa uniform growth of the basilar angle throughout the fruiting period of development, andmore intensively than in the average morphotype – mesens. Analysis of the correlation offetal age, cranial index, and basilar angle size showed even more significant anatomicalvariability. Thus, if in dolicho- and mesomorphs the basilar angle grows almost uniformlyduring the fetal period, then in brachymorphs it first intensively decreases until the 6thmonth of development, and then begins to grow until the newborn period. It should be notedthat a similar feature is observed in the dynamics of age-related changes in the facial angle,in addition to slowing down its growth from the 8th month in dolichomorphs.Conclusions. 1. Regression analysis of the correlation of age, morphotype indicators, andbasilar and facial angles made it possible to establish patterns of individual morphometricvariability of the skull in the dynamics of the fetal period of prenatal human development,as well as to identify critical periods of morphogenesis of the brain and facial parts ofthe skull, which are 6-7 months in leptins, the 6th month – for brachymorphs and the 8thmonth – for dolichomorphs. 2. The size of the basilar angle of the skull is an importantdiagnostic and prognostic indicator in combination with indices that characterize themorphotype of the individual, for monitoring the normal development of the skull andbrain of the fetus.

Exploiting relation of video segments for temporal action detection

With the development of urban intelligence, intelligent identification of human activities has become imminent. Temporal Action Detection (TAD) is designed to identify real human social activities, which is a challenging task in video understanding. The current methods mainly use global features for boundary matching or predefine all possible proposals, while ignoring the interference of background and the causal relevance of the temporal action, resulting in the generation of more redundant proposals and the decline of detection accuracy. To fill this gap, we propose a novel Dilated Convolution Locate and Action Relevant Score model called DCAR. Specifically, DCAR includes a Dilated Location Network (DL-Net) and Action Relevance Calculation (ARC) block. For the DL-Net, we design a Boundary Feature Enhancement (BFE) block, which enhances the boundary feature of actions and fuses the similar features of different channels by pooling and channel squeeze to reduce the interference of the background. Meanwhile, we also design multiple dilated convolutional structures to aggregate long contextual information in time point/interval after boundary enhancement. For the ARC block, we use the hyperbolic space distance and cross attention to calculate the causal correlation of action proposals classification, which removes the misclassification of action proposals. We conduct extensive experiments on Thumos14 and ActivityNet-1.3, which shows our method significantly improves the performance and achieves state-of-the-art results. On Thumos14, at tIoU 0.3, 0.4 and 0.5, it achieves mAP of 63.2%, 57.0% and 48.5%, respectively; On ActivityNet-1.3 it reaches 9.42% at tIoU 0.95.

Precise feature selection via non-convex regularized graph embedding and self-representation for unsupervised learning

In graph embedding learning with unsupervised feature selection fields, similarity matrices are usually obtained only from the initial noise-laden samples, and the probable association between various features is often ignored. To tackle the issues described above, this article proposes an effective unsupervised group feature selection approach via non-convex regularized graph embedding and self-representation (NLGMS). NLGMS integrates the similarity matrix and feature selection as a joint system on the basis of graph. It combines self-representation learning into the system to preserve the global structure and learns the nonconvex projection matrix to direct the procedure of feature selection. Specifically, first, NLGMS presents a unified framework for global structure learning, local structure learning, and feature selection process so the data’s intrinsic structure is excellently captured and learned adaptively. Second, nonconvex ℓ2,0-norm is enforced upon the projection matrix to perform the process of feature selection, ensuring that the optimal feature subset can be picked precisely. Nonconvex ℓ2,0-norm constraint considers the probable association between various features so that it can deliver optimal performance by considering the feature subset as an integral whole, which other convex regularized constraints often fail to achieve. Finally, the introduction to self-representation learning can facilitate the reconstruction of samples in low-rank subspaces, strengthening the model’s robustness. To tackle this challenging nonconvex model, an innovative alternative optimization technique is exploited. In addition, to validate the superiority of NLGMS, exhaustive experiments are performed on one synthetic dataset, six benchmark datasets, and one fish image dataset. The source code is available at: https://github.com/hrbai/NLGMS.

Rare Middle Triassic coleoids from the Alpine-Carpathian system: new records from Slovakia and their significance

Two stratigraphically well constrained (by ammonites and conodonts) coleoid remains have been recorded from the Triassic (Anisian) dark-grey organodetritic limestones (Ráztoka Limestone) of Western Carpathians (Hronic Nappe). The limestones deposited at the periphery of a former carbonate platform. It yields a highly diverse cephalopod fauna including nautiloids (2 taxa), ammonoids (7 taxa) and indetermined aulacoceratids. Two unusual coleoid specimens are referred to genus Mojsisovicsteuthis (M. boeckhi) and probably to a new taxon (described as Breviconoteuthis aff. breviconus herein) possessing similar morphological features of genus Breviconoteuthis (Phragmoteuthida) and/or Zugmontites. Based on index ammonites and conodonts, both records are of the uppermost Trinodosus through the lowermost Reitzi zones (Anisian—lower Illyrian). While the genus Mojsisovicsteuthis has been widely dispersed (however its records are rare), the occurrence of Breviconoteuthis and Zugmontites is strictly limited to the Alpine-Carpathian region. Comparing with the holotype and additional specimens stored in the Hungarian Natural History Museum, the overal shell of Mojsisovicsteuthis and its size has been reconstructed. Its relationship to aulacoceratids and phragmoteuthids is briefly discussed. Geochemical record (n-alkanes from the bulk rock) provided a relevant signal of the existence of algal meadows.

Paleo-geomorphic features of pre-Jurassic and its oil-controlling effect in Wuqi–Dingbian area

The Fuxian-Yan10 layers are the main oil-producing reservoirs of Jurassic in Wuqi–Dingbian area of Ordos Basin. However, due to the lack of understanding of the pattern and distribution characteristics of oil reservoirs, the benefits of exploration and development are restricted. In order to provide theoretical guidance for the study of similar geological features, based on the analysis of paleogeomorphic features and evolution, the analysis focuses on the influence of palaeo-geomorphology on oil reservoir distribution, and summarizes the main types of reservoir models in the study area. The results show that there are four types of palaeo-geomorphic units in the Wuding area: palaeo-river, slope, highland and interriver hill. In the study area, the Jurassic paleogeomorphology controls the sedimentary development and distribution from Fuxian Formation to Yan 9 Formation. The compacted structure and lithologic barrier provide good trapping conditions for the paleogeomorphic oil. Moreover, the swampy coal measures and mudstone at the top of Yan 9 play a sealing role for oil accumulation, and the bottom water was obviously driven. In addition, the pre-Jurassic deep valley was the main channels for oil migration. On this basis, it is concluded that there are four reservoir-forming models in Wuding area: slope type, river hill type, ancient river type and highland type.