dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, the African Consortium on Cervical Cancer Control Research (COFAC-Col) and the African Breast Cancer Consortium can serve as foci around which ACRA networks and hubs will be established. Finally, AORTIC has developed a registry of researchers and research projects through the African Cancer Research Registry (http://mendel2. med. upenn. edu:8080/AfricaProject/MapView. jsp). Figure  2  provides a summary of the countries in which cancer projects have been identified and included in registry This registry provides the potential for African cancer researchers to identify related projects and colleagues with similar interests. The data in this registry are also included in the Global Cancer Project Map (http://globalonc. org/ Projects/global-cancer-project-map/) developed by Global Oncology.
Ongoing research initiatives are being identified and will be included as found. However, in the future all data will be incorporated directly into the Global Cancer Project Map, which will serve as the central repository for this information in the future. While ACRA will serve as the AORTIC focus for the development of cancer research activities in Africa, it will also strive to work with other existing partners. These include funders such as the US National Cancer Institute, the International Union Against Cancer (UICC), the American Association for Cancer Research (AACR), and the American Society for Clinical Oncology (ASCO). In addition, ACRA will work with the AORTIC Education and Training Committee to foster training opportunities as research capacity is built.
, 2014)  and the fact that connection weights are fundamental to understanding cortical organization  (Ercsey-Ravasz et al. , 2013). A recent systematic comparison in the mouse  (Calabrese et al. , 2015)  revealed a correlation coefficient of r ϭ 0. 46 between log-transformed, high-resolution postmortem tractography data and quantitative tracer-based connectivity data  (Oh et al. , 2014). In contrast, van den  Heuvel et al. (2015)  reported much lower correlations (r ϭ 0. 25-0. 31) when comparing in vivo macaque tractography with two published tracer-based connectivity analyses. Given methodological limitations in data acquisition (e. g. , coarse spatial resolution, low angular resolution diffusion scans) and analysis (e. g. , a coarse cortical parcellation; see Discussion) in the latter study, this may not reflect the upper bound for tractography performance in primates.
In the present study, we used high-resolution postmortem diffusion imaging combined with complex fiber orientation modeling and probabilistic tractography to evaluate systematically tractography performance in estimating the existence and the weights of areato-area connectivity throughout cerebral cortex in Old World monkeys. As an approximation of ground truth, we used published quantitative data generated using retrograde tracers injected into 29 cortical areas in the macaque using a 91-area atlas parcellation  (Markov et al. , 2014). This constitutes the most comprehensive weighted tracer-based connectivity matrix currently available for NHPs. Tractography results were generated from diffusion imaging of postmortem brains from Old World monkeys (macaque and vervet) that have very similar cortical convolutions and, likely, connectivity patterns.
(2014)  in logarithmic (log 10 ) units. Results were represented using the Connectivity Informatics Technology Initiative data format (http://www. nitrc. org/projects/cifti/), which enables interactive visualization of connectivity maps and connectivity matrices in Connectome Workbench (http://humanconnectome. org/software/). For the tracer injections, each injection provided information restricted to the inputs to a particular area. Therefore, a complete description of both inputs and outputs is available only for the 29 ϫ 29 edge-complete subgraph of the larger 29 ϫ 91 connectivity matrix. Directionality was removed by calculating the arithmetic mean of each pair of anterograde and retrograde connection weights. We refer to the 29 ϫ 29 retrograde tracer (RT) parcellated connectivity matrix as RT 29ϫ29 , or the RT matrix for short.
dMRI data acquisition. We analyzed dMRI scans from both the left and right hemispheres of two postmortem monkey brains (PM1 and PM2), the second of which was analyzed for two separate scans: (PM2A and PM2B). PM1 involved diffusion-weighted MRI of an immersion-fixed brain from an adult male, macaque, M. fascicularis  (D'Arceuil et al. , 2007). The postmortem brain was infused with a gadolinium contrast agent that enables acquisition using short TR  (D'Arceuil et al. , 2007). Scans were acquired using a 3D multishot, spin-echo sequence on a 4. 7 T Bruker scanner with the following scan parameters: TR ϭ 250 ms; TE ϭ 31. 7 ms; matrix size ϭ 256 ϫ 128 ϫ 128; isotropic 0. 43 mm voxels. Diffusion weighting was applied in 120 uniformly distributed directions with b ϭ 8000 s/mm 2. Total imaging time was ϳ27 h.
