dataesr/scientific-paragraphs-categorization huggingface dataset:
Intelligent Wireless Sensor System Intervention Prior to the intervention, the IWSS study protocol and the management of alerts were explained to the referent CHNs. They participated in a two-hour training session on how to use the IWSS software. Thirty-four home-dwelling older adults in the Experimental Group [EG] had their IWSS installed within 72 hours of their allocation  [18]. Prior to a three-month RCT with the IWSS, a two-week assessment period evaluated the participating older adult's patterns of behavior. Individual alert thresholds for use during the RCT were chosen through discussions between the older adult (if possible), the home healthcare center, CHNs and informal caregivers. The IWSS intervention continuously recorded home-dwelling older adults' movements and changes in activity in strategic places in their living space: living room, bedroom, toilet, time spent in bed, and the number of times the refrigerator was opened.
Assessment of Acceptability Among CHNs Data about acceptability were collected throughout the study. These came from personal notes written by the principal investigator and study nurse, and the answers from the questionnaires distributed to the referent CHNs after their experience of using the IWSS in daily practice with their patients. In the absence of a validated questionnaire on the acceptability of the IWSS, a questionnaire was developed based on the scientific publications by Davis  [17] , Venkatesh et al. [20] , and Peek et al. [21]. It assessed the usefulness, ease of use, and perceptions of the use of the IWSS technology, using a 5-point Likert scale scoring from 0 "unacceptable/completely unsatisfactory" to 4 "extremely acceptable/very satisfactory". Two open-ended questions, based on the critical incident technique developed by Flanagan  [22, 23] , allowed the CHNs to express their opinions and describe their positive and negative experiences of the IWSS.
Data Analysis The acceptability of using the IWSS, perceptions of the relevance or usefulness of the alerts that it generated, and its ease of use were all analyzed via returned questionnaires and the data collected from the transcripts of the audiorecordings of CHN focus groups. To obtain a clear idea of end-users' satisfaction with the IWSS, the five-point Likert scale questionnaire answers were dichotomized: scores of 3 or 4 were recoded as "satisfied"; scores of 0 to 2 were recoded as "dissatisfied". Satisfaction rates and the alerts generated by the IWSS were analyzed and descriptive statistics were performed using version 22 of the IBM-Statistical Package for Social Sciences (IBM-SPSS ® )  [24]. A statistical significance level was established at p = 0. 05, and all tests were two-tailed.
Acceptability of the IWSS Interventions Table  1  presents a summary of the CHNs perceived rates of satisfaction taken from the acceptability questionnaire. In general, acceptability rates among CHNs were moderate to low. Perceived ease of use for the IWSS received the lowest rate of satisfaction, whereas the understandability of the IWSS software scored highest. Credibility -Ease of use -Safety -Was the IWSS interface easy to use? -Was the IWSS a reliable and appropriate one with which to ensure the safety of home-dwelling older patients? -Were the alert messages useful for adapting your patients' care? -Was the IWSS better than you expected? -Was the IWSS easy to integrate with other technologies used in your daily practice? -The IWSS did not overload daily planned care and nursing activities? -Did the IWSS provide you with useful information that is otherwise inaccessible? 29% (n = 5) 18% (n = 3) 0% (n = 0)* 0% (n = 0)* 6% (n = 1) 6% (n = 1) 6% (n = 1) Information.
Information and Training The majority of the CHNs claimed not to be familiar with the IWSS technology. All of them perceived a lack of information and training about the IWSS intervention protocol, which had an impact on the system's PEOU. The CHNs reported a need for training in the use of this technology, to prepare for its use in daily clinical practice. Despite the explanations and support offered by the Datacenter, CHNs were not clear about how to use this IWSS technology, including how to set the alert indicators and use the system's data. They mentioned a lack of support for the use of the system in complex clinical situations. The following transcript illustrates this: "First, we were coached on how to get going. Some people came to explain things: it's true, the alerts or rather the whole system isn't easy to understand if you're not using it all the time.
