dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast to the blind source separation methods, De Haan et al. have exploited the effect of light on human skin due to its unique physical characteristics, introducing chrominance based methods  [4]. The chrominance (CHROM) signals are generated from the RGB traces with the use of a skin-tone standardized linear combination compatible with different skin colors. The chrominance based methods assume that the light reflected from skin generally occupies similar coordinates in the RGB space under white illumination. This direction of the standard skin tone was experimentally estimated using their in-house database. They further advanced upon their chrominance based methods proving that the various absorption spectra of arterial blood manifest along a specific vector in a normalized RGB space, termed as the blood volume pulse vector  [14].
The first derivative of L w. r. t w required for the optimization given by where ρ = ±ρ depending on the sign of E{G(y)} -E{G(v)}, G ′ y (y) and g ′ i (w) are the first derivatives of G(y) and g i (w) w. r. t y and w respectively. The Hessian L ′′ w k is calculated as the inversion of which is not problematic because R xx being the covariance matrix of the whitened and centered signal x is an identity matrix. G ′′ y 2 (y) and g ′′ i (w) are second order derivatives and L ′′ w k is of size m × m. The first and second derivatives of autocorre- lation in Eq. 12 are not trivial and are presented in the appendix to maintain structure. The expectation in the equations were calculated by using all the samples of the input signal x. The first and second derivatives were then fed into the fmincon function of MATLAB using the interior point algorithm  [30]  to obtain the final weighting matrix w * which was then used to obtain the final rPPG signal.
System framework Figure  5  depicts the entire workflow of the procedure. Let x = [x 1 x 2 x 3 ] T be the tem- poral RGB traces with each x m , m ∈ [1 • • • 3] corresponding to the channel-wise tem- poral trace vector of size N. Each x m ,corresponding to each video frame, was obtained  (14)  Maximize : J (y) = ρ E{G(w T x)} -E{G(v)} 2 , Subject to : g i (w) ≤ 0, h(w) = E{y 2 } -1 = 0 (15) L(w, µ, ) =J (y) - 1 2γ i [max{0, ḡi (w)}] 2 -µ 2 i -h(w) + 1 2 β h(w) 2 (16) L ′ w = ρE{xG ′ y (y)} - 1 2 µE{g ′ i (w)} -E{xy} (17) L ′′ w k = ρR xx E{G ′′ y (y)} - 1 2 µE{g ′′ i (w)} - by spatial averaging of the skin pixels from the face. These candidate skin pixels were obtained by using the method proposed by Conaire et al. [31]  after performing face detection and tracking using the Viola-Jones and Kanade-Lucas-Tomasi implementations provided by the computer vision toolbox of MATLAB.
Once the rPPG signal was estimated, the window-wise heart rate was calculated from the highest peak of the FFT filtered within the limits of normal heart rate F ∈ [0. 7, 3] Hz over a temporal moving window using a step size of 0. 5 s. ICA is generally known to work better with longer temporal signals, however, all the processing was performed over a 30 s window using the weighting matrix w k obtained at window k as an initial estimate for calculation of w k+1 at the next window. This was done in order to mimic the constraints of a live scenario thereby making the analysis more relevant. The 30 s length for the window was selected as a trade-off between speed and availability for enough data for convergence. That being said, a 15 s window had to be chosen for the MMSE-HR database because of the variable and oftentimes short length of the videos.
Results and discussion The cICA algorithm was validated using our in-house UBFC-RPPG database comprising of two datasets comprising of 9 (about 21k frames) and 46 (about 94k frames) videos respectively and the public MMSE-HR  [1]  database comprising of 97 (about 105k frames) videos. The two in-house datasets are labeled as SIMPLE and REALISTIC, respectively, and are ethically approved by the human participants. The SIMPLE dataset consists of the subjects relaxed with their eyes close with a realistic background and moderately varying light conditions. For the REALISTIC dataset, the subjects were required to play a time sensitive mathematical game that aimed at augmenting their heart rate while simultaneously emulating a normal human-computer interaction scenario. The UBFC-RPPG database which is focused specifically on rPPG analysis was created using a custom C++ application for video acquisition with a Logitech C920 web camera placed at a distance of about 1 m from the subject.
