Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel curcumin nanoformulation induces apoptosis, and reduces migration and angiogenesis in liver cancer cells

Abstract Background Curcumin has been used in the treatment of several diseases; however, its low pharmacologic profile reduces its therapeutic use. Towards improving its biological activity, nanoformulations have emerged. Thus, we aimed to determine whether curcumin nanoparticles (Cur-NPs) coated with PEG/chitosan improve the treatment of liver cancer (LC) cells and underpin the molecular mechanisms underlying their anti-cancer activity. Methods Cur-NPs were synthesised in the form of Cur-PLGA-PEG/chitosan NPs. The effect of Cur-NPs was assessed in HepG2 and Huh 7 LC cells and THLE-2 normal liver cells. Results The size of synthesised Cur-NPS was determined in the standard range of 141.2 ± 47.5 nm. Compared to THLE-2 cells, LC cells treated with Cur-NPs exerted cytotoxicity at 6.25 µg/mL after 48h. Treatment of HepG-2 cells with 2.5 µg/mL of Cur-NPs inhibited cell migration and this inhibition was augmented at 10 µg/mL (p < 0.001). Treatment of chicken embryo with 5 µg/mL Cur-NPs reduced angiogenesis (p < 0.001) of 4-day-old embryos. The nanoformulation upregulated Bax and p53 and downregulated Bcl-2 in a concentration-dependent manner and subsequently induce apoptosis in HepG-2 cells. Conclusion Treatment of LC cells with Cur-NPs decreased cell proliferation, migration, and angiogenesis, and induced cell death by promoting the proapoptotic pathway. HIGHLIGHTS Curcumin nanoparticles (Cur-NPs) increase the anticancer efficiency of Curcumin against liver cancer cells. Cur-NPs induce apoptotic cell death of Liver cancer cells. Cur-NPs have ant-angiogenesis and metastasis effect.
Pediatric Acute Respiratory Distress Syndrome in South African PICUs: A Multisite Point-Prevalence Study

OBJECTIVES: To describe the prevalence of pediatric acute respiratory distress syndrome (pARDS) and the characteristics of children with pARDS in South African PICUs. DESIGN: Observational multicenter, cross-sectional point-prevalence study. SETTING: Eight PICUs in four South African provinces. PATIENTS: All children beyond the neonatal period and under 18 years of age admitted to participating PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical and demographic data were prospectively collected on a single day of each month, from February to July 2022, using a centralized database. Cases with or at risk of pARDS were identified using the 2015 Pediatric Acute Lung Injury Consensus Conference criteria. Prevalence was calculated as the number of children meeting pARDS criteria/the total number of children admitted to PICU at the same time points. Three hundred ten patients were present in the PICU on study days: 166 (53.5%) male, median (interquartile range [IQR]) age 9.8 (3.1–32.9) months, and 195 (62.9%) invasively mechanically ventilated. Seventy-one (22.9%) patients were classified as being “at risk” of pARDS and 95 patients (prevalence 30.6%; 95% CI, 24.7–37.5%) fulfilled pARDS case criteria, with severity classified as mild (58.2%), moderate (25.3%), and severe (17.6%). Median (IQR) admission Pediatric Index of Mortality 3 risk of mortality in patients with and without pARDS was 5.6 (3.4–12.1) % versus 3.9 (1.0–8.2) % (p = 0.002). Diagnostic categories differed between pARDS and non-pARDS groups (p = 0.002), with no difference in age, sex, or presence of comorbidities. On multivariable logistic regression, increasing admission risk of mortality (adjusted odds ratio [aOR] 1.02; 95% CI, 1.00–1.04; p = 0.04) and being admitted with a respiratory condition (aOR 2.64; 95% CI, 1.27–5.48; p = 0.01) were independently associated with an increased likelihood of having pARDS. CONCLUSIONS: The 30.6% prevalence of pARDS in South Africa is substantially higher than reports from other sociogeographical regions, highlighting the need for further research in this setting.
Stochastic ground motion method combining a Fourier amplitude spectrum model from a response spectrum with application of phase derivatives distribution prediction

Presentazione di un metodo per ricavare un modello di spettro di Fourier a partire da uno spettro di risposta. Tale modello viene utilizzato per generare accelerogrammi stocastici la cui non-stazionarieta e ottenuta imponendo una distribuzione nelle derivate di fase. A tal proposito una legge di predizione viene proposta in seguito all'analisi della dispersione delle derivate di fase di un database di terremoti (NGA-West1). Infine viene proposta l'applicazione di questa metodologia per ricavare il Conditional Scenario Spectra, ovvero sia un set di accelerogrammi in grado di ricostruire la storia sismica di un sito di riferimento.
Mechanism for missing data incorporated in joint modelling of ordinal responses

