Potential Matches Research Articles

Understanding factors that influence students' sense of belonging and engagement in a career exploration program: A mixed methods study

AbstractThe Talent Identification and Career Exploration (TICE) program, funded by a Jacob K. Javits grant, focuses on increasing underrepresented students’ participation in gifted and talented programs. Early identification for gifted and talented programs expands student learning experiences, and when students from marginalized groups are not identified, they are denied equal opportunity to maximize their potential and enter the appropriate career path. This study focused on sixth‐ and seventh‐grade students from a predominantly rural midwestern state who participated in TICE from 2020 to 2022. Students engaged in four modules focused on sensemaking of their strengths and potential future career matches. This mixed‐methods study used student feedback to understand the relationship between students’ feelings of belonging and learning format on student enjoyment of the modules. Results show that students’ sense of belonging was a significant predictor of student enjoyment for two of the four modules, and learning format was not a significant predictor of student enjoyment. Qualitative data revealed students valued working with each other and learned about themselves and careers for which they would be a potential match. Findings from the study can be applied to inform the development, implementation, and evaluation of school‐based career interventions for talented student populations.

User preferences in ride-sharing mathematical models for enhanced matching

Ride-sharing services have attracted significant interest due to overcrowding, limited energy resources, and environmental concerns. This study proposes a mathematical programming model that integrates user preferences into the ride-sharing problem with transfer. Passenger transfer in ride-sharing problems addresses the limitations of the matching process, especially in less-populated areas. It allows passengers to be dropped off at meeting points and continue with another driver. Moreover, considering user preferences in ride-sharing systems is crucial for enhancing efficiency and user satisfaction. Accordingly, we propose a Preference-Driven Matching Algorithm for the matching process. Our proposed algorithm captures user preferences and provides potential matches. In addition, we introduce an Iterative Enhance-and-Optimize Algorithm capable of producing high-quality solutions within short computational times. We evaluate the efficiency of these approaches across various instances, focusing on real-scale scenarios. Based on the results, the model with preferences demonstrates effective performance compared to other methods. Our findings underscore the importance of user preferences in optimizing ride-sharing problems and highlight the trade-off between efficiency and user satisfaction. By incorporating user preferences, our approach results in higher user satisfaction, a more responsive and efficient system with reduced response times, and increased demand and revenue by servicing more users.

Comparison of identification methods used in Forensic Anthropological services in four distinct institutes in Brazil and the United States

ABSTRACT Identifying unknown deceased individuals, especially those in advanced stages of decomposition, dismemberment, or burning, is a major medico-legal challenge. Forensic anthropologists play a crucial role in these cases due to the numerous characteristics that can be derived from bone remains. Their methods help evaluate the biological profile and individual characteristics, narrowing potential matches. This research investigates the identification methods used by forensic anthropologists in Medicolegal Institutes and Medical Examiner’s Offices in Brazil and the USA. Case reports from forensic anthropology institutes in Vitória, Rio de Janeiro, Porto Velho (Brazil), and New York City (USA) were analysed. In resolved cases, details such as the biological profile, trauma, decomposition extent, and individualization markers were documented. Statistical tests evaluated the association between these variables and identification outcomes. Conventional identification methods included dental analysis, DNA testing, and fingerprint analysis. Additionally, Bayesian methods, visual identification, prosthetics, and X-ray comparison proved useful. This study highlights that forensic anthropologists can employ a variety of methodologies beyond standard practices, enhancing the identification process in complex cases.

Blue-button: A tool for institution-agnostic, EHR-integrated regional clinical trial matching.

