Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatable brain network biomarkers in children in coma using task and resting-state functional MRI: a case series

The withdrawal of life-sustaining therapies is frequently considered for pediatric patients with severe acute brain injuries who are admitted to the intensive care unit. However, it is worth noting that some children with a resultant poor neurological status may ultimately survive and achieve a positive neurological outcome. Evidence suggests that adults with hidden consciousness may have a more favorable prognosis compared to those without it. Currently, no treatable network disorders have been identified in cases of severe acute brain injury, aside from seizures detectable through an electroencephalogram (EEG) and neurostimulation via amantadine. In this report, we present three cases in which multimodal brain network evaluation played a helpful role in patient care. This evaluation encompassed various assessments such as continuous video EEG, visual-evoked potentials, somatosensory-evoked potentials, auditory brainstem-evoked responses, resting-state functional MRI (rs-fMRI), and passive-based and command-based task-based fMRI. It is worth noting that the latter three evaluations are unique as they have not yet been established as part of the standard care protocol for assessing acute brain injuries in children with suppressed consciousness. The first patient underwent serial fMRIs after experiencing a coma induced by trauma. Subsequently, the patient displayed improvement following the administration of antiseizure medication to address abnormal signals. In the second case, a multimodal brain network evaluation uncovered covert consciousness, a previously undetected condition in a pediatric patient with acute brain injury. In both patients, this discovery potentially influenced decisions concerning the withdrawal of life support. Finally, the third patient serves as a comparative control case, demonstrating the absence of detectable networks. Notably, this patient underwent the first fMRI prior to experiencing brain death as a pediatric patient. Consequently, this case series illustrates the clinical feasibility of employing multimodal brain network evaluation in pediatric patients. This approach holds potential for clinical interventions and may significantly enhance prognostic capabilities beyond what can be achieved through standard testing methods alone.
Local Perturbation Limits of the Discrete Matrix Inequality Linear Quadratic Regulator Task for Differential-algebraic Systems

The theory of optimal control is connected with keeping a dynamical system at minimum cost. The situation where the system behavior is depicted by a set of linear differential equations and the cost is described by a quadratic function is considered to be the linear quadratic (LQ) task. The discussed regulation task can be clearly accomplished by the solutions Q, Y of a system of linear matrix inequalities (LMIs). This paper is devoted to conditioning of the discrete-time LMI based linear quadratic regulator task for differential-algebraic (DAE) systems. To determine the local perturbation limits of the matrix inequalities we introduce an appropriate term on the right, which is somehow perturbed. Based on the perturbation analysis we obtain narrow local perturbation limits for the LMIs’ solutions to the LQR task. The results are illustrated by an example.
Lower Mississippi River Environmental Program. Report 11. Forest Vegetation of the Leveed Floodplain of the Lower Mississippi River

Abstract : The main stem levee system and loess bluffs of the Lower Mississippi River Valley (LMRV) confine 955 miles of river in an area about 600 miles long and up to 20 miles wide. The corridor of unprotected floodplain flanking the lower river supports over one-million acres of forest; about one-quarter of the total bottomland hardwood forest estimated to remain in the LMRV. In 1982, the US Army Corps of Engineers Mississippi River Commission initiated the Lower Mississippi River Environmental Program, which included development of a geographic information system and studies of selected ecosystem components within the confined floodplain. The research reported here was designed to provide a description of forest resources of the study area, with particular attention to forest attributes commonly considered in evaluations of wildlife habitat suitability.
Reverse Takotsubo Cardiomyopathy Following COVISHIELD Vaccination: A Rare Case of an Anaphylactic Reaction