PM2A and PM2B were acquired from a perfusion-fixed brain of a healthy adult 32-month male vervet (Chlorocebus sabaeus) brain. The animal was obtained from the Behavioral Science Foundation, St. Kitts, and was socially housed in enriched environments. The experimental protocol was reviewed and approved by the Institutional Review Board of the Behavioral Science Foundation acting under the auspices of the Canadian Council on Animal Care. Preparation of the postmortem brain and the collection of data on a preclinical 4. 7 T Agilent MR scanner (at the Danish Research Centre for Magnetic Resonance) involved the ex vivo imaging setup described previously  (Dyrby et al. , 2011 (Dyrby et al. , , 2014)). The scan session started with a Ͼ4 h diffusion-weighted image (DWI) dummy scan ensuring reduction of short-term instabilities introduced in the acquisition of the DWI datasets.
In two separate scan sessions, the PM2A and PM2B were collected using a 2D pulsed-gradient-spin-echo sequence with single-line readout. Scan parameters for PM2A and PM2B were as follows: TR ϭ [5100, 6500] ms; TE ϭ  [45, 35]  ms; NEX ϭ [1, 2] (averaged offline); matrix size ϭ 128 ϫ 256; whole brain coverage of axial slices with isotropic 0. 5 mm voxels. The b-value was [4000, 7500] s/mm 2 , 3 b ϭ 0 s/mm 2 acquired in  [61, 128]  uniformly and noncollinear directions using the scheme files available from the Camino tool kit  (Cook et al. , 2006). Total imaging times for PM2A and PM2B were ϳ20 and 68. 5 h, respectively. The Yerkes19 macaque atlas. To enable accurate comparisons across results obtained in different individuals and between left and right hemispheres, tracer and MRI data were registered to a group-average "Ye-rkes19" macaque surface-based atlas  (Van Essen et al.
, 2012a). This atlas was based on structural MRI scans (T1w and T2w, 0. 5 mm isotropic) of 19 adult macaques acquired at Yerkes Primate Research Center at Emory. Each brain was processed using the minimal preprocessing pipelines developed for the Human Connectome Project (HCP)  (Glasser et al. , 2013)  to extract cortical surfaces and subcortical volumes from the structural MRI scans and align individual scans to the template. The HCP pipelines (developed for human MRI data) were adapted to work with monkey MRI. For the in vivo scans used to generate the atlas, the PreFreeSurfer pipeline aligned T1w and T2w volumes to native anterior commissure-posterior commissure space, allowing brain extraction, cross-modal registration, bias field correction, and FMRIB Software Library (FSL) analysis  (Smith et al.
Interhemispheric alignment between the left and right hemispheres was based on landmark-based alignment analogous to that performed on the macaque F99 atlas surfaces  (Van Essen et al. , 2012a)  using 45 geographically corresponding landmark contours in each hemisphere. For the two postmortem monkeys analyzed in this study, identical steps were performed for the structural preprocessing with the exception of the creation of T1w-and T2w-like images described in the next section. Data were mapped from individual experimental hemispheres to the Ye-rkes19 atlas using surface-based registration, as described below. Visualization used Connectome Workbench software (http://www. humanco nnectome. org/software/connectome-workbench. html). dMRI preprocessing. Because the diffusion data were not echo-planar imaging (EPI) based and were from fixed tissue, many typical preprocessing steps (motion correction, eddy current correction, and b0 inhomogeneity correction) were not required.
This makes all parcellated tractography matrices comparable to one another, as well as to the FLNe connection weight metric used to describe tracers. Within-area (area-toself) connections were not considered in this analysis and their connection weights were set to the minimum threshold value for both tracer and tractography datasets. Symmetrization via arithmetic mean i. e. , ͩ DT i, j ϩ DT j,i 2 ͪ was performed after computing the fractionally scaled connection weights. Because connection weight values span many orders of magnitude, most analyses were performed in log-space using log 10 connection weights. Averaging of connection weights across hemispheres was performed for corresponding vertices of the dense connectome (dDT1/3) based on interhemispheric alignment of the Yerkes19 atlas mesh.
