Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances in the Role of Nuclear Factor Erythroid-2-Related Factor 2 in Spinal Cord Injury: Regulatory Mechanisms and Therapeutic Options

Nuclear factor erythroid-2-related factor 2 (Nrf2) is a pleiotropic transcription factor, and it has been documented that it can induce defense mechanisms both oxidative stress and inflammatory injury. At present, more and more evidences show that the Nrf2 signaling pathway is a key pharmacological target for the treatment of spinal cord injury (SCI), and activating the Nrf2 signaling pathway can effectively treat the inflammatory injury and oxidative stress after SCI. This article firstly introduces the biological studies of the Nrf2 pathway. Meanwhile, it is more powerful to explain that activating the Nrf2 signaling pathway can effectively treat SCI by deeply exploring the relationship between Nrf2 and oxidative stress, inflammatory injury, and SCI. In addition, several potential drugs for the treatment of SCI by promoting Nrf2 activation and Nrf2-dependent gene expression are reviewed. And some other treatment strategies of SCI by modulating the Nrf2 pathway are also summarized. It will provide new ideas and directions for the treatment of SCI.
A Novel Convolution Computing Paradigm Based on NOR Flash Array With High Computing Speed and Energy Efficiency

Convolution is one of the key operations in signal processing and machine learning applications. In this paper, we propose a novel convolution computing paradigm based on the NOR Flash array (NFA) that is capable of executing the 2-D convolution computing in one clock cycle. In order to demonstrate the feasibility and efficiency of the proposed convolution computing paradigm, the feature extraction task on an image with the size of $20\times 20$ is executed using the NFA structure. We also prove the NOR Flash-driven convolution computing is capable of processing the image with a larger size. This paper presents a new approach to realize convolution computing with high speed and energy efficiency for the signal processing and convolution neural network.
The Role of Wilms’ Tumor Gene (WT1) Expression as a Marker of Minimal Residual Disease in Acute Myeloid Leukemia

The Minimal Residual Disease(MRD) monitoring in acute myeloid leukemia (AML) is crucial to guide treatment after morphologic complete remission, to define the need for consolidation with allogeneic stem cell transplantation (Allo-SCT), and to detect impending relapse allowing early intervention. However, more than 50% of patients with AML lack a specific or measurable molecular marker to monitor MRD. We reviewed the key studies on WT1 overexpression as a marker of MRD in AML patients undergoing an intensive chemotherapy program, including Allo-SCT. In addition, we provided some practical considerations on how to properly use WT1 expression as an MRD marker, considering its strengths and weaknesses. In order to achieve the best sensitivity and specificity, it is recommended to refer to the standardized method of European LeukemiaNet and its defined threshold (250 WT1 copies/104 Abelson (ABL) on Bone Marrow-BM and 50 WT1 copies/104 ABL on Peripheral Blood-PB), which has been validated in a large and multicenter cohort of patients and normal controls.
Scalable Flexible Electromagnetic Interference Shielding Textiles Based on MWCNTs and PEDOT:PSS

With the rise of electromagnetic radiation-based technologies, considerable attention has been drawn to developing and implementing innovative electromagnetic shielding materials. Carbon nanomaterials and conductive polymers have been appealing to both academia and industry as promising alternatives for the traditionally used metallic materials, owing to their lightness, flexibility, easy processability and resistance to corrosion, which are of special importance for textile applications. In this work, multiwalled carbon nanotubes (MWCNTs) and poly (3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) have been applied to cotton textile substrates by straightforward scalable dyeing and coating processes, respectively. These processes led to uniform and homogeneous coatings with distinct properties: the fabric coated with MWCNT presented higher thickness and lower loading of incorporated material than the textile coated with PEDOT:PSS (thickness: 995 μm vs. 208 μm; material loading: 9.4 wt.% vs. 70.7 wt.%). The electromagnetic shielding properties were outlined for each shielding textile in the frequency range of 5.85–18 GHz: an average shielding effectiveness of ~35.6 dB was obtained for MWCNT@tex, while PEDOT:PSS@tex reached ~38.3 dB. Thus, PEDOT:PSS provided enhanced radiation shielding with lower coating thickness, while the MWCNTs led to improved attenuation with less material usage. Shielding effectiveness values above 30 dB were obtained for both electromagnetic interference shielding textiles, which corresponds to an excellent classification for general use applications, such as casual clothing and maternity wear.
Large-Scale Whale-Call Classification by Transfer Learning on Multi-Scale Waveforms and Time-Frequency Features

Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations.
Mixed Layer Depth Promotes Trophic Amplification on a Seasonal Scale

The Humboldt Upwelling System is of global interest due to its importance to fisheries, though the origin of its high productivity remains elusive. In regional physical‐biogeochemical model simulations, the seasonal amplitude of mesozooplankton net production exceeds that of phytoplankton, indicating “seasonal trophic amplification. ” An analytical approach identifies amplification to be driven by a seasonally varying trophic transfer efficiency due to mixed layer variations. The latter alters the vertical distribution of phytoplankton and thus the zooplankton and phytoplankton encounters, with lower encounters occurring in a deeper mixed layer where phytoplankton are diluted. In global model simulations, mixed layer depth appears to affect trophic transfer similarly in other productive regions. Our results highlight the importance of mixed layer depth for trophodynamics on a seasonal scale with potential significant implications, given mixed layer depth changes projected under climate change.
Facile Fabrication of Graphene Oxide-based Flexible Temperature Sensor and Improving its Humidity Stability

Graphene derivatives and their composites have attracted significant research interest for the development of novel sensors. Optimizing the formulation of graphene-based composites plays a significant role in developing sensors with improved features, such as high sensitivity and humidity stability. In this work, graphene oxide (GO)-based temperature sensors with high sensitivity were developed on a flexible substrate, using a facile fabrication method, and the humidity stability of the sensors was improved by ~58% while maintaining a high sensitivity towards the temperature. GO ink was used as the sensing layer of one temperature sensor (TS1), and a composite of GO and poly(3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT:PSS) was used as the sensing layer of another temperature sensor (TS2). The resistive responses of sensors towards varying temperatures ranging from 10 °C to 80 °C were investigated. The temperature sensors showed linear responses, with slopes of -0.98 and -0.69, and correlation coefficients of 0.9994 and 0.998 for TS1 and TS2 temperature sensors, respectively. The temperature coefficient of resistance (TCR) of temperature sensors were calculated as -1.00 and -0.68 %/°C for TS1 and TS2 sensors, respectively. The sensitivity of temperature sensors towards humidity was calculated as 0.107 and 0.045 %/%RH for TS1 and TS2 temperature sensors, respectively. It was observed that adding PEDOT:PSS to GO improves the humidity stability of the temperature sensors by ~58% while maintaining a high TCR.
Unpreparedness and uncertainty: a qualitative study of African American experiences during COVID-19 pandemic

Abstract During disasters, vulnerable populations are disproportionately affected. COVID-19 disproportionately affected African American (AA) families, increasing their risk for COVID-19 morbidity and mortality. The pandemic also exacerbated existing negative milieu such as economic opportunity and access to social and healthcare services. We explored AA families’ experiences of indirect pandemic effects. Data were collected through semi-structured in-depth telephone interviews with 11 AA parent/grandparents of school-aged child (5–17 years). Line-by-line coding and thematic analysis were used to analyze and interpret the data. Three emergent themes highlighted the salient indirect effects of COVID-19 pandemic on AA families: (i) access to healthcare, (ii) access to food, and (iii) disaster unpreparedness. Participants expressed frustration with virtual healthcare services and inability to schedule in-person hospital appointments for health conditions unrelated to COVID-19. Lack of food products in stores and limited financial resources due to pandemic-related job layoffs were important food insecurity factors discussed. Unpreparedness on the part of institutions, state, and the nation, created heightened perceptions of vulnerability. Given the social vulnerability spectrum in the U.S., pandemic planning approaches that promote equity are critical if public officials are to develop effective adaptation, mitigation, response, and recovery plans that mobilize and serve diverse populations.
A Decentralized Composite Controller for Unified Voltage Control With Global System Large-Signal Stability in DC Microgrids

Bus voltage control is an essential issue in a DC microgrid (MG). In this paper, a decentralized composite controller (DCC) is proposed to unify two types of voltage controls, i.e., constant voltage mode and droop mode, and simultaneously realize global system large-signal stability of the MG. The main idea of the DCC is to partition the MG into dispatchable unit (DU) subsystems and the lumped load. A well-devised high gain observer is adopted to estimate the electrical coupling of a particular DU subsystem with other DUs and the load. Then, rather than dealing with the MG as a whole, the DCC is locally designed to offset the estimated coupling and stabilize the internal states of the DU subsystem. By doing so, each subsystem can be virtually decoupled from the MG and functions in an isolated fashion. When DU subsystems are interlinked, large-signal stabilization of the entire MG would be obtained by only guaranteeing the localized stability of individual subsystems. This stabilizing strategy has not been reported in the literature thus far. Relevant theoretical analyses are rigorously conducted by employing Lyapunov stability theorem. The effectiveness of the DCC has been verified by simulations. Experimentations show that the DCC enables faster system performance recovery and provides wider stability margin than the conventional PI controller.
Cross-Switched Multilevel Inverter Using Novel Switched Capacitor Converters

