dataesr/scientific-paragraphs-categorization huggingface dataset:
Collection data and cleaning pipeline. We propose a cleaning pipeline to remove possible inconsistencies in collection data. Unlike species-specific approaches, many studies use large numbers of species, lacking correction because of the great number of references and specialists to be consulted  38. Collection data available in global datacentres, such as GBIF, cannot carry out thorough data-correction procedures, and the quality of the records has been debated and tested in some cases  39. Some records have no locality information, or coordinates are based on cities close to the observed distribution, and may contain duplicated data or zeros as information  [38] [39] [40]. We used a pipeline that cleans collection data by removing records with a lack of geographic information  38  , and we strongly recommend the use of analytical tools to correct inconsistencies present in global databases.
Methods Species. We focused our analysis on 227 hyperdominant Amazonian tree species. The hyperdominant species are the most common tree species in Amazon, and together make up half of all trees with a diameter (dbh) over 10 cm  24. We chose only hyperdominant species to reduce the emergence of too many 'false absences' when plot data are interpolated into abundance maps. They present the largest probability of occurrence in the plots where they are present in the surrounding area. Collections. Species collections were downloaded from GBIF (August 2017, www. gbif. org). We used data from the species' complete extent of occurrence to prevent deficiencies that are associated with SDMs based on a species' partial geographic range, such as under-prediction  42. All individuals were assigned to species level; intraspecific levels were ignored.
Collections cleaning pipeline. The cleaning pipeline consisted of a two-step process to remove inconsistencies from GBIF downloaded data (GBIF records). The first step consisted of removing all records with missing latitude, longitude or locality information (imprecise georeferences)  39  and all duplicates at 0. 5-degree spatial resolution  40. With the GeoClean function from speciesgeocodeR R Package  38  we also removed coordinates assigned to capital cities, coordinates with latitude equal to longitude, coordinates equal to exactly zero; coordinates based on centroids of provinces, and corrected country references (cleaned GBIF records). In the second step we used a kernel-density estimate function to remove spatial outliers from the cleaned GBIF data, assuming that these are misidentifications or incorrect coordinates not filtered by the step described above.
This function calculates a fixed-bandwidth kernel-density estimate of the point process density function that produced the point patterns  43  , using the density. ppp function from spatstat R Package  44  to generate a kernel-density estimate. Outliers were identified and removed based on the kernel-density values for each species coordinate, using a threshold based on a quantile function from stats R Package  45  (kernel-density estimate GBIF records). The quantile threshold was set according to the number of Amazonian regions in which a species occurred, six in total as defined by ter Steege et al. 24. The quantile threshold was larger for species with narrow distribution (occurring in one to three Amazonian regions) and smaller for species with wide distribution (occurring in more than three Amazonian regions).
As some hyperdominants are very widely distributed in Amazonia a larger quantile threshold cuts off too many occurrences, removing not only outliers, but also potential correct occurrence or entire occurrence clusters. Both steps reduced the number of species collection records (Appendix S4), and the predicted area of occupancy (Appendix S5). Plot abundance data. Abundance maps were constructed using 1675 1-ha tree inventory plots well distributed across Amazonia (defined as the tropical rain forest of the Amazon basin and the Guyana Shield) from the Amazon Tree Diversity Network (ATDN) (http://atdn. myspecies. info/). All individuals with ≥10 cm diameter at breast height (dbh) were recorded within the plots  24. Because a relatively small number of collections from these plots have been deposited in herbaria, they constitute a dataset nearly independent from the NHCs.
In addition, the 3-degree distance limit causes the model to predict the absence of a species when no occurrence plots are present within a radius of 3 degrees. This setting is based on the notion that within a non-environmental model a species' extent of occurrence is restricted by dispersal limitation only  46. The maximum dispersal distance has been optimized to a 3-degree distance by determining the best match between the IDW maps and the Fisher's Alpha diversity map of all species  47. Constructing presence-only SDMs using MaxEnt. We used MaxEnt version 3. 3. 3 k 1,48 , to construct presence-only SDMs for all the 227 species. Data of 19 environmental variables were downloaded from WorldClim 6. These included variables related to temperature and precipitation. Since collinearity, the non-independence of predictor variables, potentially leads to the wrong identification of relevant predictors for the model, we used the common Spearman's rank correlation coefficient threshold of |rho| >0.
Without a bias file, sampling bias could severely reduce models accuracy. We used the bias file to produce a background file according the efforts of collection. Finally we used a convex hull around cleaned occurrences (kernel-density estimate GBIF records) of each species to estimate their extent of occurrence sensu  35  , plus a buffer of 300 km, equal to the buffer set for the IDW analysis, to crop the area of predicted environmental suitability  29, 41. The latter is our predicted area of occupancy. Data analysis. We compared collection presences and absences to IDW relative abundance to answer our first question whether NHCs are independent drawn from the unknown probability distribution. A binomial generalized linear model (logit regression) was used to determine if a significant positive relationship existed between the probability of being collected and predicted local relative abundance.
