Fibrous Dysplasia of the Parietal Bone with Focal Motor Seizures: A Case Report.

BACKGROUND Monostotic fibrous dysplasia is a benign proliferation of fibrous and osseous tissues that expand medullary bone to cause symptoms due to compression of adjacent organs and anatomical structures. Focal seizures are rarely the first sign of this kind of lesion. This report describes a young female patient with left-sided focal motor seizures associated with fibrous dysplasia presenting as a mass in the right parietal bone. CASE REPORT An 18-year-old female student with left-sided focal motor seizures presented with a mass in the right parietal bone. Computed tomography revealed an expansile mixed-density lesion on the right parietal bone, a relatively homogeneous ground-glass appearance in the outer circumferential portion, and a lucent eccentric area with thinned but sclerotic borders. Magnetic resonance imaging revealed a homogeneously hypointense signal on T1WI, a small hyperintense signal on T2WI, and avid enhancement signal intensity on post-contrast T1. Electroencephalogram showed inter-ictal epileptiform activities derived from the right fronto-central lobe. Surgical en bloc resection with a margin of normal bone and cranioplasty were performed. Histopathology showed features indicative of fibrous dysplasia, including osteoid trabeculae arranged haphazardly in a dense fibroblastic stroma, irregular trabeculae lacking conspicuous osteoblastic rimming, and intervening fibrous stroma containing cytologically bland spindle cells. The patient achieved seizure control and has remained neurologically intact. CONCLUSIONS This report has highlighted the importance of early diagnosis of fibrous dysplasia of bone to exclude primary bone malignancy or bone metastasis, to ensure rapid management and symptom control.

Nuclear Quantum Effects on the Electronic Structure of Water and Ice.

The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of the hydrogen atoms. Despite numerous previous studies, a comprehensive understanding of the nuclear quantum effects (NQEs) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

Nonlinear Effects of Hydrophobic Confinement on the Electronic Structure and Dielectric Response of Water.

Fundamental studies of the dielectrics of confined water are critical to understand the ion transport across biological and synthetic nanochannels. The relevance of these fundamental studies, however, surmounts the difficulty of probing water's dielectric constant as a function of a fine variation in confinement. In this work, we explore the computational efficiency of machine learning potentials to derive the confinement effects on the dielectric constant, polarization, and dipole moment of water. Our simulations predict an enhancement of the axial dielectric constant of water under extreme confinement, arising from either the formation of ferroelectric structures of ordered water or larger dipole fluctuations facilitated by the disruption of water's H-bond network. Our study highlights the impact of hydrophobic nanoconfinement on the dielectric constant and on the ionic and electronic structure of water molecules, pointing to the importance of geometric flexibility and electronic polarizability to properly model confinement effects on water.

Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Potential Molecular Dynamics Simulations.

Improved understanding of proton transfer in nanopores is critical for a wide range of emerging applications, yet experimentally probing mechanisms and energetics of this process remains a significant challenge. To help reveal details of this process, we developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO2 slit-pores. We find that confinement of water within pores smaller than 0.5 nm imposes strong and complex effects on water reactivity and proton transfer. Although the proton transfer mechanism is similar to that at a TiO2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen-oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. In a broader context, our study demonstrates that proton transfer in hydrophilic nanopores can be enhanced by controlling pore size, surface chemistry, and topology.

Mimosa pudica L. extract ameliorates pulmonary fibrosis via modulation of MAPK signaling pathways and FOXO3 stabilization.

ETHNOPHARMACOLOGICAL RELEVANCE: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing pulmonary disorder that has a poor prognosis and high mortality. Although there has been extensive effort to introduce several new anti-fibrotic agents in the past decade, IPF remains an incurable disease. Mimosa pudica L., an indigenous Vietnamese plant, has been empirically used to treat respiratory disorders. Nevertheless, the therapeutic effects of M. pudica (MP) on lung fibrosis and the mechanisms underlying those effects remain unclear. AIM OF THE STUDY: This study investigated the protective effect of a crude ethanol extract of the above-ground parts of MP against pulmonary fibrogenesis. MATERIALS AND METHODS: Inflammatory responses triggered by TNFα in structural lung cells were examined in normal human lung fibroblasts and A549 alveolar epithelial cells using Western blot analysis, reverse transcription-quantitative polymerase chain reaction assays, and immunocytochemistry. The epithelial-to-mesenchymal transition (EMT) was examined via cell morphology observations, F-actin fluorescent staining, gene and protein expression measurements, and a wound-healing assay. Anti-fibrotic assays including collagen release, differentiation, and measurements of fibrosis-related gene and protein expression levels were performed on TGFß-stimulated human lung fibroblasts and lung fibroblasts derived from mice with fibrotic lungs. Finally, in vitro anti-fibrotic activities were validated using a mouse model of bleomycin-induced pulmonary fibrosis. RESULTS: MP alleviated the inflammatory responses of A549 alveolar epithelial cells and lung fibroblasts, as revealed by inhibition of TNFα-induced chemotactic cytokine and chemokine expression, along with inactivation of the MAPK and NFκB signalling pathways. MP also partially reversed the TGFß-promoted EMT via downregulation of mesenchymal markers in A549 cells. Importantly, MP decreased the expression levels of fibrosis-related genes/proteins including collagen I, fibronectin, and αSMA; moreover, it suppressed collagen secretion and prevented myofibroblast differentiation in lung fibroblasts. These effects were mediated by FOXO3 stabilization through suppression of TGFß-induced ERK1/2 phosphorylation. MP consistently protected mice from the onset and progression of bleomycin-induced pulmonary fibrosis. CONCLUSION: This study explored the multifaceted roles of MP in counteracting the pathobiological processes of lung fibrosis. The results suggest that further evaluation of MP could yield candidate therapies for IPF.

