Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA\'s features for effective RRI predictions.

This study aimed to investigate the accuracy of a robotic computer-assisted implant surgery (r-CAIS) for immediate implant placement.
Patients requiring immediate implant placement in the maxillary anterior region were enrolled for r-CAIS. Before surgery, the patients underwent a cone beam computed tomography (CBCT) scan with a positioning marker. Virtual implant placement position and drilling sequences were planned. Following spatial registration and calibration, the implants were placed with the robotic system under supervision. A postoperative CBCT was taken to control the actual implant positions. The DICOM data of the virtually planned and the actually placed implant were superimposed and registered through the accuracy verification software of the robotic system. The accuracy was calculated automatically. The deviation at the mesial-distal, labial-palatal, and apico-coronal directions were recorded.
Fifteen patients with 20 implants were included. No adverse surgical events or postoperative complications were reported. The global platform, apex, and angular deviation were 0.75 ± 0.20 mm (95 % CI: 0.65 to 0.84 mm), 0.70 ± 0.27 mm (95 % CI: 0.57 to 0.82 mm), and 1.17 ± 0.73° (95 % CI: 0.83 to 1.51°), respectively. Moreover, the vertical platform and apex deviation were 0.50 ± 0.31 mm, (95 % CI: 0.35 to 0.64 mm) and 0.48 ± 0.32 mm, (95 % CI: 0.33 to 0.63 mm), respectively. All the placed implant positions were further labial and apical than the planned ones, respectively.
High accuracy of immediate implant placement was achieved with the robotic system.
Our study provided evidence to support the potential of the robotic system in implant placement, even in challenging scenarios.

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore \"chemical\" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

Comprehensive analysis of triacylglycerol (TAG) regioisomers is extremely challenging, with many variables that can influence the results. Previously, we reported a novel algorithmic method for resolving regioisomers of complex mixtures of TAGs. In the current study, the TAG Analyzer software and its mass spectrometric fragmentation model were further developed and validated for a much wider range of TAGs. To demonstrate the method, we performed for the first time a comprehensive analysis of TAG regioisomers of bovine milk fat, a very important and one of the most complex TAG mixtures in nature containing FAs ranging from short to long carbon chains. This analysis method forms a solid basis for further investigation of TAG regioisomer profiles in various natural fats and oils, potentially aiding in the development of new and healthier foods and nutraceuticals with targeted lipid structures.

Life cycle assessment (LCA) is widely used to evaluate product\'s life cycle environmental impact and identify the environmental weaknesses. However, it is difficult for existing LCA software to perform flexible LCA analysis based on the product life cycle characteristics and industry background. Meanwhile, under the existing LCA research model, product designers and manufacturers are usually not LCA evaluators, resulting in a certain time gap between the evaluation results and product improvement. Designers with less experience in green design often find it difficult to identify high environmental impact links in products at different life cycle stages and product levels, and updated products are challenging to meet various environmental restrictions. This paper establishes a multi-module product life cycle analysis model that combines product industry background that includes basic information, assessment information, structural information, and restriction information to achieve the multi-scenario of product LCA in different dimensions in a typical domain. The calculated mechanism of the dynamic power emission factor is built according to the service time and space dimensions. The proposed method forms an integrated environmental performance evaluation of household appliance (EPEHA) system. A software assessment and an optimization method are proposed to improve the EPEHA system. The results of this study show that these proposed methods can improve the timeliness and diversity of results analysis of product LCA in the field of household appliances in China. The universal data exchange format and simple operation interface of the EPEHA system enable people related to the product to quickly understand the environmental impact of the product in different scenarios, even if they lack green design knowledge and professional software training.

Digital smile design (DSD) plays an essential role in the planning of aesthetic treatments. However, most digital smile designing software programmes are expensive and require specialised training and skill for effective implementation. This paper illustrates the use of Adobe Photoshop and Microsoft PowerPoint, which are economical and user friendly for planning aesthetics in a clinical case.