Discussion By comparing tractography-created cortical connectomes systematically with published parcellated connectome data derived from neuroanatomical tracers, we have shown that current state-of-the-art tractography performs much better than chance, yet far from perfectly, in detecting neuroanatomical pathways and estimating weights corresponding to their connection strength. Our analysis benefitted from a fractional scaling normalization scheme to assess the correlation between connection weight metrics reported by these two modalities. We confirmed an exponential relationship between connection weight and interareal path length and showed that tractography remains modestly predictive after regressing out this relationship. Of the two tractography seeding strategies used, the unidirectional surface-to-surface method, producing dDT1, had a slight performance advantage over the bidirectional voxel-to-surface (dDT3) method.
Although specific to this study, this threshold offers a starting point when considering possible thresholds for other datasets, including humans and other species in which connectivity "ground truth" is completely lacking. The observed trend between connection weight and path length in both tracer and tractography is consistent with previous observations of a "noisy" exponential relationship  (Ercsey-Ravasz et al. , 2013). Regressing out this relationship indicates that, whereas path length is a major factor in estimating connection weights, tractography has useful explanatory power that transcends predictions based purely on connection path length. Algorithmic refinements that better model anatomically observed path length dependency may enhance tractography's utility in characterizing long-distance pathways  (Liptrot et al.
, 2014). The quality of postmortem dMRI imaging data used here surpasses typical spatial and angular resolutions used in conventional in vivo dMRI, though recent advances such as multiband acceleration  (Feinberg et al. , 2010; Setsompop et al. , 2012)  can reduce this gap  (Sotiropoulos et al. , 2013). A previous analysis of different fiber orientation modeling methods  (Thomas et al. , 2014)  found that the original ball and stick model  (Behrens et al. , 2007)  effectively balanced sensitivity and specificity when identifying connections and was robust to changes in angular threshold. We used an improved ball and stick model that accounts for non-mono-exponential decay  (Jbabdi et al. , 2012) , enabling estimation of up to three crossing fibers per voxel. By making these datasets freely available, other investigators will be able to compare and contrast the performance of alternative fiber orientation and tractography models.
A recent study  (van den Heuvel et al. , 2015)  reported low correlations (r ϭ 0. 26 -0. 31) between macaque in vivo tractography and two macaque tracer datasets, the CoCoMac database and the  Markov et al. (2014)  dataset analyzed in the present study. We attribute our twofold higher correlation (0. 59) to differences in analysis methods as well as data acquisition. For dMRI acquisition, we used higher spatial resolution (0. 43-0. 5 vs 1. 1 mm isotropic voxels), angular resolution (120 vs 60 diffusion directions), b values (8000 vs 1000 s/mm), and scan durations. Higher spatial and angular resolution may improve the fidelity of tractography modestly  (Sotiropoulos et al. , 2013; Thomas et al. , 2014; Jbabdi et al. , 2015; Wu and Zhang, 2016). Potentially important analysis differences include our finer-grained parcellation (the 91-area Markov parcellation vs their 39 area "WBB47" parcellation).
National Institutes of Health  (Grant  R01 MH 60974  to D. C. V. E. and Grant  F30 MH097312  to M. F. G. ), the  Engineering and Physical Sciences Research Council  (Grant  EP/L023067/1  to S. N. S. ), and the  French National Research Agency  (Grants  ANR-11-BSV4-501 ,  ANR-14-CE13-0033 ,  LabEx  CORTEX  ANR-11-LABX-0042 , and  Universitede Lyon   ANR-11-IDEX-0007  to H. K. ), and the  National Institutes of Health  (Grant  P01AG026423 ) and the  Yerkes National Primate Research Center (Office of Research Infrastructure Programs  Grant  OD P51OD11132 ) for the scans used for the macaque atlas, and to  T. Preuss  and  J. Rilling. We thank  Drs. R. Palmour  and  M. Ptito  and the  Behavioral Science Foundation of St-Kitts  ( West Indies ) for providing the Vervet monkey specimens ( PM2 ) for ex vivo imaging.