Acknowledgements V. H. F. Gomes ,  H. ter Steege  and  R. P. Salomão  are supported by grant  407232/2013-3 -PVE -MEC/MCTI/ CAPES/CNPq/FAPs. We thank  Jésus Aguirre Guttierez ,  Edwin Pos  (and others) for constructive comments on the manuscript.
Metabolic and electrophysiological measures must remain within normal values to maintain the quality of life of chronic patients. Furthermore, depending on the age and disease stage of the individual, automatic identification of risk situations is critical for emergency support. To achieve these goals, this study proposes a technological solution termed Raspcare to help both the patients in their self-care and the medical teams monitoring the patients. The solution consists of a domestic gateway equipped with a microcontroller and various interfaces to allow interaction between the platform and household devices, such as televisions, biometric sensors, blood glucose metres, non-invasive pressure gauges, smartphones and smartwatches, among others. The gateway implements a Linux OS application responsible for executing the user's health plan, which involves periodic measurements, medications and dietary care.
[12] , the use of patient monitoring platforms involves several risks because their data are stored and therefore may be leaked. Alternatively, another type of failure may occur. Unauthorised data access was considered a high risk due to weak or predictable passwords; however, a biometric sensor can be used to minimise this problem. The study conducted by Santos et al. [13]  should be noted. The authors designed the CareBox, a hardware based on an NVIDIA ION mini-PC with an Atom 1. 6 GHz processor and 2 GB RAM, an operating system based on OpenELEC distribution and the Firefox browser. The programming was based on the Python programming language. The study focused on developing an interoperability, configuration and remote update solution. The interfaces available in the solution involve Ethernet network, Wi-Fi network and infrared, High-Definition Multimedia Interface (HDMI) and Universal Serial Bus (USB) ports.
Requirements of the diabetic and hypertensive patient home monitoring platform To monitor chronic patients at home, an indispensable device is the gateway, which is the base of the telemedicine platform. This device should act as a concentrator of the data generated by the e-health sensors, storing the data locally and synchronising them with a remote database for subsequent access by the medical team. Sensor data may or may not undergo local post-processing to generate alarms. For improved chronic patient adherence to the treatment, a patient care plan prepared by the health team should be synchronised with the patient electronic calendar to guide the daily treatment routine from medication and data acquisition times to dietary and physical activity suggestions. The architecture of the telemedicine platform with the above characteristics is presented in Figure  1.
• Television • Fall detection sensor The data collected by the blood glucose and pressure metres and the fall detection sensor are stored both locally on the gateway through the local monitoring software and remotely in the web application. The gateway connection with the television enables the patient to view the alarms and the history of measurements over time. The platform also implements two types of alarms: • Alarm from the Care Plan Calendar: the care plan is the treatment proposed by the physician for the patient. The plan includes dietary recommendations and the medication and measurements the patient should take throughout the day. • Alarm indicating an abnormal situation: two abnormal situations are detected by the monitoring software. The first situation refers to blood glucose and pressure values that are compared with the abnormal values set in the monitoring software.
Sensors The sensors used in the proposed system enable telemonitoring the patient health status (blood glucose and pressure values) and activity (falls). A biometric sensor is used to scan the patient's fingerprint to ensure secure user authentication when collecting the data. To monitor the health status, patients use portable blood glucose and pressure metres certified for medical use. These devices have a communication interface to share data. The blood glucose metre used was the OneTouch UltraMini  [16] , which has memory for 500 blood glucose test results, operating range of 20-600 mg/dl, and analysis method based on a glucose oxidase biosensor. The data from the blood glucose metre are transferred using an RS232-USB converter because the gateway has a USB port. The converter implements the manufacturer's protocol for accessing data stored in the device's memory.
The blood pressure metre used was the G-Tech RW-450, which measures the wrist blood pressure of the individual. This is the first blood pressure monitor available in the Brazilian market with the capacity to transmit measurements stored in the memory to a computer. To collect data, serial communication with the blood pressure metre was used according to the manufacturer's communication protocols  [17] , as the blood pressure metre has a USB interface cable for connection with the gateway. For activity monitoring and fall detection, accelerometre and gyroscope inertial sensors are used, which are available in most smartwatches in the market. In this study, the LG Urbane smartwatch was used. To increase data security and facilitate user authentication, a biometric sensor was used. The authentication procedure makes it possible to disable alarms and interact with the platform.