The video was recorded with a frame resolution of 640 × 480 in 8-bit uncompressed RGB format at 30 frames per second. A CMS50E transmissive pulse oximeter was used to obtain the ground truth PPG data comprising of the PPG waveform as well as the PPG heart rates. The experimental setup with sample images from both the databases is depicted in Fig. 6. The MMSE-HR database is specifically geared towards research on emotion elicitation, but the videos are usable for rPPG analysis. Due to its focus towards emotion elicitation, the videos in this database comprise of a large number of facial expressions and movements which aids in assessing our method rigorously. Based on its working principle, rPPG signals should be ideally compared with PPG ground truth signals. However, the MMSE-HR dataset only provides ground truth heart rates, acquired using the BIOPAC 150 data acquisition system which were calculated using contact based ECG electrodes.
Moreover, it was also observed that the PPG heart rates were unreliable and spurious at many instances, most definitely because of movements of the subject. These two facts do make the MAE metrics in our analysis less comprehensive. This also highlights the issue of contact based measurements, where even the ground truth signals are not entirely reliable. On the other hand, our UBFC-RPPG database provides both the PPG waveforms as well as the PPG heart rates, thus furnishing the means for a more comprehensive analysis. Table  1  shows the performance comparisons between ICA and cICA and other state of the art methods, viz. , PCA  [33] , Green  [2] , CHROM  [4] , POS  [15] , and G-R  [34]. In literature, there are several works which uses ICA for rPPG extraction. However, the core algorithm for it remains the same.
The ICA implementation used for our analysis has been adapted from FastICA  [8]. Furthermore, the analysis of all the methods was performed using exactly the same pre and post processing steps like normalization, filtering and smoothing. The exact metrics of ICA from related state of the art methods such as  [6, 7]  and  [5]  could not be used because they all use their own private databases which were inaccessible to the public. However, as mentioned earlier, the core algorithm of ICA remains the same making the metrics in Table  1  applicable. Furthermore, comparison with smart ROI selection methods such as  [17, 19, 20]  and  [13]  was not deemed relevant in order to limit the comparison amongst source separation methods, and the fact that our method can easily be incorporated into an ROI selection framework.
The best metrics per method are italicized to compare their effectiveness clearly. The metrics used in our analysis are mean absolute error (MAE) in beats per minute (bpm), signal-to-noise ratio (SNR) and Pearson's correlation coefficient (r) between heart rate calculated using the rPPG signal, HR rPPG and the heart rate calculated using the ground truth PPG waveform, HR PPG. The MAE was calculated as the window-wise mean of |HR rPPG -HR PPG |, averaged per video. The windowed method is computation- ally more taxing, owing to the smaller window length, but is more realistic. The SNR (dB) was calculated as the ratio of the power of the main pulsatile component of the PPG to that of the background noise to accommodate the wide dynamic range of the signals. However, it is to be noted that the MMSE-HR database does not provide the ground truth waveforms, thereby obliging the use of the main pulsatile component of the RPPG instead of the PPG for the SNR calculation.
In this case, the SNR just represented the strength of the main pulsatile component, which in itself is a useful metric, but is not valid for the comparison with the SNR for other databases which were calculated in a different manner. As a result, the SNR values for the MMSE-HR database are not really relevant and are omitted. The MAE values, however, are relevant since it is calculated as the difference between rPPG and PPG heart rates, which are provided as ground truth for the MMSE-HR database. It was also worth assessing the resilience of the cICA algorithm against changes in parameters such as image resolution and window length. This analysis was performed in two parts. The MMSE-HR database was chosen to assess the effect of changes in image resolution since it provides images of resolution 1040 × 1392 which offer the possibility to asses scaled down versions of the frames, to 75, 35% and even to 10%, in favor of the UBFC-RPPG database which provides images of a lower resolution, viz.
640 × 480 pixels. On the other hand, due to the shorter duration of videos in the MMSE-HR database, the UBFC-RPPG REALISTIC dataset was chosen to assess the effect of changes in the window length, the assessment being done against window lengths of 10 and 20 s, respectively. Table  2  lists the average results of this assessment over the three datasets. The metrics in the last row correspond to the original results. It was observed that there was a slight decline in the performance with respect to the window length of 10 s, which is expected, owing to the lack of enough data for the ICA objective function to establish independence. Correspondingly, frames of lower resolution reflect loss of spatial information. Even though this loss is in itself not too deteriorating for the signal quality, the fact that it was coupled with the 15 s window length for the MMSE-HR database, explains the slightly higher MAE values for frames scaled down to 35 and 70%.
As expected, the amount of information loss is slightly more pronounced at a scale of 10%, depicting the slight decline in the quality of the extracted rPPG signal with reduction in resolution. The decline due to loss of spatial information can be attributed to the reduction in the total number of skin pixels used to get the average signal value per frame. The global correlation analysis using window-wise calculations between HRs from PPG versus RPPG obtained from all the videos in each dataset for the skin-segmented pixel data for one particular run is presented in Fig. 8. The metrics PRECIS 2. 5 and PRECIS 5 show the percentage of windows where δ = |HR rPPG -HR PPG | < 2. 5 and 5 bpm respectively. n represents the total number of windows used in the analysis, r is Pearson's correlation coefficient and y depicts the equation of the fitted line.
