dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to express our gratitude to all the small customary chefs from the following tribes:  Galagaoui, Pagu, Paop, Wanap I and II, (Koumac district, customary area: Hoot Ma Waap), Balagam, Parawa ,  Saint Jean-Baptiste ,  Saint Joseph ,  Saint Michel ,  Saint Pierre ,  Saint Thimothé ,  Sainte Anne  (Bondé district, customary area: Hoot Ma Waap), Gööpä (Muhéo district, customary area:  Paici Camuki) Néxöwaa, Näwéélé (Pondah district ) as well as  Isabelle Frou  and  Micheline Roépingi. We would finally like to thank  Baptiste Jacquet  for proof-reading the paper and  Jean-Louis Stilgenbauer  for his careful review of the first draft of the document.
Acknowledgements This work is supported by the  Singapore National Research Foundation  through its  Competitive Research Program  ( CRP Award  No. NRF-CRP22-2019-0004  and  Quantum engineering program ) and  Singapore Ministry of Education  ( MOE2016-T3-1-006  (S)). B. Y. acknowledges the financial support by the  European Research Council  ( ERC Consolidator Grant  " NonlinearTopo ," No. 815869 ) and the  ISF -Personal Research Grant  (No. 2932/21 ). Work at  Chongqing University  was financially supported by the  National Natural Science Foundation of China  (Grants No. 12004056 , No. 52071041 ). K. W. and T. T. acknowledge support from the  JSPS KAKENHI  (Grant Numbers  19H05790 ,  20H00354  and  21H05233 ).
In today's scenario, when cancer cases are increasing rapidly, anticancer herbal compounds become imperative. Studies on the molecular mechanisms of action of polyphenols published in specialized databases such as Web of Science, Pubmed/Medline, Google Scholar, and Science Direct were used as sources of information for this review. Natural polyphenols provide established efficacy against chemically induced tumor growth with fewer side effects. They can sensitize cells to various therapies and increase the effectiveness of biotherapy. Further pharmacological translational research and clinical trials are needed to evaluate theirs in vivo efficacy, possible side effects and toxicity. Polyphenols can be used to design a potential treatment in conjunction with existing cancer drug regimens such as chemotherapy and radiotherapy.
Methodology In the current comprehensive review, the anticancer properties of the polyphenols presented according to their molecular mechanisms of action were analyzed. To compile this study, the papers published in the following specialized databases Web of Science, Pubmed/Medline, Google Scholar, Science Direct were studied. The following MeSH terms were used for the search: "Antineoplastic Agents/pharmacology", "Antineoplastic Agents/therapeutic use", "Apoptosis/drug effects", "Cell Proliferation/drug effects", "Chemoprevention", "Cell Line, Tumor", "Disease Models, Animal", "Diet", "Neoplasms/drug therapy", "Neoplasms/prevention and control", "Polyphenols/pharmacology", "Polyphenols/ therapeutic use". In vitro and in vivo experimental pharmacological studies analyzed were processed to select the best data on the antitumor, anticancer, signaling and molecular anticancer mechanisms of polyphenols.
Cardiac auscultation is one of the most costeffective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i. e. , only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e. g. , cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset.
Moreover, the emergence of new imaging techniques and the significant decrease of valve diseases in developed countries reduced the clinical application of cardiac auscultation  [11]. As a result, auscultation has been recently neglected, and a great reduction of this clinical skill has been observed over the last decades  [10]. In underprivileged countries, the scenario is similar, with a lack of trained professionals with these skills and the resources to train them. This motivates the use of computer-aided decision systems based on auscultation to accelerate the timely diagnosis and referral of patients to specialized treatment centers  [12]. A fundamental step in developing computer-aided decision systems for CVD screening from cardiac auscultation consists in collecting large annotated datasets of heart sounds that can properly represent and characterize murmurs and anomalies in patients with different CVDs.