In this paper, a novel structure of switched capacitor converter (SCC) is proposed. First, the basic unit of the proposed SCC is presented. After that, a generalized structure of the proposed SCC is discussed. The presented SCC uses one dc source, several switches, diodes, and capacitors to produce multistep dc boosted output voltage. The capacitors in the SCC can be charged in trinary asymmetrical pattern. A detailed comparative study between the proposed and other recently developed SCC structures is presented. It is shown that the proposed SCC can produce an output voltage level with lower total standing voltage and lower maximum switch stress voltage as compared to most of the suggested SCC structures. Further, a switched capacitor multilevel inverter (SCMLI) based on cross-switched multilevel inverter has been developed and analyzed for symmetric and asymmetric dc source configurations. For fair comparison with the existing SCMLI structures, the overall cost of the structures is compared based on a cost function (CF). The proposed SCMLI provides lower cost function/output voltage level (CF/NL) in comparison with most of the other SCMLI structures for both symmetric and asymmetric dc source configurations. Extensive experimental studies validate the operation and performance of the proposed SCMLI structure.
CNN‐based UGS method using Cartesian‐to‐polar coordinate transformation

The main concern of user-guided segmentation (UGS) is to achieve high segmentation accuracy with minimal user interaction. A novel convolutional neural network (CNN)-based UGS method is proposed, which employs a single click as the user interaction. In the proposed method, the input image in the Cartesian coordinate system is first converted into the polar transformed image with the user-guided point (UGP) as the origin of the polar coordinate system. The transformed image not only effectively delivers the UGP to the CNN, but also enables a single-scale convolution kernel to act as a multi-scale kernel, whose receptive field in the Cartesian coordinate system is altered based on the UGP without any extra parameters. In addition, a feature selection module (FSM) is introduced and utilised to additionally extract radial and angular features from the polar transformed image. Experimental results demonstrate that the proposed CNN using the polar transformed image improves the segmentation accuracy (mean intersection over union) by 3.69% on PASCAL VOC 2012 dataset compared with the CNN using the Cartesian coordinate image. The FSM achieves additional performance improvement of 1.32%. Moreover, the proposed method outperforms the conventional non-CNN-based UGS methods by 12.61% on average.
Six GIScience Ideas That Must Die

Abstract. In 2015, John Brockman edited a volume of chapters contributed by leading thinkers from various domains discussing common scientific ideas hindering further scientific progress. While starting with the provocative slogan of This Idea Must Die, the book’s chapters and their authors (for most parts) do not argue that those existing – often foundational scientific theories from various domains – are false, but instead that their widespread, and often unquestioned, utilization has started to hinder the evolution of new theories. Through this work, we would like to foster a similar discussion in our community, by suggesting six ideas in GIScience/geoinformatics that may benefit from retiring to make room for new perspectives. Our suggestions are somewhat controversial, and readers are encouraged to keep an open mind.
Effect of Flywheel versus Traditional Resistance Training on Change of Direction Performance in Male Athletes: A Systematic Review with Meta-Analysis

Objective: This study aimed to systematically review and meta-analyze the effect of flywheel resistance training (FRT) versus traditional resistance training (TRT) on change of direction (CoD) performance in male athletes. Methods: Five databases were screened up to December 2021. Results: Seven studies were included. The results indicated a significantly larger effect of FRT compared with TRT (standardized mean difference [SMD] = 0.64). A within-group comparison indicated a significant large effect of FRT on CoD performance (SMD = 1.63). For TRT, a significant moderate effect was observed (SMD = 0.62). FRT of ≤2 sessions/week resulted in a significant large effect (SMD = 1.33), whereas no significant effect was noted for >2 sessions/week. Additionally, a significant large effect of ≤12 FRT sessions (SMD = 1.83) was observed, with no effect of >12 sessions. Regarding TRT, no significant effects of any of the training factors were detected (p > 0.05). Conclusions: FRT appears to be more effective than TRT in improving CoD performance in male athletes. Independently computed single training factor analyses for FRT indicated that ≤2 sessions/week resulted in a larger effect on CoD performance than >2 sessions/week. Additionally, a total of ≤12 FRT sessions induced a larger effect than >12 training sessions. Practitioners in sports, in which accelerative and decelerative actions occur in quick succession to change direction, should regularly implement FRT.
Fingerprinting the ship propulsion system: low hanging fruit or mission impossible?

