Similar Features Research Articles

A novel discriminant algorithm for differential diagnosis of mild to moderate thalassemia and iron deficiency anemia.

Mild to moderate thalassemia trait (TT) and iron deficiency anemia (IDA) are the most common conditions of microcytic hypochromic anemia (MHA) and they exhibit highly similar clinical and laboratory features. It is sometimes difficult to make a differential diagnosis between TT and IDA in clinical practice. Therefore, a simple, effective, and reliable index is needed to discriminate between TT and IDA. Data of 598 patients (320 for TT and 278 for IDA) were enrolled and randomly assigned to training set (278 of 598, 70%) and validation set (320 of 598, 30%). Stepwise discriminant analysis was used to define the best diagnostic formula for the discrimination between TT and IDA in training set. The accuracy and diagnostic performance of formula was tested and verified by receiver operating characteristic (ROC) analysis in validation set and its diagnostic performance was compared with other published indices. A novel formula, Thalassemia and IDA Discrimination Index (TIDI) = -13.932 + 0.434 × RBC + 0.033 × Hb + 0.025 ×MCHC + 53.593 × RET%, was developed to discriminate TT from IDA. TIDI showed a high discrimination performance in ROC analysis, with the Area Under the Curve (AUC) = 0.936, Youden' s index = 78.7%, sensitivity = 89.5%, specificity = 89.2%, respectively. Furthermore, the formula index also obtained a good classification performance in distinguishing 5 common genotypes of TT from IDA (AUC from 0.854-0.987). The new, simple algorithm can be used as an effective and robust tool for the differential diagnosis of mild to moderate TT and IDA in Guangxi region, China.

The Influence of Facial Feature Similarity on Preference under Different Gender

The Influence of Facial Feature Similarity on Preference under Different Gender

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS.

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

LEMD2-associated progeroid syndrome: Expanding the phenotype of the nuclear envelopathy caused by a defect in LEMD2 gene.

Nuclear envelopathies are rare genetic diseases that compromise the integrity of the nuclear envelope. Patients with a defect in LEM domain nuclear envelope protein 2 (LEMD2) leading to LEMD2-associated progeroid syndrome are exceedingly scarce in number, yet they exhibit shared clinical features including skeletal abnormalities and a prematurely-aged appearance. Our study broadens the understanding of LEMD2-associated progeroid syndrome by detailing its phenotypic and molecular characteristics in the first female and fourth reported case, highlighting a distinct impact on metabolic functions. The patient's history revealed growth delay, facial and skeletal abnormalities, and recurrent abdominal pain crises caused by hepatomegaly. Comparisons with the previously documented cases emphasized similarities in skeletal and facial features while showcasing unique variations, notably in cardiac and hepatic manifestations. Invitro experiments conducted on patient-derived peripheral blood and urinary epithelial cells and LEMD2-downregulated HepG2 cells confirmed abnormalities in the structure of the nuclear envelope in all three tissue-types. Overall, our work offers a comprehensive profile of a patient with LEMD2-related syndrome, emphasizing the hepatic involvement in the disease and broadening our understanding of clinical and molecular implications. This study not only contributes specific insights into LEMD2-related conditions but also underscores potential therapeutic paths for disorders affecting nuclear envelope dynamics.

Characterizing Extreme Events in a Fabry–Perot Laser with Optical Feedback

The study of extreme events (EEs) in photonics has expanded significantly due to straightforward implementation conditions. EEs have not been discussed systematically, to the best of our knowledge, in the chaotic dynamics of a Fabry–Perot laser with optical feedback, so we address this in the current contribution. Herein, we not only find EEs in all modes but also divide the EEs in total output into two categories for further discussion. The two types of EEs have similar statistical features to conventional rogue waves. The occurrence probability of EEs undergoes a saturation effect as the feedback strength increases. Additionally, we analyze the influence of feedback strength, feedback delay, and pump current on the probability of EEs defined by two criteria of EEs and find similar trends. We hope that this work contributes to a deep understanding and serves as inspiration for further research into various multimode semiconductor laser systems.

