Miracle-dz/UniFAR-FTUs huggingface dataset:
Biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses

Blindness affects millions of people around the world, and is expected to become increasingly prevalent in the years to come. For some blind individuals, a promising solution to restore a form of vision are cortical visual prostheses, which convert camera input to electrical stimulation of the cortex to bypass part of the impaired visual system. Due to the constrained number of electrodes that can be implanted, the artificially induced visual percept (a pattern of localized light flashes, or ‘phosphenes’) is of limited resolution, and a great portion of the field’s research attention is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is the non-invasive functional evaluation in sighted subjects or with computational models by making use of simulated prosthetic vision (SPV) pipelines. Although the SPV literature has provided us with some fundamental insights, an important drawback that researchers and clinicians may encounter is the lack of realism in the simulation of cortical prosthetic vision, which limits the validity for real-life applications. Moreover, none of the existing simulators address the specific practical requirements for the electrical stimulation parameters. In this study, we developed a PyTorch-based, fast and fully differentiable phosphene simulator. Our simulator transforms specific electrode stimulation patterns into biologically plausible representations of the artificial visual percepts that the prosthesis wearer is expected to see. The simulator integrates a wide range of both classical and recent clinical results with neurophysiological evidence in humans and non-human primates. The implemented pipeline includes a model of the retinotopic organisation and cortical magnification of the visual cortex. Moreover, the quantitative effect of stimulation strength, duration, and frequency on phosphene size and brightness as well as the temporal characteristics of phosphenes are incorporated in the simulator. Our results demonstrate the suitability of the simulator for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioural experiments. The modular approach of our work makes it ideal for further integrating new insights in artificial vision as well as for hypothesis testing. In summary, we present an open-source, fully differentiable, biologically plausible phosphene simulator as a tool for computational, clinical and behavioural neuroscientists working on visual neuroprosthetics.
Broader functions of TIR domains in Arabidopsis immunity

TIR domains are NAD-degrading enzymes that function during immune signaling in prokaryotes, plants, and animals. In plants, most TIR domains are incorporated into intracellular immune receptors. In Arabidopsis, TIR-derived small molecules bind and activate EDS1 heterodimers, which in turn activate RNLs, a class of cation channel-forming immune receptors. RNL activation drives cytoplasmic Ca2+ influx, transcriptional reprogramming, pathogen resistance and host cell death. We screened for mutants that suppress an RNL activation mimic allele and identified a TIR-containing immune receptor, SADR1. Despite functioning downstream of an auto-activated RNL, SADR1 is not required for defense signaling triggered by other tested TIR-containing immune receptors. SADR1 is required for defense signaling initiated by some trans-membrane pattern recognition receptors and contributes to the unbridled spread of cell death in lesion simulating disease 1. Together with RNLs, SADR1 regulates defense gene expression at infection site borders, likely in a non-autonomous manner. RNL mutants that cannot sustain this pattern of gene expression are unable to prevent disease spread beyond localized infection sites, suggesting that this pattern corresponds to a pathogen containment mechanism. SADR1 potentiates RNL-driven immune signaling partially through the activation of EDS1, but also partially independently of EDS1. We studied EDS1-independent TIR function using nicotinamide, an NADase inhibitor. We observed decreased defense induction from trans-membrane pattern recognition receptors and decreased calcium influx, pathogen growth restriction and host cell death following intracellular immune receptor activation. We demonstrate that TIR domains can potentiate calcium influx and defense and are thus broadly required for Arabidopsis immunity.
Mapping whole brain effects of infrared neural stimulation with positron emission tomography

The combination of neuroimaging and targeted neuromodulation is a crucial tool to gain a deeper understanding of neural networks at a circuit level. Infrared neurostimulation (INS) is a promising optical modality that allows to evoke neuronal activity with high spatial resolution without need for the introduction of exogenous substances in the brain. Here, we report the use of whole-brain functional [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) imaging during INS in the dorsal striatum, performed using a multifunctional soft neural probe. We demonstrate the possibility to identify multi-circuit connection patterns in both cortical and subcortical brain regions within a single scan. By using a bolus plus infusion FDG-PET scanning protocol, we were able to observe the metabolic rate evolution in these regions during the experiments and correlate its variation with the onset of the INS stimulus. Due to the focality of INS and the large amount of viable molecular targets for PET, this novel approach to simultaneous imaging and stimulation is highly versatile. This pilot study can pave the way to further understand the brain connectivity on a global scale.
Evidence for intraflagellar transport in butterfly spermatocyte cilia

