dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcription factor-binding sites at the unc-4 and OdsH regulatory regions To investigate the presence of different transcription factor-binding sites (TFBSs) located at the unc-4 and OdsH regulatory regions, the sequences were extracted 500 bp upstream and downstream of the genes from all species in which expression had been analysed previously. In addition, D. sechellia, D. simulans and D. mauritiana were included because in these species, OdsH is associated with hybrid sterility. For this analysis, the OdsH regulatory sequences were subjected to enrichment analysis with CiiiDER  (Gearing et al. 2019)  to identify differentially enriched TFBSs between unc-4 and OdsH by using the unc-4 sequences as background. We used the JASPAR CORE  (Castro-Mondagon et al. 2022 ) database of insect TFBSs for this analysis.
Acknowledgments We acknowledge funding from The  São Paulo Research Foundation (FAPESP)/ Université de Lyon 1  ( UdL -IDEXLyon ) Joint Call to C. M. A. C [ 2020/06238-2 ] and to CV, from the  National Council for Scientific and Technological Development (CNPq)  to C. M. A. C [ 308020/2021-9 ] and from the  International  fellowship from  UdL -IDEXLyon -FAPESP  to WVBN. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance   Code 001  to WVBN.
The authentication process was tested using three datasets AMIGOS, DEAP, and SEED. Achieving accuracy of 96:23%; 98:85%, and 99:89% respectively. The key generation process generates a set of different keys with different lengths from the same observation. Each key is unique and repeatable. The generated keys were examined using NIST test suite, scale index test, and autocorrelation test. Time complexity analysis of the key generation process was performed.
, 2021) , voice  (Shofiyah et al. , 2022) , and DNA  (Zahid et al. , 2019)  contain unique and repeatable identity information for each individual, but each of them have it's own limitation  (Sudar et al. , 2019). EEG signals have recently been proposed as a promising biometric approach. Thomas and Vinod in  (Thomas and Vinod, 2016)  studied person authentication from EEG signals during the rest state with both eye open (EO) and eye close (EC) using sample entropy and power spectral density, their system reached a genuine accept rate (GAR) of 99:7% for EO, and 98:6% for EC in the beta frequency band. Monsy in  (Monsy, 2020 ) used frequency-weighted power (FWP) which is an equivalent representation of the power in a specific frequency band. The system was examined using two EEG datasets recorder during the rest-state and achieved an equal error rate (EER) of 0.
0039 from EC resting state EEG signals. Biradar et al. and Gui et al. in   (Biradar et al. , 2022; Gui et al. , 2019)  offered an extensive survey on the use of brain signals for building biometric security systems. In crypto-systems, participants use secret keys to protect their confidential data. Keys should be long, unique and repeatable which make them very hard to generate and memorize. Biometrics have been widely used in cryptographic key generation  (Wang et al. , 2020; Knutson et al. , 2021). Recently, generating cryptographic keys from brain waves was introduced. Bajwa and Dantu in  (Bajwa and Dantu, 2016)  performed user authentication based on EEG signals, they used energy bands obtained from discrete Fourier transform and discrete wavelet transform. Their system was tested using two datasets achieving mean accuracy of 98:46% and 91:05%.
For performing neurokey generation, feature selection task was performed using normalized thresholds and segmentation window protocol. They generated a 230 bit key, and evaluated the strength of their generated key using nine NIST tests. They concluded that, different keys could be generated from different mental tasks. Nguyen et a. in  (Nguyen et al. , 2017)  used power spectral density from different frequency bands as features. They used a random initialization vector (IV) with a password for better protection for users' templates and to add more randomness for their generated keys. They evaluated their method using the EEG Alcoholism and GrazIIIa datasets, and found that their generated key has 99% regeneration success rate. Their generated key was 192 bits and they evaluated its strength using six NIST tests.
A common limitation that faces all biometric based cryptosystems is that; biometrics are noisy in nature, as they are sensitive to acquisition equipment's, environmental conditions and variation in biometric itself over time. As, cryptographic keys needs to be exact, any change in the biometric will cause variation in the generated key. The challenge in all biometric based cryptosystems is to overcome the variation in biometric itself over time and to generate multiple unique keys from the same biometric  (Sudar et al. , 2019). The objective of this work is to design a stable EEG-based personal authentication and cryptographic key generation system that overcomes the previous limitations and offers the following security aspects; Prevents Biometric data leakage; The EEG data in its original form is not stored in the system database.