Application of convolutional neural network for differentiating ovarian thecoma-fibroma and solid ovarian cancer based on MRI.

Ovarian thecoma-fibroma and solid ovarian cancer have similar clinical and imaging features, and it is difficult for radiologists to differentiate them. Since the treatment and prognosis of them are different, accurate characterization is crucial. To non-invasively differentiate ovarian thecoma-fibroma and solid ovarian cancer by convolutional neural network based on magnetic resonance imaging (MRI), and to provide the interpretability of the model. A total of 156 tumors, including 86 ovarian thecoma-fibroma and 70 solid ovarian cancer, were split into the training set, the validation set, and the test set according to the ratio of 8:1:1 by stratified random sampling. In this study, we used four different networks, two different weight modes, two different optimizers, and four different sizes of regions of interest (ROI) to test the model performance. This process was repeated 10 times to calculate the average performance of the test set. The gradient weighted class activation mapping (Grad-CAM) was used to explain how the model makes classification decisions by visual location map. ResNet18, which had pre-trained weight, using Adam and one multiple ROI circumscribed rectangle, achieved best performance. The average accuracy, precision, recall, and AUC were 0.852, 0.828, 0.848, and 0.919 (P < 0.01), respectively. Grad-CAM showed areas associated with classification appeared on the edge or interior of ovarian thecoma-fibroma and the interior of solid ovarian cancer. This study shows that convolution neural network based on MRI can be helpful for radiologists in differentiating ovarian thecoma-fibroma and solid ovarian cancer.

Correlations in abnormal synergies between the upper and lower extremities across various phases of stroke.

The flexion synergy and extension synergy are a representative consequence of a stroke and appear in the upper extremity and lower extremity. Since the ipsilesional corticospinal tract (CST) is the most influential neural pathway for both extremities in motor execution, damage by a stroke to this tract could lead to similar motor pathological features (e.g., abnormal synergies) in both extremities. However, less attention has been paid to the inter-limb correlations in the flexion synergy and extension synergy across different recovery phases of a stroke. We used results of the Fugl-Meyer assessment (FMA) to characterize those correlations in a total of 512 participants with hemiparesis post stroke from the acute phase to 1 year. The FMA provides indirect indicators of the degrees of the flexion synergy and extension synergy post stroke. We found that generally, strong inter-limb correlations (r>0.65 with all p-values<0.0001) between the flexion synergy and extension synergy appeared in the acute-to-subacute phase (<90 days). But correlations of lower-extremity extension synergy with upper-extremity flexion synergy and extension synergy decreased (down to r=0.38) around 360 days after stroke (p<0.05). These results suggest that preferential use of alternative neural pathways after damage by a stroke to the CST enhances inter-limb correlations between the flexion synergy and extension but a recovery of the CST functional and the fragmentation (remodeling) of the alternative neural substrates in the chronic phase contribute to diversity in neural pathways in motor execution, eventually leading to reduced inter-limb correlations.

A parallel corpus-based exploration of deflected agreement in Arabic varieties

Agreement in modern Arabic varieties exhibits much variation despite sharing similar features, such as feminine singular (deflected) agreement with plural controllers. The presence of deflected agreement has been attributed both to retention (Bettega and D’Anna 2022) and to a process of loss and reborrowing from Modern Standard Arabic (Al-Sharkawi 2014; Versteegh 1984}. Using evidence from a multi-dialectal parallel corpus, I argue that neither of these accounts adequately explains the variation present in the dialects. This study highlights the need to understand the language-specific changes in modern Arabic varieties and the utility of parallel corpora for exploring morphosemantic variation.