65 Background: Research indicates that most people with cancer are willing to enroll in clinical trials when offered the chance, but only 7% actually participate in cancer treatment trials. Barriers to screening and enrollment include time and effort for providers to screen patients for trials, and lack of awareness of available regional trials. Stakeholders recommended creation of free, automated screening tools and processes integrated into provider workflows, directly interfacing with site electronic health records (EHRs), that would search for regional trial eligibility. The need for such tools was independently identified in a large national survey of oncology providers. Methods: The Blue-button tool was developed to automatically identify relevant, regional clinical trials. A collaboration between ACS CAN and MITRE, a not-for-profit organization working in the public interest, created the tool’s functionality. Clinical workflow and research protocol were developed in collaboration with University of Texas Southwestern Simmons Comprehensive Cancer Center. This SMART-on-FHIR application works within existing EHR systems by automatically extracting and sending limited, deidentified patient data elements to third-party trial matching services via the FHIR mCODE standard, a core set of non-proprietary, open-source structured data elements that establishes minimum standards for the structure and content of oncology data in health records. Potential trial matches within a specified radius of the practice are returned in a standard format using the FHIR Research Study resource. Results: Blue-button extracts limited patient data from EHR and provides it in an interface allowing users to update data before sending to trial matching services. Only non-identifiable information is sent to the external clinical trial matching services, which then return prescreen trial matches for manual review to confirm patient eligibility. Thus, with a few clicks in a medical record, the tool quickly identifies comprehensive trial availability within a specified distance of the patient’s location. When used by providers, Blue-button reduces the manual burden that often prevents consideration of clinical trials and identifies regional opportunities beyond the treating institution. Conclusions: Successful feasibility testing demonstrated the effectiveness of automated regional trial screening as a model. We have launched a clinical trial at two sites to assess the effectiveness of the Blue-button tool with patients in healthcare settings. We expect patient enrollment to continue through part or all of 2025. This trial of the Blue-button tool will provide additional information on provider accessibility and uptake and will demonstrate whether EHR-integrated regional trial screening leads to increased opportunities and enrollment in cancer clinical trials. Clinical trial information: NCT05885880 .

Hierarchical Graph Neural Network: A Lightweight Image Matching Model with Enhanced Message Passing of Local and Global Information in Hierarchical Graph Neural Networks

Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This paper proposes the h-GNN, a lightweight image matching model, with improvements in the two processing modules, the GNN and matching modules. After image features are detected and described as keypoint nodes of a base graph, the GNN module, which primarily aims at increasing the h-GNN’s depth, creates successive hierarchies of compressed-size graphs from the base graph through a clustering technique termed SC+PCA. SC+PCA combines Principal Component Analysis (PCA) with Spectral Clustering (SC) to enrich nodes with local and global information during graph clustering. A dual non-contrastive clustering loss is used to optimize graph clustering. Additionally, four message-passing mechanisms have been proposed to only update node representations within a graph cluster at the same hierarchical level or to update node representations across graph clusters at different hierarchical levels. The matching module performs iterative pairwise matching on the enriched node representations to obtain a scoring matrix. This matrix comprises scores indicating potential correct matches between the image keypoint nodes. The score matrix is refined with a ‘dustbin’ to further suppress unmatched features. There is a reprojection loss used to optimize keypoint match positions. The Sinkhorn algorithm generates a final partial assignment from the refined score matrix. Experimental results demonstrate the performance of the proposed h-GNN against competing state-of-the-art (SOTA) GNN-based methods on several image matching tasks under homography, estimation, indoor and outdoor camera pose estimation, and 3D reconstruction on multiple datasets. Experiments also demonstrate improved computational memory and runtime, approximately 38.1% and 26.14% lower than SuperGlue, and an average of about 6.8% and 7.1% lower than LightGlue. Future research will explore the effects of integrating more recent simplicial message-passing mechanisms, which concurrently update both node and edge representations, into our proposed model.

A robust correspondence-based registration method for large-scale outdoor point cloud

ABSTRACT Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Efficiency and Privacy in Record Linkage: Evaluating a Novel Blocking Technique Implemented on Cryptographic Longterm Keys

