Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of serum biomarkers for necrotizing enterocolitis using aptamer-based proteomics

Introduction Necrotizing enterocolitis (NEC) is a potentially fatal intestinal disease primarily affecting preterm infants. Early diagnosis of neonates with NEC is crucial to improving outcomes; however, traditional diagnostic tools remain inadequate. Biomarkers represent an opportunity to improve the speed and accuracy of diagnosis, but they are not routinely used in clinical practice. Methods In this study, we utilized an aptamer-based proteomic discovery assay to identify new serum biomarkers of NEC. We compared levels of serum proteins in neonates with and without NEC and identified ten differentially expressed serum proteins between these groups. Results We detected two proteins, C-C motif chemokine ligand 16 (CCL16) and immunoglobulin heavy constant alpha 1 and 2 heterodimer (IGHA1 IGHA2), that were significantly increased during NEC and eight that were significantly decreased. Generation of receiver operating characteristic (ROC) curves revealed that alpha-fetoprotein (AUC = 0.926), glucagon (AUC = 0.860), and IGHA1 IGHA2 (AUC = 0.826) were the proteins that best differentiated patients with and without NEC. Discussion These findings indicate that further investigation into these serum proteins as a biomarker for NEC is warranted. In the future, laboratory tests incorporating these differentially expressed proteins may improve the ability of clinicians to diagnose infants with NEC rapidly and accurately. Graphical Abstract Overview of the study. Figure made with Biorender.com.
Genetic diagnosis of inborn errors of immunity using clinical exome sequencing

Inborn errors of immunity (IEI) include a variety of heterogeneous genetic disorders in which defects in the immune system lead to an increased susceptibility to infections and other complications. Accurate, prompt diagnosis of IEI is crucial for treatment plan and prognostication. In this study, clinical utility of clinical exome sequencing (CES) for diagnosis of IEI was evaluated. For 37 Korean patients with suspected symptoms, signs, or laboratory abnormalities associated with IEI, CES that covers 4,894 genes including genes related to IEI was performed. Their clinical diagnosis, clinical characteristics, family history of infection, and laboratory results, as well as detected variants, were reviewed. With CES, genetic diagnosis of IEI was made in 15 out of 37 patients (40.5%). Seventeen pathogenic variants were detected from IEI-related genes, BTK, UNC13D, STAT3, IL2RG, IL10RA, NRAS, SH2D1A, GATA2, TET2, PRF1, and UBA1, of which four variants were previously unreported. Among them, somatic causative variants were identified from GATA2, TET2, and UBA1. In addition, we identified two patients incidentally diagnosed IEI by CES, which was performed to diagnose other diseases of patients with unrecognized IEI. Taken together, these results demonstrate the utility of CES for the diagnosis of IEI, which contributes to accurate diagnosis and proper treatments.
Network meta-analysis of comparative efficacy of animal-assisted therapy vs. pet-robot therapy in the management of dementia

Objective This network meta-analysis aimed to compare and rank the efficacy of animal-assisted therapy (AAT) and pet-robotic therapy (PRT) in the management of dementia. Methods Relevant studies were identified by searching PubMed, EMBASE, the Cochrane Library, SCOPUS, and Web of Science (WoS) until October 13, 2022. Traditional meta-analysis was first conducted based on the random-effects model, then random network meta-analysis was conducted to determine the relative efficacy and rank probability of AAT and PRT. Results Nineteen randomized controlled trials (RCTs) were included in this network meta-analysis. Network meta-analysis revealed that PRT marginally benefited agitation alleviation compared with control (standard mean difference [SMD]: −0.37, 95% confidence interval [95%CI]: −0.72 to −0.01) although both AAT and PRT did not improve cognitive function, reduce depression, and improve Quality of Life (QoL). The SUCRA probabilities indicated that PRT ranked better than AAT in agitation, cognitive function, and QoL, although there were no differences between the two therapies. Conclusion The present network meta-analysis reveals that PRT may help alleviate agitated behaviors in people with dementia. However, future studies are warranted to establish evidence of the effectiveness of PRT and further evaluate the differences between different robot types in managing dementia.
Social cognitive neuroscience in the digital age

