Miracle-dz/UniFAR-FTUs huggingface dataset:
An immature, dedifferentiated, and lineage-deconstrained cone precursor origin of N-Myc–initiated retinoblastoma

Significance Cancers initiate through the effects of oncogenic mutations on susceptible cell-of-origin states. To explore the interplay between initiating mutations, cell-of-origin states, and cancer cell behaviors, we examined retinal cell responses to N-Myc overexpression, which induces a form of retinoblastoma distinct from the RB1-mutant subtype. Whereas pRB loss induces proliferation and tumorigenesis in maturing cone precursors that retain differentiation markers and progress through an indolent premalignant stage, N-Myc induced proliferation in immature cone precursors, deconstrained cone precursor identity, and enabled more rapid oncogenic transformation dependent on a gene (SOX9) that is normally expressed in retinal progenitor cells. These findings illustrate that distinct cone precursor states are sensitive to different cancer-initiating mutations and follow markedly different paths toward phenotypically distinct retinoblastoma tumors.
A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks

The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the MCTs. Next, we introduce an exhaustive taxonomy of the MCTs based on various design attributes and then review the literature by categorizing it into periodic and on-demand charging techniques. In addition, we compare the state-of-the-art MCTs in terms of objectives, constraints, solution approaches, charging options, design issues, performance metrics, evaluation methods, and limitations. Finally, we highlight some potential directions for future research.
Therapeutic Effects of Dimethyl Fumarate on the Rat Model of Brain Ischemia

Blood-brain barrier (BBB) disruption, inflammation, and cell death are major pathogenic mechanisms in ischemic stroke. Dimethyl fumarate (DMF) has anti-inflammatory and immune-modulatory effects. So, this study aimed to elucidate the effects of DMF on brain ischemia in the middle cerebral artery occlusion (MCAO) model. 69 Sprague-Dawley male rats were allocated into a sham group that was just subjected to surgery stress; vehicle and DMF groups, after MCAO, received vehicle or 30 mg/kg DMF for three days. Neurological scores were evaluated every day. BBB disruption was evaluated by the extravasation of Evans blue. In addition to the measurement of brain water content, the total and infarct volume, numerical density, and the total number of neurons, non-neurons, and dead neurons in the right cortex were estimated by stereological methods. RT-PCR was done to analyze the expression levels of NF-κB and Nrf2. Although brain ischemia treatment with DMF did not have a significant effect on the infarction size, it improved neurobehavioral function, BBB disruption, cerebral edema, increased number of neurons, and expression of Nrf2. It also decreased the number of dead neurons and the expression of NF-κB. DMF beneficial effects on stroke may be mediated through both increase of the Nrf2 and decrease of NF-κB expression.
Detection and Classification of MRI-Based Brain Tumor via Jaya Algorithm and Twin Support Vector Machine

Brain tumor detection and classification is one of the challenging tasks in the medical image application. Early detection of a brain tumor can help diagnosis and treatment of the patients. Magnetic Resonance Imaging (MRI) is widely used for the detection of brain tumor. Manual analysis of brain MRI, and classification of brain tumor is a tedious and time-consuming job. This paper introduces a novel approach to brain tumor segmentation and classification using BRATS 2015 datasets. Our system exploits the benefits of Jaya Algorithm (JA) as an optimization technique for finding multi-level thresholds to segment the tumor part from the MRI. Feature extraction is implemented by Gray Level Co-occurrence Matrix (GLCM), followed by Principal Component Analysis (PCA) for feature reduction. Due to its inherent distinct features and advantages, a machine-learning approach, Twin Support Vector Machine (TSVM) is used as a classifier. The prediction accuracy of the proposed system yielded up to 97.89 % with sensitivity 96.48%, 98.97 precision, 97.91% F1 Score, and 0.0798 MSE. The accuracy, sensitivity, F1 Score, and MSE are found comparable to the other state-of-arts machine learning methods.
Étude explicite de quelques n-champs géométriques