The need for such datasets is specifically crucial when considering the design of modern data-hungry machine learning techniques. Currently available annotated public PCG datasets have limited scope such that they either provide limited information regarding a general evaluation of the heart sound (normal vs. abnormal)  [13] , or the presence/absence of abnormal sounds (e. g. , murmurs, clicks, extra sounds)  [14]. Few attempts have been devoted to providing large public datasets with richer characterizations of heart sounds (see e. g. ,  [15] ). However, in these cases, heart sounds are only classified based on the underlying cardiac conditions without providing a full characterization of the specific sound signatures encountered in each recording. The hereby presented dataset aims to address these limitations, thus providing the necessary means to develop novel computer-assisted decision systems, which are able to offer a rich characterization of heart sound anomalies.
This is accomplished by collecting a large set of sounds and characterizing them using the same scales and parameters commonly used in clinical practice. The presented dataset was collected during two independent cardiac screening campaigns in the Pernambuco state, Brazil. These campaigns were organized to screen a large pediatric population in Northeast Brazil and to provide the basis for the further development of a telemedicine network  [16] ,  [17]. The gathered dataset includes the characterization of each murmur in the recorded heart sounds from different perspectives, including timing, pitch, grading, shape and quality. Moreover, multi-auscultation location recordings are provided for each patient, alongside with the corresponding annotations, which are helpful for inferring the specific source of each murmur  [18].
III. AVAILABLE HEART SOUND DATASETS In this section, we briefly present heart sound datasets that are open to the scientific community. We consider datasets that satisfy the following criteria: a) full availability of data for scrutiny; b) online accessibility; c) relevance to the current study; d) include relevant information regarding the study population (e. g. , size, age, and/or gender); and e) include relevant information regarding the audio recordings (e. g. , number, duration, or collection spots). With these criteria, we selected six datasets that will form the basis of our analysis. A summary of the selected datasets are presented in Table  I. Furthermore, some features from the proposed dataset are also presented in last row of Table  I , for comparison reasons. From this table, it is observed that the existing heart sound datasets have significantly different sampling frequencies.
While the informative spectra of the PCG is below 1 kHz (with its power spectrum mainly concentrated below 500 Hz), the diversity in the sampling frequency of existing datasets is mainly due to the audio devices and computer-based sound cards used for PCG digitization. In fact, since the heart sounds are in the audio frequency range, many research teams have preferred to use high-fidelity audio sound cards, which sample audio signals at standard rates such as 4 kHz, 8 kHz, 11025 Hz, 22050 Hz, 44100 Hz, 48 kHz, etc. In fact, none of these rates are specific to the heart sound, but for historical reasons, they are audio rates supported by standard commercialized audio devices and software  [26, Sec. 4. 5]. However, from the signal processing and machine learning perspective, oversampling way above the Nyquist rate (twice the maximum frequency of the desired signal) does not provide additional information regarding the signal and would only increase the audio length and processing load.
Based on this fact, with an appropriate anti-aliasing analog filter that avoids "spectral folding" effects  [27] , a sampling frequency of 2 kHz to 4 kHz is fully adequate for all human-and machine-based heart sound diagnosis. A. Pascal Challenge Database  [14]  This database comprises of two datasets. Dataset A was collected from an unreported size population, through a smartphone application. Dataset B was gathered using a digital stethoscope system deployed in the Maternal and Fetal Cardiology Unit at Real Hospital Português (RHP) in Recife, Brazil. It contains a total of 656 heart sound recordings from an unknown number of patients. The duration of the PCG signals range from 1 to 30 seconds and were collected at a sampling rate of 4000 Hz. The sounds were obtained from the apex point of volunteer subjects in the Dataset A.
