Mouse Clicks Research Articles

Tools and Applications of Bioinformatics in Sericulture: A Review

Rapid advances in genomic and other molecular research tools, as well as advances in information technology, have resulted in a massive volume of data relevant to molecular biology during the last several decades. The term "bioinformatics" refers to the mathematical and computational methods used to get a better understanding of biological processes. Bioinformatics commonly uses genetic sequences, which are easier to manage with computers than by hand. Although genomic and proteomic techniques have advanced our understanding of how genes and proteins contribute to physiology, biochemistry, genetics, and evolution in insects, a complete picture of genomics and proteomics and their interactions remains difficult. Meanwhile, bioinformatics developments have discovered hidden omics features in other species, leaving insects with a substantial information gap. Bombyx mori has sparked research interest in countries all over the world due to its adaptability and status as the first Lepidopteran species to have its genome sequenced. The entire genome sequence of the silkworm was reported in 2004 and updated in 2008 based on its biological and industrial value. The genome of B. mori was the first in a lepidopteran to be sequenced, and it has served as a foundation for Lepidoptera research. Bioinformatics tools and techniques not only aided in the detection of proteomic and genomic variation among species/strains, but also led to the discovery of a gap in a silkworm genome sequence that diverged during domestication. Databases of Seri-bioinformatics are an important Seri-bioresource. The accessible internet resources on silkworms and associated organisms, such as databases and informational websites like SilkDB, MorusDB, WildSilkbase, KAIKObase, SilkPathDB etc., have aid in the improvement of silkworm health, disease resistance, and productivity. These databases contain information on gene, protein, disease sequences, and have evolved as a highly beneficial platform via which the scientific community may communicate molecular data across the globe with a single mouse click, not only minimizing the gap and time but also allowing information to be accessed. This review paper will cover all of these bioinformatics tools and methodologies in order to better understand their application in silkworms and host plants.

Reaction time for correct identification of vowels in consonant-vowel syllables and of vowel segments.

Reaction times for correct vowel identification were measured to determine the effects of intertrial intervals, vowel, and cue type. Thirteen adults with normal hearing, aged 20-38 years old, participated. Stimuli included three naturally produced syllables (/ba/ /bi/ /bu/) presented whole or segmented to isolate the formant transition or static formant center. Participants identified the vowel presented via loudspeaker by mouse click. Results showed a significant effect of intertrial intervals, no significant effect of cue type, and a significant vowel effect-suggesting that feedback occurs, vowel identification may depend on cue duration, and vowel bias may stem from focal structure.

Information Technology for Maintaining Records of the It Company Facilities

Accounting of premises is the process of storing, processing and manipulating accounting data of premises. In IT companies, effective premises accounting is of particular importance: it can help reduce costs, improve staff performance, and increase business efficiency. The need to keep records of the premises of an IT company always arises in the case of many offices or many manipulations of their data. Optimal use of space is one of the key factors in ensuring the efficiency of IT companies. The convenience of operational access to all the relevant information on the premises already greatly simplifies the conduct of business for IT companies. The availability of information technology with an analysis and decision- making system reduces the complexity of some accounting processes to a few mouse clicks by one of the relevant managers of the company. Study object – is the process of accounting for premises in an IT company, including the processes of collecting, processing, analysing, and displaying information necessary for efficient use of space. Scope of research - is the information technology for maintaining records of the IT company facilities, which allows to use of the space as efficiently as possible while minimizing costs. The goal this of research is to develop an IT company’s premises accounting information technology that will serve not only as a place to store and review accounting data but also as a platform for reserving places or premises and analysing their condition in accordance with regulatory documents and laws of Ukraine. The development of information technology for the accounting of premises for IT companies is a key component for solving the problem of efficient use of space and reducing costs. The developed technology allows collecting, processing, and analysing information on the use of premises in real-time, which will ensure effective control over the use of space and allow timely response to changes in requirements. This technology should be considered as a decision-making system, which not only manipulates data but also helps analyse and select information according to the user’s relevant requests. Among the important features and functions of the premise accounting technology can be attributed: to the creation, editing and deletion of premises from the system; analysis of the condition of the premises; access to all accounting and analytical data using a chatbot, which is built using an artificial intelligence system; booking a place or room for any amount of time during the day; review of current reservations of each room: review of all created reservations of a specific user; manipulation of employee data: registration, editing and deletion of users from the system, which can only be done by the manager.