It is worth mentioning that the MAE values in Fig. 8  and Table  1  differ from those in Fig. 7 , which are averaged over 20 executions, but are obviously in range. Moreover, differences in range of 10 -2 bpm are inconsequential. The analysis of the SIMPLE dataset, as the name suggests, was quite easy for both ICA and cICA. However, cICA did remove the few outliers that are present in the case of ICA. This is reflected in the low MAE and high SNR values as shown in Table  1. This can be attributed to the fact that the subjects were generally relaxing, mostly with their eyes closed, which resulted in minimal motion artifacts. On the other hand, the REAL-ISTIC dataset was slightly more challenging since the subjects were actually working on the computer and were only requested to keep their hand still for the PPG sensor.
Similarly, the MMSE-HR database was challenging owing to it being an emotion elicitation database. There were many instances where the subjects laughed out loud, exhibited considerable movements, while regularly manifesting various facial expressions. This resulted in the usual problems arising from movement of the subjects and the face ROIs. Consequently, the presence of outliers was more pronounced for both the REALISTIC dataset and the MMSE-HR database, which cICA was able to reduce. Also, the fitting line was closer to the 45° line with cICA as compared to ICA. ∂y where each column is the product of the derivative ∂r 1 ∂y and the scalar r k and is of size N × 1. Deriving Eq. 4, r k = yT k y T , listed here for convenience, w. r. t y using the product rule of differentiation, 2   where ∂ ∂y (T k y T ) = T T k comes from the fact that the differential of T k y T , a vector, will remain the same even when it is transposed and the derivative is computed elementwise.
is essentially an optimization algorithm where the weights are randomly initialized, it was worth assessing its consistency over multiple runs. Figure 7 shows the box plot comparing the MAEs with cICA for the UBFC-SIMPLE dataset without much movement under ambient light, the UBFC-REALISTIC dataset with subjects working on a computer under ambient light, and the MMSE-HR dataset with subjects exhibiting facial expressions under indoor lighting. These tests on the datasets were performed 20 times. It is visible from the box plot that the cICA method performs consistently resulting in MAEs in the range [0. 53, 1. 96] bpm, [2. 49, 4. 1] bpm and [2. 63, 5. 86] bpm for the UBFC-SIMPLE, UBFC-REALISTIC and MMSE-HR datasets respectively.
Finally, since cICA is essentially an optimization algorithm where the weights are randomly initialized, it was worth assessing its consistency over multiple runs. Figure 7 shows the box plot comparing the MAEs with cICA for the UBFC-SIMPLE dataset without much movement under ambient light, the UBFC-REALISTIC dataset with subjects working on a computer under ambient light, and the MMSE-HR dataset with subjects exhibiting facial expressions under indoor lighting. These tests on the datasets were performed 20 times. It is visible from the box plot that the cICA method performs consistently resulting in MAEs in the range [0. 53, 1. 96] bpm, [2. 49, 4. 1] bpm and [2. 63, 5. 86] bpm for the UBFC-SIMPLE, UBFC-REALISTIC and MMSE-HR datasets respectively.
Acknowledgements J. W. and H. Y. contributed equally to this work. The Nanjing Tech University coauthors acknowledge the financial supported by the  Natural Science Foundation of Jiangsu Province of China  (Grant No. BK20170963 ), the  National Natural Science Foundation of China  (Grant Nos. 51702154  and  21573109 ), the  Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) , and the high-level talent start-up funding. The  NTU  coauthors acknowledge the financial supported by  AcRF Tier 1  (Reference No. RG103/16 ), AcRF Tier 2 ( MOE2015-T2-1-148 ), and  AcRF Tier 3  ( MOE2011-T3-1-005 ).
Background: The ataxia telangiectasia mutated (ATM) gene is a moderate-risk breast cancer susceptibility gene; germline loss-of-function variants are found in up to 3% of hereditary breast and ovarian cancer (HBOC) families who undergo genetic testing. So far, no clear histopathological and molecular features of breast tumours occurring in ATM deleterious variant carriers have been described, but identification of an ATM-associated tumour signature may help in patient management. Methods: To characterise hallmarks of ATM-associated tumours, we performed systematic pathology review of tumours from 21 participants from ataxia-telangiectasia families and 18 participants from HBOC families, as well as copy number profiling on a subset of 23 tumours. Morphology of ATM-associated tumours was compared with that of 599 patients with no BRCA1 and BRCA2 mutations from a hospital-based series, as well as with data from The Cancer Genome Atlas.