Human interactions are increasingly taking place from a distance through methods of remote interpersonal communication like video chatting and social media. While remote interpersonal communication has existed for millennia—with the first postal system arising in ∼2400 B.C.—accelerated advances in technology and the recent global COVID-19 pandemic have led to a dramatic increase in remote interpersonal communication use in daily life. Remote interpersonal communication presents a challenge to the field of social-cognitive neuroscience, as researchers seek to understand the implications of various types of remote interpersonal communication for the “social brain. ” The present paper reviews our current understanding of the social-cognitive neural network and summarizes critical differences between the neural correlates of social cognition in remote vs. face-to-face interactions. In particular, empirical and theoretical work is reviewed that highlight disparities in the neural mechanisms of social perception, evaluation of social stimuli, human motivation, evaluation of social reward, and theory of mind. Potential impacts of remote interpersonal communication on the development of the brain’s social-cognitive network are also discussed. Finally, this review closes with future directions for research on social-cognitive neuroscience in our digital technology-connected world and outlines a neural model for social cognition in the context of remote interpersonal communication. For the field of social-cognitive neuroscience to advance alongside of the ever-evolving society, it is crucial for researchers to acknowledge the implications and concepts suggested for future research in this review.
Fast Algorithm for Extracting Minimum Enclosing Rectangle of Target Image

Geometric feature extraction from image is a crucial step in image classification and recognition algorithms.It requires to calculate the Minimum Enclosing Rectangle(MER) of the image to get some attributes,such as length and width.This paper proposes an algorithm based on the combination of the vertex chain code and the discrete Green theorem to extract MER from image.According to this algorithm,it can calculate the area,centroid,and principal axis of the image easily,only with its point coordinates from the veritical or hrizontal direction of the vertex chain code.Experimental results show that the rotation method's speed is two times of the current algorithms,while the principal axis method is three times of the rotation method.
Optimization and application of a LOCI-type reduction pipeline to detect exoplanets

Adaptive optics and coronagraphy are complementary key resources required to reach both resolution and contrast conditions for direct detection of exoplanets. However, instruments are never perfect, so that swarms of bright quasi-static speckles close to the central star in the image do curb the detection of companions. This noise can be lowered either actively by better non-common path aberrations calibrations on the instrument acquiring the images it was the way explored during my internship at ESO, or passively by appropriate combination of observing strategy and data reduction technique. Among the various strategies of differential imaging, stress is put on angular differential imaging (ADI), whose main idea is to take profit of pupil tracking to freeze the quasi-static speckles of both the telescope and the instrument. In order to optimize even further the suppression of quasi-static speckles, a data post-processing algorithm called LOCI (Locally Optimized Combination of Images) has been proposed by Lafrenière, Marois, et al. (2007). The personal contribution of this thesis is aimed at implementing a personalized version of LOCI, based on the existing code of Dimitri, endeavoring algorithmic optimization of its parameters, computation of contrast curves taking into account the extinction of the companion by LOCI, and compilation of all the required steps of data reduction, from basic treatment to optimized LOCI, in a unique IDL code. Finally, the last part of this work is devoted to the reduction of different sets of images acquired by VLT/NACO with this code, attempting to detect exoplanets around specific young stars known to possess a debris disk.
An algebraic - analytic framework for the study of intertwined families of evolution operators

Empathy theory replaced semi-group theory for the study of implicit evolution equations d dτ [Bu(τ)] = Au(τ); A,B not necessarily closed: the evolution process is described with a pair of evolution operators 〈E(τ), S(τ)〉τ>0 ‘intertwined’ by the empathy relation S(τ + σ) = S(τ)E(σ). The evolution operators associated with the transition probabilities of Markov processes, integrated empathies and semigroups are further cases of intertwined evolution operators. The framework of vector-valued Schwartz distributions had limited success with certain evolution equations: their convolution theorem rests on a bounded bilinear form; thus it applies to uniformly measurable evolution families. In contrast, the empathy approach to the implicit Cauchy problem leans heavily on the Laplace transform and a convolution theorem which is applicable to strongly measurable evolution families. This dissertation introduces a new framework which efficiently implements Sauer’s approach of giving convolution a central role in the study of intertwined families. A convolution algebra (A , ∗) of homomorphisms between a test space of Banach space-valued group-domained functions and the Banach space itself lies at the heart of this new framework: (A , ∗) results from the marriage of ideas of vector-valued distributions and abstract harmonic analysis; the product ∗ generalizes the abstract convolution of the well known convolution algebra of abstract harmonic analysis. The framework replaces families of evolution operators with families of homomorphisms, and their composition with ∗; homomorphisms generalize operators and the product ∗ is more fundamental than operator composition. We call the homomorphisms, generalized operators. A new fully developed convolution theorem for families of homomorphisms subsumes the earlier approaches. The framework unifies all the resolvent equations spanning C0semigroup, empathy theory and n-times integrated semi-groups. We next construct a Hille-Yosida generation theorem for the implicit evolution equation set in the more general setting of generalized operators. This
Mathematical and numerical analysis of a mathematical model of mixed immunotherapy and chemotherapy of cancer