Information stored in the user's template if stolen, will neither allow an imposter to be falsely accepted by the system nor it will help him in regenerating the correct cryptographic key. Provides stable authentication and key generation processes; Individual's EEG samples taken in different times are almost never identical. The system accuracy was found to be stable when tested using different datasets having variation in data collection procedure, number of participants, number of electrodes, and number of recording sessions. System accuracy was also stable when training and testing data are from the same session or from different sessions that are temporally spaced. Generate a set of unique keys that could be used in different applications; From the same EEG observation the proposed system generates a set of different keys with different lengths without the need to perform any change in the system internal structure.
Each generated key is unique and repeatable. Generated keys are suitable to be used for cryptographic applications; The statistical properties of each generated Key were tested using NIST test suite, its degree of non-periodicity were examined using scale index text, and the correlation of the key and a shifted version of itself was checked using autocorrelation test. The generated keys passed all the tests and are suitable to be used as cryptographic keys. Time complexity analysis of the key generation process is performed showing that the key generation has linear complexity and is fast enough for practical applications. The rest of the paper is organized as follows; in Section 2 we introduce the basic techniques used in building our system. Section 3 gives an overview on the three datasets used for testing our proposed system.
Error correction code In our proposed key generation algorithm the error correction process is performed using Reed-Solomon (RS) coder. RS coder is a systematic block coder that have the following properties  (Shrestha and Xu, 2011; Singh, 2013) : RS coder works with multi-bit symbols, each symbol consists of m bits. The message is divided into separate blocks of data (k symbols each). It is described as RS n; k ð Þ, where n 6 2 m À 1. Parity protection information (2t symbols) is added to each block to form a self-contained code word of n symbols. Fig. 1  shown RS code configurations. The code adds 2t parity symbols and is capable of correcting t symbol errors where; t ¼ nÀk 2 ; for n À k ð Þeven nÀkÀ1 2 ; for n À k ð Þodd ( ð7Þ In RS code both the message and parity symbols are elements of Galois field (GF), for coding m bit symbols, the GF has 2 m elements  (Shrestha and Xu, 2011).
Data sets Our proposed system is examined using three publicly available datasets, AMIGOS, DEAP and SEED. Table  1 , gives an overview for the three datasets, and Fig. 2  shows the electrode positioning in each. In AMIGOS dataset  (Miranda-Correa et al. , 2021)  EEG and other physiological signals were recorded. The dataset contains two experiments one for individuals and the other for groups. In this work we are using the individuals experiment in which EEG signals were recorded from 40 participants watching 16 short emotional videos. EEG signals were recorded using 14 electrodes placed according to the 10-20 international positioning system  (Jurcak and Tsuzuki, 2007)  at a sampling rate of 128 Hz. Each observation (trial) is from 51 to 150 s according to the length of the stimuli video used.
In this work we limit each trial time to 51s, and we excluded 6 participants  (ID number 12, 21, 22, 23, 24, 33)  with invalid and corrupted data. In the DEAP dataset  (Koelstra et al. , 2012)  EEG and other physiological signals of 32 participants were recorded while each of them watching 40 one-minute musical videos. EEG signals were recorded using 32 electrodes placed according to the 10-20 international positioning system at a sampling rate of 512 Hz. The DEAP dataset is recorded in two sessions in the same day separated by a lunch break (20 trials per session). DEAP has a pre-processed version in which, the Electrooculography (EOG) artifacts were removed, signals were down-sampled to 128 Hz, and filtered from 4 to 45 Hz. Each observation (trial) is 63s in which the first 3s are baseline signals.
In this work we use the pre-processed version of DEAP and the first 3s were removed. In the SEED dataset  (Duan et al. , 2013; Zheng and Lu, 2015)  EEG signals of 15 participants were recorded while each of them watching 15 videos excerpts from Chinese movies. EEG signals were recorded using 62 electrodes placed according to the 10-20 international positioning system  (Jurcak and Tsuzuki, 2007)  at a sampling rate of 1000 Hz. The dataset was recorded during 3 sessions with an interval of approximately one week between sessions (15 trials per session for each participant). In the preprocessed version of SEED, signals were down-sampled to 200 Hz, and filtered from filter 0-75 Hz. Each observation (trial) is from 185 to 265 s according to the length of the stimuli video used. In this work we used the pre-processed version of SEED and limit each trial time to 185s.
, 2001). The output from the quantizer consists of two parts; the first part QV G which is the quantized values of V G that forms the input to the RS encoder. The second part is the quantization thresholds which is a L Â 1 vector (where L is the number of quantization levels). The quantization threshold is stored in the system database as the first part of user's template. 7. The H Â k matrix represents the coded symbols output from the RS encoder is flattened into H Ã k ð ÞÂ1 ½ vector, and forms one of the inputs to the key generation process (4. 2).
Results and discussion To evaluate the stability of our proposed technique we performed two experiments; in the first experiment training and testing data are from the same session. In the second experiment training and testing data are from different sessions. The first experiment generates the following outcomes; AMIGOS, DEAP-Case1, DEAP-Case2, SEED-Case1, and SEED-Case2. In AMIGOS, DEAP-Case1, and SEED-Case1 we use the data from the same ses-sion for both training and testing. In DEAP-case2 we collect the data from each user two sessions and use it in both training and testing processes. In SEED-Case2 we collect the data from each user three sessions and use it in both training and testing processes. The cross validation in this first experiment is performed by dividing the data into 70% training and 30% testing and performing 5fold cross validation.
The second experiment is applied only on DEAP and SEED datasets, as they contain more than one session (Dataset description section III). The second experiment generates the following outcomes; DEAP-Case3, SEED-Case3, and SEED-Case4. In DEAP-Case3 we use the data from one session for training and the other session for testing. In SEED-Case3 when we use data from two sessions for training and data from the third sessions for testing. SEED-Case4 when we use data from one session for training and data from the other two sessions for testing. The accuracy of our proposed system is evaluated using; Genuine accept rate (GAR), false reject rate (FRR), and false accept rate (FAR). The result from both the two experiments is shown in Table  2. From which we can see that using different sessions for training and testing results in slight reduction in GAR around (1%) while in  (Ashenaei et al.
Time complexity analysis In this section we perform time complexity analysis for the key generation process. The time cost is measured in the running envi-ronment: Intel(R) Core(TM) i7-6500U CPU, 16 GB RAM and MATLAB 2021a software framework. Time cost is measured using the CPU time. We generate the keys 100 times for each user in each database and average the result time. Keys with 10 different length from 128 bits to 65563 bits (2 7 to 2 16 ) where generated. Fig. 5  shows the time cost for key generation process. From which we can see that the key generation is a linear process, which is justified as the key generation is performed using SHAKE-265 cryptographic hash function which is linear with complexity O n þ m ð Þ where n and m are the sizes of input and output respectively. The hash function input is the reshaped QV G vector output from the quantizer with size N Ã N þ 1 ð Þ =2 ½ where N is the number of electrodes (Section 4.
2). Since, AMIGOS dataset recorded from 14 electrodes, DEAP from 32 electrodes and SEED from 62 electrodes, the input vector size for AMIGOS, DEAP, and SEED dataset are 105, 528, and 1953 samples respectively. Difference in input vector size justifies the difference in time required for key generation that appears clearly in Fig. 5b. The key generation time from AMIGOS dataset is between 0. 84 to 10. 3 ms, for DEAP dataset between 1. 8 to 11. 6 ms, and for SEED dataset between 8 and 18. 5 ms. For reasonable key length the proposed key generation process is suitable for practical applications.
Security analysis Our proposed system offers several security aspects summarized in the following points; The original user data is not stored in the system database, so even if an imposter steal a genuine user template he will neither be able to pass the authentication process nor regenerate the cryptographic key. The quantization thresholds for each participant is stored in his template in the system database. Since we used a scalar quantizer in which the quantization levels are determined using multi-Otsu thresholding method, each participant will have his own quantization thresholds. This results in reducing the FAR in our system. Also in the user template we stored the H Â 2t ½ symbols which represents the parity symbols results from the RS encoder. Storing user specific parity symbols gave us two benefits.
Limitations of the study Although it has been validated that the proposed system offers a stable personal authentication and cryptographic key generation mechanism, there are some limitations that need to be addressed in future work. First, the proposed method was examined using only three datasets with maximum three sessions one week apart. These results are limited, if the time separation between sessions are wider (months or years), the system performance is unknown. More experiments needs to be performed using datasets with larger number of sessions that are temporal separated over longer periods of time. Second, in this work we used a window size of 10s to avoid the influence of participant affective state on the authentication and key generation processes. Several studies  (Nguyen et al.
Conclusion In this work, we present a system for personal authentication and cryptographic key generation based on EEG signals. The personal authentication process was performed using the raw EEG data without feature extraction by generating covariance matrices from the N-channel EEG signals and representing them as points (SPD matrices) on a Riemannian manifold. Then geometric mean was generated from each observation, followed by vectorization, quantization, and error correction processes (Section 4). The raw EEG data is not stored in the system database, even if an intruder steals a genuine user template, he will neither be capable of passing the authentication process nor regenerating the cryptographic key. The personal authentication process was tested using three publicly available datasets AMIGOS, DEAP and SEED.
Obtaining a GAR of 96:23%; 98:85% and 99:89% respectively if the training and testing data are from the same session, and 98:34% and 99:25% from the DEAP and SEED datasets when the training and testing data are from different sessions (Section 5). The achieved results are comparable to other techniques in the literature  (Table (3). A set of different cryptographic keys with different lengths is generated from each user sample, without the need for changing any internal system configuration. The user just needs to specify the required key length during both the enrollment and verifica-tion processes. For testing the statistical properties of the generated keys, were tested using NIST test suite, scale index text, and autocorrelation test. The generated keys passed all the tests and are suitable to be used as cryptographic keys (Section 5.
Dantu, 2016) mental tasks, 1 session). Alcoholism-(120 subject, 64 electrode, 1 session). 230 bit Using 18 electrodes GAR(91:05%). (Damaševicius Self-collected database-(42 subjects, 17 electrodes, rest Up to 400 EER(0:024), and TPR(99:74%) et al. , 2018) state, 1 session). bit. (Nguyen et al. , Alcoholism. 192 bit EER(0:079) in Gamma band. 2017) GrazIIIa -(3 subjects, 60 electrodes, six sessions). 192 bit EER(0:0018) in Gamma band. (Nguyen et al. , GrazIIIa 256 bit Using 32 electrodes EER(0:4%) in Gamma band. 2019) DEAP 256 bit EER(2:83%) in the Gamma band. (Yang et al. , 2017) Self-collected database -(10 subjects, 4 electrodes, 21 bits FAR(1:83%), ERR(1:875%). different tasks, 4 session in 1 week). Our System AMIGOS 128, 265, GAR(96:23%), FAR(0:039%). 512,. DEAP 128, 265, Same session GAR(98:845%), FAR(0:092%).
A 10 GHz ECR ion source (PK-GANESA) with a new magnetic field topology was developed in a GANIL-Pantechnik collaboration. The performance of this source is analyzed through simulations of electron trajectories over a time of 20 μs to ensure both rf-heating and magnetic confinement, using the code TrapCAD. The electron energy distribution functions obtained from the simulations are characterized with respect to radio frequency, heating power, and simulation time. The results are compared with a more traditional 10 GHz ECR source (NANOGANIII) presently used at GANIL. Our study demonstrates an improvement of electron confinement (a factor 10 increase) with increasing rf heating power which should in principle lead to the production of highly charged ions, at variance with the measured production of highcharge-state ions, significantly lower than for the NANOGANIII source.
