Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow Cytometric Detection of Malignant Blasts in Cerebrospinal Fluid: A Biomarker of Central Nervous System Involvement in Childhood Acute Lymphoblastic Leukemia

Despite the excellent prognosis for children and adolescents with acute lymphoblastic lymphoma (ALL), the involvement of the central nervous system (CNS) represents a major therapeutic challenge. Patients who develop CNS relapse have a very poor prognosis, and since current methods cannot reliably identify patients with CNS involvement or patients at high risk of CNS relapse, all children with ALL receive CNS-directed treatment. The current golden standard for detecting CNS involvement is the assessment of cytomorphology on cytospin slides of cerebrospinal fluid (CSF). This technique is inadequate due to low sensitivity and reproducibility. Flow cytometric analysis of CSF represent a novel, highly specific and sensitive technique for the detection of leukemic cells in the CNS. In prospective studies, CSF flow cytometry demonstrated two to three times higher rates of CNS involvement at diagnosis of childhood ALL than conventional cytospin, and especially demonstrated superior sensitivity in detecting low-level CNS disease. CNS involvement determined via flow cytometry has been linked to a higher risk of CNS relapse and poor outcomes in several studies. In this review, we discuss the central analytical concepts of CSF flow cytometry and summarize the current evidence supporting the use of flow cytometric detection of malignant blasts as a biomarker of CNS involvement in childhood ALL.
Assessment of hepatitis C monitoring adherence after viral eradication in veterans with substance use to improve care and surveil reinfection

Introduction Hepatitis C virus (HCV) incidence rates are rising for patients with substance use and/or SUDs. Guidelines provide monitoring recommendations to ensure remission after successful treatment. The study's objective was to identify gaps in follow-up for patients with documented substance use and/or SUD through assessment of adherence to guideline-recommended HCV RNA lab 12 months post-treatment. Methods Patients treated for HCV through the Veteran Health Indiana Hepatitis C Pharmacy Clinic were retrospectively evaluated. Subjects were categorized based on the provider assigned for follow-up care after 12-week sustained virologic response (SVR12) labs (primary care provider [PCP] or HCV provider). The primary outcome was HCV RNA obtained 11 to 13 months post-treatment. Secondary outcomes were HCV RNA detected post-treatment, substance use, engagement in substance use treatment, and engagement with social work. Results Two hundred forty-one patients were included in the HCV provider cohort and 139 in the PCP cohort. Forty-one patients did not have a specified clinic for follow-up treatment, and 20 patients did not achieve SVR12. Sixty-one patients (28%) in the HCV provider cohort completed a 12-month HCV RNA within 11 to 13 months post-treatment vs 15 patients (11%) in the PCP cohort (P ≤ .01). One patient had HCV RNA detected post-treatment. Discussion This study reveals inadequate HCV post-treatment follow-up for patients with substance use and/or SUD. SUD is a chronic disease that requires continued monitoring to prevent complications. Further studies are needed to identify reinfection rates and improvements of care in this population.
Physiological responses and stress levels of high-speed rail train drivers under various operating conditions - a simulator study in China

ABSTRACT The stress level of high-speed rail (HSR) train drivers directly impacts their job performance and thus the safety of HSR operations. This paper attempts to develop a quantitative understanding of train drivers’ stress levels and the contributing factors by the experimental study conducted in a realistic HSR simulator. An extensive statistical analysis found that the ultra-short-term heart rate variability metrics could differentiate different stress levels. Three different machine-learning classifiers were evaluated for stress detection, including support vector machine (SVM), random forests (RF), and K-nearest neighbour (KNN). The RF model was shown to perform the best in terms of robustness and classification accuracy. Moreover, the research found that the driver’s stress level should be detected rather than the type of stressor. The findings from this research could contribute to the development of real-time HSR driver condition monitoring systems and the improvement of current HSR operation safety regulations.
The State of Agricultural Mechanics in the Preparation of School-Based Agricultural Education Teachers

Agricultural mechanics is a primary subject area in school-based agricultural education (SBAE). Despite a high prevalence in SBAE, preservice and in-service SBAE teachers have frequently expressed concerns regarding teaching agricultural mechanics. Early 21st century research documented the characteristics of preservice SBAE teacher training in agricultural mechanics, although more recent studies have suggested that the agricultural mechanics training requirements have changed. This study sought to compare the agricultural mechanics preparation of preservice SBAE teachers between 2000 and 2021. A survey instrument was distributed to a representative of every institution with an undergraduate SBAE teacher preparation program in the contiguous United States, resulting in an 86.7% response rate. The results were compared to previous studies from 2000 and 2005. A reduction in the average number of required agricultural mechanics credit hours was found. Faculty perceptions of importance and preservice teacher preparation had increased slightly; however, deficits were found in all competency groups. Further research is warranted to determine the perceptions of preservice and in-service SBAE teachers regarding agricultural mechanics. Recommendations include that teacher educators evaluate current preparation methods for preservice SBAE teachers and provide professional development in the areas where the greatest training deficits were observed.
Adaptive Control of Second-Order Safety-Critical Multiagent Systems With Nonlinear Dynamics