Measurement of forward charged hadron flow harmonics in peripheral PbPb collisions at sNN=5.02TeV with the LHCb detector

Flow harmonic coefficients, vn, which are the key to studying the hydrodynamics of the quark-gluon plasma (QGP) created in heavy-ion collisions, have been measured in various collision systems and kinematic regions and using various particle species. The study of flow harmonics in a wide pseudorapidity range is particularly valuable to understand the temperature dependence of the shear viscosity to entropy density ratio of the QGP. This paper presents the first LHCb results of the second- and the third-order flow harmonic coefficients of charged hadrons as a function of transverse momentum in the forward region, corresponding to pseudorapidities between 2.0 and 4.9, using the data collected from PbPb collisions in 2018 at a center-of-mass energy of 5.02 TeV. The coefficients measured using the two-particle angular correlation analysis method are smaller than the central-pseudorapidity measurements at ALICE and ATLAS from the same collision system but share similar features. ©2024 CERN, for the LHCb Collaboration 2024 CERN

Features of the gas transport function of blood in hyperpigmentations of the skin

BACKGROUND: Acquired hyperpigmentation is widespread in the population and significantly affects the quality of life of the patients. It is known that one of the clinical signs of skin hyperpigmentation is localized hyperkeratosis in the lesion associated with a high level of cell proliferation and cell saturation with melanin. Cellular proliferation and hyperkeratosis are associated with increased local metabolism rates. AIM: Study of erythrocyte gas transport function in women with skin hyperpigmentation compared to the control group. MATERIALS AND METHODS: To achieve the objective, the concentrations of lactic acid, pyruvic acid and 2,3-diphosphoglycerate levels in venous blood erythrocytes were investigated. RESULTS: The phenomenon of skin hyperpigmentation is accompanied by a restructuring of blood cell metabolism aimed at preserving oxygen and energy homeostasis of skin structures. The obtained data indicate redistribution of oxygen in cellular structures and tissues, which is accompanied by a significant increase in lactate concentration and formation of local tissue hypoxia. CONCLUSION: The formation of skin pigmentation is considered as a physiological mechanism in response to inflammation, mainly associated with ultraviolet radiation. The general strategy of the course of the inflammatory process under normal regulation or dysregulation has similar features, but there are also differences. It seems necessary to further study systemic adaptation mechanisms in dysregulation and formation of skin hyperpigmentation.

DMS: Low-overlap Registration of 3D Point Clouds with Double-layer Multi-scale Star-graph.

Registering 3D point clouds with low overlap is challenging in 3D computer vision, primarily due to difficulties in identifying small overlap regions and removing correspondence outliers. We observe that the neighborhood similarity can be utilized to detect point correspondence, and the consistent neighborhood correspondence can be used as a criterion to detect robust overlapping regions. So that a Double-layer Multi-scale Star-graph (DMS) structure is proposed to detect robust correspondences using two different types of multi-scale star-graphs. The first-layer Multi-scale Neighbor Feature Star-graphs (MNFS) takes each point as the center and its multi-scale nearest neighbors as the leaves. The MNFS enables to establish the initial correspondence candidate set between the two point clouds based on multi-scale neighborhood topology and feature similarity. Subsequently, each pair of corresponding points find their nearest neighbors within the correspondence sets to construct a Multi-scale Matching Star-graphs (MMS) on each side, so the mutual correspondence relationships between the MMS vertices are identified. These identified mutual correspondences are treated as vertices to construct the Multi-scale Correspondence Star-graphs (MCS), that indicate the relationships among the correspondences. We design edge weight and vertex weight criterion in MCS to detect only the robust correspondence set that has strong neighborhood consistency, so as to reject the outliers. Finally, the point cloud registration is conducted based on the detected robust correspondence. The experimental results demonstrate clearly that the proposed DMS method exhibits superior robustness when compared to existing state-of-the-art registration algorithms. The code of this study will be available at https://github.com/HualongCao/DMS.