In this paper the concept of ship propulsion system “fingerprinting” is explored as an alternative for data driven models that require extensive measured datasets collected over long periods of ship operation. As a first exploratory step a model of a ship in bollard pull conditions is linearised and its transfer functions are determined. Subsequently limited experimental data, involving sinusoidal excitation of the system input at a wide range of frequencies, is used to determine the system parameters. The resulting parameter estimates compare well against previously determined values. Although the developed ideas are far from ready to be used on full scale, the authors believe that the approach is promising enough to be developed further towards full scale application.
ON A CLASS OF VERTEX FOLKMAN NUMBERS

1. Notations. We consider only finite, non-oriented graphs, without loops and multiple edges. The vertex set and the edge set of a graph G will be denoted by V (G) and E(G), respectively. We call p-clique of G any set of p vertices, each two of which are adjacent. The largest natural number p, such that the graph G contains a p-clique is denoted by cl(G) (the clique number of G).
An Investigation into the Pressures Experienced by Medical Masters Students during the COVID-19 Pandemic Based on the Perceived Stress Scale-14 and Its Alleviation Methods

The COVID-19 pandemic can be seen as a traumatic event during which time medical students have been required to perform dual roles both as students and as medical workers. In this study, we aimed to use the Perceived Stress Scale (PSS-14) to judge the pressures on medical students and to identify effective ways to relieve these pressures. In this cross-sectional study, the subjects were recruited under informed consent according to the Declaration of Helsinki. Students undertaking Masters degrees at the China-Japan Union Hospital of Jilin University were randomly selected and data were collected through questionnaire surveys. Our data showed significant differences in the levels of pressure experienced by Masters students (p < 0.05). In the student population that showed increased pressure, females were significantly more stressed than males (p < 0.05). In addition, the pressures persisted after a holiday period (p < 0.05) but were reduced by undertaking recreational activities. The psychological pressures resulting from the COVID-19 pandemic were higher in female medical students compared to males. We showed that recreational activities including chatting with friends reduced psychological pressures in female medical students.
N-Methyltransferase CaASHH3 Acts as a Positive Regulator of Immunity against Bacterial Pathogens in Pepper

Proteins with conserved SET domain play a critical role in plant immunity. However, the means of organization and functions of these proteins are unclear, particularly in non-model plants such as pepper (Capsicum annum L.). Herein, we functionally characterized CaASHH3, a member of class II (the ASH1 homologs H3K36) proteins in pepper immunity against Ralstonia solanacearum and Pseudomonas syringae pv tomato DC3000 (Pst DC3000). The CaASHH3 was localized in the nucleus, and its transcript levels were significantly enhanced by R. solanacearum inoculation (RSI) and exogenous application of salicylic acid (SA), methyl jasmonate (MeJA), ethephon (ETH), and abscisic acid (ABA). Knockdown of CaASHH3 by virus-induced gene silencing (VIGS) compromised peppers’ resistance to RSI. Furthermore, silencing of CaASHH3 impaired hypersensitive-response (HR)-like cell death response due to RSI and downregulated defense-associated marker genes, including CaPR1, CaNPR1, and CaABR1. The CaASHH3 protein was revealed to affect the promoters of CaNPR1, CaPR1, and CaHSP24. Transiently over-expression of CaASHH3 in pepper leaves elicited HR-like cell death and upregulated immunity-related marker genes. To further study the role of CaASHH3 in plant defense in vivo, CaASHH3 transgenic plants were generated in Arabidopsis. Overexpression of CaASHH3 in transgenic Arabidopsis thaliana enhanced innate immunity against Pst DC3000. Furthermore, CaASHH3 over-expressing transgenic A. thaliana plants exhibited upregulated transcriptional levels of immunity-associated marker genes, such as AtNPR1, AtPR1, and AtPR2. These results collectively confirm the role of CaASHH3 as a positive regulator of plant cell death and pepper immunity against bacterial pathogens, which is regulated by signaling synergistically mediated by SA, JA, ET, and ABA.
Rechargeable Battery Cabinet Deployment for Public Bike System

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the ${e}$ lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings.