Vital Details Research Articles

Mining Twitter Data for Business Intelligence Using Naive Bayes Algorithm for Sentiment Analysis

Today social media has grown to be a big player in the way businesses and organizations operate, especially with the coronavirus pandemic increasing the online footprint of organizations. The use of data from social media to drive business intelligence is now of growing interest to both researchers and business owners. Business owners can now utilize platforms like Twitter to learn about their target audience and improve their business processes to meet their growing needs. Twitter makes it easy to see what is going or about to go viral and vital details like why it is going viral and the players behind it. This research aims to help business owners’ especially small and medium enterprises and start-ups gain a competitive advantage in their industry by using the crowd wisdom opportunity via social media. The proposed system is based on Twitter and crawls the platform for relevant data, including; locations, trends, and important actors (influencers) within a specified field; the system cleans the data and presents the information in an actionable format. Python was used for Twitter data mining, and sentiment analysis of the tweets was done using Naive Bayes classifiers.

HPTLC analysis and anti-inflammatory activity of Plectranthus amboinicus (Lour.) leaves

Plectranthus amboinicus (Lour.) Spreng. are generally used in Chinese folk medicine for treating fever, cough, mumps, sore throats, and mosquito bite. The current study was therefore carried out to provide vital pharmacological details about the leaf. This study investigated the anti-inflammatory properties of ethanolic extract from Plectranthus amboinicus. The leaves of Plectranthus amboinicus were rinsed, air dried and then powdered using machine. The ethanol extract of the plant was used for phytochemical analysis to identify the constituents present in the plants. HPTLC analysis was carried out to investigate its secondary metabolite profile by using the solvent mixture. The anti-inflammatory effect was analysed by inhibition of protein denaturation assay. The hypotonicity induced haemolysis was observed from 100µg/ml to 500µg/ml of extract concentration. The results proved that the ethanolic leaf extract of P.amboinicus has antiinflammatory activity. The presence of flavonoid, quercetin in plant extract was confirmed by HPTLC analysis and these flavonoids may be responsible for the antiinflammatory activity. Further studies are needed to the development of potential drug that may be used for various pharmacological activities.

Consolidated EHR Workflow for Endoscopy Quality Reporting.

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone. We hypothesize that extracting comprehensive colonoscopy quality metrics from consolidated procedure documents using computational linguistic techniques, and integrating it with discrete EHR data can improve quality of screening and cancer detection rate. As a first step, we developed an algorithm that links colonoscopy, pathology and imaging documents by analyzing the chronology of various orders placed relative to the colonoscopy procedure. The algorithm was installed and validated at the University of Arkansas for Medical Sciences (UAMS). The proposed algorithm in conjunction with Natural Language Processing (NLP) techniques can overcome current limitations of manual data abstraction.

Diverse Features Fusion Network for video-based action recognition

The two-stream convolutional network has been proved to be one milestone in the study of video-based action recognition. Lots of recent works modify internal structure of two-stream convolutional network directly and put top-level features into a 2D/3D convolution fusion module or a simpler one. However, these fusion methods cannot fully utilize features and the way fusing only top-level features lacks rich vital details. To tackle these issues, a novel network called Diverse Features Fusion Network (DFFN) is proposed. The fusion stream of DFFN contains two types of uniquely designed modules, the diverse compact bilinear fusion (DCBF) module and the channel-spatial attention (CSA) module, to distill and refine diverse compact spatiotemporal features. The DCBF modules use the diverse compact bilinear algorithm to fuse features extracted from multiple layers of the base network that are called diverse features in this paper. Further, the CSA module leverages channel attention and multi-size spatial attention to boost key information as well as restraining the noise of fusion features. We evaluate our three-stream network DFFN on three public challenging video action benchmarks: UCF101, HMDB51 and Something-Something V1. Experiment results indicate that our method achieves state-of-the-art performance.

Cannulation strategies in type A aortic dissection: a novel insight narrative review.

This review highlights vital details that can be easily overlooked and discuss how to identify and fix failed cannulation from another novel insight. Appropriate arterial cannulation strategy during cardiopulmonary bypass (CPB) in Stanford type A aortic dissection (AAD) is highly necessary to reach satisfactory perfusion effects and appreciable clinical outcomes. Despite several previously published reviews on cannulation strategies in AAD, most focus on the advantages and disadvantages by comparing various cannulation strategies. In fact, most of evidence came from retrospective studies. More importantly, however, some important details and novel approaches maybe overlooked due to variety reasons. These overlooked details also make sense in clinical practice. Papers related to cannulation refer to type AAD were retrieved and analyzed from the PubMed and Medline database. The key words such as “aortic dissection”, “cannula”, “cannulation”, “cannulation strategy”, “cerebral perfusion”, “type I aortic dissection” were conducted and analyzed. In addition, we looked at some new and very significant specific perfusion techniques such as anterograde cerebral perfusion combined with retrograde inferior vena caval perfusion (RIVP) and reperfusion via the right carotid artery before surgery. The arterial cannulation site and strategy should be determined individually. Monitoring measures are very necessary in the whole procedure.

Three-dimensional stability diagram of Al–Mg–O inclusions in molten steel

Stability diagrams are of great importance for investigating deoxidation equilibrium and controlling inclusions in molten steel. Combined with programming for calculation and drawing, the three-dimensional stability diagrams of MgO, MgO·Al2O3, and Al2O3 in the Fe–Al–Mg–O system were successfully drawn, which can visually explain the meaning of the iso-oxygen contours and the borderlines among different phases. They also offer straight evidence that the borderline method is imperfect. The three-dimensional stability diagrams can visually show more vital details that cannot be illustrated by the iso-oxygen contours. The most significant advantage of three-dimensional stability diagrams is not only judging the most stable phase in the specific component area but also visually showing the transformation trend among the phases from a three-dimensional perspective, especially when the equilibrium surfaces of these phases are relatively close. Besides, an interesting transformation of inclusions in the Fe–Al–Mg–O system was found. When the magnesium content is higher than 0.0065% and the aluminum content is lower than 0.21%, the equilibrium products of aluminum-magnesium composite deoxidation may be transformed from MgO to MgO·Al2O3 and Al2O3 in turn, which can be directly observed from the three-dimensional stability diagram based on the calculations.

Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention Guidance for Image Fusion

Image fusion integrates a series of images acquired from different sensors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , infrared and visible, outputting an image with richer information than either one. Traditional and recent deep-based methods have difficulties in preserving prominent structures and recovering vital textural details for practical applications. In this article, we propose a deep network for infrared and visible image fusion cascading a feature learning module with a fusion learning mechanism. Firstly, we apply a coarse-to-fine deep architecture to learn multi-scale features for multi-modal images, which enables discovering prominent common structures for later fusion operations. The proposed feature learning module requires no well-aligned image pairs for training. Compared with the existing learning-based methods, the proposed feature learning module can ensemble numerous examples from respective modals for training, increasing the ability of feature representation. Secondly, we design an edge-guided attention mechanism upon the multi-scale features to guide the fusion focusing on common structures, thus recovering details while attenuating noise. Moreover, we provide a new aligned infrared and visible image fusion dataset, RealStreet, collected in various practical scenarios for comprehensive evaluation. Extensive experiments on two benchmarks, TNO and RealStreet, demonstrate the superiority of the proposed method over the state-of-the-art in terms of both visual inspection and objective analysis on six evaluation metrics. We also conduct the experiments on the FLIR and NIR datasets, containing foggy weather and poor light conditions, to verify the generalization and robustness of the proposed method.

Legal Project Management: Projectifying the Legal Profession

Post-Global Financial Crisis, global law firms and in-house departments have started to take up ‘Legal Project Management’ (LPM). LPM adopts and adapts project management methods for the law context as a means of streamlining, planning and costing legal work. This article examines LPM as an aspiring driver of managerialist change within the legal profession. In its reframing of all legal matters as ‘projects’, LPM is also an example of a more specific type of managerialist change, ‘projectification’: the process by which work activities, and our activities generally, are being organised and shaped as projects or temporary endeavours. Though we know managerialism is occurring, our understanding of how it manifests in, and is promoted by, specific practices and discourses within the workplace organisation is under-developed in the law context. It may be tempting to read managerialism as sullying traditional professionalism. But an extensive body of literature has documented the interactions of professional and managerial imperatives that result in what has been described as a hybridisation of different logics or belief systems. This article adds vital detail to the existing literature about managerialism within the legal profession by looking closely at LPM as projectification. To do so, it utilises Mirko Noordegraaf’s three dimensions of professionalism that represent core points of distinction: coordination of work, authority or the grounds for legitimacy, and values at stake. Through these facets, it analyses LPM’s somewhat contradictory aspects, illustrating the schismatic nature of projectification as both exciting and empowering, and ethically risky and dehumanising.

"Hear me!"

I am respectfully submitting a narrative essay to this journal. As a faculty member at a residency program, I got interested in contributing this essay after my experience caring for a disabled patient. I presumed we tend to imperfectly decipher what our patients’ needs are and my experience highlighted the need to be more sensitive and less dismissive to patients with disabilities. I started with the assumption that there were minimal teaching points to the house staff since this was an overt outpatient placement case. I was wrong and learnt much more than I expected. As faculty physicians, we tend to highlight pertinent clinical data to the learners and inadvertently gloss over vital nonclinical details that ultimately are as important. This patient was very succinct with her demands and understandably upset with our blatant conjectures with our daily mundane clinical rounds and consults. Taking time to listen to her, having a team meeting in her room and coordinating her care with nursing and medical colleagues was not only a learning experience but made me a better physician and teacher. She was the focus and her needs were met, not ours. I have no financial conflict of interest, and the patient was aware I intended to share my experience with my peers. I will appreciate any feedback and opportunity to learn and improve this narrative with expected revisions.

Manifold Learning for Real-World Event Understanding

Information coming from social media is vital to the understanding of the dynamics involved in multiple events such as terrorist attacks and natural disasters. With the spread and popularization of cameras and the means to share content through social networks, an event can be followed through many different lenses and vantage points. However, social media data present numerous challenges, and frequently it is necessary a great deal of data cleaning and filtering techniques to separate what is related to the depicted event from contents otherwise useless. In a previous effort of ours, we decomposed events into representative components aiming at describing vital details of an event to characterize its defining moments. However, the lack of minimal supervision to guide the combination of representative components somehow limited the performance of the method. In this paper, we extend upon our prior work and present a learning-from-data method for dynamically learning the contribution of different components for a more effective event representation. The method relies upon just a few training samples (few-shot learning), which can be easily provided by an investigator. The obtained results on real-world datasets show the effectiveness of the proposed ideas.

Dynamical systems analysis using many-task interactive cloud computing

The article states an approach for developing a distributed application architecture for dynamic systems analysis in a hybrid cloud. The problem that was posed as an example is based on the calculation of the Lyapunov characteristic exponents for trajectories of dynamical systems in phase space. Parallelization of the computational process is achieved by supplying the list of running tasks for computing Lyapunov characteristic exponents, depending on the result of finished tasks during calculations. We have implemented a distributed application to make this experiment automated. The architecture of application elements is established on the principles of the enterprise desktop grid and cloud systems. A vital detail of the application architecture is the deployment process based on a cloud hybrid computing environment using the Jupyter server running on the DigitalOcean cloud platform and the Everest cloud platform simultaneously with the Maple package.

Using the Model for Improvement and Plan-Do-Study-Act to effect SMART change and advance quality.

Using the Model for Improvement and Plan-Do-Study-Act to effect SMART change and advance quality.

Quality of Life Dilemmas in Pediatric Patients with Epilepsy Residing in Long-Term Care Facilities

AbstractChildren with chronic care or complex medical needs, such as epilepsy, depend on advocates to ensure their safety and well-being, a role typically filled by parents or extended family. Close participation to provide vital history and details is quintessential to help guide appropriate management decisions to ensure optimal neurodevelopmental outcome. However, when these vulnerable children are placed in the foster care system, their need for an advocate becomes even more paramount. Unfortunately, this can be lacking and can lead to a breakdown in their medical care. Their complex medical conditions often can be intimidating to most potential foster families and may result in placement in long-term care facilities. This, in turn, presents additional obstacles that can hinder optimal care such as lack of consistent, dedicated caretakers. This void not only impacts their outcome and medical care but also leads to excessive use of limited healthcare resources as well as unwanted adverse reactions that can be prevented if a reliable source of history is available. As a result, there is a need for dedicated resources to help provide devoted advocates for these vulnerable children.

Conceptual compression&#x0D; of a text as a component of communicative&#x0D; competence

The article presents an example how to work with students on the text conceptual compression. The skill of compression and transferring the information&#x0D; of a text is a solid part of communicative competence and it seems to be crucial&#x0D; in the process of verbal communication including professional conversation.&#x0D; There was conducted a beta testing among the 1st year university students in order to evaluate the skills to compress and transfer information. The examination&#x0D; shows that the secondary school graduates do not perform well being asked to&#x0D; compress the information of a text, they are not able to use adequate ways to&#x0D; transform the texts, the final text product does not include all vital conceptual details. The subject group of students was offered a special training, which included a number of exercises aimed on teaching to compress texts. The article&#x0D; also includes the designed course which was applied. As the result of such a focused instruction on text compression studies, the students learned how to highlight the key topics and to use the needed ways of compression. What is more,&#x0D; the final products appeared to be more logical. Hence the proposed research&#x0D; leads to the conclusion that not only such specially designed training is vitally&#x0D; needed, but also that the designed by the researchers exercises are effective.

LNPSS: SAR image despeckling based on local and non-local features using patch shape selection and edges linking

Abstract Removal of speckle noise from Real SAR images is a significant task for the better segmentation, target detection, target recognition or other processing. In this paper, a novel complex strategy is presented based on carefully analyzing the effect of the speckle noise on the histogram. The histogram of a patch is expanded in the image corrupted with speckle noise. To overcome this, we introduced some rules to remove both noise and preserve the crucial features (edges). Owing to the use of patch shape selection automatically, the patch's histogram modification is more effective. Our method, also, is being able to reconstruct some missing edges by using the kirsch filter and edge linking approach. Our satisfactory results indicate that the proposed approach has a competitive despeckling performance in terms of both visual quality assessment and preserving vital details.

Wavelet based deep learning approach for epilepsy detection.

Electroencephalogram (EEG) signal contains vital details regarding electrical actions performed by the brain. Analysis of these signals is important for epilepsy detection. However, analysis of these signals can be tricky in nature and requires human expertise. The human factor can result in subjective and possible erroneous epilepsy detection. To tackle this problem, Machine Learning (ML) algorithms were introduced, to remove the human factor. However, this approach is counterintuitive in nature as it involves using complex features for epilepsy detection. Hence to tackle this problem we have introduced a wavelet based deep learning approach which eliminates the need of feature extraction and also performs significantly better on smaller datasets compared to the present state of the art ML algorithms. To test the robustness of our model we have performed a binary (2-way) and ternary (3-way) classification using our model. It is found that the model is much more accurate than the present state of the art models and since it uses deep learning it also eliminates the need of feature extraction.

Safety profile of iron polymaltose infusions

ABSTRACTObjectives: Iron polymaltose infusions are one type of iron infusate used to treat iron deficiency used in countries such as Australia. We looked at the safety profile of this infusion at one metropolitan hospital in Melbourne, Australia and the associated demographics of those who had reactions.Methods: A retrospective review of the medical records all adult patients who had an iron polymaltose infusion at Western Hospital over 12 months was performed. Basic demographics, infusion indication, starting hemoglobin and ferritin, vital signs and medical details of patients who had an adverse reaction were recorded.Results: There were 1103 patients who had iron infusions in a 12-month review period between 2017 and 2018. Adverse drug reactions occurred in 2.7% of infusions with no mortalities. The most common reaction was urticaria. No cases of anaphylaxis were recorded; however, four cases were associated with marked hypotension which resolved with fluid resuscitation and cessation of the infusion. Overall, vital signs remained clinically stable pre and post-iron polymaltose infusions.Conclusion: Iron polymaltose has a low rate of adverse drug reactions; yet, serious side-effects such as hypotension may occur. Perturbations in hemodynamics within the first 20 min suggest close monitoring is necessary in the initial stages of infusion.

Spectroscopic Characterization of the Interaction between Dopamine and Human Serum Albumin

The interactions of HSA with DA have received great attention nowadays due to its significant effect in the biomedical field and overall health. The main aim of this work is to examine the interaction between DA and HSA at physiological conditions. Upon addition of DA to HSA, the fluorescence emission was quenched with quenching constant Kq = 1.32 × 109 L⋅mol−1⋅s−1 and the binding constant of DA with HSA is found to be K = 4.4 × 102 mainly indicating dynamic quenching. The HSA conformation was altered upon binding of DA to HSA with an increase in α-helix and a decrease in β-sheets suggesting unfolding of HSA secondary structure due to weak intermolecular interaction with HSA. In view of the evidence presented, it is important to understand the details of the interactions of HSA with DA which will be crucial in the design of new DA-inspired drugs and help revealing vital details to better understand the HSA’s role as a transporter for many drugs.

Structure and function of L-threonine-3-dehydrogenase from the parasitic protozoan Trypanosoma brucei revealed by X-ray crystallography and geometric simulations.

Two of the world's most neglected tropical diseases, human African trypanosomiasis (HAT) and Chagas disease, are caused by protozoan parasites of the genus Trypanosoma. These organisms possess specialized metabolic pathways, frequently distinct from those in humans, which have potential to be exploited as novel drug targets. This study elucidates the structure and function of L-threonine-3-dehydrogenase (TDH) from T. brucei, the causative pathogen of HAT. TDH is a key enzyme in the metabolism of L-threonine, and an inhibitor of TDH has been shown to have trypanocidal activity in the procyclic form of T. brucei. TDH is a nonfunctional pseudogene in humans, suggesting that it may be possible to rationally design safe and specific therapies for trypanosomiasis by targeting this parasite enzyme. As an initial step, the TDH gene from T. brucei was expressed and the three-dimensional structure of the enzyme was solved by X-ray crystallography. In multiple crystallographic structures, T. brucei TDH is revealed to be a dimeric short-chain dehydrogenase that displays a considerable degree of conformational variation in its ligand-binding regions. Geometric simulations of the structure have provided insight into the dynamic behaviour of this enzyme. Furthermore, structures of TDH bound to its natural substrates and known inhibitors have been determined, giving an indication of the mechanism of catalysis of the enzyme. Collectively, these results provide vital details for future drug design to target TDH or related enzymes.

Adapted K-Core Decomposition and Visualization for Functional Magnetic Resonance Imaging Connectivity Networks.

Medical imaging modalities, such as functional magnetic resonance imaging (fMRI) are being increasingly used to study the human brain. Analysis of the images has led to findings describing diseases, such as schizophrenia and post-traumatic stress disorder. One of the most widely used methods of analysis involves creating functional connectivity network (FCN) abstractions. These summarize the temporal relationships between regions of interest (ROIs) in the brain and can be used to easily compare subjects, e.g. healthy against schizophrenia. Visual analytics is widely used to facilitate such analysis, with existing approaches designed to enable and simplify detailed interpretation of single networks and pairs of networks in comparison. Prior to such detailed analysis, grouping and aggregation is often performed on the data, which is a time consuming and difficult task. Existing methods for doing this are commonly statistical, while others visualize the cohort without presenting vital network details of the individual FCNs. Thus, there is an opportunity for alternative visual analytics to facilitate the grouping by incorporating the network details. Graph decomposition, such as k-core decomposition, can be used to simplify the representation of networks, while retaining these vital network details. In this study, we propose an adapted k-core decomposition algorithm and visualization, which calculates the connected component information of nodes in the FCNs, a key detail in analysis. Our visualization combines this information with the decomposition to display more details about FCNs at a high-level than contemporary approaches. We present a prototype of our method, demonstrating the ability to group and aggregate the data without the loss of vital network details for further detailed analysis.