Dans [PRID], Pridham a montre que tout n-champs d'Artin M admet une presentation en tant que schema simplicial X. → M, telle que le schema simplicial X satisfait a certaines proprietes notees par G.Pn,k de [GROTH]. Dans la presentation (.→ X2 → X1 → X0 → M), le schema X1 represente une carte pour X0 x MX0. Donc, la lissite de X0 → M est equivalente a la lissite des deux projections ә0,ә1 : X1 → X0. Ce sont les deux premieres parties de la condition de Grothendieck-Pridham, notees G.P1,0 et G.P1,1. Dans [BENZ12] nous avons introduit un n-champ d'Artin M des elements de Maurer-Cartan d'une dg-categorie. On a construit une carte, et on a deja fait la preuve des premieres conditions de lissite explicitement. Pour tout n et tout 0 ≤ k ≤ n Pridham considere un schema note MatchΛkn(X) avec un morphisme Xn → MatchΛkn(X). On construira explicitement le schema simplicial de Grothendieck-Pridham X, on montrera la lissite formelle de cette carte precedente, ainsi que M est un n-champ geometrique.
Relaxation-weighted 23Na magnetic resonance imaging maps regional patterns of abnormal sodium concentrations in amyotrophic lateral sclerosis

Objectives: Multiparametric magnetic resonance imaging (MRI) is established as a technical instrument for the characterisation of patients with amyotrophic lateral sclerosis (ALS). The contribution of relaxation-weighted sodium (23NaR) MRI remains to be defined. The aim of this study is to apply 23NaR MRI to investigate brain sodium homeostasis and map potential alterations in patients with ALS as compared with healthy controls. Materials and Methods: Seventeen patients with ALS (mean age 61.1 ± 11.4 years, m/f = 9/8) and 10 healthy control subjects (mean age 60.3 ± 15.3 years, m/f = 6/4) were examined by 23NaR MRI at 3 T. Regional sodium maps were obtained by the calculation of the weighted difference from two image data sets with different echo times (TE1 = 0.3 ms, TE2 = 25 ms). Voxel-based analysis of the relaxation-weighted maps, together with 23Na concentration maps for comparison, was performed. Results: ROI-based analyses of relaxation-weighted brain sodium concentration maps demonstrated increased sodium concentrations in the upper corticospinal tracts and in the frontal lobes in patients with ALS; no differences between ALS patients and controls were found in reference ROIs, where no involvement in ALS-associated neurodegeneration could be anticipated. Conclusion: 23NaR MRI mapped regional alterations within disease-relevant areas in ALS which correspond to the stages of the central nervous system (CNS) pathology, providing evidence that the technique is a potential biological marker of the cerebral neurodegenerative process in ALS.
Influence of Left Atrium Volume Index on effectiveness of Thoracoscopic Ablation in the Treatment of Atrial Fibrillation

Research objective: establish the impact of Left Atrium Volume Index (LAVI) on on effectiveness of Thoracoscopic Ablation (TSA) in the Treatment of Atrial Fibrillation (AF) and define the risk factors for manifestation of supraventricular arrhythmias in the long-term follow-up resultsMethods. Prospective cohort study of 121 patients with AF (from 2018 to 2021) who performed TSA. The patients were divided into two groups: patients with increased LAVI (group I), patients with normal LAVI less than 34 ml/m2 (group II). Results. According to echocardiography, the mean LAVI was 45.48 ± 9.3 ml/m2 and 28.59 ± 4.13 ml/m2 in groups I and II, respectively (p = 0.012). The mean value of left ventricular ejection fraction (LVEF) according to Teicholz in group I was 61.62 ± 7.041%, in group II 63.57 ± 6.16% (p = 0.8). Spearman's сorrelation analysis showed the relation-ship between LAVI and LVEF before surgery and in the long-term follow-up period, that is agreed with world literature data about contribution left atrial (LA) to left ventricular (LV) function. According to our study, only LAVI < 34 ml/m2 is a risk factor for arrhythmia after TSA. Effectiveness TSA in I group was 77.8%, that is lower than the II group – 88.9%. 3 months after TSA, 20 (17%) patients were required catheter ablations (CA), mainly in I group.Conclusions. Research results showed that an increase in LAVI significantly reduces the effectiveness of TSA in the long-term period by 11.1% compared with LAVI < 34 ml/m2. That are conform with other data of previously submitted works.
A Cross-Cultural Study of Trust Building in Autonomous Vehicles