The biometric sensor used was the GT-511C1R. The biometric sensor used has a CPU ARM Cortex M3 Core, optical sensor with 14 × 12. 5 mm effective area, universal asynchronous receiver/transmitter (UART) communication interface operating at 3. 3 V and storage capacity for 20 fingerprints. The communication between this sensor and the gateway uses the sensor transistor-transistor logic (TTL) serial communication in addition to some resistors to change the sensor voltage (5 V) to the gateway voltage (3. 3 V), and the manufacture's communication protocols  [18]  were used to identify the user for data input.
Gateway The data concentrator or gateway is a device able to collect, process and communicate data on patient health and activities. The data collected by the sensors are processed and analysed in real-time through embedded algorithms (signal processing and machine learning) to identify risk situations and generate alerts sent to the cloud through the Internet. The cloud remotely stores and processes the data. The data may be accessed and monitored remotely through the user's personal devices, including televisions, smartphones, smartwatches, tablets and laptops. The Raspcare platform includes another gateway function. This is the patient care plan, which establishes the treatment routine, measurements, medications and dietary recommendations. To ensure that the patient adequately follows the care plan and that treatment is effective, the proposed alarm generation module generates alarms according to the Care Plan Calendar, which is implemented in the gateway and is synchronised with Google Calendar.
Therefore, both the gateway connected to the television and the smartphone or smartwatch receive the alarm notification in the form of a task scheduled in Google Calendar. The loading of the Care Plan Calendar is addressed in the next section. The hardware used to develop the platform was the Raspberry Pi 2 [19], a microcomputer with an embedded Linux operating system and Raspbian distribution, an adaptation from the Debian distribution developed by the manufacturer. This microcomputer has a 900 MHz ARM Cortex-A7 processor and 1 GB of RAM memory. Figure  2  presents the communication technologies used in each Raspcare platform device. A base was designed and developed through 3D printing to accommodate the gateway and its peripherals, as shown in Figure  3. A frontal view depicts the position of the Raspberry board, and a top-down view shows the space for the cables and connections of the sensors.
Application modules The Raspcare platform has three main application modules: client application, web application and fall detection application for smartwatch. As shown in Figure  4 , the applications communicate through the cloud. The fall detection application installed in the smartwatch sends the fall alert to the web application only through an HTTP request, which subsequently inserts the alert into the database. Similarly, the client application installed on the gateway connected to the Internet synchronises the blood glucose and pressure measurement data stored locally with the web application, which inserts them into the remote database. Each patient is identified through their unique ID in all operations between applications. The client application was developed in Python language [20] due to its large number of libraries, vast documentation and efficiency.
The interface was designed using the pyQt4 framework [21], and a MySQL database was used to store the data. The database modelled consists of seven tables, namely, three tables for the data generated by the blood glucose and pressure metres with the identification (Integer Regarding the identification and data manipulation criteria, ID columns with an auto-increment setting were used in all tables, and all types of data are recorded in MySQL Standard Edition. Libraries were also used to insert data into Google Calendar (Calendar API) such that by establishing a connection with the Internet, the client application will synchronise reminders in the Google Calendar of the user's personal account with prior user permission. Synchronisation with Google Calendar occurs when the Internet is available.
If the user account is not registered, the browser window opens, requesting the Gmail user login and password data, which are recorded in the database for future synchronisations. Thus, the user will receive notifications on their personal tablet, smartphone or smartwatch devices. For remote access to the data generated by the client application and by the fall detection application, the web application has a database that synchronises the data from the blood glucose and pressure measurements and the fall events detected, enabling the health team to monitor the patient remotely through a conventional webpage. The web application stores some configuration data in the database, including the login and password of the consultant physician, name and e-mail of the consultant physician and ID and name of each patient.
If the activity is a blood glucose or pressure measurement, prior patient biometric identification is required to allow the client application to collect the data. If the patient has an Internet connection, the client application sends the data to a remote server located in the cloud to allow the healthcare professional to process and analyse the data. The fall detection application WatchAlert  [14]  was integrated into the Raspcare platform. WatchAlert is executed in two devices, a smartwatch and a smartphone. Sensor data are collected in the smartwatch. Then, data are sent to the smartphone for processing. If a fall occurs, it is identified. Data on falls were integrated into the database of the web application for combined analysis with the health monitoring data. For WatchAlert configuration, the users must enter their personal data, such as address, e-mail and telephone number, as shown in Figure  6.
WatchAlert is turned on or off through the smartphone application. Data on user activities and health stored in the database may be analysed and processed using an analysis and procession module of the application. This module aims to extract risk information for the user and to interpret it as an alarm that is sent to the actors (healthcare professionals and family members) involved in the care of the user. In the present version of the Raspcare platform, only the use of thresholds was implemented. The thresholds are preset according to abnormal blood glucose and pressure values.