This case report describes a 47-year-old female healthcare worker who developed reverse takotsubo cardiomyopathy (TTC) following the administration of the COVISHIELD COVID-19 vaccine. Within minutes of receiving the vaccine, she experienced acute shortness of breath, nausea, and restlessness, along with a significant drop in blood pressure. She was diagnosed with an anaphylactic reaction and promptly treated with adrenaline and fluids. In the intensive cardiac care unit, she exhibited hypotension, had vision loss, and developed pulmonary edema. Further evaluation revealed abnormal ECG findings, elevated troponin levels, and reduced left ventricular ejection fraction (LVEF). Coronary angiography ruled out obstructive coronary artery disease. The patient gradually improved over several days and was discharged with a recovered left ventricular function. This case highlights the occurrence of TTC triggered by anaphylaxis to the COVID-19 vaccine and emphasizes the need for preparedness to manage such emergencies in vaccination centers.
94 GHz Butler Matrix-Integrated MTM SIW Beamforming Array in Glass IPD

In this letter, we propose a low-profile beamforming array operating in the W-band, which is based on the Butler matrix integrated with substrate-integrated waveguide metamaterial (MTM) antennas. The proposed MTM antenna achieves an enhanced bandwidth through the use of multiple resonance modes of the composite right/left-hand structure. The implementation of the proposed array is done using a low-cost glass-based integrated-passive-device (IPD) technology. The microstrip-type Butler matrix with crossover structures is designed, and its bandwidth of 12.8% at the W-band is verified with measurements. Experimental verifications of the radiation patterns are conducted to demonstrate the beamforming characteristics of the proposed IPD antenna array.
Investigating the Impact of Over Qualification on Employee Performance, Satisfaction and Employee Relations

– In the contemporary job market, the phenomenon of overqualification has emerged as a prevalentissue with potential implications for both employees and organizations. Overqualification occurs whenindividuals possess qualifications, skills, or educational backgrounds that surpass the requirements of theircurrent job positions. This research aims to investigate the impact of overqualification on employeeperformance, job satisfaction, and interpersonal relations within the workplace. Employing a mixed-methodresearch design, this study collected data from a diverse sample of employees through quantitative surveysa confirmatory factor analysis (CFA) using SPSS Amos to analyze data collected from employees in variousindustries and educational institutes. The quantitative analysis revealed a significant positive correlationbetween overqualification and employee performance, indicating that individuals who perceive themselvesas overqualified may experience good job performance. Additionally, overqualification was found to havea positive association with job satisfaction, as overqualified employees reported higher levels of overall jobsatisfaction compared to their adequately qualified counterparts. The qualitative findings complementedthe quantitative results by providing deeper insights into the emotional experiences of overqualifiedemployees, highlighting feelings of underutilization, frustration, and disengagement. Furthermore, theresearch indicated that overqualification can detrimentally impact interpersonal relations in the workplace,potentially leading to communication challenges and good relations among employees.
Effects of ice-phase cloud microphysics in simulating wintertime precipitation

We compare two numerical experiments to investigate the effects of ice-phase cloud microphysical processes on simulations of wintertime precipitation in the southwestern United States. Results of these simulations, one with and the other without ice-phase microphysics, suggest that an inclusion of ice-phase microphysics plays a crucial role in simulating wintertime precipitation. The simulation that employs both the ice and water-phase microphysics better reproduced the observed spatial distribution of precipitation compared to the one without ice-phase microphysics. The most significant effect of ice-phase microphysics appeared in local production of precipitating particles by collection processes, rather than in local condensation.
Exploring Parametric and Mechanistic Differences between Expi293FTM and ExpiCHO-STM Cells for Transient Antibody Production Optimization