In the Dataset B, sounds were collected from four cardiac auscultation locations on healthy and unhealthy children. Up to our knowledge, no additional data regarding the auscultation location is provided. The sounds were recorded in clinical and non-clinical environments, and divided into normal, murmur, extra heart sound, and artifact classes in Dataset A. In Dataset B, the sounds were divided into normal, murmur, and extra systole classes. Furthermore, in Dataset B, the positions of fundamental heart sounds were manually annotated by clinicians. B. PhysioNet/CinC Challenge 2016 Database  [28]  This database merges nine independent databases: the Massachusetts Institute of Technology heart sounds database (MITHSDB), the Aalborg University heart sounds database (AADHSDB), the Aristotle University of Thessaloniki heart sounds database (AUTHHSDB), the Khajeh Nasir Toosi University of Technology heart sounds database (TUTHSDB), the University of Haute Alsace heart sounds database (UHAHSDB), the Dalian University of Technology heart sounds database (DLUTHSDB), the Shiraz University adult heart sounds database (SUAHSDB), the Skejby Sygehus Hospital heart sounds database (SSHHSDB), and the Shiraz University fetal heart sounds database (SUFHSDB).
As part of the PhysioNet/CinC 2016 Challenge, the data has been divided into training and testing sets, and it contains a total of 2435 heart sound records from 1297 patients. The duration of the PCG signals ranges from 8 to 312. 5 seconds. Since the data was collected using different devices with different sampling rates, each PCG signal has been downsampled to 2000 Hz  [13]   1. The sounds were obtained from four different auscultation locations (aortic, pulmonary, tricuspid, and mitral) on both healthy (normal) and pathological (abnormal) subjects, with a variety of diseases including heart valve diseases and coronary artery diseases. The training and test sets are unbalanced, with the number of normal records being greater than abnormal records. The sounds were recorded at clinical and non-clinical environments, and divided into normal, abnormal and unsure classes.
C. HSCT-11 (2016) [29] Spadaccini et al. published an open access database on heart sounds. The dataset was designed to measure the performance of biometric systems, based on auscultation. This dataset is composed of 206 patients and a total of 412 heart sounds, collected from four auscultation locations (mitral, pulmonary, aortic, tricuspid). The data was recorded using the ThinkLabs Rhythm digital electronic stethoscope, with a sampling rate of 11025 Hz and a resolution of 16 bits per sample. No information is provided regarding the health condition of each subject. [34]  technology. The sounds were recorded at 4 kHz from the Mitral point. Finally, two cardiac physiologists manually annotated the beginning and ending of each fundamental heart sound.
E. Fetal PCG Dataset [31] Cesarelli et al. collected and published 26 fetal heart sounds from different pregnant women, over the last months of their singleton physiological pregnancies. The patients were healthy and aged between 25 to 35 years. The sounds were recorded at 333 Hz with 8 bits resolution. [32] ,  [35]  The electro-phono-cardiogram (EPHNOGRAM) project focused on the development of low-cost and low-power devices for recording sample-wise simultaneous electrocardiogram (ECG) and PCG data. The database, has 69 records acquired from 24 healthy young adults aged between 23 and 29 (average: 25. 4±1. 9) in 30 minute stress-test sessions during resting, walking, running and biking conditions (using indoor fitness center equipment). The synchronous ECG and PCG channels have been sampled at 8 kHz with a resolution of 12 bits (with 10.
5 effective number of bits). In some records, the environmental audio noises have also been recorded through additional auxiliary audio channels, which can be used to enhance the main PCG channel signal quality through signal processing. This data is useful for simultaneous multi-modal analysis of the ECG and PCG, as it provides interesting insights about the inter-relationship between the mechanical and electrical mechanisms of the heart, under rest and physical activity. No manual segmentation has been made, but a MATLAB code is provided that automatically and accurately detect all the R-peaks from the ECG. Such peaks can be used to locate the S1 and S2 components of the PCG.