Efficient privacy-preserving record linkage (PPRL) is essential for integrating data from different providers without exposing personally identifiable information (PII). This study investigates the effectiveness of a novel blocking technique, implemented on Bloom filters (a space-efficient probabilistic data structure), to enhance efficiency and maintain privacy in a real-world evaluation. This methodology involves the utilization of Anonlink, an open-source Python-based PPRL system which generates a type of Bloom filter, Cryptographic Longterm Keys (CLK), for secure record linkage. Initially, the relevant PII fields of the two datasets undergo anonymization into CLKs. Utilizing the CLK’s property of similarity preservation, we create manageable blocks of records based on bits in common within the Bloom filters. This allows us to reduce the number of comparisons of non-matching records to improve linkage efficiency. Finally, record linkage is performed to identify potential matches within the blocked datasets. This blocking technique for CLKs is evaluated in terms of efficiency and record matching precision, aiming to determine the optimal balance between the two factors. Preliminary results indicate a significant reduction in computational burden, with recall minimally affected. Moreover, the implemented blocking technique poses no additional risks of privacy breaches. Preliminary evaluation of the blocking technique shows a promising avenue for secure and efficient data integration, especially in datasets with PII, warranting further investigation for validation and wider application.

Post-mortem to ante-mortem facial image comparison for deceased migrant identification

The identification of deceased migrants is a global challenge that is exacerbated by migration distance, post-mortem conditions, access to ante-mortem data for comparison, inconsistent international procedures and lack of communication between arrival and origin countries. Due to low technology requirements, fast speed analysis and ease of transferring digital data, facial image comparison is particularly beneficial in those contexts, especially in challenging scenarios when this may be the only initial ante-mortem data available to identify the deceased. The Facial Identification Scientific Working Group (FISWG) professional guidelines for facial image comparison were developed for living facial appearance, and, therefore, a tailored protocol for the application of post-mortem to ante-mortem facial image comparison was proposed and evaluated in this research. The protocol was investigated via an inter-observer and an accuracy study, using 29 forensic cases (2001–2020) from the University of Milan, provided by the Laboratory of Forensic Anthropology and Odontology. In order to replicate a migrant identification scenario, each post-mortem subject was compared to all 29 ante-mortem targets (841 comparisons). The protocol guided the practitioner through stages of facial image comparison, from broad (phase 1) to more detailed (phase 3), eventually leading to a decision of ‘exclusion’ or ‘potential match’ for each post-mortem to ante-mortem case (phase 4). In phase 4, a support scale was also utilised to indicate the level of confidence in a potential match. Each post-mortem subject could be recorded with multiple potential matches. The protocol proved to be useful guide for facial image comparison, especially for less experienced practitioners and the inter-observer study suggested good reproducibility. The majority (82–96%) of ante-mortem subjects were excluded at the first stage of the protocol, and 71 full post-mortem to ante-mortem facial image comparisons were carried out. On average, two or three potential matches were recorded for each post-mortem subject. The overall accuracy rate was 85%, with the majority (79%) of ante-mortem non-targets correctly excluded from the identification process. An increased number and quality of available ante-mortem images produced more successful matches with higher levels of support. All potential matches involving non-targets received low levels of support, and for 73% of the post-mortem subjects, the ante-mortem target was the only recorded potential match. However, two ante-mortem targets were incorrectly excluded (one at the first stage of the protocol) and therefore changes to the protocol were implemented to mitigate these errors. A full protocol and a practical recording chart for practitioner use is included with this paper.

“I take hot showers so I can practice burning in hell” A corpus analytical study of Tinder female profiles in the UK

The focus of this article is the linguistic choices made by women-seeking-men (WSM) and women-seeking-women (WSW) on Tinder profiles in the UK, which builds on prior corpus-analytical research on dating profiles (Baker 2018; Collins 2019). Data was collected through TinderBotz, a scraping tool which gathered the information publicly displayed on Tinder profiles. Then, to ensure an ethical research praxis, it assigned each profile random identification numbers that guaranteed the anonymity of the users. The corpus consists of two subcorpora (WSM.C and WSW.C) each containing 405 profiles (average number of words=20.15) from women (18-24 y.o.) based in the UK. Given that profile creation on Tinder consists of two parts (the first being semi-guided and the second part, aka the ‘about me’ section, being fully creative), each sub-corpora (WSM.C and WSW.C) was in turn divided into two parts. UK.WSM.T.P.C.1 and UK.WSW.T.P.C.1 include the semi-guided part of the profile while UK.WSM.T.P.C.2 and UK.WSW.T.P.C.2 include the ‘about me’ section). Sketch Engine was used to search for significant n-grams and keywords in both sub-corpora and chi-square tests were conducted to determine the significance of our findings. Our results showed that WSM had less elaborate profiles, mentioned physical attributes (e.g., height) and often redirected potential matches to other platforms (i.e., Instagram). Meanwhile, WSW had more creative ‘about me’ sections in which they described their identity in greater detail (e.g., leatherdyke). Overall, both groups showed similar profiles in terms of work and relationship status.