We analyse the problem of two clinically inseparable, repeatedly measured responses of ordinal type by also incorporating their missingness process. In our application these are the therapeutic effect and extent of side effects of fluvoxamine. In the case of a composite end point, the scientific questions addressed can be answered only when the responses are modelled jointly. As an extension of the methodology, several missingness not at random models were fitted to a set of observed data and shown to yield approximately the same result as their missingness at random counterparts, although it affects precision. In addition, the effect of various identifying restrictions on multiple imputation is investigated. An alternative numerical approximation method is suggested to reduce computational time.
Application of biomaterials and nanotechnology in corneal tissue engineering

Corneal diseases are among the most common causes of blindness worldwide. Regardless of the etiology, corneal opacity- or globe integrity-threatening conditions may necessitate corneal replacement procedures. Several procedure types are currently available to address these issues, based on the complexity and extent of injury. Corneal allograft or keratoplasty is considered to be first-line treatment in many cases. However, a significant proportion of the world’s population are reported to have no access to this option due to limitations in donor preparation. Thus, providing an appropriate, safe, and efficient synthetic implant (e.g., artificial cornea) may revolutionize this field. Nanotechnology, with its potential applications, has garnered a lot of recent attention in this area, however, there is seemingly a long way to go. This narrative review provides a brief overview of the therapeutic interventions for corneal pathologies, followed by a summary of current biomaterials used in corneal regeneration and a discussion of the nanotechnologies that can aid in the production of superior implants.
Assessing the costs of ozone pollution in India for wheat producers, consumers, and government food welfare policies

Significance We assess the economic burden of air pollution on India’s wheat production, quantifying the cost of ozone pollution for producers, consumers, and the government in three policy scenarios. Previous studies overlooked the economic dynamics of supply changes and food security policies that are crucial in India’s regulated grain markets. Ozone mitigation would cause a net loss of USD 0.24 to a gain of 4.2 billion in total social welfare. The minimum support price policy protects the farmers’ welfare, but increases government costs, causing a net welfare loss. Both alternative policies allow prices to fall, resulting in overall increases in net social welfare but causing losses to producers. Therefore, farmer support policies need reexamination to maximize the social welfare of pollution mitigation for all stakeholders.
Lumbar joint position sense measurement of patients with low back pain

Lumbar position sense can be assessed by measurement instruments including the goniometer, isokinetic dynamometry, and electronic motion monitoring equipment, which have demonstrated relatively high reliability. This literature provides a comprehensive overview of influencing factors of lumbar position sense measurement, including repositioning method, fatigue degree, and posture during the reposition. It highlights the significant role of muscle proprioception, which contributes to greater accuracy in active reposition compared to passive reposition. The differences in lumbar position sense with different measurement positions may be explained by the presence of mechanoreceptors in the load-bearing structures of the lumbar spine, especially in the facet joint capsules. These mechanoreceptors play a crucial role in providing sensory feedback and proprioceptive information pertaining to the position and movement of the lumbar spine. Individuals with low back pain (LBP) demonstrate alterations in lumbar position sense compared to those without LBP. The auto motor sensory feedback transmission mechanism of patients with non-specific LBP was more unstable than that of healthy people. These findings suggest that lumbar position sense may play a potential role in the development and perpetuation of LBP. At present, the commonly used clinical assessment methods for determining position sense include both active and passive repositioning. However, neither method exhibits high sensitivity and specificity, leading to the poor comparability of relevant studies and posing challenges for clinical application.
Early Visual Deprivation Impairs Functional Development of the Visual Ventral Stream