In this article, we propose a distributed adaptive control architecture for leader-following consensus of uncertain multiagent systems with second-order nonlinear dynamics. A nonlinear reference model system captures an ideal behavior of the agents for the leader-following consensus problem. We design a modified nonlinear reference model system and propose a distributed model reference adaptive control architecture to suppress the effects of system uncertainties without a strict knowledge of their magnitude and rate upper bounds. Consequently, each agent evolves within a (possibly different) prescribed distance from the corresponding modified reference system trajectories. Based on an input-to-state stability analysis, it is shown that the trajectories of the modified reference model system can get arbitrarily close to the trajectories of the ideal reference model system. As a result, the trajectories of the agents evolve within a user-specified prescribed distance from their ideal system trajectories, satisfying the safety constraints. The key feature of the presented control architecture in this article is the elimination of the ad hoc tuning process for the adaptation rate that is conventionally required in model reference adaptive control systems to ensure safety. An illustrative numerical example finally demonstrates the efficacy of the proposed distributed adaptive control architecture.
Management of lung cancer patients during COVID-19 pandemic: dos, don’ts and don’t knows

Aim: During the coronavirus disease 2019 (COVID-19) pandemic two needs have overlapped: on one hand continuing to provide the best care for patients with lung cancer and preventing the spread of the virus between patients and healthcare professionals on the other hand. Due to the pandemic’s unpredictable duration, physicians had to evaluate the risk/benefit ratio of anti-cancer therapeutic strategy to do the best for their patients and to protect patients themselves, as well as healthcare workers. Methods: Systematic literature research was performed with the aim to assess the available guidelines for the management of lung cancer patients during the COVID-19 pandemic. Thirteen potentially relevant articles were selected and recommendations have been divided into three main categories: dos, don’ts and don’t knows. Results: All guidelines and recommendations highlighted the relevance of being able to delay, if possible and based on risk stratification, and curative interventions. The selected recommendations should be considered adaptable and flexible because they might be contextualized on the basis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prevalence and the availability of diagnostic-therapeutic resources. Conclusions: It remains of fundamental importance to discuss each diagnostic and therapeutic decision with the patient taking into account risks and benefits that might vary from case to case.
Effect of Aging on Educational Differences in the Risk of Cognitive Impairment: A Gender-Specific Analysis Using Korean Longitudinal Study of Aging (2006–2016)

This study examined the effect of aging on gender-specific educational differences in the risk of cognitive impairment using a nationally representative sample of 4278 men and 5495 women aged 45 years and older from the dataset of the Korean Longitudinal Study of Aging. Sociodemographics, lifestyle, and medical conditions were included as covariates in the mixed logistic regression analysis models. The prevalence of cognitive impairment was higher in women than in men at baseline. The risk of cognitive impairment in each age group decreased with education in both men and women. The risk by educational rank was worse at lower levels and increased with age, more so for women than men. Aging appears to widen the impact of educational differences on the risk of cognitive impairment and is more unfavorable for women than for men. Public health policies regarding population aging need to consider this and identify the target population to reduce both the level of and the difference in the risk of cognitive impairment.
Boundary Extraction Enhanced Inverse Scattering Method for Microwave Mammography

Microwave mammography is a promising alternative from the X-ray based imaging modality, in terms of compactness, low cost and cell-friendly exposure. This paper focuses on the accuracy enhancement by incorporating breast surface boundary extraction into the inverse scattering analysis algorithm, known as distorted born iterative method (DBIM). Many studies have reported that the reconstruction accuracy of the DBIM or other inverse scattering algorithms largely depends on an initial estimate of boundary surrounding object, namely, skin surface shape of breast media. The Envelope method achieves highly accurate boundary extraction by processing a group of reflection time-delays. The accuracy of boundary extraction of Envelope depends on that of time-delay estimation, which is mostly processed by the filter based on waveform matching between observed and reference signals. However, the coupling effect between an antenna and a breast surface deforms an observed waveforms from reference one. To mitigate this problem, this paper introduces the finite-difference time-domain (FDTD) -based waveform correction for more accurate boundary extraction. Numerical simulations based on realistic breast phantoms derived from magnetic resonance imaging (MRI) demonstrate that the proposed method significantly enhances the accuracy for the DBIM based reconstruction of dielectric profile in highly heterogeneous case.
Combining biogeographical approaches to advance invasion ecology and methodology