The Interface and the IWSS Proof of Concept CHNs found that ease of use was negatively influenced by an imprecise software interface, especially for setting alert indicators. This resulted in under-or over-evaluations of older patients' risks of a deteriorating health status, causing either irrelevant alerts or an absence of alerts in true situations of significantly deteriorating health status. The IWSS technology failed to detect two falls, probably because of a lack of suitably positioned sensors in very old houses with multiples small rooms. This led to questions about the adaptability of the IWSS system's proof of concept in different housing. Finally, the CHNs did mention that the ability to receive IWSS alert messages on mobile phones or tablets was beneficial. However, poor mobile telephone connections in mountainous rural areas were a major barrier to both the system's ease of use and usefulness, and to responding to emergencies if necessary.
Additionally, recent studies have documented the effectiveness of innovative e-health and Nanotechnology, Biotechnology, Information technology and Cognitive science in the management of chronic diseases like heart failure or type II diabetes in older patients  [30, 31]. There is significant potential for combining IWSS and e-health technologies to support patients with chronic diseases in both community and primary care settings, but the potential downsides of adopting new technologies should not be forgotten, and nor should patient-identified needs and concerns. Our findings highlighted that dealing with alert indicators was problematic for nurses, due to a lack of information about proof of concept and using the IWSS software. This corroborated recent research findings about using information and communication technologies to take care of older adults with dementia, which suggested that healthcare providers should be directly involved in the conceptualization of innovative technology as well as developing its usefulness and ease of use  [32].
Study Limitations This study had various limitations. The first concerns its selective exploration of two components of the acceptability concept. Another limit was the use of non-validated and auto-constructed, non-exhaustive questionnaire exploring satisfaction and acceptability. In addition to the relatively small sample, it seems important to mention that data were collected via a single district home healthcare center, as this raises questions as to the transferability of our findings: extending them to other technologies or regions should be done with great caution. Finally, our small study population of CHNs was particularly unfamiliar with innovative technologies, thus transferring and generalizing our findings to other CHNs is impossible.
B. Optimization Methodology The heart of the whole algorithm is the optimization engine; our optimization algorithm is developed in MATLAB and implemented in the MOGA optimization tool MATLAB. GA is responsible for the exploration of the solution space in the search for optimal solutions. In general, the best individuals in a population tend to reproduce. At the beginning we generate the individual randomly n times (n represents the population size). The individual is made up of binary code string encoding a particular sized Op-Amp fitness of every individual can be got, and then the GA can be used to choose the better individuals as the parents of the next generation. After crossing and mutating, the new generation is produced. Performing the above works iteratively the goal will be achieved in the end (Fig.
6 )  [5]. In the program elaborated with MATLAB toolbox, every individual is presented by a binary code string. Fig. 3  shows that telescopic Op-Amp contains 9 CMOS transistors and biasing current (I bias ). As a total there are 11 parameters to be adjusted and each gene of the chromosome stands for one parameter. Thus, the parameter vector is compressed to  [9] : [W 1 , L 1 , W 3 , L 3 , W 5 , L 5 , W 7 , L 7 , W 9 , L 9 , I bias ]. The size of the transistors M 2 , M 4 , M 6 , and M 8 are equal to M 1 , M 3 , M 5 , and M 7 , respectively. The goal of multi-objective optimization is the finding a vector x of decision variables satisfying constraints to give acceptable values to all objective functions fi(x). In this MOGA, six performances are considered: DC gain, unity-gain band width, phase margin, power, circuit area, and slew rate.