A screenshot-based task mining framework for disclosing the drivers behind variable human actions

Robotic Process Automation (RPA) enables subject matter experts to use the graphical user interface as a means to automate and integrate systems. This is a fast method to automate repetitive, mundane tasks. To avoid constructing a software robot from scratch, Task Mining approaches can be used to monitor human behavior through a series of timestamped events, such as mouse clicks and keystrokes. From a so-called User Interface log (UI Log), it is possible to automatically discover the process model behind this behavior. However, when the discovered process model shows different process variants, it is hard to determine what drives a human’s decision to execute one variant over the other. Existing approaches do analyze the UI Log in search for the underlying rules, but neglect what can be seen on the screen. As a result, a major part of the human decision-making remains hidden. To address this gap, this paper describes a Task Mining framework that uses the screenshot of each event in the UI Log as an additional source of information. From such an enriched UI Log, by using image-processing techniques and Machine Learning algorithms, a decision tree is created, which offers a more complete explanation of the human decision-making process. The presented framework can express the decision tree graphically, explicitly identifying which elements in the screenshots are relevant to make the decision. The framework has been evaluated through a case study that involves a process with real-life screenshots. The results indicate a satisfactorily high accuracy of the overall approach, even if a small UI Log is used. The evaluation also identifies challenges for applying the framework in a real-life setting when a high density of interface elements is present.

Sandpaper-assisted preparation of microstructured multifunctional PANI/PVA composite hydrogel film for high-sensitivity pressure sensors with accurate pressure identification and motion monitoring

Bio-inspired microstructure surface flexible piezoresistive sensors offer an attractive opportunity for a variety of wearable device applications. Unfortunately, the realization of high-performance piezoresistive sensors still faces great challenges owing to the balance between responsitivity and sensing capability of them, as well as being limited by the level of deformation of their surface structures. Here, we propose a simple and efficient, low-cost sandpaper-assisted solution casting-scraping strategy to fabricate PANI/PVA hydrogel film with the reverse random height distribution (RHD) spinosum microstructure on the surface that is simultaneously conductive, flame retardant, and EMI shielding. The flexible piezoresistive sensor designed based on this film can detect pressure from 0.01 N to 20 N with a responsitivity of 77.7263 kPa−1 in the region of 0.03183–1.27324 kPa, a quick response/recovery delay of 18 ms/30 ms, and a good reliability within 2000 load-unload cycles. Notably, this piezoresistive sensor can accurately identify objects of different masses and monitor physiological signals of human motion, and can also produce significant current responses to extremely slight mouse clicks and handwriting styles. The successful preparation of multifunctional PANI/PVA microstructured film provides a new idea to explore the potential applications of piezoresistive sensors in flexible wearable electronic devices.

Learning analytics with iLearn Insights

iLearn Insights is an innovative online application to address the challenge of engaging, motivating and supporting students with automated personalised email for those at risk of failure. It allows academics to identify students who are engaging or otherwise with the Learning Management System (LMS) content, thus allowing early intervention, and ultimately improved retention in units by providing targeted support to a student cohort. In contrast to systems that provide only static data reports, this vendor-independent innovation was built in-house to analyse and visualise student learning data in relation to access patterns, forum activity, learning activity submission and grades to identify a student cohort that needs support, as well as enabling teaching staff to send personalised emails to students on the basis of their level of engagement and/or performance. Feedback has a powerful impact on learning, but students frequently highlight it as an area that can be improved in tertiary education (Dawson and Henderson, 2019). iLearn Insights provides graphical representation of LMS data that enables visual personalised feedback to students. Within three mouse clicks, academic staff can trigger a range of automated communications to commend high-achieving students, offer additional assistance to lower performing students or to recapture disengaged students. These emails can be a targeted to a group of students and configured by the unit convenor to contain motivating information including the top five resources accessed by classmates; a student’s mark in comparison to the class average; number or percentage of students that have already submitted an assignment; clickable links; and support resources for students falling behind. ILearn Insights was developed based on four principals of learning analytics design knowledge: integration, agency, reference frame and dialogue (Wise, 2014). It has been observed that targeted visual feedback with clickable links is the most effective way to engage students quickly.?The positive impact of iLearn Insights is demonstrated by its rapid uptake by teaching staff across Macquarie University. When it launched in Session 1, 2020, after 18 months of piloting, there were 478 users across 763 units (subjects) who sent 125334 targeted personalised emails. In Session 1, 2023 iLearn Insights was used by approximately 808 users across 40 departments and learning support areas and 1237 units, sending over 316576 targeted emails to encourage students to engage with learning activities or offer support. That represents an increase of 169% of users, 162% of units and 253% of emails in three years. These personalised email exchanges have led to enhanced student engagement, which is critical for student success (Kahu and Nelson, 2018; De Villiers and Werner, 2018; McClenney et al, 2012; Klem and Connell, 2004).