In the model organism insect Drosophila melanogaster short cilia assemble on spermatocytes that elaborate into 1.8 mm long flagella during spermatid differentiation. A unique feature of these cilia/flagella is their lack of dependence on intraflagellar transport (IFT) for their assembly. Here, we show that in the common butterfly Pieris brassicae, the spermatocyte cilia are exceptionally long: about 40 μm compared to less than 1 μm in Drosophila. By transmission electron microscopy, we show that P. brassicae spermatocytes display several features not found in melanogaster, including compelling evidence of IFT structures and features of motile cilia.
Glyco engineered pentameric SARS-CoV-2 IgMs show superior activities compared to IgG1 orthologues

Immunoglobulin M (IgM) is the largest antibody isotype with unique features like extensive glycosylation and oligomerization. Major hurdles in characterizing its properties are difficulties in the production of well-defined multimers. Here we report the expression of two SARS-CoV-2 neutralizing monoclonal antibodies in glycoengineered plants. Isotype switch from IgG1 to IgM resulted in the production of pentameric IgMs, comprising of correctly assembled 21 human protein subunits. All four recombinant monoclonal antibodies carried a highly reproducible human-type N-glycosylation profile, with a single dominant N-glycan species at each glycosite. Both pentameric IgMs exhibited increased antigen binding and virus neutralization potency, up to 390-fold, compared to the parental IgG1. Collectively, the results may impact on the future design of vaccines, diagnostics and antibody-based therapies and emphasize the versatile use of plants for the expression of highly complex human proteins with targeted posttranslational modifications.
Optical Genome Mapping for detecting Homologous Recombination Deficiency (HRD) in human breast cancers.

Homologous recombination deficiency (HRD) leads to genomic instability that marks HRD tumor genome with a specific genomic scar. Present in many cancers, HRD is important to be detected as it is associated with a hyper-sensitivity to some classes of drugs, in particular the PARP inhibitors. Here, we investigate the use of structural variants detected by the Optical Genome Mapping (OGM) technology to identify HRD tumors. We first compared the performance of OGM and whole genome sequencing (WGS) in an HRD triple negative breast carcinoma (TNBC) carrying a germline BRCA2 deleterious mutation. We showed the excellent performance of OGM and its ability to recognize subclonal events not detected by WGS. We then analyzed by RVA OGM data from fifteen TNBC samples from the clinical trial RadioPARP. We defined two features characteristic of HRD. Tandem duplication (TD) and insertion events were found increased in HRD tumors. We showed that insertion calls were probably mostly TD too small to be called as TD by RVA. The insertion/TD feature fully discriminated HRD from all homologous recombination proficient (HRP) TNBC but one. This outlier carried a CCNE1 amplicon probably explaining the excess of insertion/TD. Total numbers of translocations were similar in HRP and HRD TNBC. We suggested a novel feature, translocations and intra-fusions isolated from another event by 3 megabases. Isolated translocations and intra-fusions perfectly discriminated HRD from HRP TNBC. Our results demonstrate that the OGM technology is an affordable way of getting an insight of the structural variants present in solid tumors, even with low tumoral cellularity. It represents an alternative technology for HRD diagnosis, which should now be evaluated in independent series of tumors of different tissue origins.
A combined DTI-fMRI approach for optimizing the delineation of posteromedial vs. anterolateral entorhinal cortex

In the entorhinal cortex (EC), attempts have been made to identify the human homologue regions of the medial (MEC) and lateral (LEC) subdivision using either functional magnetic resonance imaging (fMRI) or diffusion tensor imaging (DTI). However, there are still discrepancies between entorhinal subdivisions depending on the choice of connectivity seed regions and the imaging modality used. While DTI can be used to follow the white matter tracts of the brain, fMRI can identify functionally connected brain regions. In this study, we used both DTI and resting-state fMRI in 103 healthy adults to investigate both structural and functional connectivity between the EC and associated cortical brain regions. Differential connectivity with these regions was then used to predict the locations of the human homologues of MEC and LEC. Our results from combining DTI and fMRI support a subdivision into posteromedial (pmEC) and anterolateral (alEC) EC and reveal a discrete border between the pmEC and alEC. Furthermore, the EC subregions obtained by either imaging modality showed similar distinct connectivity patterns: While pmEC showed increased connectivity preferentially with the default mode network, the alEC exhibited increased connectivity with regions in the dorsal attention and salience networks. Optimizing the delineation of the human homologues of MEC and LEC with a combined, cross-validated DTI-fMRI approach allows to define a likely border between the two subdivisions and has implications for both cognitive and translational neuroscience research.
A proposed standard method for polarimetric calibration and calibration verification

