Miracle-dz/UniFAR-FTUs huggingface dataset:
Cerebrospinal fluid metabolite alterations in patients with different etiologies, diagnoses, and prognoses of disorders of consciousness

Medical management of disorders of consciousness (DoC) is a growing issue imposing a major burden on families and societies. Recovery rates vary widely among patients with DoC, and recovery predictions strongly influence decisions on medical care. However, the specific mechanisms underlying different etiologies, consciousness levels, and prognoses are still unclear.
Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling

Abstract Aims Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone. This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility. Methods Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40–60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45–55, 35–65, and 30–70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up. Results Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45–55 model had the highest number of parameters predictive for post-ablation VT-recurrence. Conclusion Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.
A rapid, high‐yield and bioinspired synthesis of colloidal silver nanoparticles using Glycyrrhiza glabra root extract and assessment of antibacterial and phytostimulatory activity

Silver nanoparticles (AgNPs) have emerged as highly effective antimicrobial agents against multidrug‐resistant (MDR) pathogens. This study aims to employ green chemistry principles for AgNP synthesis involving phytochemical‐rich extract from Glycyrrhiza glabra roots. The approach highlights using renewable feedstocks, safer chemicals, minimum byproducts, and process scale‐up. The synthesis of AgNPs was assessed using a surface plasmon resonance band at 420 nm, and structural properties were characterized using TEM, x‐ray diffraction, Fourier‐transform infrared spectroscopy, and X‐ray photoelectron spectroscopy. This method enables the production of high‐yield dispersions of AgNPs with desired physicochemical characteristics, including dark yellow solution, size (~20 nm), spherical to an oval shape, crystal structure, and stable colloidal properties. The antimicrobial activity of AgNPs was investigated against the MDR bacteria strains of gram‐positive (Staphylococcus aureus) and gram‐negative (Escherichia coli). This work reveals that the antimicrobial activity of AgNPs can be influenced by bacterial cell wall components. The results demonstrate the strong interaction between AgNPs and E. coli, exhibiting a dose‐dependent antibacterial response. The green approach facilitated the safer, facile, and rapid synthesis of colloidal dispersions of AgNPs, providing a sustainable and promising alternative to conventional chemical and physical methods. Furthermore, the effect of AgNPs on various growth parameters, including seed germination, root and shoot elongation, and dry weight biomass, was assessed for mung bean seedlings. The results revealed phytostimulatory effects, suggesting the promising prospects of AgNPs in the nano‐priming of agronomic seeds.
Functional anatomy of shoulder muscles in the Pacific white‐sided dolphin (Lagenorhynchus obliquidens)

Most intrinsic muscles of the forelimb in dolphins are either degenerated or lost; however, the muscles around the shoulder joint are well preserved. We dissected the forelimbs of Pacific white‐sided dolphins and constructed a full‐scale model of the flipper to compare and examine their movements following dissection. The humerus was oriented at approximately 45° ventrally from the horizontal plane of the dolphin and 45° caudally from the frontal plane. This maintains the neutral position of the flipper. The deltoideus and pectoralis major muscles were inserted into the body of the humerus, and the flipper was moved in the dorsal and ventral directions, respectively. A large tubercle, known as the common tubercle, was observed at the medial end of the humerus. Four muscles were inserted into the common tubercle: the brachiocephalicus, supraspinatus, and cranial part of the subscapularis, which laterally rotated the common tubercle. Subsequently, the flipper swung forward, and its radial edge was lifted. Conversely, the medial rotation of the common tubercle caused by the coracobrachialis and the caudal part of the subscapularis was accompanied by backward swinging of the flipper and lowering of the radial edge. These findings suggest the function of the flipper as a stabilizer or rudder is caused by the rotation of the humerus's common tubercle.
Forced Convection Cooling of Electric Motors Using Enhanced Surfaces