Understanding the causes of plant invasions requires that parallel field studies are conducted in the native and introduced ranges to elucidate how biogeographical shifts alter the individual performance, population success and community‐level impacts of invading plants. Three primary methods deployed in in situ biogeographical studies are directed surveys, where researchers seek out populations of target species, randomized surveys and field experiments. Despite the importance of these approaches for advancing biogeographical research, their relative merits have not been evaluated. We concurrently deployed directed surveys, randomized surveys and in situ field experiments for studying six grassland plant species in the native and introduced ranges. Metrics included plant size, fecundity, recruitment, abundance and invader impact, as well as soil properties and root associations with putative fungal mutualists and pathogens. Consistent with key invasion hypotheses, Bromus tectorum experienced increased size and fecundity in the introduced range linked to population increases and significant invader impacts, along with altered fungal associations. However, performance differences did not predict population increases and invader impacts across species. A notable finding was that disturbance facilitated greater recruitment in the introduced range for most species, thereby playing a crucial, though underappreciated, role in driving invader success. Directed surveys consistently generated information on plant performance and fungal associations. However, soil sampling suggested that directed surveys may have been biased towards disturbed conditions for half the species. Randomized surveys generated robust data for population comparisons and impact, but generally failed to produce performance metrics for species that were uncommon or flowered outside the peak sampling window. Field experiments controlled for bias and confounding factors and provided rare information on recruitment and disturbance effects, but poor recruitment in the native range and ethical constraints on growing invaders in the introduced range hindered comparisons of performance and plant–fungal interactions. Synthesis. Each method had strengths and weaknesses. However, when combined they provided complementary information to paint the most complete biogeographical picture to date for several introduced plants. We propose a hybrid approach to optimize biogeographical studies.
TaylorCSROA-based Deep Residual Network: An Optimization driven Deep Network for the multilevel spinal cord disease classification

ABSTRACT Spinal cord is a cylindrical shape located in Central Nervous System (CNS) and extended between the medulla oblongata and lumbar vertebrae. The main task in the classification of multilevel spinal cord disease in Computed Tomography (CT) is the accuracy. In this research, a Taylor Crow Search-Rider Optimization Algorithm (TaylorCSROA) is developed for accurate classification of spinal cord disease. The segmentation is done through the adaptive thresholding method. The Sparse Fuzzy C-Means clustering (Sparse FCM) algorithm is implemented for the localization of disc. The features, like connectivity, Local Optimal-Oriented Pattern (LOOP), statistical, image-level, Grid-based shape features, and tetrolet features are extracted. The spinal cord disease is classified into intervertebral disc (IVD) bulges, the cal sac compressing, bone marrow disease, central or foraminal stenosis, annular tears, scoliosis, end plate degeneration, endplate defects (modic type), facet connection, and ligamentum flavum hypertrophy, and spondylolisthes is by training the Deep Residual Network using the developed TaylorCSROA. The developed TaylorCSROA algorithm derived from the integration of Taylor Series, Crow Search Algorithm (CSA), and the Rider Optimization Algorithm (ROA). When compared with the existing spinal cord disease classification methods, the developed method obtained a maximum accuracy of 0.9102, sensitivity of 0.8945, and specificity of 0.9245.
Towards single-charge heavy ion beams driven by an ultra-intense laser

The acceleration of super-heavy ions from an ultra-thin lead target irradiated by a femtosecond laser pulse with an intensity in the range of ∼1022–1023 W cm−2 was investigated using an advanced 2D3V particle-in-cell code. It is shown that by properly selecting the laser pulse parameters, it is possible to produce a practically single-charge Pb ion beam with multi-GeV ion energies and the laser-to-ions energy conversion efficiency approaching 30%. At the laser intensity of 1023 W cm−2, Pb ions with the charge state Z = 72 carry over 90% of the total energy of all ions, while the peak intensity and peak fluence of the Pb+72 ion beam are at least two orders of magnitude higher than for other types of ions. In addition, the Pb+72 ion beam is more compact and has a smaller angular divergence than those for other types of ions. The above properties of the Pb+72 ion beam mean that further energy-efficient purification of the beam from other types of ions is possible, even in simple ion transport and selection systems.
Abstract \(필히 영문 작성 요망\)