DELIVERING SUSTAINABLE CHANGE OF POSTOPERATIVE CARE DOCUMENTATION PRACTICES IN SPINAL SURGERY THROUGH QUALITY IMPROVEMENT FRAMEWORKS

AbstractINTRODUCTIONIn the NHS the structure of a “regular healthcare team” is no longer the case. The NHS is facing a workforce crisis where cross-covering of ward-based health professionals is at an all-time high, this includes nurses, doctors, therapists, pharmacists and clerks. Comprehensive post-operative care documentation is essential to maintain patient safety, reduce information clarification requests, delays in rehabilitation, treatment, and investigations. The value of complete surgical registry data is emerging, and in the UK this has recently become mandated, but the completeness of post-operative care documentation is not held to the same importance, and at present there is no published standard. This project summarises a 4-stage approach, including 6 audit cycles, >400 reviewed operation notes, over a 5 year period.OBJECTIVETo deliver a sustainable change in post operative care documentation practices through quality improvement frameworks.METHODSStage 1: Characterise the problem and increase engagement through: SMART aims, process mapping, hybrid action-effect and driver diagram and stakeholder analysis. Multi disciplinary stakeholders were involved in achieving a consensus of evidence-based auditable criteria. Stage 2: Baseline audit to assess current practice. Stage 3: Intervention planning by stakeholders. Stage 4: Longitudinal monitoring through run charts and iterative refinement.RESULTSStage 1: Process mapping identified numerous downstream effects of the absence of critical information from operation notes, and the action-effect diagram highlighted the multiple unnecessary mitigating actions performed by ward staff. An MDT consensus was achieved on 15 essential criteria for complete documentation, including important negative fields. Interest-influence matrix identified stakeholder groups with high influence but low interest who needed engagement to deliver change. Stage 2: Baseline audit demonstrated unexpectedly poor documentation: >75% compliance in 4 criteria, and <50% compliance in 10 criteria, which elevated the interest of key stakeholders. Stage 3: A post-operative care template based on the 15 criteria was embedded within the existing IT software. It allowed use of existing operative templates, with a non-overwriting suffix requiring only two mouse clicks. Stage 4: Re-audit at 3 and 12 months showed improved and sustained compliance. At 24 months compliance had declined. Questionnaire of template usage identified problems of criteria response options, and lack of awareness of template by newly appointed staff. Template update improved compliance over the next 6 months (>75% compliance in 11 criteria). Finally, a further reaudit conducted 12 months after the template update (5 years post baseline audit) showed a sustained improvement in compliance (>75% compliance in 13 criteria).CONCLUSIONSSimple innovation through quality improvement frameworks has changed documentation practices by 1) achieving a consensus from stakeholders, 2) a “shock and awe” moment to highlight existing poor documentation and increase engagement 3) implementing change which fit easily into existing systems, 4) respecting autonomy rather than enforcing change and 5) longitudinal monitoring using run charts and an iterative process to ensure the template remains fit for purpose. This model has now successfully been translated to other subspecialities within the orthopaedic department.Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Guiding pre-service teachers’ visual attention through instructional settings: an eye-tracking study