VII. CONCLUSION This work has demonstrated the utility of an evolutionary algorithm for automating electronic design using algorithms called multi-objective genetic algorithms, which have the ability to deal with a problem of multi-objective optimization with two or more goals and taking account also the constraints. A program based on MOGAs has been developed to optimize the performances of a telescopic operational amplifier by determining its CMOS transistors sizes in order to design an optimized 8-bit 40-MS/s low power Multiplying Digital-to-Analog Converter (MDAC) for a pipelined ADC. The optimization procedure was implemented using MATLAB optimization toolbox, and the circuit simulation results were obtained from Spectre. The Op-Amp was designed in AMS 0. 18µm CMOS technology. Monte Carlo analysis of the optimized telescopic Op-Amp shows that DC-gain, unit gain bandwidth, and phase margin prove the effectiveness of the approach in the MDAC design where the design space is too complicated to be done with the classical methods within a short time.
Knowing the position of protein structures within the membrane is crucial for fundamental and applied research in the field of molecular biology. Only few web resources propose coordinate files of oriented transmembrane proteins, and these exclude predicted structures, although they represent the largest part of the available models. In this article, we present TMPL (http://www. dsimb. inserm. fr/TMPL/), a database of transmembrane protein structures (a-helical and b-sheet) positioned in the lipid bilayer. It is the first database to include theoretical models of transmembrane protein structures, making it a large repository with more than 11 000 entries. The TMPL database also contains experimentally solved protein structures, which are available as either atomistic or coarse-grained models.
Based on such algorithms, the PDBTM  (6)  and OPM  (5)  databases have remained for a decade the only two resources of membrane assignments. This low number of databases is problematic regarding the fact that transmembrane protein structures may actually have different but equally valid orientations in the lipid bilayer, due to the orientational disorder that exists in the biological membranes  (7). For example, the transmembrane domain of the phospholamban (PDB code: 1zll) has a measured helical tilt which varies from 10 to 28 depending on the studies, the latest of which reports a value of 13 , with an experimental error of 4  (8). Thus, it is certainly no coincidence that different algorithms produce different membrane orientations for this protein structure (Figure  1 ). For such cases, an additional point of view appears necessary for providing an alternative to the membrane assignments from OPM and PDBTM.
In addition, another important need has to be met regarding the coarse-grained models of proteins, since they are included in none of the two existing databases, despite the popularity of this structural representation. While the recent MemProtMD (9) does include coarse-grained models of transmembrane proteins, this database belongs to a different category from OPM and PDBTM, since its entries are protein structures inserted into an explicit membrane environment. Although this representation can reveal local bilayer deformations, MemProtMD cannot provide any delimitation of the transmembrane segments (nor any calculations of the tilt angles and hydrophobic thickness), as do OPM and PDBTM with their planar bilayers. One may regret that none of the existing databases includes predicted structures of transmembrane proteins, especially when considering (i) the few native structures available for these proteins, (ii) the steady progress in protein structure modeling and (iii) the growing number of theoretical models available in specialized databases, such as ModBase  (10)  and SWISS-MODEL  (11).
Also regrettable is the lack of quality assessment for the orientation of the structures in the lipid bilayer. Indeed, the membrane assignments found in the existing databases are actually predictions made by computational methods-sometimes confirmed by the very few experimental data availablethe reliability of which needs to be evaluated. This can be achieved with different types of scoring functions, in particular those that have been recently developed for membrane protein structures  (12) (13) (14). In this report, we present TMPL, a new database of transmembrane protein models positioned in the lipid bilayer. The database includes atomistic and coarsegrained protein models-such as those used with the MARTINI force field (15)-oriented in the membrane by the ANVIL algorithm (2), which has been designed to process both representations of protein structure.
The protein structures in TMPL are either experimental or theoretical models, whose quality is assessed with the membranespecific statistical potential MAIDEN  (12). This inclusion of predicted structures makes TMPL a large database, with >11 000 entries at its launch. The TMPL database is expected to grow along with the number of native and predicted structures available, and through its interfacing with the OREMPRO web server (16) (a pipeline of the ANVIL algorithm and the MAIDEN method), which allows users to deposit their own models.