A robust and numerically-efficient method based on two moving average filters, followed by a dynamic event-related threshold, has been developed to detect P and T waves in electrocardiogram (ECG) signals as a proof-of-concept. Detection of P and T waves is affected by the quality and abnormalities in ECG recordings; the proposed method can detect P and T waves simultaneously through a unique algorithm despite these challenges. The algorithm was tested on arrhythmic ECG signals extracted from the MIT-BIH arrhythmia database with 21,702 beats. These signals typically suffer from: (1) non-stationary effects; (2) low signal-to-noise ratio; (3) premature atrial complexes; (4) premature ventricular complexes; (5) left bundle branch blocks; and (6) right bundle branch blocks. Interestingly, our algorithm obtained a sensitivity of 98.
Analysis of the P and T waves of the ECG is essential, as their shape and duration can be severely altered by certain pathologies. Changes in P waves may indicate defective intra-atrial conduction, hypertrophic conditions of the atria and atrio-ventricular conduction, among other abnormalities  [5]. In some pathological conditions, T wave morphology may also vary because of altered ventricular activation  [5]. Researchers have attempted to employ a variety of methods to detect P and T waves, each with its own strengths and limitations. For example, in  [6] , the discrete cosine transform (DCT) was used for discrimination of P waves on a few ECG segments from the MIT-BIH database. However, detection generally failed when the amplitude was very small. In  [7] , a Bayesian sampling algorithm (Partially Collapsed Gibbs Sampler, PCGS) was used to detect P and T waves in the PhysioNet QT interval database.
Even though both waves were accurately detected at the same time, their characterization strongly depended on an a priori model assumption that was not verified, especially in pathological conditions. Moreover, it is has a high computational overhead. In  [8] , a template-based correlation method was tested on the Physionet QT database. Despite rather satisfactory detection rates, the authors stated that estimation uncertainty is higher when signal quality is poor, thus demonstrating the lack of robustness of the method for noisy signals. The study in  [9]  proposed a Field Programmable Gate Array (FPGA) based system combined with the identification of the slopes of the waves of interest. The method was validated on the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database from Physionet.
Nevertheless, only a portion of the database was examined, and half of the recordings (out of 12 s) was employed for algorithm training. Li et al. [10]  proposed a method for detecting monophasic P and T waves based on quadratic spline wavelets with compact support, but without validation on an ECG database. De Azevedo et al. [11]  used a neural network with asymmetric basis functions to extract the features of the P waves on the MIT-BIH database. However, they did not mention the detection rate. Strumillo  [12]  demonstrated a nonlinear signal decomposition method based on nested median filters. It was tested on the QT database for detecting T wave offset, but the detection rate was not reported. Martinez et al. [13]  presented a generalized method for the discrimination of P and T waves based on quadratic spline wavelets and the derivative of a Gaussian as a smoothing function and tested it on the QT database.
The biorthogonal wavelet transform was performed in  [14]  for P wave detection, but database source is not detailed. Chouhan et al. [15]  used the first derivative with adaptive thresholds for simultaneous P and T wave detection. Most of the algorithms in the literature detect either P or T waves separately. Many attempts have been made to find a satisfying universal solution for P and T wave detection. Moreover, most of these algorithms reported high P and T wave detection performance with high rates after excluding some segments or beats from the used records. Difficulties arise mainly because of the diversity of the P and T waveforms, low signal-to-noise ratio (SNR) and the artifacts accompanying the ECG signals  [16] , especially in the presence of arrhythmia  [17]. The performance of the existing P and T wave detection algorithms is still inefficient and needs to be tested on long recordings rather than short ECG segments, such as those signals found in the well-known MIT-BIH database  [18, 19].
The recordings included in this dataset present a wide variety of cardiac disturbances, such as such as premature atrial complexes (PAC), premature ventricular complexes (PVC), left bundle branch blocks (LBBB), and right bundle branch blocks (RBBB). Through this proof-of-concept study, we demonstrate a numerically-efficient and robust algorithm. We show that the proposed algorithm can detect P and T waves, despite the effect of pathological conditions on their properties-in particular, morphology and duration.
