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. Key takeaways from this article are that (1) standardization with NWB requires non-trivial design choices; (2) the general practice of standardization in the lab promotes data awareness and literacy, and improves transparency, rigor, and reproducibility in our science; (3) we offer several feature suggestions to ease the extensibility, publishing/sharing, and usability for NWB standard and users of NWB data.

Risk Factors and Profiles of Falls Among Inpatients in Vietnam: A Multicenter Nested Case-Control Study.

Purpose: Falls among inpatients represent a significant global health concern and are among the leading causes of accidental death. However, hospital falls are context- and population dependent. This study aimed to investigate the risk factors contributing to falls and the fall profiles among Vietnamese inpatients. Methods: A nested case-control study was conducted at nine public hospitals in Ho Chi Minh City. For every fall identified through the medical fall incident reporting system, four controls (ie, nonfall patients) were also selected from medical records within the same department and timeframe. Medical records were extracted, which included detailed information about the falls. Results: Among 101 fall cases and 404 nonfall controls, several risk factors for falls were found, including reduced strength and mobility (OR=3.08, 95% 1.30-7.30), nocturia (OR=9.08, 95% CI 4.04-20.45), having more than two diseases (OR=2.76, 95% CI 1.53-4.98), using walking aids (OR=23.26, 95% CI 10.20-53.03), using medical devices (OR=3.44, 95% CI 1.92-6.15) and using antiepileptics (OR=3.94, 95% CI 1.22-12.77). About 19.8% of the falls occurred within the first 24 hours from admission and the most common time of falls was from 0:00 am to 5:59 am (44.6%). The patient bed and bathroom were the most frequent locations for falls, accounting for 44.55% and 37.62% of the cases, respectively. More than 40% of the falls occurred when the patients were with their personal caregivers. Conclusion: Although intervention programs can use these risk factors to target those who have a high risk of falling, to optimize resources, such programs should consider the fall patterns found in our study.

Machine learning demonstrates the impact of proton transfer and solvent dynamics on CO2 capture in liquid ammonia.

Direct air capture of CO2 using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO2 adsorption process in condensed phase amines remain poorly understood. This work combines machine learning potentials, enhanced sampling and grand-canonical Monte Carlo simulations to directly compute experimentally relevant quantities to elucidate the mechanism of CO2 chemisorption in liquid ammonia as a model system. Our simulations suggest that CO2 capture in the liquid occurs in a sequential fashion, with the formation of a metastable zwitterion intermediate. Furthermore, we identified the importance of solvent-mediated proton transfer and solvent dynamics, not only in the reaction pathway but also in the efficiency of CO2 chemisorption. Beyond liquid ammonia, the methodology presented here can be readily extended to simulate amines with more complex chemical structures under experimental conditions, paving the way to elucidate the structure-performance of amines for CO2 capture.

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.

Exploring Extravasation in Cancer Patients.

Extravasation, the unintended leakage of intravenously administered substances, poses significant challenges in cancer treatment, particularly during chemotherapy and radiotherapy. This comprehensive review explores the pathophysiology, incidence, risk factors, clinical presentation, diagnosis, prevention strategies, management approaches, complications, and long-term effects of extravasation in cancer patients. It also outlines future directions and research opportunities, including identifying gaps in the current knowledge and proposing areas for further investigation in extravasation prevention and management. Emerging technologies and therapies with the potential to improve extravasation prevention and management in both chemotherapy and radiotherapy are highlighted. Such innovations include advanced vein visualization technologies, smart catheters, targeted drug delivery systems, novel topical treatments, and artificial intelligence-based image analysis. By addressing these aspects, this review not only provides healthcare professionals with insights to enhance patient safety and optimize clinical practice but also underscores the importance of ongoing research and innovation in improving outcomes for cancer patients experiencing extravasation events.

Uterine Angioleiomyoma: Clinical and Histopathologic Differentiation of an Underrecognized Mimicker of Uterine Leiomyoma.

Angioleiomyoma is an uncommon benign neoplasm of mesenchymal origin that arises from perivascular smooth muscle cells. This soft tissue neoplasm usually occurs in the dermal or subcutaneous tissues of the extremities, head and neck, or trunk with fewer than 40 reported angioleiomyomas arising in the uterine corpus. Herein we report a uterine angioleiomyoma in a 44-year-old G5P4 Hispanic woman with a longstanding history of recurrent abdominal pain, pelvic organ prolapse, abnormal uterine bleeding, anemia, and hypertension. The patient underwent surgical treatment with total laparoscopic hysterectomy with bilateral salpingectomy and a uterosacral ligament suspension. Uterine angioleiomyoma was diagnosed post-operatively based on gross and microscopic features. The location of the uterine angioleiomyoma within the myometrium corresponded with contrast enhancement apparent on preoperative imaging. This and other uterine angioleiomyomas have characteristic imaging, macroscopic, and microscopic features which distinguish it from leiomyoma. Enhancing awareness of this underrecognized entity will facilitate precise diagnosis and thereby enable improved understanding of the clinicopathological characteristics of uterine angioleiomyoma.

Depletion of lipid storage droplet-1 delays endoreplication progression and induces cell death in Drosophila salivary gland.

Perilipins are evolutionarily conserved from insects to mammals. Drosophila lipid storage droplet-1 (LSD-1) is a lipid storage droplet membrane surface-binding protein family member and a counterpart to mammalian perilipin 1 and is known to play a role in lipolysis. However, the function of LSD-1 during specific tissue development remains under investigation. This study demonstrated the role of LSD-1 in salivary gland development. Knockdown of Lsd-1 in the salivary gland was established using the GAL4/UAS system. The third-instar larvae of knockdown flies had small salivary glands containing cells with smaller nuclei. The null mutant Drosophila also showed the same phenotype. The depletion of LSD-1 expression induced a delay of endoreplication due to decreasing CycE expression and increasing DNA damage. Lsd-1 genetically interacted with Myc in the third-instar larvae. These results demonstrate that LSD-1 is involved in cell cycle and cell death programs in the salivary gland, providing novel insight into the effects of LSD-1 in regulating salivary gland development and the interaction between LSD-1 and Myc.

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.

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.

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.

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.

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.

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).

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.