In this study, a previously published mathematical model of mixed immunotherapy and chemotherapy of tumors is considered. The stability analysis of the tumor-free equilibrium obtained in the previous study of this model is flawed. In this paper, a suitable analysis is performed to correct this error, and the parameter conditions for the stability of the tumor-free equilibrium are obtained. The stability condition gives an indicator of the host's ability to fight a cancer. The parameter conditions are examined using experimental data from clinical studies to show that the immune system is able to control a small tumor, and the host's ability to fight a cancer depends on individual variation. A numerical method based on the continuation technique is employed for one-parameter bifurcation analysis of the mathematical model with periodically pulsed therapies. The unstable fixed point curve provides a good approximation of the maximum tumor burden as a function of the dosage. Chemotherapy-induced lymphocyte damage, which may cause treatment failure, is observed in the numerical simulation. The numerical method also produces a set of combined chemotherapy and immunotherapy dosages from which an efficient and safe combination of dosages can be determined.
Hybrid Brain–Computer Interface System Using Auditory Stimulation and Speech Imagination Paradigms for Assistive Technology

In the case of paralysis with visual and tactile impairments, brain–computer interfaces (BCIs) based on auditory and mental imagery paradigms are alternative methods for controlling external devices. This study demonstrates the use of a hybrid BCI via auditory stimulation and speech imagination for assistive technology. The proposed auditory BCI using Thai vowel and numeral stimulus patterns as well as multi-loudspeaker position settings for multi-command BCI are investigated. To avoid auditory stimulation during resting periods, a speech imagery method is used to enable an audio stimulator. We observe the classification efficiency of speech imagery and auditory BCIs from selected electroencephalogram channels using the proposed algorithms. We examine the efficiency of using the proposed BCI in the presence of background noise (speech). One command is created using the proposed speech-imagination paradigm. Four commands are created using the proposed auditory stimulation paradigm. We design an experiment to verify the proposed BCI paradigm and classification algorithms for real-time processing. The results show that the average classification accuracy of the proposed auditory BCI using numeral stimuli and scatter patterns without speech noise is 72.2% to 83.3%, respectively. The efficiency under background noise is approximately two times lower than that without background noise.
Low Power Bio-Amplifier Using Charge Distribution Technique at CMOS 45nm Technology:A Review

Analog design is still a challenge for designer working at lower technology node due degradation in devices and circuit performance and increased short channel non ideal effects. Medical equipment needs a high gain low noise amplifier frequently in medical data acquisition and signal processing. CMOS differential amplifiers are suitable choice for designer due its high value of gain, noise margin and CMRR. Multiple stages of amplifiers further improve gain but at the cost other analog performance. Use of low and high VT transistors are preferred for the transistor connected to input signal and load respectively. Multiple stages of PMOS load are also improves the overall gain with the increase in number transistors. To achieve high gain self-bias operational transconductance amplifiers are suitable choice with higher W/L ratio of PMOS at output stage amplifier. CMOS based high gain self-bias double-stage amplifier were designed at 45nm technology node using Cadence Virtuoso. DC and transient analysis are used for performance evaluation to find suitable amplifier topology and circuit schematic.
Superprophet inequalities for independent random variables

As well as having complete knowledge of the future, a superprophet can also alter the order of observation as it is presented to a player without foresight, whose strategy is known to the prophet. It is shown that a superprophet can only do twice as well as his counterpart, if the underlying random sequence is independent.
A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

: Deep learning and deep neural networks, particularly Convolutional Neural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications. Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces. Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques. This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets. Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers. Compares them based on their accuracy. This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.
β-COP Suppresses the Surface Expression of the TREK2

K2P channels, also known as two-pore domain K+ channels, play a crucial role in maintaining the cell membrane potential and contributing to potassium homeostasis due to their leaky nature. The TREK, or tandem of pore domains in a weak inward rectifying K+ channel (TWIK)-related K+ channel, subfamily within the K2P family consists of mechanical channels regulated by various stimuli and binding proteins. Although TREK1 and TREK2 within the TREK subfamily share many similarities, β-COP, which was previously known to bind to TREK1, exhibits a distinct binding pattern to other members of the TREK subfamily, including TREK2 and the TRAAK (TWIK-related acid-arachidonic activated K+ channel). In contrast to TREK1, β-COP binds to the C-terminus of TREK2 and reduces its cell surface expression but does not bind to TRAAK. Furthermore, β-COP cannot bind to TREK2 mutants with deletions or point mutations in the C-terminus and does not affect the surface expression of these TREK2 mutants. These results emphasize the unique role of β-COP in regulating the surface expression of the TREK family.
SWaCo: Safe Wafer Bin Map Classification With Self-Supervised Contrastive Learning