Accurate calibration of polarimetric sensors is critical to reducing and analyzing phenomenology data, producing uniform polarimetric imagery for deployable sensors, and ensuring predictable performance of polarimetric algorithms. It is desirable to develop a standard calibration method, including verification reporting, in order to increase credibility with customers and foster communication and understanding within the polarimetric community. This paper seeks to facilitate discussions within the community on arriving at such standards. Both the calibration and verification methods presented here are performed easily with common polarimetric equipment, and are applicable to visible and infrared systems with either partial Stokes or full Stokes sensitivity. The calibration procedure has been used on infrared and visible polarimetric imagers over a six year period, and resulting imagery has been presented previously at conferences and workshops. The proposed calibration method involves the familiar calculation of the polarimetric data reduction matrix by measuring the polarimeter's response to a set of input Stokes vectors. With this method, however, linear combinations of Stokes vectors are used to generate highly accurate input states. This allows the direct measurement of all system effects, in contrast with fitting modeled calibration parameters to measured data. This direct measurement of the data reduction matrix allows higher order effects that are difficult to model to be discovered and corrected for in calibration. This paper begins with a detailed tutorial on the proposed calibration and verification reporting methods. Example results are then presented for a LWIR rotating half-wave retarder polarimeter.
A fast‐opening switch for use in REB diode experiments

The operating characteristics of a plasma‐filled fast‐opening switch and its performance in pinching experiments on Sandia’s Proto I accelerator (double sided, 2 MV, 7.2 Ω, 24 nsec each side) are described. The growth of a sheath between the plasma and the cathode of the switch is the mechanism responsible for the switch impedance rising from 0.1 to 50 Ω in 10 nsec. The effect of this switch on diode prepulse and machine turn‐on transient suppression is discussed.
Residual Neural Network for Predicting Super-enhancers on Genome Scale

Residual neural network (ResNet) is a Deep Learning model introduced by He et al. [13] in 2015 to enhance traditional Convolutional neural networks for computer vision problems. It uses skip connections over some layer blocks to avoid vanishing gradient problem. Currently, many researches are focused to test and prove the eﬃciency of the ResNet on diﬀerent domains such as genomics. In this paper, we propose a new ResNet model for predicting super-enhancers on genome scale. In fact, the prediction of super-enhancers (SEs) has prominent roles in biological and pathological processes; especially that related to the detection and progression of tumors. The obtained results are very promising and they proved the performance of our proposal compared to the CNN results.
Strong Consistency of Bayes Estimates in Stochastic Regression Models

Under minimum assumptions on the stochastic regressors, strong consistency of Bayes estimates is established in stochastic regression models in two cases: (1)When the prior distribution is discrete, the p.d. f.fof i.i. d. random errors is assumed to have finite Fisher informationI=? ∞? ∞(f? )2/fdx<∞; (2)for general priors, we assumefis strongly unimodal. The result can be considered as an application of a theorem of Doob to stochastic regression models.
A Comparison of the Bad as Old and Superimposed Renewal Models

For the bad as old (BAO) model for system reliability, asymptotic distributions for time between failures and time of rth failure are obtained. For series systems, inequalities are obtained for the BAO and superimposed renewal (SR) models for distributions of number of failures and time between failures. Approximations for the distribution of number of failures are obtained for the BAO model for new, large series systems. Consideration is given to the use of BAO probabilities as approximations to SR probabilities for series systems.
Predicting Interest in Engineering Majors: The Role of Critical Agency and Career Goals

This research paper investigates how students’ latent diversity influences students' disciplinary choices in engineering. Students bring interests, beliefs, and attitudes (i.e., latent diversity) to engineering education, shaping their environment and their classmates' experience. In turn, the cultural norms, standards, and structures within engineering culture convey implicit and explicit messages about particular ways of being, thinking, knowing that are valued. This dialectic process does not occur equitably; as students persist, the pressure to conform leads them to either norm, leave engineering, or labor to pioneer new spaces. And, this process is also entrenched in the history and process of engineering education that is raced, classed, and gendered. To add an additional layer of complexity, engineering is not a monolith with varying rates of participation by race and gender across engineering disciplines. Each engineering discipline has a unique culture and better understanding these differences can provide useful ways to change engineering disciplines to be more inclusive. It is in this complex landscape that this research begins to investigate how latent diversity may be linked to students’ disciplinary pathways. First, we can identify opportunities to nurture ways of being, thinking, and knowing that would otherwise be pushed out (forms of latent diversity that are difficult to see but foster equity and inclusion nonetheless). Second, it can allow us to better tailor courses to fit students’ interests and needs, thus increasing student belonging, innovation, and adoption of new ideas. To this end, we explore two research questions: (1) Do students’ engineering beliefs, career priorities, and field interests predict interest across several disciplines of engineering? ; and (2) Are the relationships between students’ beliefs and discipline interests moderated by patterns of representation and parity?
Evaluation of Asset Pricing Models in the South African Equities Market