Rapidly producing drug-like antibody therapeutics for lead molecule discovery and candidate optimization is typically accomplished by large-scale transient gene expression technologies (TGE) with cultivated mammalian cells. The TGE methodologies have been extensively developed over the past three decades, yet produce significantly lower yields than the stable cell line approach, facing the technical challenge of achieving universal high expression titers for a broad range of antibodies and therapeutics modalities. In this study, we explored various parameters for antibody production in the TGE cell host Expi293FTM and ExpiCHO-STM with the transfection reagents ExpiFectamineTM and polyethylenimine. We discovered that there are significant differences between Expi293FTM and ExpiCHO-STM cells with regards to DNA complex formation time and ratio, complex formation buffers, DNA complex uptake trafficking routes, responses to dimethyl sulfoxide and cell cycle inhibitors, as well as light-chain isotype expression preferences. This investigation mechanistically dissected the TGE processes and provided a new direction for future transient antibody production optimization.
Unsupervised Word-Sense Disambiguation Using Bilingual Comparable Corpora

An unsupervised method for word-sense disambiguation using bilingual comparable corpora was developed. First, it extracts word associations, i.e., statistically significant pairs of associated words, from the corpus of each language. Then, it aligns word associations by consulting a bilingual dictionary and calculates correlation between senses of a target polysemous word and its associated words, which can be regarded as clues for identifying the sense of the target word. To overcome the problem of disparity of topical coverage between corpora of the two languages as well as the problem of ambiguity in word-association alignment, an algorithm for iteratively calculating a sense-vs.-clue correlation matrix for each target word was devised. Word-sense disambiguation for each instance of the target word is done by selecting the sense that maximizes the score, i.e., a weighted sum of the correlations between each sense and clues appearing in the context of the instance. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora together with the EDR bilingual dictionary showed that the new method has promising performance; namely, the Fmeasure of its sense selection was 74.6% compared to a baseline of 62.8%. The developed method will possibly be extended into a fully unsupervised method that features automatic division and definition of word senses.
Acquisition Phase-------- R-----------------------t----------------1-------Training Bilingual

This paper presents a statistical Word Sense Disambiguation with application in Portuguese-Chinese Machine Translation systems. Due to the limited availability of Portuguese-Chinese resources in the form of digital corpora and annotated Treebank, an unsupervised learning and a non-aligned bflingual corpus are applied. The proposed method first identifies words related to each of the ambiguous words based on their surrounding words and relative distance. A mathematical model is then applied in the identification of the most suitable sense of an ambiguous word in terms of the related words. All the senses discovered are converted into a set of rules and stored in the Sense Knowledge base for later use in disambiguation and translation process. Preliminary experiment results show an improvement of 6%o in asgning correctiy the corresponding translation over the baseline method. Index Terns-Word Sense Disambiguation, Natural Language Processing, Machine Translation
Learning Spatial–Temporal Consistency for Satellite Image Sequence Prediction

As an extremely challenging spatial–temporal sequence prediction task, satellite image sequence prediction has various and significant applications in real-world scenarios. Although a lot of deep learning prediction models are developed for spatial–temporal sequence prediction, the methods still deliver unsatisfactory performance in terms of keeping spatial–temporal consistency, which leads to inaccurate and blurry satellite image sequence predictions. To maintain spatial–temporal consistency and achieve high-quality satellite image sequence prediction, we propose a novel and effective spatial–temporal consistency network (STCNet). In STCNet, a multilevel motion memory-based predictor is proposed to accurately predict motion patterns of satellite image sequences to ensure temporal consistency. Then, a time-variant frame discriminator is carefully designed and proposed, which can enhance the perception quality of predicted frames to guarantee spatial consistency and simultaneously maintain the motion coherency of predicted sequences. Moreover, a scheduled sampling strategy is proposed to reduce the optimizing difficulty and better train the proposed method. Comprehensive experiments conducted on satellite image sequences from the FY-4A meteorological satellite verify the effectiveness, applicability, and adaptability of our proposed method compared to state-of-the-art approaches under challenging scenarios.
Strong Haze‐Black Carbon‐Climate Connections Observed Across Northern and Eastern China