In complex classroom situations, pre-service teachers often struggle to identify relevant information. Consequently, classroom videos are widely used to support pre-service teachers’ professional vision. However, pre-service teachers need instructional guidance to attend to relevant information in classroom videos. Previous studies identified a specific task instruction and prompts as promising instructions to enhance pre-service teachers’ professional vision. This mixed-methods eye-tracking study aimed to compare pre-service teachers’ visual attention to information relevant for classroom management in one of three instructional conditions. Participants viewed two classroom videos and clicked a button whenever they identified situations relevant to classroom management in the videos. They got either (1) a specific task instruction before video viewing (n = 45), (2) attention-guiding prompts during video viewing (n = 45), or (3) a general task instruction (n = 45) before video viewing as a control group. We expected a specific task instruction and prompts to better guide participants’ visual attention compared to a general task instruction before video viewing because both experimental conditions contained informational cues to focus on specific dimensions of classroom management. As both a specific task and prompts were assumed to activate cognitive schemata, resulting in knowledge-based processing of visual information, we expected the specific task instruction to have a similar attention-guiding effect as prompts during video viewing. Measurements were conducted on an outcome level (mouse clicks) and on a process level (eye tracking). Findings confirmed our hypotheses on an outcome level and in part on a process level regarding participants’ gaze relational index. Nevertheless, in a disruptive classroom situation, participants of the prompting condition showed better attentional performance than participants of the other conditions regarding a higher number of fixation and a shorter time to first fixation on disruptive students. Further qualitative analyses revealed that, when observing classroom videos without instructional guidance, pre-service teachers were less likely to identify disruptive situations in the video and more likely to attend to other situations of classroom management concerning the teachers’ action. We discuss advantages of both attention-guiding instructions for pre-service teacher education in terms of the economy of implementation and the salience of situations.

Impact of Covid-19 Pandemic on User Search Behavior: A Case Study of Postgraduate Students

Relevance Feedback (RF) is crucial for building a user profile which is a fundamental element of different intelligent systems such as information retrieval, information filtering, and personalization. RF is affected by a number of contextual factors such as mood, stress level, and sentimental state of the user. Covid-19 pandemic imposed dramatic changes to the user environment as well as the search context. This paper investigates user’s search behaviour to identify the differences in the behavior between the contexts before and during the Covid-19 pandemic. This can be practically translated into identifying the differences in the relationship between the implicit feedback and the explicit relevance level between the two contexts. For this purpose, we conducted three user studies (i) Pre-COVID-19, (ii) Mid COVID-19 and (iii) after Covid-19. A user study was conducted on the same group of users on the three user studies. The Pre-COVID-19 user study took place before the pandemic started and the Mid-COVID-19 user study took place three months after the beginning of the pandemic. After Covid-19 stage took place after 18 months of the pandemic. A linear regression model was developed for each user study using IBM-SPSS. The analysis showed a significant variation in the user behavior between the two studies due to the COVID-19 context and its impact on user search behaviour. Also, two new RF parameters in Mid-COVID-19 were shown to have a significant relationship with the explicit user interest which were Mouse Clicks and Page/Down strikes. Furthermore, the comparison between the two models showed that the second regression model achieved a higher accuracy level that is attributed to the common behavioral change imposed by the pandemic.

Uncovering associations between users' behaviour and their flow experience

ABSTRACT Flow experience is one of the most ambitious targets of any user interface designer. However, it has remained elusive to evaluate how well user interfaces give rise to flow experience outside conducting invasive self-reporting-based questionnaires, which remove the users from the flow experience and can't be massively applied. At the same time, otherwise, well-built systems do track the behaviour of users on the interface, and therefore, user behaviour data could act as a reliable proxy for assessing the experience of users. Currently, there is little empirical research or data about which indices of user behaviours might correspond with having a flow experience as well as the different psychological constituents of the flow experience. Therefore, facing the challenge of using users' behaviour data to model users' experience, we investigated the associations between users' behaviour data (e.g. mouse clicks, activity time in the system, and average response time) and their self-reported flow experience by using data mining (i.e. associations rules) analysing data from 204 subjects. Results demonstrate that the speed of users' actions negatively affects the flow experience antecedents while also positively affecting the loss of self-consciousness. Our study advances the literature, providing insights to identify users' flow experience through behaviour data.

Attending to what's important: what heat maps may reveal about attention, inhibitory control, and fraction arithmetic performance.

Math proficiency is an important predictor of educational attainment and life success. However, developing mathematical competency is challenging, and some content (e.g., fractions) can be enigmatic. Numerous factors are suspected to influence math performance, including strategy knowledge, attention, and executive functions. In two online studies, we investigated the relationship between adults' fraction arithmetic performance, confidence judgments, inhibitory control (a component of executive functions), and attention to strategy-relevant fraction components. We explored the utility of heat maps (based on mouse clicks) to measure adults' attention to strategy-relevant fraction arithmetic components (operationalized according to each mathematical operation). In Study 1, attending to strategy-relevant fraction components was correlated with inhibitory control, but this finding did not replicate in Study 2. Across both studies, inhibitory control and attention to strategy-relevant fraction components were correlated with arithmetic accuracy. Intraindividual variability in participants' attention to strategy-relevant fraction components was also found. Our findings suggest that heat map questions may be a viable alternative to assess participants' attention during fraction tasks and that attention to specific fraction-arithmetic problem features is related to problem-solving accuracy.

Quick Guide: Programming a Gaming Mouse for PACS to Optimize Radiology Workflow.

The increasing demand for diagnostic imaging has added to the radiologists' workload, highlighting the shortcomings of conventional computer mice. Radiologists grapple with inefficiencies from frequent mouse clicks and keyboard shortcuts required for various PACS functions. These inefficiencies contribute to cognitive strain, errors, and repetitive strain injuries. High-performance gaming mice, known for their precision in the gaming world, offer multiple custom buttons and superior tracking. These features can streamline radiology tasks. Utilizing a gaming mouse tailored for radiology tasks can substantially enhance efficiency. Our guide offers a step-by-step approach to harnessing the gaming mouse's capabilities for radiology tasks, ensuring radiologists can enhance their workflow and minimize injury risks. Although the guide uses a Logitech gaming mouse for demonstration, it is designed to be intuitive, helping users adapt to their unique needs across different modalities, subspecialties, and various radiology viewer software. Importantly, its fundamental concepts are transferrable to other mouse brands or models with similar customization software.

Hybrid CNN-transformer network for interactive learning of challenging musculoskeletal images

Background and objectivesSegmentation of regions of interest (ROIs) such as tumors and bones plays an essential role in the analysis of musculoskeletal (MSK) images. Segmentation results can help with orthopaedic surgeons in surgical outcomes assessment and patient's gait cycle simulation. Deep learning-based automatic segmentation methods, particularly those using fully convolutional networks (FCNs), are considered as the state-of-the-art. However, in scenarios where the training data is insufficient to account for all the variations in ROIs, these methods struggle to segment the challenging ROIs that with less common image characteristics. Such characteristics might include low contrast to the background, inhomogeneous textures, and fuzzy boundaries. Methodswe propose a hybrid convolutional neural network – transformer network (HCTN) for semi-automatic segmentation to overcome the limitations of segmenting challenging MSK images. Specifically, we propose to fuse user-inputs (manual, e.g., mouse clicks) with high-level semantic image features derived from the neural network (automatic) where the user-inputs are used in an interactive training for uncommon image characteristics. In addition, we propose to leverage the transformer network (TN) – a deep learning model designed for handling sequence data, in together with features derived from FCNs for segmentation; this addresses the limitation of FCNs that can only operate on small kernels, which tends to dismiss global context and only focus on local patterns. ResultsWe purposely selected three MSK imaging datasets covering a variety of structures to evaluate the generalizability of the proposed method. Our semi-automatic HCTN method achieved a dice coefficient score (DSC) of 88.46 ± 9.41 for segmenting the soft-tissue sarcoma tumors from magnetic resonance (MR) images, 73.32 ± 11.97 for segmenting the osteosarcoma tumors from MR images and 93.93 ± 1.84 for segmenting the clavicle bones from chest radiographs. When compared to the current state-of-the-art automatic segmentation method, our HCTN method is 11.7%, 19.11% and 7.36% higher in DSC on the three datasets, respectively. ConclusionOur experimental results demonstrate that HCTN achieved more generalizable results than the current methods, especially with challenging MSK studies.

Kompiuteryje atliekamų pasikartojančių veiksmų automatizavimo sistemos kūrimas

As technology advances and time saves time, more and more tasks are being automated, reducing the need for manual work. The process of automation is beneficial in computer-based work as well. This article analyzes automation systems for mouse clicks, their benefits, and their applications in various fields. Project problem. The automated click systems do not provide flexible, reliable, and convenient algorithmic functions for the use of complex marketing systems, mobile games, or a broader market segment. The objective of this work is to create a system that simulates mouse clicks, swipes, and text input, allowing for repetitive actions, creating and saving action execution algorithms. The theoretical part discusses similar automated systems, their advantages and disadvantages, text and image recognition algorithms, and popular libraries. The compliance of the systems with the ISO/IEC 9126 standard is analyzed. The programming language chosen for development is C++, Qt framework, the Tesseract library is used for text recognition, while the OpenCV library is used for graphic objects. The practical part presents the project of the created automated action system, the development process, the system itself, and the system's application possibilities. The system includes 10 control tools: text, color, and image recognition systems, mouse click, text input, and swipe simulation systems, automated action deletion, algorithm execution/stop, settings selection, menu collapse/expand, and system shutdown. The system allows for the creation of mouse click, swipe, and text input algorithms, defining conditions, creating action instructions, and sharing instructions on a forum. The system recognizes texts, colors, and images. Keywords: automating click operations, automatic actions system, automated optical character recognition, automatic clicker.

Easykin: a flexible and user-friendly online tool for forensic kinship testing and missing person identification.

In forensic kinship testing and missing person identification, it is a fundamental question to choose the most informative reference relatives, select appropriate genotyping systems, and evaluate the weight of evidence comprehensively. Despite that several useful tools have been developed, they have not addressed these questions satisfactorily. In this paper, we develop a flexible and user-friendly online tool, Easykin, to address the aforementioned issues. It has some promising features: (i) Pedigrees can be constructed easily and presented intuitively with just a few mouse clicks. (ii) System power can be estimated before testing based on certain set of markers and reference relatives. (iii) The pruning function of EasyKin enables users to choose appropriate subsets of available references. (iv) Parameters at a specific LR for a single case may ease evidence interpretation. (v) The user interface (UI) is an HTML-based dashboard, which is friendly to both professional and non-professional users and can be used anytime and anywhere. Here, we presented three common cases as examples to demonstrate how kinship testing and missing person identification can be improved with EasyKin. In conclusion, this tool provides a one-stop solution for forensic use, that is, instructing users to choose appropriate kits and reference relatives before testing, calculating LR in the testing, and providing parameters for data interpretation after testing. EasyKin is freely available at https://forensicsysu.shinyapps.io/EasyKin/ .

IDLaS-NL – A platform for running customized studies on individual differences in Dutch language skills via the Internet

We introduce the Individual Differences in Language Skills (IDLaS-NL) web platform, which enables users to run studies on individual differences in Dutch language skills via the Internet. IDLaS-NL consists of 35 behavioral tests, previously validated in participants aged between 18 and 30 years. The platform provides an intuitive graphical interface for users to select the tests they wish to include in their research, to divide these tests into different sessions and to determine their order. Moreover, for standardized administration the platform provides an application (an emulated browser) wherein the tests are run. Results can be retrieved by mouse click in the graphical interface and are provided as CSV file output via e-mail. Similarly, the graphical interface enables researchers to modify and delete their study configurations. IDLaS-NL is intended for researchers, clinicians, educators and in general anyone conducting fundamental research into language and general cognitive skills; it is not intended for diagnostic purposes. All platform services are free of charge. Here, we provide a description of its workings as well as instructions for using the platform. The IDLaS-NL platform can be accessed at www.mpi.nl/idlas-nl.

Improving health care efficiency one click at a time.

Computers are an integral component of modern hospitals. Mouse clicks are currently inherent to this use of computers. However, mouse clicks are not instantaneous. These clicks may be associated with significant costs. Estimated costs associated with 10 additional clicks per day for 20 000 staff exceed AU$500 000 annually. Workflow modifications that increase clicks should weigh the potential benefits of such changes against these costs. Future investigation of strategies to reduce low-value clicks may provide an avenue for health care savings.