Impact of Color Space and Color Resolution on Vehicle Recognition Models.

In this study, we analyze both linear and nonlinear color mappings by training on versions of a curated dataset collected in a controlled campus environment. We experiment with color space and color resolution to assess model performance in vehicle recognition tasks. Color encodings can be designed in principle to highlight certain vehicle characteristics or compensate for lighting differences when assessing potential matches to previously encountered objects. The dataset used in this work includes imagery gathered under diverse environmental conditions, including daytime and nighttime lighting. Experimental results inform expectations for possible improvements with automatic color space selection through feature learning. Moreover, we find there is only a gradual decrease in model performance with degraded color resolution, which suggests the need for simplified data collection and processing. By focusing on the most critical features, we could see improved model generalization and robustness, as the model becomes less prone to overfitting to noise or irrelevant details in the data. Such a reduction in resolution will lower computational complexity, leading to quicker training and inference times.

TriHealth Cancer Institute’s collaboration with the Tempus AI TIME program impact on clinical trial operations and enrollment.

1553 Background: TriHealth Cancer Institute has the largest personalized medicine program serving Cincinnati. TriHealth’s EMR integration with Tempus AI TIME allows for patient screening using an AI powered software TApp that matches to a broad clinical trial portfolio. Each patient is followed using LINK portal and trials can be activated rapidly via TIME’s operational model. The goal of this collaboration is to increase TriHealth’s patients' access for clinical trials. Methods: The TApp is software utilizing subject data, trial eligibility criteria, and NLP models for AI matching to TIME studies. Unique TApp searches occur whenever there is new patient data or changes to eligibility criteria. All TApp matches are reviewed by Tempus AI nurses followed by confirmatory screening by TriHealth’s staff. Trials were activated using the TIME program’s standardized operational methods including a pre-negotiated rate card, trials agreement, and a central IRB. Data included number of patients screened, unique TApp searches, trial activations, consents, operational metrics, and financial impact. Results: From January 2022 - March 2023, TApp performed 8,653,397 unique searches for 135 TIME trials on TriHealth’s population of 18,823 patients. During this period, 985 trial eligibility criteria were modified and 200,776 patient clinical updates occurred. There were 2,032 potential matches (1,944 interventional, 88 observational) for 45 different trials. Tempus AI nurses spent 389 hours reviewing all 1,944 interventional matches and of these sent 284 to TriHealth for evaluation. For TIME studies, 28 patients consented (18 interventional, 10 observational) and 7 trials were activated (4 interventional, 3 observational). Total trial billable revenue for TIME studies was approximately $241,500 ($34,500 average per trial). Regarding non-TIME trials, 3 patients consented on 3 interventional studies. Activation time for non-TIME trials compared to TIME trials was 186 days versus 37 days. Conclusions: TriHealth’s partnership with TIME evaluated 18,800+ patients for 135 trials. Over 8.6M unique TApp searches resulted in 2,000+ patients screened by nurses. Operationally, TIME trials resulted in a 2.3X increase in the number of trial activations, an 80% reduction in days to activation, and 9.3 fold increase in enrollment compared to non-TIME studies. The use of AI enabled patient screening, combined with a structured trial activation process, improved patient enrollment at a community health system. [Table: see text]

Effect of a novel artificial intelligence (AI) –enabled multi-trial matching system on patient matching using real-world data.