Radiative absorption of black carbon (BC) is of great importance in air quality and climate change. Simultaneous mitigation of haze pollution and climate warming requires an in‐depth understanding of haze‐BC‐climate connections based on direct observations. We simultaneously performed measurements in the North China Plain (NCP) and the Yangtze River Delta (YRD) during the winter of 2018–2019, when cross‐regional haze transport frequently occurred. We found that BC particles exhibited a thick coating thickness (100–120 nm) under haze pollution conditions in the NCP and were then transported to the YRD with regional haze development. BC particles in the downwind region showed a bimodal distribution of core‐size‐resolved aging degree, which can be used to separate and quantify local and regional BC. The results showed that the regional haze transport from the NCP brought 1–3 μg m−3 BC into the YRD. These regional BC particles exhibited ∼1.6 times stronger absorption efficiency than locally emitted BC. The direct radiative effects of BC in the downwind region were estimated to be enhanced by ∼2 times when regional haze pollution was transported from the NCP to the YRD. The amplification of BC's climate effect during regional haze pollution was attributed to the increases in both the mass concentrations and light absorption efficiency of BC, which were responsible for 40%–50% and 50%–60%, respectively. Our work identified strong haze‐BC‐climate connections across the NCP and YRD and highlighted the importance of BC in the effects of haze pollution on regional climate.
Biopolymers and Their Application in Bioprinting Processes for Dental Tissue Engineering

Dental tissues are composed of multiple tissues with complex organization, such as dentin, gingiva, periodontal ligament, and alveolar bone. These tissues have different mechanical and biological properties that are essential for their functions. Therefore, dental diseases and injuries pose significant challenges for restorative dentistry, as they require innovative strategies to regenerate damaged or missing dental tissues. Biomimetic bioconstructs that can effectively integrate with native tissues and restore their functionalities are desirable for dental tissue regeneration. However, fabricating such bioconstructs is challenging due to the diversity and complexity of dental tissues. This review provides a comprehensive overview of the recent developments in polymer-based tissue engineering and three-dimensional (3D) printing technologies for dental tissue regeneration. It also discusses the current state-of-the-art, focusing on key techniques, such as polymeric biomaterials and 3D printing with or without cells, used in tissue engineering for dental tissues. Moreover, the final section of this paper identifies the challenges and future directions of this promising research field.
Components of the Nucleotide Salvage Pathway Increase Frog Virus 3 (FV3) Replication

Viruses are obligate intracellular parasites that alter host metabolic machinery to obtain energy and macromolecules that are pivotal for replication. Ranavirus, including the type species of the genus frog virus 3 (FV3), represent an ecologically important group of viruses that infect fish, amphibians, and reptiles. It was established that fatty acid synthesis, glucose, and glutamine metabolism exert roles during iridovirus infections; however, no information exists regarding the role of purine metabolism. In this study, we assessed the impact of exogenously applied purines adenine, adenosine, adenosine 5′-monophosphate (AMP), inosine 5′-monophosphate (IMP), inosine, S-adenosyl-L-homocysteine (SAH), and S-adenosyl-L-methionine (SAM) on FV3 replication. We found that all compounds except for SAH increased FV3 replication in a dose-dependent manner. Of the purines investigated, adenine and adenosine produced the most robust response, increasing FV3 replication by 58% and 51%, respectively. While all compounds except SAH increased FV3 replication, only adenine increased plaque area. This suggests that the stimulatory effect of adenine on FV3 replication is mediated by a mechanism that is at least in part independent from the other compounds investigated. Our results are the first to report a response to exogenously applied purines and may provide insight into the importance of purine metabolism during iridoviral infection.
Finite Non-Commutative Associative Algebras as Carriers of Hidden Discrete Logarithm Problem

The article introduces new finite algebras attractive as carriers of the discrete logarithm problem in a hidden group. In particular new 4-dimensional and 6-dimensional finite non-commutative algebras with associative multiplication operation and their properties are described. It is also proposed a general method for defining finite non-commutative associative algebras of arbitrary even dimension m ≥ 2. Some of the considered algebras contain a global unit, but the other ones include no global unit element. In the last case the elements of the algebra are invertible locally relatively local bi-side units that act in the frame of some subsets of elements of algebra. For algebras of the last type there have been derived formulas describing the sets of the (right-side, left-side, and bi-side) local units. Algebras containing a large set of the global single-side (left-side and right-side) units and no global bi-side unit are also introduced. Since the known form of defining the hidden discrete logarithm problem uses invertibility of the elements of algebra relatively global unit, there are introduced new forms of defining this computationally difficult problem. The results of the article can be applied for designing public-key cryptographic algorithms and protocols, including the post-quantum ones. For the first time it is proposed a digital signature scheme based on the hidden discrete logarithm problem.
NL3DLogTNN: An Effective Hyperspectral Image Denoising Method Combined Non-Local Self-Similarity and Low-Fibered- Rank Regularization

Hyperspectral image denoising is an important research topic in the field of remote sensing image processing. Recently, methods based on non-local low-rank tensor approximation have gained widespread attention towing to their ability to fully exploit non-local self-similarity. However, existing non-local low-rank tensor approximation methods fall short in capturing the correlations between various modes in hyperspectral images, thus failing to achieve the optimal approximation. To solve this issue, a novel three-directional log-based tensor nuclear norm (3DLogTNN)–based non-local hyperspectral image denoising model NL3DLogTNN is proposed. The correlation between the various modes of the model was obtained by performing TNN decomposition in three directions on the extracted non-local comparable blocks, better capturing the global low-rank property of the image. To effectively solve the proposed NL3DLogTNN model, we developed an approximate alternating direction method of multipliers (ADMM)-based methodology and offered a thorough numerical convergence proof. Extensive experiments are conducted on hyperspectral image datasets with simulated noise and real-world noise, which demonstrated that the proposed NL3DLogTNN model outperforms state-of-the-art methods in terms of quantitative and visual performance evaluation.
Tensor-Based Angle Estimation for Bistatic MIMO Radar Systems With Multislot Gain-Phase Error

In this article, a tensor-based angle estimation algorithm is proposed for bistatic multiple-input multiple-output (MIMO) radar systems with a multislot gain-phase error (MSGPE). First, the received signals are constructed into a complex-valued third-order parallel factor model that contains information, such as transmit MSGPE, receive MSGPE, directions of departure (DODs), and directions of arrival (DOAs). Then, a two-stage tensor-based angle algorithm is proposed for antenna arrays with the MSGPE. In the first stage, the MSGPE information part and angle information part are separated by the least-squares Khatri–Rao factorization. In the second stage, the DODs and DOAs are obtained jointly based on spatial smoothing and real-valued processing techniques under eliminating the influence of MSGPE. Compared with the existing competitive algorithms, the proposed algorithm achieves an improved performance and is applicable for both the coherent and noncoherent targets. Simulation results show the superiority of the proposed tensor-based angle estimation algorithm.
Extracao de Estrategias Operacionais Otimas de Sistemas de Distribuicao de Agua Utilizando Algoritmos Geneticos Multiobjetivo e Aprendizado de Maquina – Aplicacao ao Sistema de Macro-distribuicao de Agua de Goiania

The disordered growth of cities and the lack of funding for the basic sanitation system have rendered water supply systems complex and difficult to operate. Efficient operation of the system is an essential tool to lengthen its life as much as possible, ensuring perfect service to the consumers, besides keeping electric energy and maintenance costs within acceptable levels. It is essential to know the system for effective operation, since through the system, with tools such as hydraulic simulation models, optimization and definition of operational rules, it is possible to create ideal conditions to operate the system units, no longer de- pending exclusively on personal experience and keeping the model reliable. This study proposes to develop a computa- tional model aiming at the optimum operational control of the drinking water macrodistribution system for the city of Goiania, and the results showed that satisfactory opera- tional strategies can be produced for the system, replacing the personal judgment of the operation