Copyright held by the owner/author(s). AVS, ’18 East Lansing, MI USA ACM 0-12345-67-8/90/01. http://dx.doi. org/xxxx/xxxxxxxxxx Abstract Trust in autonomous vehicles (AVs) has become as a key determinant of drivers' acceptance of AVs and explanations are often at the heart of this trusting relationship. This study explores, in consideration of cultural and personality differences, how explanation timing and permission of AV's action affect trust building in the AV. To examine the research question, we design a video-based online survey platform and collect data from participants with different cultural backgrounds and personalities. We expect that our results will have important implications for the design of AV's information delivery.
Bacterial consortium as a model for studying the response of the microbial community of the Verkhnekamsk salt mining region to combined pollution

The unique ecosystem of industrial development area of the Verkhnekamsk salt deposit (VSD) (Perm krai, Russia) is characterized by the combined effect of salinization and contamination by organic pollutants, including polycyclic aromatic hydrocarbons (PAH). The purpose of the present study was to examine the degradative potential in relation to naphthalene, as a model PAH, under different salinity of bacterial consortium SMB3, previously isolated from soil of the VSD region, as well as the effect of long-term exposure to high salinity on the taxonomic composition of the consortium. The consortium SMB3 was able to grow on naphthalene both in the presence of NaCl up to 90 g/L, and in its absence. With an increase in the concentration of NaCl to 90 g/L, the growth rate of the consortium decreased by 2.1 times (compared with that of the consortium in a salt-free medium), and the naphthalene utilization estimated after 72 hours of cultivation decreased by 22.9 times. As a result of long-term cultivation in a mineral medium with naphthalene in the presence of 70 g/L NaCl, moderately halophilic strains Halomonas sp. SMB31 and Salinicola socius SMB35 T , not using naphthalene as the sole source of carbon and energy, and naphthalene degraders �hodococcus spp. SMB37 and SMB38 were shown to preserve in the consortium, while strains Glutamicibacter sp. SMB32, Microbacterium sp. SMB33, ‘‘ Thalassospira permensis ’’ SMB34 T , not growing on naphthalene, were eliminated. Thus, using the model experiments with the bacterial consortium SMB3, it has been shown that the soil autochthonous microbocenosis in the VSD salt-mining area is able to degrade persistent toxic organic compounds in a wide range of salinity, and prolonged exposure to a high salt concentration leads to a decrease in species richness.
An Online Data-Driven Method to Locate Forced Oscillation Sources From Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)

Forced oscillations may jeopardize the secure operation of power systems. To mitigate forced oscillations, locating the sources is critical. In this paper, leveraging on Sparse Identification of Nonlinear Dynamics (SINDy), an online purely data-driven method to locate the forced oscillation is developed. Validations in all simulated cases (in the WECC 179-bus system) and actual oscillation events (in ISO New England system) in the IEEE Task Force test cases library are carried out, which demonstrate that the proposed algorithm, requiring no model information, can accurately locate sources in most cases, even under resonance condition and when the natural modes are poorly damped. The little tuning requirement and low computational cost make the proposed method viable for online application.
Reliability analysis and improvement of nanoscale CMOS digital circuits

Reliability degradation due to transistor aging is a major challenge in the design of digital integrated circuits for current and future technology nodes. Bias Temperature Instability (BTI) is the dominant aging mechanisms in digital circuits. BTI gradually increases the device’s threshold voltage (Vth) over the lifetime, which in turn degrades circuit speed, and ultimately, it may cause a faulty operation. Circuit performance degradation due to BTI is highly dependent on the operating conditions such as the executed workloads by the circuit and the operating temperature. Moreover, variability in devices parameter due to process variations aggravate the reliability issues. These effects need to be properly accounted during circuit design phase to obtain electronic products with improved lifetime. Conventional worst-case one-time guardbans to deal with aging due to BTI are becoming too large with technology scaling, which leads to conservative designs with reduced performance. This thesis addresses BTI aging issues from two different perspectives: 1. Aging Monitoring: Aging sensors are introduced in the circuit critical paths (CP) in order to detect when they are close to violate timing specifications due to BTI. Then, failure-prevention actions (i.e. increase system clock period) can take place. This thesis proposes a methodology to efficiently identify, at an early design phase, the critical paths of the circuit that should be monitored for reliable on-line aging tracking. The major challenges addressed for critical path
Synthetic Turbulent Inflow Generation for Simulations of Turbulent Wall-bounded Flows

An efficient and realistic synthetic turbulent inflow generation is highly demanded in the simulations of wall bounded turbulent flows. In this work, a generative variational auto-encoder (VAE) architecture is applied to generate the three components of the velocity field of an in-compressible turbulent channel flow at a grid resolution of 385 × 512 . The training data is from direct numerical simulations. The VAE network is designed based on convolutional neural networks, and the computational code is implemented using PyTorch. The results show a qualitative agreement with realistic data. Detailed analysis shows that the generated flow fields are more accurate at large scales although significant improvements can be made.
Design of Network Intrusion Detection Model Based on TCA

The traditional machine learning model cannot effectively identify the new network traffic data set, resulting in the model failure. Therefore, in this paper, by analyzing the problems of current network intrusion detection (NID) and combining the application of transfer theory in the detection model, a NID model based on transfer component analysis (TCA) is proposed. Among them, the specific mathematical derivation of the algorithm and the detection process of transfer model are introduced in detail. Then, the classification performance of KNN and SVM based on TCA algorithm for network abnormal traffic is compared. The results show that the TCA algorithm proposed in this paper can effectively improve the accuracy of NID, which is meaningful to expand the application scope of network abnormal traffic detection scheme based on machine learning.
Design the Optimal Number of Components in a Grid-Connected Hybrid Power Generation System

Hybrid power generation systems are an effective solution for the variable generated power of renewable energy sources. In this paper, a grid-connected hybrid power generation system including wind turbines, solar panels, wave generators and power storage batteries for a village in the Chabahar Bay in southeastern of Iran is optimally designed. The purpose of selecting this region is to investigate the economic feasibility of using wave generators in hybrid power generation systems. In the design, during various operating scenarios simulated with Homer software, adding power generation resources to the village will provide part of the required power locally, which will reduce the cost of purchasing electricity from the power grid, and even profitability is also achieved with the sale of electricity to the power grid. On the other hand, the reliability of power grid is improved in terms of the Loss of Power Supply Probability (LPSP) index.
Reward ameliorates depressive-like behaviors via inhibition of the substantia innominata to the lateral habenula projection

The lateral habenula (LHb) is implicated in emotional processing, especially depression. Recent studies indicate that the basal forebrain (BF) transmits reward or aversive signals to the LHb. However, the contribution of the BF-LHb circuit to the pathophysiology of depression still needs to be determined. Here, we find that the excitatory projection to the LHb from the substantia innominata (SI), a BF subregion, is activated by aversive stimuli and inhibited by reward stimuli. Furthermore, chronic activation of the SI-LHb circuit is sufficient to induce depressive-like behaviors, whereas inhibition of the circuit alleviates chronic stress–induced depressive-like phenotype. We also find that reward consumption buffers depressive-like behaviors induced by chronic activation of the SI-LHb circuit. In summary, we systematically define the function and mechanism of the SI-LHb circuit in modulating depressive-like behaviors, thus providing important insights to better decipher LHb processing in the pathophysiology of depression.
Developing a two-parabolic band model for thermoelectric transport modelling using Mg2Sn as an example

Thermoelectrics is a field driven by material research aimed at increasing the thermal to electrical conversion efficiency of thermoelectric (TE) materials. Material optimisation is necessary to achieve a high figure of merit (zT) and in turn a high conversion efficiency. Experimental efforts are guided by the theoretical predictions of the optimum carrier concentration for which generally the single parabolic band (SPB) model is used which considers the contribution to electronic transport only from the majority carriers’ band. However, most TE materials reach peak performance (maximum zT) close to their maximum application temperature and when minority carrier effects become relevant. Therefore, single band modelling is insufficient to model the behaviour of TE materials in their most practically relevant temperature range. Inclusion of minority effects requires addition of the minority carrier band and necessitates the use of a two-band model—the simplest and, for most cases, sufficient improvement. In this study, we present a systematic methodology for developing a two-band model using one valence and one conduction band for any given TE material. The method utilises in part the SPB model and in part a simple cost function based analysis to extract material parameters like density of states masses, band gap, deformation potential constant etc., based on easily available experimental data. This simple and powerful method is exemplified using Mg2Sn, chosen due to its low band gap, the availability of experimental data in a wide range of dopant concentrations and its practical importance, being an end member of the highly popular Mg2(Si,Sn) solid solutions. Using the experimental data for p- and n-type Mg2Sn from literature, a two-band model was obtained. Optimum carrier concentration and maximum zT were predicted from both SPB and two-band models and at 650 K pronounced differences between the two models, which could prevent realisation of maximum zT, were observed, demonstrating the practical necessity to model the effect of minority carriers.
Distributed Economic Dispatch for Microgrids Tracking Ramp Power Commands

When in grid-connected mode of operation, distributed generators (DGs) within the microgrid (MG) can coordinate to act as a single entity to provide services to the bulk grid. The DGs can coordinate their power production to minimize the total operating cost, which is known as the distributed economic dispatch. Various methods have been proposed to solve the MG economic dispatch problem (EDP) in a distributed fashion, under the assumption that DGs’ power output, in aggregate, follows a constant or slowly varying power command. However, when the MG is providing frequency regulation service, or the internal load is highly dynamic, the MG EDP becomes dynamic, and the state-of-the-art distributed approaches cannot guarantee optimality. In this paper, we propose a distributed economic dispatch algorithm for MGs providing frequency regulation service, as an example of a dispatch profile with ramp commands. A consensus protocol guaranteeing zero steady-state error for ramp inputs is integrated into the EDP to find the optimal solution in a distributed way. With the proposed algorithm, the MG is able to tightly follow a time-varying regulation signal while maintaining an optimal economic dispatch for all the DGs within. We validate the proposed method using regulation signals from PJM and demonstrate the algorithm on a hardware-in-the-loop testbed.
Is Business Research Shaping the Circle? Systematic and Bibliometric Review of Circular Economy Research

A circular economy (CE) transforms the linear system into a resource flow model based on reusing products and materials and increasing lifetime periods. This academic work aims to review the current CE research status from business, economic, and managerial (BEM) research perspectives. We carried out a systematic and bibliometric analysis to gather information on the current state of the art applications and learn about the leading research topics and sources. To reach these goals, we reviewed 962 research papers published in journals indexed on the Web of Science. After analysing the articles, three categories emerged worldwide: literature reviews, case studies, and frameworks and guidelines based on the current closed-loop system approach. Results evidence that BEM research in the CE is focused on the existing barriers to adopting a CE. More concretely, findings show that CEs are being slowed by the fact that citizens and companies do not know how to be circular. At the same time, the article showcases how the BEM areas and the recurring topics in CE research are increasingly being developed by collaborations between engineers and economists to design and create robust and measurable closed-loop models.