Probing the complexity of wood with computer vision: from pixels to properties.

We use data produced by industrial wood grading machines to train a machine learning model for predicting strength-related properties of wood lamellae from colour images of their surfaces. The focus was on samples of Norway spruce (Picea abies) wood, which display visible fibre pattern formations on their surfaces. We used a pre-trained machine learning model based on the residual network ResNet50 that we trained with over 15 000 high-definition images labelled with the indicating properties measured by the grading machine. With the help of augmentation techniques, we were able to achieve a coefficient of determination (R2) value of just over 0.9. Considering the ever-increasing demand for construction-grade wood, we argue that computer vision should be considered a viable option for the automatic sorting and grading of wood lamellae in the future.

Distance-Dependent Evolution of Electronic States in Kagome-Honeycomb Lateral Heterostructures in FeSn.

In this work, we demonstrate the formation and electronic influence of lateral heterointerfaces in FeSn containing Kagome and honeycomb layers. Lateral heterostructures offer spatially resolved property control, enabling the integration of dissimilar materials and promoting phenomena not typically observed in vertical heterostructures. Using the molecular beam epitaxy technique, we achieve a controllable synthesis of lateral heterostructures in the Kagome metal FeSn. With scanning tunneling microscopy/spectroscopy in conjunction with first-principles calculations, we provide a comprehensive understanding of the bonding motif connecting the Fe3Sn-terminated Kagome and Sn2-terminated honeycomb surfaces. More importantly, we reveal a distance-dependent evolution of the electronic states in the vicinity of the heterointerfaces. This evolution is significantly influenced by the orbital character of the flat bands. Our findings suggest an approach to modulate the electronic properties of the Kagome lattice, which should be beneficial for the development of future quantum devices.

Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots.

Much of the complexity and diversity found in nature are driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics, and they can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.

Generation of induced pluripotent stem cell line (VRISGi004-A) from a healthy female donor by reprogramming erythroid progenitor cells.

We generated a human induced pluripotent stem cell (hiPSC) line from erythroid progenitor cells (EPCs) of a 20-year-old female healthy donor using Sendai virus vector encoding Yamanaka factors OCT3/4, SOX2, c-MYC, and KLF4. The established hiPSCs showed a standard morphology and expression of typical undifferentiated stem cell markers, a normal karyotype (46, XX), and demonstrated potential for differentiation in vitro. Furthermore, they were successfully differentiated into cardiomyocytes that expressed cardiomyocyte-specific markers. The iPSC line and iPSC-derived cardiomyocytes will provide new avenues for future drug testing/development and personalized cell therapy for cardiovascular diseases (CVDs).

The Reach of Calls and Text Messages for Mailed FIT Outreach in the PROMPT Stepped-Wedge Colorectal Cancer Screening Trial.

BACKGROUND: Mailed fecal immunochemical test (FIT) outreach can improve colorectal cancer screening participation. We assessed the reach and effectiveness of adding notifications to mailed FIT programs. METHODS: We conducted secondary analyses of a stepped-wedge evaluation of an enhanced mailed FIT program (n = 15 clinics). Patients were stratified by prior FIT completion. Those with prior FIT were sent a text message (Group 1); those without were randomized 1:1 to receive a text message (Group 2) or live phone call (Group 3). All groups were sent automated phone call reminders. In stratified analysis, we measured reach and effectiveness (FIT completion within 6 months) and assessed patient-level associations using generalized estimating equations. RESULTS: Patients (n = 16,934; 83% Latino; 72% completed prior FIT) were reached most often by text messages (78%), followed by live phone calls (71%), then automated phone calls (56%). FIT completion was higher in patients with prior FIT completion versus without [44% (Group 1) vs. 19% (Group 2 + Group 3); P < 0.01]. For patients without prior FIT, effectiveness was higher in those allocated to a live phone call [20% (Group 3) vs. 18% (Group 2) for text message; P = 0.04] and in those who personally answered the live call (28% vs. 9% no call completed; P < 0.01). CONCLUSIONS: Text messages reached the most patients, yet effectiveness was highest in those who personally answered the live phone call. IMPACT: Despite the broad reach and low cost of text messages, personalized approaches may more successfully boost FIT completion.

Features and networks of the mandible on computed tomography.

The mandible or lower jaw is the largest and hardest bone in the human facial skeleton. Fractures of the mandible are reported to be a common facial trauma in emergency medicine and gaining insights into mandibular morphology in different facial types can be helpful for trauma treatment. Furthermore, features of the mandible play an important role in forensics and anthropology for identifying gender and individuals. Thus, discovering hidden information of the mandible can benefit interdisciplinary research. Here, for the first time, a method of artificial intelligence-based nonlinear dynamics and network analysis are used for discovering dissimilar and similar radiographic features of mandibles between male and female subjects. Using a public dataset of 10 computed tomography scans of mandibles, the results suggest a difference in the distribution of spatial autocorrelation between genders, uniqueness in network topologies among individuals and shared values in recurrence quantification.

Bioconversion of Shrimp Waste into Functional Lipid by a New Oleaginous Sakaguchia sp.

Chitin, the second most abundant biomolecule after cellulose in nature, is a significant aquaculture by-product, and is estimated at 6-8 million tons annually. Chitin is composed of monomeric N-acetylglucosamine (NAG) which can be seen as an alternative feedstock for biotechnology. Microbial functional lipids have gained attention due to their bioactivity and sustainable production. In this study, a new oleaginous yeast strain named Sakaguchia sp. HKC2 was found to be able to use NAG as the carbon source for growth and accumulate functional lipids such as PUFAs and carotenoids. When cultured on the NAG-containing medium, strain HKC2 exhibited slower growth and slower intracellular lipid accumulation compared to those on a glucose-containing medium. However, the lipids obtained from HKC2 grown on NAG medium were richer in PUFAs. Notably, torularhodin-a powerful bioactive carotenoid-was found in all HKC2 cultures on NAG, while torulene was abundant in glucose medium. These findings highlight a novel avenue for utilizing aquatic by-products and unlocking their potential.

Diagnosis of pathological speech with streamlined features for long short-term memory learning.

BACKGROUND: Pathological speech diagnosis is crucial for identifying and treating various speech disorders. Accurate diagnosis aids in developing targeted intervention strategies, improving patients' communication abilities, and enhancing their overall quality of life. With the rising incidence of speech-related conditions globally, including oral health, the need for efficient and reliable diagnostic tools has become paramount, emphasizing the significance of advanced research in this field. METHODS: This paper introduces novel features for deep learning in the analysis of short voice signals. It proposes the incorporation of time-space and time-frequency features to accurately discern between two distinct groups: Individuals exhibiting normal vocal patterns and those manifesting pathological voice conditions. These advancements aim to enhance the precision and reliability of diagnostic procedures, paving the way for more targeted treatment approaches. RESULTS: Utilizing a publicly available voice database, this study carried out training and validation using long short-term memory (LSTM) networks learning on the combined features, along with a data balancing strategy. The proposed approach yielded promising performance metrics: 90% accuracy, 93% sensitivity, 87% specificity, 88% precision, an F1 score of 0.90, and an area under the receiver operating characteristic curve of 0.96. The results surpassed those obtained by the networks trained using wavelet-time scattering coefficients, as well as several algorithms trained with alternative feature types. CONCLUSIONS: The incorporation of time-frequency and time-space features extracted from short segments of voice signals for LSTM learning demonstrates significant promise as an AI tool for the diagnosis of speech pathology. The proposed approach has the potential to enhance the accuracy and allow for real-time pathological speech assessment, thereby facilitating more targeted and effective therapeutic interventions.

A perspective on neuroscience data standardization with Neurodata Without Borders.

Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science.

Great mimicker: definite isolated cardiac sarcoidosis masquerading as hypertrophic cardiomyopathy.

A healthy man in his 50s was hospitalised after presenting with chest pain and dyspnoea. An echocardiogram revealed asymmetrical septal hypertrophy, leading to a diagnosis of hypertrophic cardiomyopathy. Due to progressive conduction abnormalities during his hospitalisation, further evaluation was performed. Cardiac MRI revealed dense late gadolinium enhancement of the septum in the area of hypertrophy. Additionally, fluorodeoxyglucose-positron emission tomography demonstrated increased uptake within the same region, suggestive of active inflammation. Subsequent endomyocardial biopsy showed non-caseating granulomatous inflammation, consistent with cardiac sarcoidosis. Treatment with prednisone and methotrexate was initiated, and an implantable cardioverter-defibrillator was placed following thorough risk stratification. This case highlights the importance of multimodality imaging and the pursuit of a tissue diagnosis in the evaluation of cardiomyopathy.