Sources of protein structures Most of the protein structures in TMPL come from external databases and were selected for containing the term 0 transmembrane 0 in either their keywords, title or description (Figure  2 ). Experimental models were downloaded from the RCSB PDB (www. rcsb. org), while theoretical models generated by comparative modeling were downloaded from ModBase (modbase. compbio. ucsf. edu) and SWISS-MODEL (swissmodel. expasy. org) databases. Additionally, de novo predicted structures were taken from the top-ranked models of the EVfold_membrane dataset  (17). For all the theoretical models, secondary structures were assigned with the DSSP algorithm  (18). The coarse-grained models in TMPL were generated from experimental structures of transmembrane proteins, by using the 0 martinize.
py 0 script  (19). Thus, the coordinate files of coarse-grained structures are compatible with the MARTINI force field and describe the spatial arrangement of backbone beads (BB) and side-chain beads (SC1 to SC4). Besides the aforementioned databases, TMPL has two minor-yet qualitatively important-sources of transmembrane protein models. The first of these sources is constituted by the structures that users can deposit through the TMPL website. Indeed, an original feature of TMPL is the possibility for users to upload protein models and their membrane assignments to the database. This is made possible by the connection between the TMPL database and our OREMPRO web server for orienting protein structures into the lipid bilayer. As an example, the first model deposited in this way is a theoretical model of the human red blood cell DARC protein, generated in our laboratory  (20) , and whose native structure still remains unsolved.
Membrane assignment and quality assessment The transmembrane segments of all the models in TMPL have been delimited by using our algorithm for positioning protein structures in the lipid bilayer, named ANVIL, which is capable of processing both atomistic and coarsegrained representations of protein structure. This computational method is based on a membrane propensity scale, which by default is binary (a value of þ1. 0 or À1. 0 is attributed to each residue type, depending on its tendency to be located inside or outside the membrane, respectively) but can be modified on the OREMPRO web server. For all the membrane assignments in TMPL that have been automatically generated, the default membrane propensity scale has been used. When a user-deposited structure is oriented using a different scale, the modified propensity values is recorded in the TMPL database and displayed on the protein entry page (see online supplementary material for Figure  S1 ).
It must be noted that monotopic segments (i. e. those which do not completely span across the membrane) are not counted as transmembrane, unlike in other databases (see online supplementary material for Figure  S1 ). After a protein model is oriented in the lipid bilayer, the structural quality of the transmembrane domain is assessed by using the MAIDEN pseudo-energy function  (12). This scoring function is a statistical potential of mean force  (23)  that has been trained on a dataset of native transmembrane Predicted, native, and 0 martinized 0 structures from other databases are selected for being annotated as 0 transmembrane 0 proteins, before being submitted to the OREMPRO server. The latter is a pipeline of the ANVIL and MAIDEN methods, which are aimed at the orientation and assessment of transmembrane protein structures, respectively.
Entry codes in TMPL Every protein structure in TMPL has a unique accession code, whose pattern is dependent on whether the entry is a theoretical or an experimental model. The entry codes of native structures-and their 0 martinized 0 versions-include the PDB ID, which is followed by the representation ('AT' for atomic, or 0 CG 0 for coarse-grained), and ends by 0 D 0 (default) or 0 G 0 (geometric), this terminal letter corresponding to the methods used by the orientation algorithm to determine the membrane boundaries. Thus, the pattern of the accession codes for native structures can be written as: TM[PDB ID][ATjCG][DjG]. For predicted structures, the entry code includes the UniProtKB AC of the protein chain, the source of the model, i. e. 0 S 0 (SWISS-MODEL), 0 M 0 (ModBase), 0 O 0 (ORION), 0 E 0 (EVfold), or 0 U 0 (user-deposited), the number of the model (from 1 to 99; because there may be more than one model of the same protein), and the positioning method employed by ANVIL ('D' or 0 G 0 ).