Neo Banks: A Paradigm Shift in Banking

Purpose: In terms of market structure and competitiveness, Indian banking has seen major changes. The future of banking services is being revolutionised by technical and digital advancements. Technology advancements are anticipated to have a significant impact on the global financial sector in the near future. Innovations in payments, lending, asset management, and insurance put financial institutions' business models and strategies to the challenge, but they also offer opportunities to both newcomers and existing market players. Fintech is a term used to characterise emerging technological advancements in the financial services sector due to an increased reliance on information technology. The concept of new- generation banking is resonating with traditional banks' increased use of technology through in-house upgrades or partnerships with fintechs. This in turn is driving the banking sector toward new business models, innovative products, and services. Clients nowadays, particularly retail customers, want banks to offer them efficient, dependable, and customised services. Therefore, there is a need to offer financial services that are both affordable and accessible to everyone. Narrow banks. One such cutting-edge strategy used by fintech companies is neo banking. Neo banking is a financial industry buzzword that is revolutionising India's Fintech industry. Neo banks, which have been widely successful, are now expanding into India and giving users access to financial services with just a mouse click. Neo banks can offer banking services and products online through partnerships with traditional banks despite not having a physical presence. Neo banks provide cutting-edge banking options to their clients, particularly the tech-savvy generation and the underbanked citizens of our nation. Therefore, this research paper aims to introduce this concept of neo banking to a wider range of consumers and attempts to make a study on Neo bank in its present and future by analyzing its pros and cons through SWOC and ABCD Framework Analysis. Design/Methodology/Approach: Research gap is identified after a literature search on Neo banks. Since this concept is in its inception in India, there is a lot of scope for research. Secondary data is analysed to understand the concept of neo banks and SWOC and ABCD framework analysis is conducted on Neo banks. Findings/ Results: This research article explores the new paradigm shift in providing financial services to individuals and businesses. Since Neo banks are in its inception stage, this paper attempts to understand the advantages, challenges, and benefits of Neo banking through ABCD and SWOC analysis. In addition, the paper also focuses on 5 prominent neo banks in India and the growth and future for Neo banking in India. Type of Article: Review-based exploratory study.

Association of single-click radiomic classifier with response and prognosis in non-small cell lung cancers (NSCLC) treated with immune checkpoint inhibitors.

8574 Background: Radiomics has shown promise to non-invasively phenotype disease and address the limitations of extant biomarkers (e.g. PD for immune checkpoint inhibitors (ICI) in cancers, such as NSCLC. However, considerable barriers to the clinical adoption of these tools remain, such as their dependence on precise annotation of tumor extent by experienced clinical users. Here, we demonstrate a radiomic solution that requires only a single user mouse click within one or more target lesions on a baseline CT scan, to contour tumors in 3D and generate a patient-level radiomic prediction of response and outcome in ICI treated NSCLC patients. Methods: 1778 CT scans from 1261 patients were used to develop and validate an interactive, semi-automated tool for predicting ICI outcomes in NSCLC patients prior to therapy. A user click based deep learning contouring model was trained and validated on 1146 patients, then used to create annotations for radiomic analysis. A least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model was utilized to select features associated with post-ICI overall survival (OS) and derive a radiomic risk score within a training cohort (n=74) that can separate patients into high and low risk groups. The model was tested on held out pre-treatment CTs of 41 ICI recipients from 2 institutions for association with OS, progression-free survival (PFS), and objective response (OR). Results: A total of 77 lesions were identified and segmented within the testing set. Average volume per lesion was 54.10 mL and per patient was 101.60 mL. OR was observed in 48.70% of patients. A threshold of -0.31 defining high and low radiomic risk groups was chosen based on optimal separation within the training set (HR=2.59 [95% 1.48~4.50], p=0.0009). Radiomic risk groups significantly stratified patients by OS (C-index=0.64, HR=3.03 [95% 1.15~8.02], p=0.03) and PFS (C-index=0.59, HR=3.20 [95% 1.13~9.10], p=0.03). Radiomic IO risk group was independently prognostic of clinical variables (Table 1) and further predicted ICI response with AUC=0.74 [95% 0.71-0.78]. Conclusions: From a single click in target lesions, our model was able to predict response and prognosis of ICI recipients from a baseline radiology scan. Additional multi-site validation and prospective evaluation will assess the value of the radiomics classifier as a decision support tool in the clinic. [Table: see text]

Reduction in Radiologist Interpretation Time of Serial CT and MR Imaging Findings with Deep Learning Identification of Relevant Priors, Series and Finding Locations

Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies. The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processingand descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL. Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65seconds; p<0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%; p<0.001). Less mouse clicks (-17.2%) were needed for finding evaluation with TL, and mouse distance traveled was reduced by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%; p<0.001). Readers were able to measure a given finding in 94.4% of cases on the series initially proposed by TL as most relevant series for comparison. The heatmaps showed consistently simplified mouse movement patterns with TL. A deep learning tool significantly reduced both the amount of user interactions with the radiology image viewer and the time needed to assess findings of interest on cross-sectional imaging with relevant prior exams.