IV. METHODS The presented dataset was collected as part of two mass screening campaigns, referred to as "Caravana do Coração" (Caravan of the Heart) campaigns, conducted in the state of Paraíba, Brazil between July and August 2014 (CC2014) and June and July 2015 (CC2015), see Figures  3  and 4. The data collection was approved by the 5192-Complexo Hospitalar HUOC/PROCAPE institution review board, under Real Hospital Portugues de Beneficiencia em Pernambuco request. The CC2014 and CC2015 screening campaigns were promoted by a non-governmental organization (NGO) from Pernambuco, "Círculo do Coração" (Heart Circle -CirCor) and funded by the Health Secretary of the neighbouring State, Paraíba. These caravans occurred during a seven-year partnership program, which established the Pediatric Cardiology Network, an integral line of care -from screening to surgery and follow-up -for under-served children with heart diseases in Paraíba.
The collected dataset is provided online on PhysioNet  [18]. Note that, in order to manage the organization of public data challenges on murmur grading and classification, only 70% of the gathered dataset was publicly released in the PhysioNet repository (randomly selected through stratified random sampling). The remaining 30%, which is currently held as a private repository for test purposes (as unseen data), it will be released on the same PhysioNet repository after the corresponding data challenge. The procedure of data gathering and the properties of the dataset are detailed in the following sections.
A. The Caravana do Coração Campaigns The studied population included participants who volunteered for screening within the study period. Patients younger than 21 years of age with a parental signed consent form (where appropriate) were included. A total of 2,061 participants attended the 2014 and 2015 campaigns, with 493 participants being excluded for not meeting the eligibility criteria. Furthermore, 116 patients attended both screening campaign. In our database, these patients are identified and highlighted. All participants completed a socio-demographic questionnaire and subsequently underwent clinical examination (anamnesis and physical examination), nursing assessment (physiological measurements), and cardiac investigation (chest radiography, electrocardiogram, and echocardiogram). Data quality assessment was performed and all entries were screened for incorrectly entered or measured values, inconsistent data, or for the presence of outliers, and omitted in case of any inconsistency.
The resulting entries were then compiled in a spreadsheet to reflect the socio-demographic and clinical variables used in our dataset. Subsequently, an electronic auscultation was performed and audio samples from four typical auscultation spots were collected; all samples were collected by the same operator for the duration of the screening, in a real clinical setting. Two independent cardiac physiologists individually assessed the resulting PCG audio files for signal quality. As a result, 119 participants did not meet the required signal quality standards. In another words, the patient recordings do not allow a safe and trustworthy murmur characterization and description, see Table  V.
B. Demographic and Clinical Information The collected dataset includes 1568 participants, 787 (50. 2%) were male and 781 (49. 8%) were female. Regarding age categories, following the National Institute of Child Health and Human Development (NICHD) pediatric terminology  [36] , 988 are children (63. 0%), 311 infants (19. 80%), 127 adolescents (8. 1%), 9 young adults (0. 6%), 11 neonates (0. 7%) and 110 pregnant women (8. 1%); in 12 patients no age data is provided (0. 8%). Finally, with regards to ethnicity, 1298 participants are mixed race (82. 8%), 249 (15. 9%) white, and 1. 4% of other ethnic backgrounds. Table  II  summarizes the population demography. The mean age (± standard deviation) of the participants is 73. 4 ± 0. 1 months, ranging from 0. 1 to 356. 1 months, as summarized in Table  III.
C. Heart Sounds and Annotations The collected dataset includes a total number of 215780 heart sounds, 103853 heart sounds (51 945 S1 and 51 908 S2 waves) from CC2014 and 111927 (56449 S1 and 55478 S2 waves) from the CC2015. In the CC2014 screening campaign, 540 recordings were collected from the Aortic point, 497 from the Pulmonary point, 603 from the Mitral point, 461 from the Tricuspid point and 5 from an unreported point. Between 1 to 10 records have been recorded per patient, with an average of 3. 2 recordings per patient. In the CC2015 screening campaign, 817 recordings were collected from the Aortic point, 793 from the Pulmonary point, 812 from the Mitral point, 754 from the Tricuspid point, and one extra sound from an unreported point. Overall, between 1 to 4 records exist per patient, with an average of 3.