Defect patterns exhibited in wafer bin maps (WBMs) can provide essential clues about critical process failures to field engineers. In modern manufacturing processes, the automatic WBM defect pattern classification is critical for yield improvement. Although it is difficult to collect sufficient labels while a lot of unlabeled data is given, most existing studies have mainly used only labeled WBM data. Moreover, the unlabeled out-of-distribution (OOD) WBMs are inevitably collected. It degrades the performance of semi-supervised models. To this end, we propose a method for safe wafer bin map classification with self-supervised contrastive learning (SWaCo) to effectively exploit the unlabeled data with OOD. We propose a loss function to utilize label information in the pre-training step to learn more suitable representations for the downstream WBM classification task. The negative labeled examples of the same class as the anchor are used for additional positive examples. Moreover, we investigate proper data augmentation for WBM self-supervised contrastive learning. To evaluate the performance and the applicability of the proposed method, experiments are conducted on a public benchmark WBM dataset, WM-811K. The results demonstrate that the proposed method achieves better classification accuracy than existing methods, especially when only a small amount of class information is given.
An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions

This paper proposes an efficient metamodeling approach for uncertainty quantification of complex system based on Gaussian process model (GPM). The proposed GPM‐based method is able to efficiently and accurately calculate the mean and variance of model outputs with uncertain parameters specified by arbitrary probability distributions. Because of the use of GPM, the closed form expressions of mean and variance can be derived by decomposing high‐dimensional integrals into one‐dimensional integrals. This paper details on how to efficiently compute the one‐dimensional integrals. When the parameters are either uniformly or normally distributed, the one‐dimensional integrals can be analytically evaluated, while when parameters do not follow normal or uniform distributions, this paper adopts the effective Gaussian quadrature technique for the fast computation of the one‐dimensional integrals. As a result, the developed GPM method is able to calculate mean and variance of model outputs in an efficient manner independent of parameter distributions. The proposed GPM method is applied to a collection of examples. And its accuracy and efficiency is compared with Monte Carlo simulation, which is used as benchmark solution. Results show that the proposed GPM method is feasible and reliable for efficient uncertainty quantification of complex systems in terms of the computational accuracy and efficiency. Copyright © 2016 John Wiley & Sons, Ltd.
Survival Status and Predictors of Mortality Among Patients Admitted to Pediatric Intensive Care Unit at Selected Tertiary Care Hospitals in Ethiopia: A Prospective Observational Study

Background: Advances in pediatric intensive care have dramatically improved the prognosis for critically ill patients. The study aimed to determine the survival status and predictors of mortality among patients admitted to the pediatric intensive care unit at selected tertiary care hospitals in Ethiopia. Methods: A health facility-based prospective observational study from October 2020 to May 30, 2021, was conducted in a selected tertiary care hospital in Ethiopia. Kaplan Meier was used to compare patient survival experiences and Cox regression was used to identify independent predictors of ICU mortality. The hazard ratio was used as a measure of the strength of the association, and a P-value of <. 05 was considered to declare statistical significance. Results: Of 206 study participants, 59 (28.6%) patients died during follow-up time, and the incidence of mortality was 3.6 deaths per 100 person-day observation (95% CI: 2.04-5.04 deaths per 100 person-days). Respiratory failure 19 (32.2%) was the commonest cause of death followed by septic shock 11(18.6). In-ICU complications (AHR: 2.13; 95% CI: 1.02, 4.42; P = .04), sepsis diagnosis (AHR: 2.43; 95% CI: 1.24, 4.78; P = .01), GCS < 8 (AHR: 1.96; 95% CI: 1.12, 3.43; P = .02), use of sedative drugs (AHR: 2.40; 95% CI: 1.16, 4.95; P = .02) were linked with increased risk of in-ICU mortality. In contrast, the use of mechanical ventilation was associated with decreased mortality (AHR: 0.45; 95% CI: 0.21, 0.92; P = .03). Conclusion: The study found a high incidence of in-ICU mortality among admitted pediatric patients in selected Ethiopian tertiary care hospitals. In-ICU complications, sepsis diagnosis, GCS < 8, and patient use of sedative drugs were independent predictors of in-ICU mortality. Prudent follow-up is warranted for those patients with the aforementioned risk factors.
Maternal Education and Their Offspring’s Income in China

In this paper, I study the relationship between maternal education and offspring income higher education are more likely to cultivate offspring with higher education. Using 2012 and 2014 China Labor Force Dynamics Survey, I find that one year increase of maternal education is associated with a 1.7% increase in children’s income. In studying the mechanism, I suggest that maternal education improves offspring’s income via improving children’s education. Hence, I conclude that women with a strong individual educational background devote more time and energy to their children. Consequently, their offspring earn a higher income.