Asset pricing models have been of interest since their origin in modern finance. The Capital Asset Pricing Model is a widely used tool and is one of the early developed asset pricing models in modern finance. There are continual improvements of this model with the evident multifactor models of Fama and French (2015), Carhart (1997) and the South African two – factor arbitrage pricing models of Van Rensburg (2002) and Laird-Smith et al. (2016). This research empirically investigates the performance of eight-different multi-factor asset pricing models in describing average portfolio returns in the South African Johannesburg Stock Exchange. We find that the Carhart (1997) four factor model comprising of the market factor, size factor, value factor and the momentum factor is the most parsimonious model and thus better explains the average portfolio returns in the South African JSE. This model is an improvement of the Fama and French (1992) three factor model. Additionally, we investigate the performance of the two factor Asset Pricing Theory (APT) model of Laird-Smith et al. (2016) and Van Rensburg (2002) that consists of the South African Financial Index (SAFI) and the South African Resources Index (SARI). We observe that the model performs better than the traditional CAPM that is widely used in industry. Adding the SAFI and the SARI to the six-factor model results in an eight-factor model that has a significant improvement in explaining average returns. The results indicate that the market factor, the South African Financial Index and the South African Resources Index (SARI) poorly explain each other but their linear combination improves the eight-factor asset pricing model in explaining average portfolio returns in the South African market. The eight – factor model comprises of the market, size, value, investment, profitability, momentum factors and the two South African indices namely, the South African Financials Index (SAFI) and the South African Resources Index (SARI).
Comparative Study between Egypt and Saudi Arabia to Empirically Examine Students’ E-learning Acceptance in Educational Private Sector during COVID 19 Pandemic

: The aim of the study is to empirically compare between students acceptance of E-learning systems in developing countries such as Egypt and Saudi Arabia especially after the pandemic of COVID19 and the accountability of all education sectors worldwide on e-learning systems and platforms. This takes place through an extension of the technology acceptance model (TAM). Identifying the factors that influence the acceptance of e-learning systems help to improve student satisfaction and enhance educational systems to become more effective and efficient. This will help universities to develop better marketing strategies. Due to the spread of COVID 19, the study adopted a quantitative approach through online questionnaires. Students were asked to fill out the questionnaire. This is to compare students‟ experience of Moodle in the College of Management and Technology in Egypt compared to students‟ experience of LMS in College of Business in Saudi Arabia. The results showed that students accept e-learning systems when they perceive them as useful and easy to use. The results of the study confirm the original TAM findings and extend it. Computer self-efficacy and internet self-efficacy had a direct impact on students‟ perception of the ease of the e-learning system in both countries. Behavioral intention had no significant effect on Saudi students‟ acceptance of e-learning system while it had direct significant effect on Egyptian students.
Frequency-dependent critical current and transport ac loss of superconductor strip and Roebel cable

The frequency-dependent critical current of a superconductor strip and Roebel cable has been studied using a 2D finite element simulation. It is shown that the critical current of the superconductor increases with frequency as f1/n, where n is the exponent of the power law flux creep model. Transport ac loss in a superconductor strip decreases with frequency as f − 2/n when the amplitude of the applied ac current is far less than its critical current. However, when the applied current is large and becomes comparable to the critical current, the transport ac loss decreases with frequency as 1/f. The analytical results are substantiated with available experimental data and the results of a 2D finite element simulation.
Significance and expression of p-Akt in oral squamous cell carcinoma

Background: Oral cancer is considered as the sixth-most common malignant neoplasm in the world and the most common type is squamous cell carcinoma (SCC). Akt is a serine/threonine-protein kinase, that acts in the regulation of different signaling downstream pathways, including; proliferation, growth, cell metabolism, angiogenesis and survival. The objective of the present study was to show the expression of p-Akt and its prognostic effect in oral SCC (OSCC). Materials and Methods: A total of 72 cases of OSCC were included in this study; the expression of the Akt gene was done on 4 μm tumor sections using immunohistochemical staining of Akt antibody. Results: Histopathological examination showed that: (43) were GIII, (13) cases were GII, and (16) cases were GI. The positivity of immunohistochemical staining were appear as brown stain. Results showed increase expression of p-Akt in most cases of oral SCC and mean expression about (54.86 ± 31.58), and the expression of p-Akt was increased with the progression of the histological grade of the tumor. Conclusion: High expression of p-Akt and its relation with the progression of tumor grade and invasion of tumor may indicate the relationship between p-Akt expression and aggressiveness and progression of the tumor.