B. Electron energy distribution functions The electron energy distribution functions (EEDF) were determined for all the trapped electrons at 20 μs. The EEDF are shown for NANOGANIII [Fig. 3(a) ] and PK-GANESA [Fig. 3(b) ]. While NANOGANIII's EEDF displays a Boltzmann-like behavior ∝ ffiffiffi ffi E p exp ð-E=kTÞ, PK-GANESA's one exhibits a simple exponential behavior ∝ exp ð-E=kTÞ. A curve was fitted on each histogram data resulting in a temperature equivalent kT of around 6. 54 keV for NANOGANIII and 2. 45 keV for PK-GANESA. Thus, the average electron energy is divided by a factor of ∼2. 7 with respect to NANOGANIII. The NANOGANIII EEDF has a longer high-energy tail than that corresponding to a Maxwellian distribution with a temperature of kT ¼ 6. 54 keV fit, meaning that electrons are trapped efficiently to experience ECR heating and reach tens of keV of kinetic energy for a sizable fraction of the electron population (over ∼62 keV for the 1% of the most energetic electrons inside the distribution).
III. SIMION SIMULATIONS The above analysis indicates that the rf-heating dynamics are in principle favorable for the production of HCS ions in PK-GANESA. In the present study and in order to attempt an explanation of the measured differences of ion-charge spectra between NANOGANIII and PK-GANESA, we have tested the following hypothesis: HCS ions are indeed produced with PK-GANESA in volumes where hot electrons are confined but are unable to escape the source inner volume through the exit aperture on the symmetry axis. For such a test, we have employed the SIMION simulation package  [10]. SIMION has been developed to calculate charged-particle tracks (both electrons and ions), once the rf electric field, electrostatic, and/or magnetostatic field maps are defined. We used the final electron spatial distribution, with the hypothesis that the HCS ions will be generated in regions where high-energy electrons are confined.
, 1995)  (or E2F/CHR  (Fischer et al. , 2022b) ), with a GC-rich cell cycle dependent element (CDE) that may be bound by E2F4 or E2F5, and an AT-rich cell cycle gene homology region (CHR) that may be bound by LIN54, the DNAbinding subunit of MuvB  (Müller and Engeland, 2010; Zwicker et al. , 1995). Later studies indicated that DREAM may also bind promoters with a single E2F binding site, a single CHR element, or a bipartite E2F/CHR-like element (CLE), and concluded that E2F and CHR elements are required for the regulation of G1/S and G2/M cell cycle genes, respectively  (Fischer et al. , 2016b; Müller et al. , 2017). The Target gene regulation database of p53 and cell-cycle genes  (Fischer et al. , 2022a)  was reported to include putative DREAM binding sites for many human genes, based on separate genome-wide searches for 7 bp-long E2F or 5 bp-long CHR motifs, but the predicted sites were not tested experimentally.
Our results in p53 Δ31/Δ31 mice led us to hypothesize that these phenotypic traits might partly result from gene repression mediated by the p53-DREAM pathway, which incited us to design a genome-wide approach relying on gene ontology and bone marrow cell differentiation to identify p53-DREAM targets related to hematopoiesis. Furthermore, mice and humans with germline increases in p53 activity may also exhibit microcephaly or cerebellar hypoplasia  (Simeonova et al. , 2013; Toki et al. , 2018) , and cerebellar hypoplasia may be observed in a subset of patients with Disease Models & Mechanisms • DMM • Accepted manuscript bone marrow failure syndromes, including patients with the Hoyeraal-Hreidarsson syndrome  (Hoyeraal et al. , 1970; Hreidarsson et al. , 1988)  or 27% of patients with Fanconi anemia  (Fiesco-Roa et al.
We observed that p53 activation correlated with cell differentiation in this system, because genes known to be transactivated by p53 (e. g. Cdkn1a, Mdm2) were induced, whereas genes repressed by p53 (e. g. Rtel1, Fancd2) were downregulated after tamoxifen withdrawal (Fig. 1A ; see also Fig. S1  for additional examples of p53-regulated genes). Thus, to investigate the impact of p53 on telomere biology we performed a gene ontology (GO) analysis of the expression data obtained with this system (Gene Expression Omnibus #GSE21299), which relied on 45,101 microarray probes corresponding to 20,627 genes, of which 17,461 are associated with a GO term according to the Gene Ontology enRIchment anaLysis and visuaLizAtion tool (GOrilla)  (Eden et al. , 2009). We focused on genes downregulated at least 1.