Relationship between BMI and prognosis of chronic heart failure outpatients in Vietnam: a single-center study.

Background: Insufficient data exists regarding the relationship between body mass index (BMI) and the prognosis of chronic heart failure (CHF) specifically within low- and middle-income Asian countries. The objective of this study was to evaluate the impact of BMI on adverse outcomes of ambulatory patients with CHF in Vietnam. Methods: Between 2018 and 2020, we prospectively enrolled consecutive outpatients with clinically stable CHF in an observational cohort, single-center study. The participants were stratified according to Asian-specific BMI thresholds. The relationships between BMI and adverse outcomes (all-cause death and all-cause hospitalization) were analyzed by Kaplan-Meier survival curves and Cox proportional-hazards model. Results: Among 320 participants (age 63.5 ± 13.3 years, 57.9% male), the median BMI was 21.4 kg/m2 (IQR 19.5-23.6), and 10.9% were underweight (BMI <18.50 kg/m2). Over a median follow-up time of 32 months, the cumulative incidence of all-cause mortality and hospitalization were 5.6% and 19.1%, respectively. After multivariable adjustment, underweight patients had a significantly higher risk of all-cause mortality than patients with normal BMI (adjusted hazard ratios = 3.03 [95% CI: 1.07-8.55]). Lower BMI remained significantly associated with a worse prognosis when analyzed as a continuous variable (adjusted hazard ratios = 1.27 [95% CI: 1.03-1.55] per 1 kg/m2 decrease for all-cause mortality). However, BMI was not found to be significantly associated with the risk of all-cause hospitalization (p > 0.05). Conclusion: In ambulatory patients with CHF in Vietnam, lower BMI, especially underweight status (BMI < 18.5 kg/m2), was associated with a higher risk of all-cause mortality. These findings suggest that BMI should be considered for use in risk classification, and underweight patients should be managed by a team consisting of cardiologists, nutritionists, and geriatricians.

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.

BACKGROUND: Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without. METHODS: The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression. RESULTS: The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve. CONCLUSION: These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.

A large-scale empirical investigation of specialization in criminal career.

We use a comprehensive longitudinal dataset on criminal acts over 6 years in a European country to study specialization in criminal careers. We present a method to cluster crime categories by their relative co-occurrence within criminal careers, deriving a natural, data-based taxonomy of criminal specialization. Defining specialists as active criminals who stay within one category of offending behavior, we study their socio-demographic attributes, geographic range, and positions in their collaboration networks relative to their generalist counterparts. Compared to generalists, specialists tend to be older, are more likely to be women, operate within a smaller geographic range, and collaborate in smaller, more tightly-knit local networks. We observe that specialists are more intensely embedded in criminal networks, suggesting a potential source of self-reinforcing dynamics in criminal careers.

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters.

We show that in silico design of DNA secondary structures is improved by extending the base pairing alphabet beyond A-T and G-C to include the pair between 2-amino-8-(1'-ß-d-2'-deoxyribofuranosyl)-imidazo-[1,2-a]-1,3,5-triazin-(8H)-4-one and 6-amino-3-(1'-ß-d-2'-deoxyribofuranosyl)-5-nitro-(1H)-pyridin-2-one, abbreviated as P and Z. To obtain the thermodynamic parameters needed to include P-Z pairs in the designs, we performed 47 optical melting experiments and combined the results with previous work to fit free energy and enthalpy nearest neighbor folding parameters for P-Z pairs and G-Z wobble pairs. We find G-Z pairs have stability comparable to that of A-T pairs and should therefore be included as base pairs in structure prediction and design algorithms. Additionally, we extrapolated the set of loop, terminal mismatch, and dangling end parameters to include the P and Z nucleotides. These parameters were incorporated into the RNAstructure software package for secondary structure prediction and analysis. Using the RNAstructure Design program, we solved 99 of the 100 design problems posed by Eterna using the ACGT alphabet or supplementing it with P-Z pairs. Extending the alphabet reduced the propensity of sequences to fold into off-target structures, as evaluated by the normalized ensemble defect (NED). The NED values were improved relative to those from the Eterna example solutions in 91 of 99 cases in which Eterna-player solutions were provided. P-Z-containing designs had average NED values of 0.040, significantly below the 0.074 of standard-DNA-only designs, and inclusion of the P-Z pairs decreased the time needed to converge on a design. This work provides a sample pipeline for inclusion of any expanded alphabet nucleotides into prediction and design workflows.