• The MIT-BIH database contains 30-min recordings for each patient, which is considerably longer than the recordings in many other databases, such as the Common Standards for Electrocardiography (CSE) database that contains 10-s recordings  [20]. • Arrhythmic ECG signals provided by the MIT-BIH Arrhythmia Database are impacted by multiple factors that affect signal quality. For example, we noted premature atrial complexes, non-stationary effects, premature ventricular complexes, low signal-to-noise ratio, left bundle blocks, and right bundle blocks. These challenges provide an opportunity to test the robustness of the P and T wave detection algorithm. These issues are expected to present significant difficulties for any ECG signal analysis algorithm  [21]. The selected ten ECG recordings from the MIT-BIH Arrhythmia Database contain a total of 21,702 beats and each was a 30-minute recording.
The selected ten ECG recordings from the MIT-BIH Arrhythmia Database contain a total of 21,702 beats and each was a 30-minute recording. The sampling frequency was 360 Hz with an 11-bit rate resolution over a 10 mV range. The quality of the Leads varied and therefore we selected the Lead with the higher quality signals (Lead I). Annotations of P and T waves in MIT-BIH is described in  [22]  and can be downloaded from http://www. elgendi. net/databases. htm.
QRS Removal The first stage of QRS removal is to detect R peaks, and then the QRS complex can be removed from each beat to make the P and T wave dominant. The removal is carried out by setting the values before and after the R peak to zeros. Note that annotation of the R peaks has already been completed and provided in the MIT-BIH Arrhythmia Database, and thus there is no need to detect R peaks. The signal is set to zero in a range equal to 0. 083 ms before the R peak and for 0. 166 ms after the R peak. Figure  3b  shows the result of removing QRS complexes from the filtered signal of Figure  3a.
Results P and T waves are successfully detected using the proposed algorithm, including merged P and T waves, LBBB, RBBB, PVC, and PAC, in arrhythmic ECG signals from the MIT-BIH Database. Moreover, the algorithm combated different types of noise such as high-frequency, noise baseline Figure  5. Pseudocode of the proposed P and T waves detection algorithm. The R i -1 T min represents the minimum interval between the T wave and current the R peak, while R i -1 T max is the maximum interval between the T wave and the current the R peak. Here, P min R i represents the minimum interval between the P wave and next the R peak, and P max R i stands for the maximum interval between the P wave and next the R peak. (a) Generating blocks of interest (b) P wave search intervals (c) T wave search intervals.
Results P and T waves are successfully detected using the proposed algorithm, including merged P and T waves, LBBB, RBBB, PVC, and PAC, in arrhythmic ECG signals from the MIT-BIH Database. Moreover, the algorithm combated different types of noise such as high-frequency, noise baseline wander, and low SNR. It can be seen in Figure  6  that the ECG signals contained these challenges. Given these results, the algorithm is promising in detecting P and T waves in noisy ECG signals. The algorithm is applied across three different types of normal rhythms as seen in Figure  6a-c : (1) without U waves (record 100); (2) with U waves (record 103); and (3) with negative polarization (record 108). The P and T wave in a normal sinus rhythm are relatively easier to detect as the P-wave, QRS, T wave do exist and are relatively salient to detect  [5].
Discussion The performance of the P and T wave detection algorithm on the MIT-BIH Database is shown recording by recording in Table  2. Comparisons to other published detectors are provided in Table  3. Two statistical parameters (SE and +P) were used to evaluate the performance of the proposed P and T wave algorithm. Even though the MIT-BIH Arrhythmia Database includes 48 ECG recordings, most of the P and T detection algorithms published by other researchers used few recordings or segments of these signals, as shown in Table  3. Literature citing P and T wave detection in the MIT-BIH Database have limitations, such as the ability only detect certain beats or segments. Perhaps the rationale behind this was that no annotation of P and T waves and there was thus no benchmark. Table  3  shows that most of the algorithms published in the literature were not applied to full recordings.
However, the proposed algorithm was able to successfully handle full recordings with high performance, when compared to recent and well-known publications in the field of study. Essentially, the proposed detection algorithm can combat non-stationary effects, low SNR, PACs, PVCs, LBBBs, and RBBBs, and is thus numerically efficient and has the ability to simultaneously detect P and T waves. The preliminary results are promising, especially after testing the algorithm on 10 recordings drawn from the MIT-BIH Database; however, applying the algorithm on the entire dataset is the next logical step to further test the robustness of the detector. Other arrhythmias such as atrial fibrillation, junctional tachycardia, paroxysmal supraventricular tachycardia atrial flutter, and multifocal atrial tachycardia are the next step in investigating the application of our algorithm.