e13501 Background: Currently clinical research teams at sites are manually matching patients to clinical trials, where each trial in our experience takes at least 25 minutes using highly trained dedicated resources. This limits cancer patients’ clinical trial options and enrollment rates. Moreover, the lack of NGS testing results may also negatively impact matching to clinical trials in targeted therapies. PCC, a consortium of 7 of the Top 20 Pharmaceutical companies, collaborated with Massive Bio to demonstrate how cancer patients’ matching rates can be increased if patients are prescreened to multiple trials simultaneously (i.e. multi-trial matching) instead of a single trial and NGS testing results are integrated into the matching process. Methods: Massive Bio has developed a first-in-class AI-enabled matching system to extract 180 structured clinical data from medical records using computer vision and natural language processing and a recommendation system for matching patients to parameterized inclusion/exclusion (I/E) criteria from over 14,000 actively recruiting interventional clinical trials.Our analysis included a sample of 5,707 cancer patients from the Massive Bio Synergy AI real-world database, corresponding to tumor types in 23 selected trials. We developed a decision-tree algorithm for retrospective matching analysis using modeled I/E criteria and compared these results to the theoretical matching rate (TMR) for each trial based on tumor type and biomarker prevalence, extent of disease at diagnosis and prior treatment history. Results: 690 patients matched to at least one trial were matched to 1.82 times more trials in multi-trial matching; 2.10 times more potential matches were obtained with hypothetical 100% NGS testing; and 99.9% matching time was reduced with AI enabled system; key findings are summarized (Table). Conclusions: This study confirms the underutilization of NGS technology and substantial benefits of AI and NGS in cancer trial matching. Their use resulted in a nearly two-fold increase in potential patient eligibility for trials, and an unprecedented reduction in manual effort, underscoring a significant advancement in the efficiency of the clinical trial matching process. A prospective country-wide analysis is under consideration. [Table: see text]

Criminal Investigation Tracker with Suspect Prediction

This initiative seeks to craft an inclusive and intuitive web-based platform tailored for criminal identification via image and video surveillance, leveraging cutting-edge facial recognition technology. The system integrates a registration portal for inputting data and images of known criminals, utilizing OpenCV and advanced facial recognition algorithms to securely analyze and store their facial attributes. In addition to allowing users to upload images for analysis, the system offers immediate feedback on potential matches with registered criminals. Moreover, the video surveillance module extends this capability to short videos, employing video analytics to identify faces within the footage. The platform ensures real-time feedback for successful identifications and provides an advanced feature enabling users to download details of identified criminals in an Excel format. By amalgamating state-of-the-art technology with an intuitive interface, this project endeavors to bolster law enforcement endeavors by furnishing an efficient and precise tool for criminal identification and tracking. Its objectives encompass developing a robust system for identifying and tracking criminals through advanced facial recognition algorithms and OpenCV technology, designing a user-friendly web interface for seamless navigation across various modules, and establishing a secure and efficient registration section for compiling comprehensive databases of facial features.

Facet-Dependent Oxygen Evolution Reaction Activity of IrO2 from Quantum Mechanics and Experiments.

The diversity of chemical environments present on unique crystallographic facets can drive dramatic differences in catalytic activity and the reaction mechanism. By coupling experimental investigations of five different IrO2 facets and theory, we characterize the detailed elemental steps of the surface redox processes and the rate-limiting processes for the oxygen evolution reaction (OER). The predicted complex evolution of surface adsorbates and the associated charge transfer as a function of applied potential matches well with the distinct redox features observed experimentally for the five facets. Our microkinetic model from grand canonical quantum mechanics (GC-QM) calculations demonstrates mechanistic differences between nucleophilic attack and O-O coupling across facets, providing the rates as a function of applied potential. These GC-QM calculations explain the higher OER activity observed on the (100), (001), and (110) facets and the lower activity observed for the (101) and (111) facets. This combined study with theory and experiment brings new insights into the structural features that either promote or hinder the OER activity of IrO2, which are expected to provide parallels in structural effects on other oxide surfaces.