Interaction with the television Communication via television is a differentiating characteristic of the proposed technology because it benefits from the availability of a television set in virtually all Brazilian homes and is a form of entertainment for the elderly. A solution independent of the brand and the technology used in the television was sought, requiring only an HDMI communication port with HDMI-CEC protocol. The HDMI-CEC communication protocol enables communication and control between devices through an HDMI cable. Some hexadecimal commands can be sent to turn the television on or off or change the channel. To use the technology, the library libCEC available for the Linux operating system and an HDMI cable with CEC protocol support were used. Some commands used to control the television are outlined in Table  1.
Generation of alarms The Raspcare platform consists of modules that automatically analyse the signals generated by the devices. The result of this analysis may produce two types of alarms: • Abnormal blood glucose and pressure measurements: every time the gateway client application detects an abnormality after measuring blood glucose and pressure values (comparing  the measured value with a preset value in the application), an alarm is triggered, and three messages are generated. One of them is sent to the television, and a window opens that indicates the reason for the alarm (see Figure  13 ). Another message is sent to the web application, indicating the type of alarm and the monitored patient. Finally, a third message is generated by the web application that sends an e-mail to the consultant physician (see Figure  14 ).
The study suggests that the starting point for using this technology is the consultant physician, who prepares and loads the patient care plan to the gateway that will be installed in the patient's home. The availability of wireless networks does not prevent the gateway from alerting the patient about tasks and collecting the data from the blood glucose and pressure metres. User authentication is crucial for data security, which was achieved by a practical and secure solution based on a fingerprint sensor. Conversely, integration with smartphones and wearable devices, such as smartwatches, was also implemented because these options enable the user to move while maintaining the support provided by the gateway in terms of task alerts and adding fall detection and logging functions. The selection of devices is used to monitor chronic patients with hypertension and diabetes.
Other chronic diseases are also indicated for monitoring but are outside the scope of this study. The present study was limited to discussing the technology that helps with chronic patient self-care and the problem of one user per consultant physician. The issues inherent to the monitoring of several concurrent users by the same team are outside the scope of this study and will be addressed in future studies. A pilot project aiming at testing the proposed technology is expected in the near future. At least 10 chronic patients with diabetes and hypertension above 70 years old and with a high risk of fall will be selected to test such technology. Internet access at home will be required. After obtained ethical approval and the signed informed consent of the patient, patient will be monitored for at least 6 months, and sensor data will be recorded.
Acknowledgements The study was funded by the  National Council for Scientific and Technological Development  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) )  Universal Call ,  Process 487098/2013-7 ;  Coordination for the Improvement of Higher Education (Coordenação de Aperfeicoamento de Pessoal de Nível Superior (CAPES) );  Science and Information Technology and Communication in South America (STIC AmSud) Call ; and  Espírito Santo Research Support Foundation (Fundação de Amparo à Pesquisa e Inovação do Espírito Santo (FAPES))  Grant  TO 041/2016.
1b ). Differential gene expression analysis of stimulated compared to unstimulated adipocytes showed 2,160 differentially expressed genes (DEGs) that were significantly up-regulated  (1, 239)  or down-regulated (921) (Fig. 1c ). Among the up-regulated genes we found CREM  [34]  (1. 5 log 2 FC) and PPARGC1A  [35]  (2. 5 log 2 FC), both known to be up-regulated in response to NE, thereby validating our experimental approach. In order to ascertain which pathways were acutely stimulated in white adipocytes upon NE treatment, we performed a pathway enrichment analysis using the Reactome database. Up-regulated genes were enriched for circadian cycle and gene-related signaling pathways (Fig. 1d ), primarily nerve growth factor (NGF), platelet-derived growth factor (PDGF), VEGF, and WNT/Beta-catenin.
TFs with high, normalized enrichment scores (NES ≥ 3) were identified in each network, corresponding to an estimated false discovery rate of less than 0. 01, which we used as threshold for our dataset. In the transcriptional regulatory network of up-regulated TFs we found a total of 147 overrepresented TFs, 60 of them with differentially expressed targets, comprising 10 MRs and 50 LRs (Additional file 1: Table  S2 ). In the regulatory network of down-regulated TFs, we found a total of 143 TFs, 81 of them with differentially expressed targets, comprising four MRs and 77 LRs (Additional file 1: Table  S3 ). Due to the fact that IEGs do not require de novo protein synthesis for their expression, that they acutely respond to NE stimulation, and that they are TFs  [22] , we assume that our MRs may be IEGs.