Database updates Thanks to the automated procedure for generating TMPL, our database will be regularly updated, with a frequency that depends on the source of protein models. Thus, every 6 months the PDB, ModBase and SWISS-MODEL databases will be searched for new structures of transmembrane proteins, which will constitute a major update. Smaller and more frequent updates of our database will be performed for every predicted structure that we will produce with ORION, and for every user-deposited model. Finally, the entries in TMPL may be subject to change, if any correction appears necessary. In such a case, the old entry page and its content will be superseded and unreferenced, while being still accessible through the new entry page. Of course, the creation date of each entry in TMPL is recorded and displayed on its page.
Search and browse TMPL The protein structures in TMPL can be accessed by filling a search form with any of several data types: the protein name or any keywords from its description, the organism (genus and/or species), the type of model (experimental or theoretical), the structural representation (atomistic or coarse-grained), or the type of transmembrane protein structure (a-helical or b-sheet). Users can also search TMPL by using entry codes from UniProt, PDB and Pfam databases. A quick search can be performed from anywhere in the website, by using the text area of the TMPL navigation bar, and will match protein keywords, organism, or entry codes. Alternatively to the search, our database can be browsed by either protein families or organisms. The protein families correspond to the Pfam families to which belong the transmembrane domains of the protein structures in TMPL.
Information and interactive visualization Every entry page displays the following information about the protein structure (Figure  3 ): the name or title of the structure, the organism, the representation, the type of transmembrane protein, the Pfam Acc (if any), the PDB ID or UniProtKB AC (depending on whether the model is experimental or theoretical, respectively), the membrane assignment by ANVIL, the structural assessment by MAIDEN, and the creation date. The membrane assignment is itself composed of the number, sequence positions and tilt angles of the transmembrane segments and the hydrophobic thickness. The protein structure and the membrane planes can be visualized directly on the web page, thanks to the WebGL protein viewer PV  (24) , which offers an interactive visualization of protein 3 D models represented as ribbon diagrams (or spheres for the coarsegrained models).
Users depositions and alternative assignments Allowing users to deposit models in TMPL-which is made easy by the connection with our OREMPRO web server-has a 2-fold purpose: (i) completing the database with native or predicted protein structures that have not yet been included and (ii) uploading structures of transmembrane proteins that are already part of TMPL, but with an alternative membrane assignment. Indeed, the inclination of a protein in a biological membrane is not a constant value, due to the thermodynamic entropy of the lipid bilayer environment  (7) , hence the possibility for a given structure to have different, yet equally valid, membrane assignments. For example, the native structure of the bacteriorhodopsin monomer (PDB 1py6) has two plausible orientations in the lipid bilayer, as we have previously shown  (2) , and both are included in TMPL as distinct entries.
In this case, the positioning depends on the input coordinate file: entry 0 TM1PY6ATD1 0 is the membrane assignment of the bacteriorhodopsin taken from the RCSB PDB, whereas 0 TM1PY6ATD2 0 corresponds to the structure of the same protein, but taken from the OPM database and oriented in the membrane using OREMPRO. Some protein structures from OPM differ from their PDB counterparts in that they have been subjected to structural adjustments (5) (never reported on OPM entry pages), which may impact the assignments produced by OREMPRO. In the case of 1py6, these modifications bring about an improvement: the transmembrane domain has 7 membrane-spanning helices (instead of 5) and a better MAIDEN score. For further updates of TMPL, we will consider a systematical use of these structural adjustments, which can be performed by various external methods, such as the web servers PPM (5), PDB_Hydro  (25) , and PDB_REDO  (26).
These are essentially either structural predictions mistakenly annotated as 0 putative transmembrane 0 , or truncated theoretical models of actual transmembrane proteins, e. g. those for which only the non-membrane (globular) domain has been modeled. These 0 false positives 0 (which can also be found in other databases, e. g. OPM entries 1ipe and 3nw8) will be discarded intermittently-whenever encountered or reported-or systematically, as soon as a new version of ANVIL can detect transmembrane proteins with a high statistical sensitivity, to avoid discarding true transmembrane proteins.