Potential prediction in aqueous organic redox-targeting flow batteries: DFT calculation and experimental validation

Aqueous organic redox flow batteries (AORFBs) face challenges of low energy density, which can be addressed by the strategy of redox-targeting (RT) reaction integrating solid materials (SMs) with redox mediators (RMs). However, the potential matching between SM and RM is demanding and complex. In this work, we establish a precise density functional theory (DFT) protocol to predict redox potential in RT-AORFB with anthraquinone-2,7-disulfonic acid (AQDS) derivatives as RM and poly(N-anthraquinoyl pyrrole) (PAQPy) as SM. Theoretical redox potentials are calculated from the Gibbs free energy (GFE) of various molecular models. The results suggest a precise potential match for 1,8-dihydroxyanthraquinone-2,7-disulfonic acid (1,8-DHAQDS) and PAQPy (-1.08 V and -1.09 V vs. SHE). Additionally, hydrogen bonding is involved to make simulation results more realistic, demonstrating a positive potential shift with increased GFE difference for both AQDS derivatives and PAQPy. To further elucidate the influencing mechanism of hydrogen bonding, electrostatic potential (ESP) and HOMO-LUMO gap are integrated together with GFE. The results indicate the introduction of hydrogen bonding results in extended distance for electron tunneling and a larger HOMO-LUMO gap, leading to higher GFE difference and a positive potential shift. Remarkably, results of experimental validations agree well with theoretical potential calculation. Based on predictions results, the RT-AORFB is successfully constructed with the well-matched 1,8-DHAQDS and PAQPy, exhibiting a 3.86-fold capacity enhancement compared to blank AORFB with 1,8-DHAQDS. The integrated DFT approach with GFE, ESP and HOMO-LUMO gap in this work emerges as a promising method for accurately predicting redox potentials in RT systems.

Abstract 1754: Test-the-test: Clinical utility of comprehensive whole exome sequencing (WES) and RNA-seq for lymphoma patients

Abstract Background: Due to the rapidly evolving landscape of targeted therapies, there is an unmet need for comprehensive molecular profiling to guide treatment decisions for patients with lymphoma. To meet this need, we designed a pilot study to assess the feasibility and turnaround time (TAT) of a comprehensive WES and whole transcriptome sequencing (RNA-seq) assay (BostonGene Tumor PortraitTM test) in large B-cell lymphoma (LBCL) patients for clinical decision-making (NCT05464823). Design: Patients aged ≥18 years with histologically documented LBCL requiring therapy were eligible. Formalin-fixed paraffin-embedded tissues underwent WES, RNA-seq, and copy number analysis (CNA) with concomitant peripheral blood or saliva for germline DNA sequencing. Samples with <20% tumor purity and median coverage of <142x for tumor and/or <95x for normal samples were labeled as quantity not sufficient (QNS). Genomic and transcriptomic data were profiled to uncover clinically relevant biomarkers and match patients with clinical trials on ClinicalTrials.gov. Results: To date, 63 patients received WES and RNA-seq, with 45 full Tumor PortraitTM test clinical reports delivered and 3 partial reports for QNS samples. Cell of origin (COO, n = 41), LymphGen (n = 41), and lymphoma microenvironment (LME, n = 42) classifications were applied. The median TAT was 8 days for advanced reports, and 76% of reports had a TAT ≤9 days. WES identified frequent TP53 (n = 13) and CDK2NA (n = 5) alterations. COOs were designated as activated (n = 16, 39%) or germinal center (n = 25, 61%) LBCL. The LymphGen classifier showed the EZB MYC- (n = 9, 22%), A53 (n = 6, 15%), and MCD (n = 6, 15%) subtypes as the most prevalent. LME classification revealed most LMEs as mesenchymal (n = 30, 71%). Immune-depleted (n = 7, 17%), immune-inflamed (n = 4, 10%), and germinal center-like (n = 1, 2%) LMEs were less prevalent, but the immune-depleted LME was more common in patients with relapse. Clinically significant findings, such as ABC COO (n = 16), TP53 loss (n = 13), DHITsig+ (n = 6), and CARD11 mutations associated with ibrutinib resistance (n = 3), were identified in 33 samples. On average, 8 clinical trials were identified per delivered report. Conclusion: Our results show the clinical utility and acceptable TAT of using a comprehensive WES and RNA-seq assay on a cohort of lymphoma patients. The produced BostonGene Tumor PortraitTM test reports included findings on significant alterations, LymphGen and LME subtypes, COOs, and potential clinical trial matches. These robust findings, coupled with the rapid TAT, demonstrate the feasibility of using integrated WES and RNA-seq to guide clinical decision-making in lymphoma patients. Citation Format: Dai Chihara, Kumudha Balakrishnan, Gita Masand, Anna Novokreshchenova, Alexander Bagaev, Nikita Kotlov, Ekaterina Postovalova, Eduardo Shugaev-Mendosa, Yuliya Gracheva, Anna Love, Krystle Nomie, Nathan Fowler, Christopher R. Flowers, Jason Westin. Test-the-test: Clinical utility of comprehensive whole exome sequencing (WES) and RNA-seq for lymphoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1754.