The clinicopathological features of adult thyroid tumors with DICER1 mutation

A total of 37 cases of thyroid tumors with pathological features suggestive of DICER1 gene mutation were selected to detect the DICER1 gene and BRAF gene using Sanger sequencing. A total of 10 patients (27.0%) exhibited DICER1 gene mutation all of whom were female with an age of [M(Q1, Q3)] 38.0 (30.5, 47.5) years. All patients had wild-type BRAFV600E gene. The ultrasound examination showed high-low echogenic well-demarcated intra-thyroidal nodules with abundant peripheral and internal blood flow signals in the DICER1 mutated thyroid tumor. The tumor was confined within the thyroid gland, with a diameter of (3.68±1.31) cm. The pathological features are as follows: the majority of tumors are encapsulated, which mainly composed of large follicles rich in colloid and some are small and micro follicles. The nucleus is round and deeply stained or slightly light stained, small to medium-sized, with occasional nuclear grooves and a lack of nuclear pseudoinclusion bodies within the nucleus. Immunohistochemical staining shows that Ki67 proliferation index of approximately 2%-10%. All cases were followed up for 11 to 18 months, and there was no recurrences or distant metastase. This study confirmed that the DICER1 gene mutation is mutually exclusive with the BRAFV600E gene mutation. The thyroid tumor with DICER1 mutation are in big size and are more common in young females with a good prognosis. Cases with the wild-type DICER1 gene may exhibit similar morphological features, and molecular testing is recommended. If somatic DICER1 mutation is confirmed, patients should undergo germline mutation testing to rule out DICER1 syndrome in order to define whether genetic counseling is necessary.

Interval prediction of crude oil spot price volatility: An improved hybrid model integrating decomposition strategy, IESN and ARIMA

Crude oil price volatility prediction are important for energy policymaking and investment risk avoidance, and have attracted a great deal of global attention. In recent years, although some studies have proposed methods for prediction crude oil price volatility, most of them are point prediction, which are difficult to provide richer reference information for managers. Therefore, an improved hybrid interval prediction model is proposed. The hybrid model combines data feature extraction and quantification techniques, fuzzy information granulation(FIG), improved echo state network (IESN), and autoregressive integrated moving average model (ARIMA). The hybrid model is able to accurately portray the different features present in the volatility of crude oil spot price. The features are quantified in order to merge similar feature components and improve the prediction effect. On this basis, IESN, which possesses strong nonlinear learning ability, is used to predict high-frequency sequences, and ARIMA, which has strong linear carving ability, is used to predict low-frequency sequences and residual terms. By “divide and conquer”, excellent prediction intervals are obtained. West Texas intermediate (WTI) crude oil spot and Brent crude oil spot are selected for analysis, and consider the influence of futures on interval construction. The effect of volatility intervals is validated from the pricing benchmark and time scale dimensions, respectively. The experimental results show that the proposed model has optimal performance in all scenarios. Take the performance of WTI on the daily scale as an example, its PICWC value is 0.8205. Compared with other benchmark models, it improves by 137.4504, 3.1118, 1.0506, and 0.4875, respectively. The proposed model provides a reliable new idea for crude oil spot volatility interval prediction.

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 &lt;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.

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.

Like Father, Like Daughter - Ectodermal Dysplasia-Syndactyly Syndrome: A Case Report.

Ectodermal dysplasia-syndactyly syndrome 1 (EDSS1) is an exceedingly rare condition associated with mutations in the PVL4 gene. It is characterised by sparse, brittle hair, eyebrows and eyelashes, abnormal dentition and nails, along with bilateral cutaneous syndactyly involving the fingers and toes. We report a 2-year-old girl who presented to us with bilateral complete simple syndactyly of the third and fourth web spaces of the hands, along with bilateral syndactyly of both feet involving the second to fourth toes. Upon examination, sparse hair and eyebrows, along with abnormal dentition, were noted. Thorough clinical examination and genetic analysis were conducted on the affected child and her father, who exhibited similar clinical features. Genetic analysis revealed a homozygous nonsense mutation in the PVL4 gene in both individuals. According to the literature, EDSS1 has been reported in only 10 families worldwide, and there are no reported cases from India. Level of Evidence: Level V (Therapeutic).

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.