Assessing the quality of land is a very important step that precedes the planning of land use and taking management decisions; for example, in the agricultural field, it can be used to evaluate the suitability of the land for planting crops, determine the suitable irrigation system type, or adjust the agricultural inputs such as fertilizers and pesticides according to the requirements of each zone in the land. The spatial-temporal dynamic nature of land characteristics entails also updated evaluation process and updated management plan. The present paper tries to exploit the advances in information and communication technologies to develop a conceptual design of a dynamic system that accommodates the spatial-temporal dynamics of the agricultural soil characteristics to realize a land suitability assessment (LSA) based on a factor analysis method. The proposed design combines IoT technologies, web development, database, and digital mapping and tries to consolidate the system with other functionalities useful for decision support and suitable for different cases. The paper conducted a survey and made comparisons to select the best technologies that fit the current use case implementation and presents its reproducible conceptual modeling by developing the static and dynamic views through schemas, diagrams, message sequence charts, IoT messaging topic tree, pseudocode, etc. The functionality of the design was validated with a simple implementation of the system model. To our knowledge, there is no previous significant contribution that has addressed a LSA IoT use case. The proposed design automates the LSA process for more accurate decision-making, saving cost, time, and effort consumed in repeated field trips. It is characterized by flexibility and centralization in its offered services of spatial analysis, detection, visualizations, and status monitoring. The design also allows for remote control of field machinery.

BACKGROUND: Advances in health have highlighted the need to implement technologies as a fundamental part of the diagnosis, treatment, and recovery of patients at risk of or with health alterations. For this purpose, digital platforms have demonstrated their applicability in the identification of care needs. Nursing is a fundamental component in the care of patients with cardiovascular disorders and plays a crucial role in diagnosing human responses to these health conditions. Consequently, the validation of nursing diagnoses through ongoing research processes has become a necessity that can significantly impact both patients and health care professionals.
OBJECTIVE: We aimed to describe the process of developing a mobile app to validate the nursing diagnosis \"intolerance to physical activity\" in patients with acute myocardial infarction.
METHODS: We describe the development and pilot-testing of a mobile system to support data collection for validating the nursing diagnosis of activity intolerance. This was a descriptive study conducted with 11 adults (aged ≥18 years) who attended a health institution for highly complex needs with a suspected diagnosis of coronary syndrome between August and September 2019 in Floridablanca, Colombia. An app for the clinical validation of activity intolerance (North American Nursing Diagnosis Association [NANDA] code 00092) in patients with acute coronary syndrome was developed in two steps: (1) operationalization of the nursing diagnosis and (2) the app development process, which included an evaluation of the initial requirements, development and digitization of the forms, and a pilot test. The agreement level between the 2 evaluating nurses was evaluated with the κ index.
RESULTS: We developed a form that included sociodemographic data, hospital admission data, medical history, current pharmacological treatment, and thrombolysis in myocardial infarction risk score (TIMI-RS) and GRACE (Global Registry of Acute Coronary Events) scores. To identify the defining characteristics, we included official guidelines, physiological measurements, and scales such as the Piper fatigue scale and Borg scale. Participants in the pilot test (n=11) had an average age of 63.2 (SD 4.0) years and were 82% (9/11) men; 18% (2/11) had incomplete primary schooling. The agreement between the evaluators was approximately 80% for most of the defining characteristics. The most prevalent characteristics were exercise discomfort (10/11, 91%), weakness (7/11, 64%), dyspnea (3/11, 27%), abnormal heart rate in response to exercise (2/10, 20%), electrocardiogram abnormalities (1/10, 9%), and abnormal blood pressure in response to activity (1/10, 10%).
CONCLUSIONS: We developed a mobile app for validating the diagnosis of \"activity intolerance.\" Its use will guarantee not only optimal data collection, minimizing errors to perform validation, but will also allow the identification of individual care needs.

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

In this work, an adaptive software architecture is proposed for the generation of experiences for hotel promotion and marketing, based on Case-based Reasoning (CBR) that uses the attributes and user characteristics and immersive 360° videos. Considering that immersion in virtual reality (VR) environments can trigger responses in various dimensions, such as affective, cognitive, attitudinal, and behavioral dimensions, these dimensions are evaluated in immersive environments with 360° videos. To validate the results obtained with the software architecture, a quasi-experimental study was conducted through the evaluation of the experience, consisting in the visualization of the environments of a boutique hotel, with a sample of a randomly selected group of young people. The contribution of this work lies in the use of 360° VR videos, for the visualization of the hotel characteristics and environments according the user profiles, to evaluate the affective, cognitive and attitudinal and behavioral responses and their influence on the booking intention and attitude. Finally, conclusions and recommendations for future work have been established.