dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of phosphorylated and total Pyk2 levels in human brain samples The brain samples were collected through a brain donation programme dedicated to neurodegenerative dementias coordinated by the NeuroCEB Brain Bank Network. The informed consent for post-mortem examination and research studies was signed by the legal representative of each patient in patient's name, as allowed by the French law and approved by the local ethics committee and the brain bank has been officially authorized to provide samples to scientists (agreement AC-2013-1887). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committees and with the 1964 Declaration of Helsinki. The brain banks fulfil criteria from the French Law on biological resources including informed consent, ethics review committee and data protection (article L1243-4 du Code de la Santepublique, August 2007).
, 2017). We evaluated potential changes in the expression of PTK2B in the brains of Alzheimer's disease patients compared to healthy individuals by taking advantage of three publicly available RNAseq datasets: Mayo Clinic data that probed the temporal cortex  (Allen et al. , 2016) ; ROSMAP data that probed the dorsolateral prefrontal cortex  (De Jager et al. , 2018) ; and the MSBB data that probed four different brain areas: BA 10, BA 22, BA 36 and BA 44  (Wang et al. , 2018). This allowed us to investigate potential gene expression changes in brain regions that are affected at different pathological stages of Alzheimer's disease  (Braak and Braak, 1991). We observed a decrease in PTK2B expression in Alzheimer's cases compared to healthy controls in all brain regions analysed (Supplementary Table  2 ); however, after multiple testing correction, this decrease was significant only in the BA 22 (16.
69% decrease; P adj ¼ 1. 99 Â 10 À02 ), BA 36 (28. 89% decrease; P adj ¼ 8. 49 Â 10 À05 ) and BA 44 (15. 18% decrease; P adj ¼ 3. 87 Â 10 À02 ) regions of the MSBB dataset, as well as in the ROSMAP dataset (28. 89% decrease; P adj ¼ 4. 57 Â 10 À03 ). Consistent with the RNAseq data, we observed a decreasing trend in Pyk2 total protein levels and an increasing trend in the p-Pyk2 protein levels in the brains of Alzheimer's patients compared to healthy controls (Supplementary Fig. 9 ). These changes resulted in a significant increase in the relative phosphorylation of Pyk2 in the hippocampus (4. 1-fold increase in the p-Pyk2:Pyk2 ratio; P ¼ 0. 0274; Wilcoxon rank-sum test; 17 cases versus 6 controls) and in the cortex (3. 6-fold increase in the p-Pyk2:Pyk2 ratio; P ¼ 0. 0274; Wilcoxon rank-sum test; 17 cases versus 7 controls), suggesting a compensatory mechanism.
Acknowledgements The authors thank the  BICeL platform of the Institut Biologie de Lille. The authors thank  Laurent Pradier  and  Philippe Bertrand  at  Sanofi  for fruitful discussions. The authors thank  Karine Blary  at the  IEMN Lille  for the microfabrication work. The authors thank the vectorology platform Transbiomed for lentivirus production. The authors thank  Charles Duyckaerts  and the  'NeuroCEB' Brain Bank (GIE Neuro-CEB   BB-0033-00011 ) for providing the brain tissue samples. The results published here are in whole or in part based on data obtained from the  AMP-AD Knowledge Portal  (https://adknowledgeportal. synapse. org/). These data were generated from post-mortem brain tissue collected through the  Mount Sinai VA Medical Center Brain Bank  and were provided by  Dr. Eric Schadt  from  Mount Sinai School of Medicine.
Time integration scheme Historically, fire has been developed for the md code imd  [32] , which implements a Leapfrog integrator for both dynamics and quenched-dynamics simulations. Thus, the published algorithm implicitly used Leapfrog, and the effect of this choice on fire has not been addressed yet. In the md code lammps  [9] , fire doesn't use the same md integrator that is used for regular dynamics (Velocity Verlet), but a dedicated integrator. In the current implementation  (12 Dec 2018)  this is the Explicit Euler method. Explicit Euler integration is not commonly used in classical md, where the requirement for energy conservation over long time periods suggests symplectic integrators  [30, 33]. To investigate the influence of the integrator, we implemented Euler Explicit (Algorithm 3), Euler Semi-implicit (Algorithm 4) and Velocity Verlet (Algorithm 6) methods.
Acknowledgments J. G is thankful for the financial support by the  German Research Foundation (DFG)  through the  priority program SPP 1594 "Topological Engineering of Ultra-Strong Glasses ". F. H acknowledges financial support by the  DFG  through projects  C3  (atomistic simulations) of  SFB/Transregio 103 (Single Crystal Superalloys ). Z. X. acknowledges financial support by the  German Science Foundation (DFG)  via the research training group GRK 1896 "In Situ Microscopy with Electrons, X-rays and Scanning Probes". W. N. acknowledges financial supports by the  European Union , within the starting grant ShapingRoughness ( 757343 ) and the advanced grant PreCoMet (339081). EB gratefully acknowledges the funding from  European Research Council (ERC)  through  the project "microKIc " (grant agreement No.
Introduction Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data. These successful methods construct a dendrogram -a tree structure -that enables a natural exploration of data which is very suitable even for non-expert users. Various tools offer intuitive top-down or bottom-up exploration strategies, zoom-in and zoom-out operations, etc. Let us consider the following real-life scenario: a social science researcher would like to understand the structure of specific scientific domains based on a large corpus of publications, such as dblp or Wiley. A contemporary approach is to construct a word embedding  [23]  of the key terms in publications, that is, to map terms into a high-dimensional space such that terms frequently used in the same context appear close together in this space (for the sake of simplicity, we omit interesting issues such as preprocessing, polysemy, etc.
20: return H Datasets: To partially see the scalability of our algorithm but also to avoid too long running times we had to limit the size of the datasets to few thousand vectors. To be able to compare the results, we run the tests on datasets of same size that we fixed to 1000 vectors. -The first dataset is composed of 1000 randomly generated 2-dimensional points. -To test the algorithm on real data and in our motivating scenario, the second dataset was created from the Wiley collection via their API 2. We extracted the titles and abstracts of the scientific papers and trained a word embedding model on the data of a given period of time by using the classical SGNS algorithm from Mikolov et al. , 2013  [22]  following the recommendation of  Levy et al. , 2015 [20]. We set the vocabulary size to only 1000 key words per year even though this dataset allows us to extract up to 50000 of them.
Conclusion and future work We propose an overlapping hierarchical clustering framework. We construct a quasi-dendrogram hierarchical structure to represent the clusters that is however not necessarily a tree (of specific shape) but a directed acyclic graph. In this way, at each level, we represent a set of possibly overlapping clusters. We experimentally evaluated our method using several datasets and also our new similarity measure that hence proved its usefulness. If the clusters present in the data show no overlaps, the obtained clusters are identical to the clusters we can compute using agglomerative clustering methods. In case of overlapping and nested clusters, however, our method results in a richer representation that can contain relevant information about the structure of the clusters of the underlying dataset.
However, it is not monitoring soil nutrients. Method Smart Fertilizer Software  [19]  describes crop monitoring based on pre-existing soil testing results but it is not performing in situ nutrient status monitoring. Implementation Farmer Pro [20] offers a fertilizer control system and prescription maps to increase yield. Other systems are based on satellite images for crop monitoring, but they do not actually monitor the soil conditions. The objective of the project, called SARMENTI, is to develop a multi-sensor, low power IoT secure node to provide decision support to farmers by monitoring in real-time and in situ soil nutrients and gaseous emission just above the ground over the whole crop lifecycle. More precisely, the SARMENTI node will measure soil macronutrients N, P and K in their ionic form of NO 3 , NH 4 , PO 4 and K thanks to a "soil probe" buried in the soil.
The soil probe will integrate electrochemical sensors, namely, nano-wire sensors together with potentiometric ISE sensors, taking advantage of measurement redundancy and specific property of each technology. A hygroscopic membrane will attract water from the soil, avoiding integration of a power-hungry active pump usually used to exact water from a soil sample. The SARMENTI node will also measure methane and ammonia emissions with Metal Oxide Semiconductor (MOS) gas sensors coupled with edge-AI techniques for gas recognition. From the sensing part, this paper will focus on presenting the details of the gaseous sensors. Sensors will be configured to measure on a daily basis over crop lifecycles and sent data to the cloud for further analysis, based on the dedicated platform that will be presented in Section 4.
The soil reservoir, or cupola, is split in two parts, each part allocates an AFE; this should protect against (or minimize) potential leakage current between the blocks of sensors. Both sub-reservoirs are fixed to a central column that conducts the AFE wiring up to the external flexible cable that links to the SDL. Each soil reservoir has holes in the bottom to allow releasing eventual water excess. Covering each AFE module there are two layers of foam to attract the humidity around and help keeping wet the sensor region. A textile network prevents the reservoir from being filled with soil material. The central column acts as support for the reservoirs and for the water sprinklers. Being open inside it also allocates the Smart Data Logger-Soil Probe cables. A water collector, placed farther from the Soil Probe soil surface, might be useful to collect rain water.
The release of water is dosed by an electronically controlled valve spraying along a configurable period of time before the measurement takes place. A liquid level detector is placed in the water collector to prevent from opening the valve if there is no water. A sprinkler is placed over each sensor module, so to throw sprayed water. The sprinkler surface would be open, for instance like a grid, so to allow passing through the water and fertilizers that will naturally arrive from rain and humanly programmed watering. Figure  4  shows an overview of an AFE module. Each front end contains the necessary analog circuits to adapt and filter the weak and noisy sensor signals. They also integrate a microcontroller to process and store the resulting values and to send these values to the Smart Data Logger on demand by means of a wired communication link that also provides the power supply.
Each soil reservoir has holes in the bottom to allow releasing eventual water excess. Covering each AFE module there are two layers of foam to attract the humidity around and help keeping wet the sensor region. A textile network prevents the reservoir from being filled with soil material. The central column acts as support for the reservoirs and for the water sprinklers. Being open inside it also allocates the Smart Data Logger-Soil Probe cables. A water collector, placed farther from the Soil Probe soil surface, might be useful to collect rain water. The release of water is dosed by an electronically controlled valve spraying along a configurable period of time before the measurement takes place. A liquid level detector is placed in the water collector to prevent from opening the valve if there is no water.
The enclosure embeds a holder where to plug/unplug the detachable and disposable sensor block. The sensor block is connected internally to the AFE main board by means of a watertight interface. The electronic board (or main board) is placed on an internal support screwed with columns. A watertight connector mounted on the PCB allows linking the AFE to the Smart Data Logger. Figure  5  shows the final drawing of the Potentiometric Sensors AFE mechanical file. Sensors 2020, 20, x FOR PEER REVIEW 7 of 29 The AFE module is watertight. The enclosure embeds a holder where to plug/unplug the detachable and disposable sensor block. The sensor block is connected internally to the AFE main board by means of a watertight interface. The electronic board (or main board) is placed on an internal support screwed with columns.
A watertight connector mounted on the PCB allows linking the AFE to the Smart Data Logger. Figure  5  shows the final drawing of the Potentiometric Sensors AFE mechanical file. The final module realized is quite big (160 × 160) mm 2. This is due to the type of connector required to link the sensor electrodes and the electronic board, to the type of connector to link the Smart Data Logger, and to the enclosure style to provide robustness and ingress protection.
On the other side of the probe, the UV sensor needs to be oriented in the appropriate direction. STMicroelectronics motion sensors such as LSM6DSL (STMicroelectronics, Catania, Italy), and compass SM303A (STMicroelectronics, Catania, Italy), are used for this purpose. Data coming from the motion sensors and from the compass allow to check the attitude of the Air Probe over time. In addition, the vibration of the pumps for cleaning the gas accumulation chamber is periodically verified by the low-cost accelerometer on board. This allows verifying that the pumps are working properly. The processing of attitude and pumps check is run on demand. The computation is done by the dedicated on-board microcontroller for running this kind of algorithms. Finally, Metal Oxide Semiconductor (MOS) gas sensors are suitable for many applications in different industries.
Smart Data Logger The Smart Data Logger (SDL) integrates all the features required to implement the application, namely, the acquisition and storage of the data generated by the Air Probe and the Soil Probe, the management of the rechargeable battery, its charger and a solar panel as harvester, a GPS as localizer, an accelerometer to estimate the attitude, and a simple user interface based on a button and three LEDs. Figure  8  shows the Smart Data Logger hardware block diagram. The wireless connectivity is implemented using two wireless communication technologies: a Bluetooth5 module enabling the link to a Mobile Device, and a LoRa module with which the Smart Data Logger becomes an endpoint, connecting to the Back-End Server through a LoRa Gateway. The solar cell is connected to a harvester that transfers the collected energy to a battery charger.
The battery has been dimensioned with enough capacity to supply the node with the harvested energy along periods with no or not enough sunshine. The selected type of battery pack embeds all the required safety protections and a fuel gauge based on Impedance Track TM (RRC power solutions, Germany, Homburg) delivering accurate Time-To-Empty & Remaining Capacity predictions over the lifetime, readable with an I2C interface. The manufacturer provides all the compliance information. A push button IC powered by the battery allows switching ON and OFF the node in a proper and controlled way. A physical push button managed by the user is the only control available in the node. A load switch controlled by the push button IC enables or disables the main supply line. From The wireless connectivity is implemented using two wireless communication technologies: a Bluetooth5 module enabling the link to a Mobile Device, and a LoRa module with which the Smart Data Logger becomes an endpoint, connecting to the Back-End Server through a LoRa Gateway.
The Smart Data Logger provides power supply and a UART to each one to communicate with it. In order to achieve isolation, each supply and communication port is linked to the AFEs by isolator ICs. In addition, the Soil Probe has been designed so to maximize isolation. A third pair power supply and UART is available so as to allow adding a third AFE, making possible to add a set of commercial sensors to be able to compare performances and to add other required ones like a commercial moisture sensor. There is one connector per AFE, all of different mode of insertion, to avoid erroneous connections. In any case the firmware is capable of recognizing the type of AFE connected and what its identity is. The same care is taken with other connected modules, sometimes by using other type of connector, sometimes by selecting connectors with different number of pins.
The Smart Data Logger integrates a valve driver and an input for the liquid level sensor, to implement the water collector. This module also has its own connector. A Serial NOR Flash memory has been embedded to store the measurements, the device and connected modules status, all timestamped. Three LEDs are available to provide basic status information on the module in stand-alone mode (with no link to a Smartphone or the Back-End Serve). Special care must be taken regarding their consumption, so better than being ON, they blink at a very low duty cycle. A Real Time Clock (RTC) available in the selected microcontroller and supplied by the battery is implemented to allow timestamping with up-to-date date/time provided by the Mobile Device during configuration. Two communication links are available and used according to the use case: a Bluetooth 5.
• The Application implements the use-case. Protection against potential Cyber Attacks is integrated on top of the Application layer. The firmware is developed using IAR Workbench (IAR Systems, Uppsala, Sweden). Figure  13  shows a view of the IDE and the Smart Data Logger layered firmware and part of the main routine. A testing tool named PCTool was developed in C# to verify most of the Smart Data Logger features and to execute the acceptance tests before integration to the rest of the system. It also acts as node simulator for interfacing the Back-End-Server. A testing tool named PCTool was developed in C# to verify most of the Smart Data Logger features and to execute the acceptance tests before integration to the rest of the system. It also acts as node simulator for interfacing the Back-End-Server.