Purpose To probe the dynamic alternations of neural networks in real-time visual processing after visual deprivation (VD) removal. Methods A prospective cross-sectional study was conducted. Twenty children with a history of early binocular VD caused by congenital cataracts and 20 matched typically developing (TD) children were enrolled. The event-related potential (ERP) data were obtained via high-density electroencephalography. ERP data were analyzed based on three components (P1, N170, and P2), three test conditions (objects, human faces, and Chinese characters), and peak time and region of interest (ROI) chosen on a grand average head map collapsed from the averaged waveform of each group. Source localization and alpha power spectrum density were applied to define the functional pattern of brain areas and evaluate the attention function. Results The VD group showed significantly lower P1 amplitudes than the TD group under all conditions in peak ROIs, which were situated in the left occipito-temporal region. For both VD and TD groups, there were strong N170 effects in the character and human face conditions in the component's peak ROIs. Furthermore, source mapping indicated that the VD group generally showed significantly lower activation in the visual cortex and ventral stream, whereas the beyond network areas (mostly frontal areas) intensively participated in functional compensation in the VD group. The VD group showed significant poststimulus alpha desynchronization in object recognition. Conclusions Our research described the mechanisms of visual networks after early binocular VD removal. Our findings may provide a new basis for the poor visual recovery after early binocular VD removal and offer clues for visual recovery strategies.
Geometric Motion Planning for Systems With Toroidal and Cylindrical Shape Spaces

Kinematic motion planning using geometric mechanics tends to prescribe a trajectory in a parameterization of a shape space and determine its displacement in a position space. Often this trajectory is called a gait. Previous works assumed that the shape space is Euclidean when often it is not, either because the robotic joints can spin around forever (i.e., has an 𝕊1 configuration space component, or its parameterization has an 𝕊1 dimension). Consider a shape space that is a torus; gaits that “wrap” around the full range of a shape variable and return to its starting configuration are valid gaits in the shape space yet appear as line segments in the parameterization. Since such a gait does not form a closed loop in the parameterization, existing geometric mechanics methods cannot properly consider them. By explicitly analyzing the topology of the underlying shape space, we derive geometric tools to consider systems with toroidal and cylindrical shape spaces.
Chromatin conformational changes at human satellite II contribute to the senescence phenotype in the tumor microenvironment

Significance In this study, we demonstrated that DNA-fluorescence in situ hybridization, a robust technique widely used in clinical practice, can detect cellular senescence in cultured cells and clinical samples by targeting hSATII decompaction. We also identified and characterized cells bearing senescence-like phenotype with activation of inflammatory gene loci from single-cell epigenomic data using our newly developed analysis pipeline, RepATACR, for repetitive sequences. This approach can be applied to other datasets and will help to elucidate the significance of cellular senescence in cancer.
FBLNet: FeedBack Loop Network for Driver Attention Prediction

The problem of predicting driver attention from the driving perspective is gaining increasing research focus due to its remarkable significance for autonomous driving and assisted driving systems. The driving experience is extremely important for safe driving, a skilled driver is able to effortlessly predict oncoming danger (before it becomes salient) based on the driving experience and quickly pay attention to the corresponding zones. However, the nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods, and the current methods usually follow the technique line of saliency prediction methods to predict driver attention. In this paper, we propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure. By over-and-over iterations, FBLNet generates the incremental knowledge that carries rich historically-accumulative and long-term temporal information. The incremental knowledge in our model is like the driving experience of humans. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention. Our model exhibits a solid advantage over existing methods, achieving an outstanding performance improvement on two driver attention benchmark datasets.
On the Generalized Likelihood Ratio Test and One-Class Classifiers

One-class classification (OCC) is the problem of deciding whether an observed sample belongs to a target class. We consider the problem of learning an OCC model that performs as the generalized likelihood ratio test (GLRT), given a dataset containing samples of the target class. The GLRT solves the same problem when the statistics of the target class are available. The GLRT is a well-known and provably optimal (under specific assumptions) classifier. To this end, we consider both the multilayer perceptron neural network (NN) and the support vector machine (SVM) models. They are trained as two-class classifiers using an artificial dataset for the alternative class, obtained by generating random samples, uniformly over the domain of the target-class dataset. We prove that, under suitable assumptions, the models converge (with a large dataset) to the GLRT. Moreover, we show that the one-class least squares SVM (OCLSSVM) with suitable kernels at convergence performs as the GLRT. Lastly, we prove that the widely used autoencoder (AE) classifier does not generally provide the GLRT.
: A Visual Analytics Approach for Interactive Video Programming

Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional challenges for effectively composing and evaluating labeling functions. In this paper, we propose VideoPro, a visual analytics approach to support flexible and scalable video data programming for model steering with reduced human effort. We first extract human-understandable events from videos using computer vision techniques and treat them as atomic components of labeling functions. We further propose a two-stage template mining algorithm that characterizes the sequential patterns of these events to serve as labeling function templates for efficient data labeling. The visual interface of VideoPro facilitates multifaceted exploration, examination, and application of the labeling templates, allowing for effective programming of video data at scale. Moreover, users can monitor the impact of programming on model performance and make informed adjustments during the iterative programming process. We demonstrate the efficiency and effectiveness of our approach with two case studies and expert interviews.
Vertical Structure of Buoyancy Transport by Ocean Baroclinic Turbulence

Ocean mesoscale eddies enhance meridional buoyancy transport, notably in the Antarctic Circumpolar Current where they contribute to setting the deep stratification of the neighboring ocean basins. The much‐needed parameterization of this buoyancy transport in global climate models requires a theory for the overall flux, but also for its vertical structure inside the fluid column. Based on the quasi‐geostrophic dynamics of an idealized patch of ocean hosting an arbitrary vertically sheared zonal flow, we provide a quantitative prediction for the vertical structure of the buoyancy flux without adjustable parameters. The prediction agrees quantitatively with meridional flux profiles obtained through numerical simulations of an idealized patch of ocean with realistic parameter values. This work empowers modelers with an explicit and physically based expression for the vertical profile of buoyancy transport by ocean baroclinic turbulence, as opposed to the common practice of using arbitrary prescriptions for the depth‐dependence of the transport coefficients.
The impact of input node placement in the controllability of brain networks

Network control theory can be used to model how one should steer the brain between different states by driving a specific region with an input. The needed energy to control a network is often used to quantify its controllability, and controlling brain networks requires diverse energy depending on the selected input region. We use the theory of how input node placement affects the longest control chain (LCC) in the controllability of brain networks to study the role of the architecture of white matter fibers in the required control energy. We show that the energy needed to control human brain networks is related to the LCC, i.e., the longest distance between the input region and other regions in the network. We indicate that regions that control brain networks with lower energy have small LCCs. These regions align with areas that can steer the brain around the state space smoothly. By contrast, regions that need higher energy to move the brain toward different target states have larger LCCs. We also investigate the role of the number of paths between regions in the control energy. Our results show that the more paths between regions, the lower cost needed to control brain networks. We evaluate the number of paths by counting specific motifs in brain networks since determining all paths in graphs is a difficult problem.
A data-centric deep learning approach to airway segmentation

The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique to segment the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway trees at different scales. In terms of segmentation performance (dice similarity coefficient), our method outperforms the baseline model by 2.5% on average when a combined loss is used. Further, our proposed technique has a low GPU usage and high flexibility enabling it to be deployed on any 2D deep learning model.
Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education

Large Language Models are increasingly being used for various tasks including content generation and as chatbots. Despite their impressive performances in general tasks, LLMs need to be aligned when applying for domain specific tasks to mitigate the problems of hallucination and producing harmful answers. Retrieval Augmented Generation (RAG) allows to easily attach and manipulate a non-parametric knowledgebases to LLMs. Applications of RAG in the field of medical education are discussed in this paper. A combined extractive and abstractive summarization method for large unstructured textual data using representative vectors is proposed.
Quantum simulation of dissipation for Maxwell equations in dispersive media

The dissipative character of an electromagnetic medium breaks the unitary evolution structure that is present in lossless media, both dispersive and optical. In dispersive media, dissipation appears in the Schrodinger representation of Maxwell equations as a sparse diagonal operator occupying an $r$-dimensional subspace. Trotterization of the evolution enables us to isolate the non-unitary part associated with dissipation from the unitary part that corresponds to the lossless case. Given the fact that the unitary part can be implemented on $n$ qubits based on the discretization and the dimensionality of the pertinent fields we propose two dilation techniques for handling the dissipative evolution part. Our first algorithm is based on realization of dissipation as a quantum amplitude channel where an unspecified environment interacts with the lossless system and produces the non-unitary evolution. Therefore, the combined system-environment is now closed and evolves unitary. This physical unitary dilation process requires only one ancillary qubit as environment resulting in a scaling of $\textit{O}(2^{n-1}n^2)$ elementary gates, in the implementation of the total system-environment unitary evolution operator. On the other hand, by treating the non-unitary part with the LCU method, exploiting the diagonal structure of dissipation, we obtain an optimized counterpart for the previous result. The non-unitary part can now be implemented in $\textit{O}(2^{n})$ elementary gates. Under the light of our results, a full-wave quantum simulation of electromagnetic wave propagation and scattering in complex media is deemed possible in the near future.