Copy number variation analysis Copy number variation (CNV) analysis using the Affymetrix OncoScan SNP array (Thermo Fisher Scientific, Santa Clara, CA, USA) was performed on the 23 FFPE ATM-associated tumours, including 2 tumours from 2 A-T participants (Table  1 ). Data were analysed with the Genome Alteration Print (GAP) method, which takes into account both ploidy and large-scale genomic rearrangements  [18, 26]. Copy number ranged from zero to eight copies, and all segments exceeding eight copies were ascribed eight-copy status. Chromosome number was estimated by the sum of the copy numbers detected at the peri-centric regions. Output processing files derived from the GAP tool were analysed using VAMP in-house software  [27]  to define the boundaries of regions recurrently altered in ATM-mutated tumours.
Copy loss and gain for near-diploid tumours were called for the segments with zero or one copy and four or more copies, respectively. Copy loss and gain for near-tetraploid tumours were called for the segments with less than or equal to two and six or more copies, respectively. LOH status was ascribed to regions having monoallelic content, regardless of copy number. LOH associated with copy loss was referred as LOH/loss. Breakpoints (changes in the copy number or major allele counts within chromosomes) in each genomic profile were characterised on the basis of resulting absolute copy number profile and after filtering for regions with < 50 SNP variations. Recurrent alterations (CNV, LOH) among the cohort were obtained using the CNTools R package (version 1. 24. 0; R Foundation for Statistical Computing, Vienna, Austria) and a homemade script.
Validation of LOH status at ATM locus using microsatellites Tumour and matched blood DNA were evaluated on a subset of participants by using a PCR-based LOH assay with four fluorescence-labelled microsatellite markers (namely D11S1113, D11S1819, D11S2179 and D11S1778) spanning a 14. 4-Mbp region encompassing the MRE11A and ATM genes. Capillary electrophoresis was performed on the ABI 3500xL DNA analyser. Raw electrophoretic data were analysed with GeneMarker software version 1. 3 (SoftGenetics, LLC, State College, PA, USA) to assess allele ratios. We considered LOH at the ATM locus when the allele ratio fell below 50% in the tumour DNA sample.
Statistical analysis Statistical analyses were performed using STATA version 14. 1 software (StataCorp, College Station, TX, USA). Two-tailed tests with a 5% significance level were used throughout. Logistic regressions were used to assess the level of association between the presence of an ATM variant and various features of interest when comparing the ATM series with the PICBIM series. Fisher's exact test (FET) was used to assess molecular subtype differences between ATM-associated tumours and breast tumours from sporadic cases from The Cancer Genome Atlas (TCGA)  [31]  and from the Norwegian series  [32].
Genome-wide copy number and LOH profiles of ATM breast tumours High-quality SNP array data were obtained for 23 FFPE breast tumours; 2 tumours were from A-T subjects, and 21 tumours were from HetAT subjects. Tumour ploidy inferred from the absolute segmental copy number profiles and genotype status by the GAP method  [26]  identified 16 of 23 (70%) near-tetraploid tumours and 7 of 23 (30%) near-diploid tumours. CNVs and regions of LOH were subsequently determined by taking into account the ploidy of each tumour. No evidence of the homologous recombination deficiency (HRD) signature as measured by large-scale state transition genomic signature  [18, 35]  was observed among the ATM-associated tumours (Fig. 2a ). Because 'two-hit' inactivation of the causative gene is regarded as a principal feature of molecular pathogenesis of most hereditary tumours, we next examined the LOH status of ATM-associated tumours at 11q22-23 in the tumours from HetAT participants.
Comparison with publicly available data We used the publicly available data from TCGA  [31]  accessible through cBioportal  [38]  to investigate whether the genes listed in Table  3  were specifically lost in ATM-associated tumours. A total of 745 TCGA tumour samples with available CNV data were used. Those tumours were from individuals who developed invasive primary breast tumours and did not carry a deleterious variant in ATM, BRCA1 or BRCA2. In addition to LOH at the ATM locus, which was observed more frequently in ATM-associated tumours (67%) than in the TCGA 'sporadic' tumours (40. 1%) (P = 0. 02), several genes at other loci appeared more frequently lost in ATM-associated tumours, including TPTE (21p11. 2-p11. 1), GSTT1, GSTTP1 and GSTTP2 (22q11. 23), as well as LCP1, RB1 (13q14), YWHAE, USP6, RABEP1 and MAP2K4 (17p13.