Snapshot-Optimal Real-Time Ride Sharing

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.

A search tool based on language modelling developed for The Index of Middle English Prose

Non-standardised early vernaculars present a problem for search tools due to the high degree of variation. The challenge lies in the variation found in orthography, syntax, and lexicon between titles, incipits, and explicits in manuscript copies of the same work. Traditional search methods relying on exact string matching or regular expressions fail to address these variations comprehensively. This project presents a web-based search tool specifically designed to handle linguistic and textual variation. The software is made available as a part of the Index of Middle English Prose (IMEP). The search tool addresses the issue of variation by utilizing a database of incipits and explicits, character-based n-gram language models (LMs) built with the Stanford Research Institute Language Modelling (SRILM) toolkit, and a fuzzy search script (IMEP: FSS) written in Python. The tool optimizes for recall, retrieving multiple potential matches for a search string, without attempting to identify the ‘correct’ one. The search process involves looking up exact matches in the database while simultaneously using the fuzzy search script to evaluate the incipits and explicits against a model of the search string, followed by a match of the search string against models of the incipits and explicits. This two-step process shortens the processing time, which would otherwise be unreasonably long, because while using SRILM to match the search string against each incipit or explicit in the IMEP for precision could be time-consuming, running a first step where all texts are matched against a single LM built from the search string allows for faster processing. A web application, built using Django and Docker, combines the results of the direct database lookup and the fuzzy search script, presenting them as a list with exact matches followed by fuzzy matches ordered by increasing model perplexity. The tool is made available Open Access and can be adapted to other datasets.

A Web Portal for Tracking and Finding Missing Person Using Aadhaar Enabled Biometrics

The Aadhaar-Enabled Biometric Web Portal for Finding Missing Persons research aims to address the critical issue of locating missing individuals by leveraging Aadhaar-enabled biometric data. With the widespread adoption of Aadhaar cards in India, this research uses the unique biometric information linked to Aadhaar cards to create a web portal. Through this portal, users can input relevant biometric data, such as fingerprints or iris scans, which are then compared with the Aadhaar database. The system utilizes advanced biometric matching algorithms to accurately identify potential matches, thereby aiding law enforcement agencies and concerned individuals in locating missing persons efficiently. By integrating Aadhaar-enabled biometrics into a user-friendly web interface, this research facilitates swift and accurate identification, offering a valuable tool in the effort to reunite missing individuals with their families and loved ones.

Criminal Investigation with the Help of Face Recognition

The Criminal Investigation and Face Recognition System (CIFRS) streamlines investigations with an intuitive interface and automated face recognition. Investigators effortlessly manage cases, set parameters, and upload facial images for analysis against a database. Real-time notifications alert investigators to potential matches, contributing to investigative efficiency. CIFRS supports diverse law enforcement management systems, allowing the download of case reports and data in various formats. The system also offers practice scenarios to familiarize users with face recognition technology. Keywords – Criminal Investigation, Face Recognition, Database Management, Law Enforcement.