Electric motors are extensively engaged in industrial and commercial applications such as automobiles, energy-conversion systems, elevators, electronic devices, machine tools, and aircraft carriers. Due to the significant internal heat generation, it is usually a challenge to design and manufacture high efficiency, high reliability, and low cost electric motors with superior performance. One of the efficient ways to dissipate the heat generated in the electrical motor is by using extended surfaces (i.e. heat sinks). These surfaces are extruded from the stator and air is drawn though them by a cooling fan. This cooling approach is simple to be implemented and has zero carbon emission to the environment. Air is allowed to flow in the mini rectangular cross sectional channels in between the ribbed fins. This project is intended to study numerically the effect of varying ribs spacing and ribs heights on heat removal efficiency, accounting for the relative change in heat transfer coefficient and pressure drop compared to those for a smooth flow channel. The study is conducted to simulate the airflow field, and heat transfer for a plate heat sink using ANSYS V.16.
Effects of adjuvant berberine therapy on acute ischemic stroke: A meta‐analysis

We conducted a meta‐analysis to evaluate the clinical efficacy of berberine (BBR) in treating acute ischemic stroke (AIS), explore its anti‐inflammatory effects, and assess its potential applications for AIS patients. We comprehensively searched nine databases from inception until July 1, 2022, to identify clinical trials investigating the use of BBR in treating AIS. We performed statistical analyses using RevMan5.4 software and focused on primary outcomes such as inflammatory markers as well as secondary outcomes including immune system indicators, relevant biomarkers, carotid artery atherosclerosis, and adverse reactions. Our analysis included data from 17 clinical trials involving 1670 patients with AIS. Our results revealed that BBR in combination with conventional treatment significantly reduced levels of high‐sensitivity C‐reactive protein (hs‐CRP), macrophage migration inhibitory factor (MIF), interleukin‐6 (IL‐6), complement C3, hypoxic inducible factor‐1 α (HIF‐1α), cysteine protease‐3 (Caspase‐3), the national institutes of health stroke scale (NIHSS), triglycerides (TG), low‐density lipoprotein cholesterol (LDL‐C), carotid intima‐media thickness (IMT), the number of unstable plaques, and carotid crouse score on ultrasound when compared with conventional treatment alone. Furthermore, combining BBR with conventional treatment may improve the overall effective rate. Therefore, our findings suggest that BBR can be used as an adjuvant therapy for AIS due to its ability to reduce inflammatory cytokine levels, providing a novel therapeutic option for AIS. However, larger randomized controlled trials are necessary to confirm these results.
Improving Bitswap Privacy with Forwarding and Source Obfuscation

IPFS is a content-addressed decentralized peer-to-peer data network, using the Bitswap protocol for exchanging data. The data exchange leaks the information to all neighbors, compromising a user's privacy. This paper investigates the suitability of forwarding with source obfuscation techniques for improving the privacy of the Bitswap protocol. The usage of forwarding can add plausible deniability and the source obfuscation provides additional protection against passive observers. First results showed that through trickle-spreading the source prediction could decrease to 40 %, at the cost of an increased content fetching time. However, assuming short distances between content provider and consumer the content fetching time can be faster even with the additional source obfuscation.
Formal Control of New England 39-Bus Test System: An Assume-Guarantee Approach

This work is concerned with an assume-guarantee approach to compositionally control a New England 39-bus Test System (NETS). The proposed scheme is based on the new notion of robust simulation functions with disturbance refinement alongside the composition of multiple subsystems to tackle the difficulties associated with scalability, also known as the curse of dimensionality. In our proposed setting, we approximate concrete subsystems with abstractions that have lower dimensions (a.k.a. reduced-order models) while providing mathematical guarantees on controller synthesis through the quantification of an upper bound on the closeness between output trajectories of original systems and their abstractions. We propose two control methods to provide guarantees for NETS: one using the principle of interconnected synchronous machines and another considering the power flows in the network between neighbouring subsystems.
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization

Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate models that suffer from scalability issues and are sensitive to observations with different scales and noise types across tasks. Moreover, they often overlook the uncertainty associated with task similarity. This leads to unreliable task adaptation when only limited observations are obtained or when the new tasks differ significantly from the related tasks. To address these limitations, we propose a novel meta-learning BO approach that bypasses the surrogate model and directly learns the utility of queries across tasks. Our method explicitly models task uncertainty and includes an auxiliary model to enable robust adaptation to new tasks. Extensive experiments show that our method demonstrates strong anytime performance and outperforms state-of-the-art meta-learning BO methods in various benchmarks.
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering

Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised setting, specifically the one-class setting, in which we assume the availability of a set of normal (i.e., anomaly-free) images for training. In this paper, we consider a more challenging scenario of unsupervised AD, in which we detect anomalies in a given set of images that might contain both normal and anomalous samples. The setting does not assume the availability of known normal data and thus is completely free from human annotation, which differs from the standard AD considered in recent studies. For clarity, we call the setting blind anomaly detection (BAD). We show that BAD can be converted into a local outlier detection problem and propose a novel method named PatchCluster that can accurately detect image- and pixel-level anomalies. Experimental results show that PatchCluster shows a promising performance without the knowledge of normal data, even comparable to the SOTA methods applied in the one-class setting needing it.
On the Definition of Marginal Ice Zone Width

AbstractSea ice features a dense inner pack ice zone surrounded by a marginal ice zone (MIZ) in which the sea ice properties are modified by interaction with the ice-free open ocean. The width of the MIZ is a fundamental length scale for polar physical and biological dynamics. Several different criteria for establishing MIZ boundaries have emerged in the literature—wave penetration, floe size, sea ice concentration, etc.—and a variety of definitions for the width between the MIZ boundaries have been published. Here, three desirable mathematical properties for defining MIZ width are proposed: invariance with respect to translation and rotation on the sphere; uniqueness at every point in the MIZ; and generality, including nonconvex shapes. The previously published streamline definition is shown to satisfy all three properties, where width is defined as the arc length of a streamline through the solution to Laplaces’s equation within the MIZ boundaries, while other published definitions each satisfy only one.
A new expansion of the Coulomb potential and linear $r_{ij}$ terms

In this work a new expansions of Coulomb potential and interparticle distance, linear term $r_{12}$ are proposed. Except the singularities, the expansions converge to the exact value in the whole coordinate space including the vicinity of the singularities or correlation cusps points. The disadvantage of the expansion is, that it leads to complicated goniometric functional forms. Anyway, for their simplification, we used the method of their expansion developed by the author in the past.
Fine error bounds for approximate asymmetric saddle point problems

The theory of mixed finite element methods for solving different types of elliptic partial differential equations in saddle point formulation is well established since many decades. This topic was mostly studied for variational formulations defined upon the same product spaces of both shape- and test-pairs of primal variable-multiplier. Whenever either these spaces or the two bilinear forms involving the multiplier are distinct, the saddle point problem is asymmetric. The three inf-sup conditions to be satisfied by the product spaces stipulated in work on the subject, in order to guarantee well-posedness, are well known. However, the material encountered in the literature addressing the approximation of this class of problems left room for improvement and clarifications. After making a brief review of the existing contributions to the topic that justifies such an assertion, in this paper we set up finer global error bounds for the pair primal variable-multiplier solving an asymmetric saddle point problem. Besides well-posedness, the three constants in the aforementioned inf-sup conditions are identified as all that is needed for determining the stability constant appearing therein, whose expression is exhibited. As a complement, refined error bounds depending only on these three constants are given for both unknowns separately.
Fault Diagnosis for Discrete Event Systems Modeled By Bounded Petri Nets

Fault diagnosis is an important problem in the manufacturing industry. It has been extensively studied in the past few decades both in time‐driven systems and discrete event systems. This paper presents a Petri net diagnoser for online fault diagnosis of discrete event systems modeled by bounded labeled Petri nets. First, we present the concept and some properties of an extended basis reachability graph. Next, based on such a graph, we construct a Petri net diagnoser that is used to determine if a fault has occurred. Finally, an example is given to illustrate the application of the proposed diagnoser.
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images

Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape models use consistent shape representation across all the samples in a given cohort, which helps to compare shapes and identify the variations that can detect pathologies and help in formulating treatment plans. In medical imaging, computing these shape representations from CT/MRI scans requires time-intensive preprocessing operations, including but not limited to anatomy segmentation annotations, registration, and texture denoising. Deep learning models have demonstrated exceptional capabilities in learning shape representations directly from volumetric images, giving rise to highly effective and efficient Image-to-SSM networks. Nevertheless, these models are data-hungry and due to the limited availability of medical data, deep learning models tend to overfit. Offline data augmentation techniques, that use kernel density estimation based (KDE) methods for generating shape-augmented samples, have successfully aided Image-to-SSM networks in achieving comparable accuracy to traditional SSM methods. However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models. This paper introduces a novel strategy for on-the-fly data augmentation for the Image-to-SSM framework by leveraging data-dependent noise generation or texture augmentation. The proposed framework is trained as an adversary to the Image-to-SSM network, augmenting diverse and challenging noisy samples. Our approach achieves improved accuracy by encouraging the model to focus on the underlying geometry rather than relying solely on pixel values.
PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection

Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optimal performance. To address this, we propose PseudoCell, an object detection framework to automate centroblast detection in WSI (source code is available at https://github.com/IoBT-VISTEC/PseudoCell.git). This framework incorporates centroblast labels from pathologists and combines them with pseudo-negative labels obtained from undersampled false-positive predictions using the cell's morphological features. By employing PseudoCell, pathologists' workload can be reduced as it accurately narrows down the areas requiring their attention during examining tissue. Depending on the confidence threshold, PseudoCell can eliminate 58.18-99.35% of non-centroblasts tissue areas on WSI. This study presents a practical centroblast prescreening method that does not require pathologists' refined labels for improvement. Detailed guidance on the practical implementation of PseudoCell is provided in the discussion section.
Quantification of Uncertainty with Adversarial Models

Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a divergence function and the posterior. Current methods such as Deep Ensembles or MC dropout underperform at estimating the epistemic uncertainty, since they primarily consider the posterior when sampling models. We suggest Quantification of Uncertainty with Adversarial Models (QUAM) to better estimate the epistemic uncertainty. QUAM identifies regions where the whole product under the integral is large, not just the posterior. Consequently, QUAM has lower approximation error of the epistemic uncertainty compared to previous methods. Models for which the product is large correspond to adversarial models (not adversarial examples! ). Adversarial models have both a high posterior as well as a high divergence between their predictions and that of a reference model. Our experiments show that QUAM excels in capturing epistemic uncertainty for deep learning models and outperforms previous methods on challenging tasks in the vision domain.
Opinion formation by belief propagation: A heuristic to identify low-credible sources of information

With social media, the flow of uncertified information is constantly increasing, with the risk that more people will trust low-credible information sources. To design effective strategies against this phenomenon, it is of paramount importance to understand how people end up believing one source rather than another. To this end, we propose a realistic and cognitively affordable heuristic mechanism for opinion formation inspired by the well-known belief propagation algorithm. In our model, an individual observing a network of information sources must infer which of them are reliable and which are not. We study how the individual's ability to identify credible sources, and hence to form correct opinions, is affected by the noise in the system, intended as the amount of disorder in the relationships between the information sources in the network. We find numerically and analytically that there is a critical noise level above which it is impossible for the individual to detect the nature of the sources. Moreover, by comparing our opinion formation model with existing ones in the literature, we show under what conditions people's opinions can be reliable. Overall, our findings imply that the increasing complexity of the information environment is a catalyst for misinformation channels.
A Poincaré Formula for Differential Forms and Applications

We prove a new general Poincaré-type inequality for differential forms on compact Riemannian manifolds with nonempty boundary. When the boundary is isometrically immersed in Euclidean space, we derive a new inequality involving mean and scalar curvatures of the boundary only and characterize its limiting case in codimension one. A new Ros-type inequality for differential forms is also derived assuming the existence of a nonzero parallel form on the manifold.
InfoSync: Information Synchronization across Multilingual Semi-structured Tables

Information Synchronization of semi-structured data across languages is challenging. For instance, Wikipedia tables in one language should be synchronized across languages. To address this problem, we introduce a new dataset InfoSyncC and a two-step method for tabular synchronization. InfoSync contains 100K entity-centric tables (Wikipedia Infoboxes) across 14 languages, of which a subset (3.5K pairs) are manually annotated. The proposed method includes 1) Information Alignment to map rows and 2) Information Update for updating missing/outdated information for aligned tables across multilingual tables. When evaluated on InfoSync, information alignment achieves an F1 score of 87.91 (en<->non-en). To evaluate information updation, we perform human-assisted Wikipedia edits on Infoboxes for 603 table pairs. Our approach obtains an acceptance rate of 77.28% on Wikipedia, showing the effectiveness of the proposed method.