As reported in the COSMIC database b This region contains PPP3CC, SORBS3, PDLIM2, BIN3, BIN3-IT1 and EGR3 c This region contains WWP2 and the microRNA MIR140 d This region contains TEKT4P2, TPTE and four miRNAs (MIR3648-1, MIR3648-2, MIR3687-1, MIR3687-2) e This region contains the pseudogenes GSTTP1 and GSTTP2(range 35×-37×). CNV patterns obtained from WGS data for frozen tumours T0015-L, T0077-L and T0077-R were compared with CNV patterns obtained in the OncoScan analysis in the corresponding FFPE tumours. Loss/LOH was confirmed by whole-genome analysis for loci 8p21. 3, 11q21-q24. 2 (containing ATM), 13q13. 3-q32. 3, 16q22. 1 and 17p13. 3, whereas discordant results were obtained at locus 21p11. 2-p11. 1 for one tumour and at locus 22p11. 23 for two tumours (Fig. 3a). Divergent ploidy estimations between the WGS analysis (ploidy 3.
This work was supported by Inserm and Ministère de la Recherche (grants 01P0751, 01P0752, 01P0753, 01P0754 and 01P0755), Electricité de France (conseil scientifique de Radioprotection d'EDF; grants EP 2002-03, EP 2004-03 and RB 2016-22), Fondation de France (grants 2001009761 and 2005011201), La Ligue (grants PRE04/NA, PRE07/NA and PRE2015 LNCC/NA), La Ligue Comité du Maine et Loire, La Ligue Comité de Paris (grant RS16/75-72), MGEN Union, ITMO Santé Publique d'AVIESAN (grant AAP12-COH-110), Institut National du Cancer (grant INCa-9578), the comprehensive cancer centre SiRIC (Site de Recherche Intégrée sur le Cancer; grant INCa-DGOS-4654), Fondation ARC pour la recherche sur le cancer (grant PJA 20151203365), and the Agence Nationale de la Recherche, program investissements d'avenir (grant ANR-10-EQPX-03).
Acknowledgements We are most grateful to the families who so willingly participated in the study. We acknowledge  David Gentien  and  Cécile Reyes  for technical expertise. We thank all the  CoF-AT collaborating cancer clinics: Institut Curie, Paris :  Dominique Stoppa-Lyonnet ,  Bruno Buecher ,  Antoine de Pauw  and  Sophie Lejeune-Dumoulin ;  Hôpital Arnaud de Villeneuve , Montpellier: Isabelle.
a As reported in the COSMIC database b This region contains PPP3CC, SORBS3, PDLIM2, BIN3, BIN3-IT1 and EGR3 c This region contains WWP2 and the microRNA MIR140 d This region contains TEKT4P2, TPTE and four miRNAs (MIR3648-1, MIR3648-2, MIR3687-1, MIR3687-2) e This region contains the pseudogenes GSTTP1 and GSTTP2(range 35×-37×). CNV patterns obtained from WGS data for frozen tumours T0015-L, T0077-L and T0077-R were compared with CNV patterns obtained in the OncoScan analysis in the corresponding FFPE tumours. Loss/LOH was confirmed by whole-genome analysis for loci 8p21. 3, 11q21-q24. 2 (containing ATM), 13q13. 3-q32. 3, 16q22. 1 and 17p13. 3, whereas discordant results were obtained at locus 21p11. 2-p11. 1 for one tumour and at locus 22p11. 23 for two tumours (Fig. 3a). Divergent ploidy estimations between the WGS analysis (ploidy 3.
Funding This work was supported by Inserm and Ministère de la Recherche (grants 01P0751, 01P0752, 01P0753, 01P0754 and 01P0755), Electricité de France (conseil scientifique de Radioprotection d'EDF; grants EP 2002-03, EP 2004-03 and RB 2016-22), Fondation de France (grants 2001009761 and 2005011201), La Ligue (grants PRE04/NA, PRE07/NA and PRE2015 LNCC/NA), La Ligue Comité du Maine et Loire, La Ligue Comité de Paris (grant RS16/75-72), MGEN Union, ITMO Santé Publique d'AVIESAN (grant AAP12-COH-110), Institut National du Cancer (grant INCa-9578), the comprehensive cancer centre SiRIC (Site de Recherche Intégrée sur le Cancer; grant INCa-DGOS-4654), Fondation ARC pour la recherche sur le cancer (grant PJA 20151203365), and the Agence Nationale de la Recherche, program investissements d'avenir (grant ANR-10-EQPX-03).