5 recordings per patient. The heart sound signals were collected using a Littmann 3200 stethoscope embedded with the DigiScope Collector  [34]  technology, an illustration is presented in Figure  5. A diagram of the graphical user interface is presented in Figure  6. The signal was sampled at 4KHz and with a 16-bits resolution. Furthermore. the heart sound signals are normalized within the [-1, 1] range. The PCG files from the CC2014 and CC2015 campaigns, had an average duration of 28. 7 seconds and 19. 0 seconds, respectively. A Mann-Whitney U hypothesis test on the signal duration distributions was performed, a p-value 0. 001 was observed. For more details refer to Figure  7. Murmurs were present in 305 patients within the collected dataset. Out of these, 294 patients had only a systolic murmur, 1 patient had only a diastolic murmur, and 9 patients had both systolic and diastolic murmurs, as summarized in Table  V.
V. DATA LABELLING The acquired audio samples were automatically segmented using the three algorithms proposed by  [33] ,  [30]  and  [38]. These algorithms detect and identify the fundamental heart sounds (S1 and S2 sounds) and their corresponding boundaries. As a result, a set of annotation recommendations for each heart sound signal were provided to the two cardiac physiologists. The two cardiac physiologists independently inspected the resulting algorithms' output on mutually exclusive data. In other words, they did not over-read each other's annotations. Nevertheless, experts were free to use or neglect the generated recommendations. The expert first inspected the automatic segmentation annotations and re-annotated the misdetections. For each heart sound signal, the expert started by randomly selecting one of the corresponding annotation recommendation, and one of two actions follows: • In case of agreement with the algorithm's segmentation output, the audio file and its corresponding annotation file were included in the dataset.
The audio and the corresponding segmentation annotation file names are in ABCDE_XY. wav format, where ABCDE is a numeric patient identifier and XY is one of the following codes, corresponding to the auscultation location where the PCG was collected on the body surface: PV corresponds to the Pulmonary point; TV corresponds to the Tricuspid  point; AV corresponds to the Aortic point, MV corresponds to the Mitral point, and finally PhC for any other auscultation location. If more than one recording exists per auscultation location, an integer index proceeds the auscultation location code in the file name, i. e, ABCDE_nXY. wav, where n is an integer. Furthermore, each audio file has its own corresponding annotation segmentation file. The segmentation annotation file is composed by three distinct columns: the first column corresponds to the time instance, where the wave was detected for the first time; the second column correspond to the time instance, where the wave was detected for the last time; the third column correspond to an identifier, that uniquely identifies the detected wave.
In such cases, murmurs were classified by default as grade I/VI. Moreover, the cases classified as grade III/VI, actually include murmurs that could potentially be of grade III/VI or higher, since discrimination among grades III/VI, IV/VI, V/VI, and VI/VI are associated with palpable murmurs, also known as a thrills  [40] , which can only be assessed via physical in-person examination. A cardiopulmonologist manually classified and characterized murmur events blindly, and independent of other clinical notes. The sounds were recorded in an ambulatory environment. Different noisy sources have been observed in our dataset, from the stethoscope rubbing noise to a crying or laughing sound in the background. Thus, the automatic analysis of CirCor DigiScope dataset is indeed a hard task. On the other hand, the proposed dataset is a representative sample of real case environments where CAD systems must operate.
VI. DISCUSSION Given the large number of participants from the CC2014 and CC2015 screening campaigns, the CirCor DigiScope dataset is a representative sample of the rural and urban populations in Northeast Brazil, Paraíba. The dataset focuses on a pediatric population, 63% of the patients were children, 20% of the patients were infants. Furthermore, and due to the challenges associated with the care of individuals with complex CHD, exceptionally, a few young adults that voluntarily asked to participate in the screening campaigns were also examined. Overall, the screened population demonstrates a generally good clinical condition, with most clinical and physiological parameters within the normal range for the age. Despite the generally good clinical condition of the participants, a variety of congenital and acquired diseases have been found.