Data Analytics Platform The Back-End Server Platform is based on Atos Codex Datalake Engine  [29]  (Atos, Paris, France), which is a fully integrated Cloudera solution (Cloudera, Santa Clara, CA, USA). It is the first Datalake appliance fully virtualized with OpenStack (OpenStack Foundation, Austin, TX, USA), that is certified by Cloudera. The appliance includes a comprehensive data management software stack, based on Cloudera Enterprise from Cloudera. The software stack represents a modern platform for machine learning and analytics optimized for the cloud and it leverages the Bull Sequana S200 server capabilities  [30]. It provides the means to collect data from Internet-connected devices. It acts as a smart data hub, managing analytics and routing data streams to their specified applications for effective service use.
System Integration, Verification and Validation Verification of each system component is performed under laboratory conditions, including the nutrient sensor calibration with agronomic soil tests and establishing gas sensor range of concentrations. Figure  22  shows PCTool main screen, use to perform functional testing. PCTool development was started by CSEM in the frame of previous EU projects as testing tool and features have been added for SARMENTI. PCTool is capable of directly communicate with each one of the sensor electronics, with the Air Probe, and with the Smart Data Logger, in order to verify each communication protocol and most of the functionalities. As an example, Figure  23  shows a screen to test one of the Soil Probe group of sensors. It allows configuring the internal circuit and to launch a test to measure the sensor values and to read the results.
MOX sensors are typically sensitive to many gasses but it is not possible to distinguish one target gas from others. The air probe approach is based on the MOX sensing material temperature modulation and data processing by a multi-layer perceptron (MLP) neural network. The neural network is formed by an input layer with same number of extracted features and output layer with a number of neurons equal to the target gasses to be identified. The number of neurons in the hidden layer is determined by iterations with the aim to minimize the classification error. Extracted pattern called gas fingerprint is formed by a single computed MOX resistance value in DC mode and multiple time discrete values of MOX resistances coming from micro hot plate temperature modulation. Temperature modulation is executed by sensor internal current level driving circuitry.
The MLP neural network is depicted in Figure  25. MOX resistance values filtered from DC component and its 1st harmonic are reported in y-axis, while the driving current level in least significant bit (LSB) is in x-axis. This combination represents the input dataset for the MLP neural network. The Air Probe embeds a co-processor based on STM32 H7 microcontroller that runs all the Air Probe processing routines by including the Artificial Intelligent algorithms for enhancing the gas sensor selectivity feature. MOX sensors are typically sensitive to many gasses but it is not possible to distinguish one target gas from others. The air probe approach is based on the MOX sensing material temperature modulation and data processing by a multi-layer perceptron (MLP) neural network. The neural network is formed by an input layer with same number of extracted features and output layer with a number of neurons equal to the target gasses to be identified.
The number of neurons in the hidden layer is determined by iterations with the aim to minimize the classification error. Extracted pattern called gas fingerprint is formed by a single computed MOX resistance value in DC mode and multiple time discrete values of MOX resistances coming from micro hot plate temperature modulation. Temperature modulation is executed by sensor internal current level driving circuitry. The MLP neural network is depicted in Figure  25. MOX resistance values filtered from DC component and its 1st harmonic are reported in y-axis, while the driving current level in least significant bit (LSB) is in x-axis. This combination represents the input dataset for the MLP neural network. The maximum value from all output neurons (last block in Figure  25 ) identifies the related gas type.
As example, in the next reported case, we have developed a specific multi-layer perceptron neural network for the Air probe with 51 neurons as input layer, 8 neurons as hidden layer and 8 neurons as output layer. Extracted features as input are the single computed gas concentration MOX resistance value in DC mode and 50 values of MOX resistances coming from micro hot plate temperature modulation by internal current level driving circuitry. MOX resistance values from DC component and its 1st harmonic represent the input dataset for the MLP. In each graph in Figure  26,  x-axis shows the current level in LSB, while y axis represents the MOX resistance value filtered from DC component and its 1st harmonic called f(R). The MLP has been trained with the dataset reported in Table  1  with the aim to recognize these gas fingerprints on field.