A time-division multiplexing (TDM) comparator scheme has been proposed as an effective signature analyzer for on-chip analog response compaction and pass/fail decision with minimum hardware overhead [6]. By applying this scheme to the oscillation test methodology (OTM), the oscillation frequency can be measured indirectly as well as the oscillation amplitude to increase the fault coverage. In general, one of the concerns for the analog BIST is the noise performance of the test technique, but it has not been well analyzed in most previous techniques. This paper provides a comprehensive analysis of noise and jitter, so its performance can be better understood in noisy environment.
Neural Mechanisms of Motor Dysfunction in Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review

Background: Despite the prevalence of motor symptoms in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), their underlying neural mechanisms have not been thoroughly studied. Objective: This review summarizes the neural underpinnings of motor deficits in MCI and AD. Methods: We searched PubMed up until August of 2021 and identified 37 articles on neuroimaging of motor function in MCI and AD. Study bias was evaluated based on sample size, availability of control samples, and definition of the study population in terms of diagnosis. Results: The majority of studies investigated gait, showing that slower gait was associated with smaller hippocampal volume and prefrontal deactivation. Less prefrontal activation was also observed during cognitive-motor dual tasking, while more activation in cerebellar, cingulate, cuneal, somatosensory, and fusiform brain regions was observed when performing a hand squeezing task. Excessive subcortical white matter lesions in AD were associated with more signs of parkinsonism, poorer performance during a cognitive and motor dual task, and poorer functional mobility. Gait and cognitive dual-tasking was furthermore associated with cortical thickness of temporal lobe regions. Most non-gait motor measures were only reported in one study in relation to neural measures. Conclusion: Cross-sectional designs, lack of control groups, mixing amnestic- and non-amnestic MCI, disregard of sex differences, and small sample sizes limited the interpretation of several studies, which needs to be addressed in future research to progress the field.
New Mechatronics Education Initiatives

The number of associate level degree programs in Mechatronics is growing rapidly across the country. These programs are trying to meet the increasing nationwide need for technicians that can install, maintain, troubleshoot, and service subsystems that often integrate pumps, motors, valves, sensor, and interface software that are responsible for the operation and/or control of manufacturing processes. These technicians are typically identified as mechanical, automation, robotics, and/or mechatronics technicians. These associate degree level programs are also aligning their curriculum with several international, industry valued credentials to best prepare the 2-year mechatronics technicians to be ready for work in the growing number of highly automated facilities across the country. This paper reviews several of these evolving programs; how they have updated electrical and/or mechanical technology programs to include current technology and industry workforce need; how the industry credentials are impacting their programs, students and employers; and their efforts to build capacity by partnering with local high schools to better meet the needs of employers in their communities.
Subchronic toxicity of magnesium oxide nanoparticles to Bombyx mori silkworm

Despite many research efforts devoted to the study of the effects of magnesium oxide nanoparticles (MgO NPs) on cells or animals in recent years, data related to the potential long-term effects of this nanomaterial are still scarce. The aim of this study is to explore the subchronic effects of MgO NPs on Bombyx mori silkworm, a complete metamorphosis insect with four development stages (egg, larva, pupa, month). With this end in view, silkworm larvae were exposed to MgO NPs at different mass concentrations (1%, 2%, 3% and 4%) throughout their fifth instar larva. Their development, survival rate, cell morphology, gene expressions, and especially silk properties were compared with a control. The results demonstrate that MgO NPs have no significant negative impact on the growth or tissues. The cocooning rate and silk quality also display normal results. However, a total of 806 genes are differentially expressed in the silk gland (a vital organ for producing silk). GO (Gene Ontology) results show that the expression of many genes related to transporter activity are significantly changed, revealing that active transport is the main mechanism for the penetration of MgO NPs, which also proves that MgO NPs are adsorbed by cells. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis demonstrates that the longevity regulating pathway-worm, peroxisome and MAPK signaling pathway are closely involved in the biological effects of MgO NPs. Overall, subchronic exposure to MgO NPs induced no apparent negative impact on silkworm growth or silks but changed the expressions of some genes.
A High-Efficiency Super-Junction MOSFET based Inverter-Leg Configuration using a Dual-Mode Switching Technique

High-efficiency power converters have benefits of minimizing energy consumption, reducing costs, and realizing high power densities. The silicon super-junction MOSFET is an attractive device for high-efficiency applications. However, its highly non-linear output capacitance and the reverse recovery properties of its intrinsic diode must be addressed when used in voltage source converters. A dual-mode switching technique operating in conjunction with intrinsic diode deactivation circuitry is proposed in this paper. The technique is demonstrated in an 800-W inverter-leg configuration operating from a 400-V DC voltage rail and switching at 20 kHz. Intended applications include machine drives. The full-load efficiency reaches approximately 98.7% and no forced cooling is needed.