Multiple Matching Research Articles

International online mentorship for translational researchers

BackgroundTranslational researchers (TRs) fulfill various roles across clinical, educational, and research domains with the ultimate goal of positively impacting patients. Mentorship has been recognized as an important means of career support for TRs, particularly when navigating the complex translational research pipeline and adapting to evolving roles. In response, the Erasmus + PATHWAY project developed and piloted an extra-curricular online preparatory course and mentorship program in 2019 and 2020 to help TRs build mentorship skills, develop their careers, and create online peer-to-peer learning opportunities.MethodsTo assess the pilot online mentorship program, a longitudinal exploratory mixed method study was conducted.ResultsMentees and mentors from both years reported that they joined the program to learn mentorship skills, gain career support, and expand their (international) network. Analysis of evaluation forms indicated that the online preparatory course was evaluated largely positively, with participants suggesting improvements for future iterations. Results of a follow-up survey in 2022 revealed that mentorship was considered helpful in supporting TRs’ work in translational research, and an online mentorship program was useful, provided it included interactive online training, multiple mentee-mentor matching rounds, compatible time zones and professional experience for matched pairs, active program moderation with offline activities, and effective online tools.ConclusionsThis study revealed the mentorship needs of TRs and their recommendations for international online mentorship. The innovative PATHWAY program’s online format, mentee-driven matching, and preparatory training for both mentees and mentors contribute to the development of mentorship for the general translational community that could potentially have broader applications, especially in a post-COVID-19 environment.

Nicotine dependence and incident psychiatric disorders: prospective evidence from US national study.

We examined the prospective associations between nicotine dependence and the likelihood of psychiatric and substance use disorders in the general adult population. Participants came from a nationally representative sample of US adults aged 18 years or older, who were interviewed 3 years apart in the National Epidemiologic Survey on Alcohol and Related Conditions (Wave 1, 2001-2002; Wave 2, 2004-2005). The primary analyses were limited to 32,671 respondents (13,751 male (47.9% weighted); mean age of 45 years (SD = 0.18)) who were interviewed in both waves. We used multiple regression and propensity score matching (PSM) to estimate the strength of independent associations between nicotine dependence related to the use of tobacco products at Wave 1 and incident psychiatric disorders at Wave 2. Psychiatric disorders were measured with a structured interview (Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV). All analyses adjusted for multiple potential confounders, including childhood (family history of substance use disorders, parental loss, vulnerable family environment), early-adolescence (self-esteem, social deviance, conduct disorder), late-adolescence (education, personality and psychiatric disorders), adulthood (divorce, stressful life events, social deviance, quality of life, history of alcohol or other substance use disorder), and sociodemographic factors. Multiple regression analysis and PSM converged in indicating that nicotine dependence was associated with significantly increased incidence of any psychiatric disorder (OR = 1.39(95%CI:1.20;1.60)), including substance use disorders (OR = 1.91(95%CI:1.47;2.47)), and anxiety disorders (OR = 1.31(95%CI:1.06;1.62)). Population Attributable Risk Proportions were substantial, ranging from 12.5%(95%CI:8.10;17.0) for any psychiatric disorder to 33.3%(95%CI:18.7;48.0) for any other drug use disorder. Supplementary analyses also indicated significant associations between nicotine dependence and persistence of psychiatric and substance use disorders among patients having a disorder at Wave 1. In the general adult population, nicotine dependence is associated with an increased likelihood for several psychiatric and substance use disorders. Given its high prevalence, these findings have important public health implications.

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.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.

Romosozumab followed by denosumab versus denosumab only: a post hoc analysis of FRAME and FRAME extension.

Osteoanabolic-first treatment sequences are superior to oral bisphosphonates for fracture reduction and bone mineral density (BMD) gain. However, data comparing osteoanabolic medications, with the more potent antiresorptive, denosumab (DMAb), are limited. We analyzed FRAME and FRAME Extension data to assess BMD and fracture incidence in patients treated with romosozumab (Romo) followed by DMAb (Romo/DMAb) versus DMAb (DMAb/DMAb) for 24months. In FRAME, women aged ≥55years (total hip [TH] or femoral neck [FN] T-score: -2.5 to -3.5) were randomized to Romo or placebo for 12months followed by DMAb for 12months. In FRAME Extension, both cohorts received DMAb for another 12months. This post hoc analysis compared BMD change and fracture incidence in patients on Romo/DMAb (months 0-24) versus DMAb/DMAb (months 12-36). Patient characteristics were balanced by propensity score weighting (PSW) and sensitivity analyses were conducted using PSW with multiple imputation (PSW-MI) and propensity score matching (PSM). Unmeasured confounding was addressed using E-values. After PSW, over 24months, compared with DMAb/DMAb, treatment with Romo/DMAb produced significantly greater BMD increases at the lumbar spine [LS], TH, and FN (mean differences: 9.3%, 4.4%, and 4.1%, respectively; all p<0.001). At month 24, in women with a baseline T-score of -3.0, the probability of achieving a T-score > -2.5 was higher with Romo/DMAb versus DMAb/DMAb (LS: 92% versus 47%; TH: 50% versus 5%). In the Romo/DMAb versus DMAb/DMAb cohorts, new vertebral fractures were significantly reduced (0.62% versus 1.26% [odds ratio = 0.45; p=0.003]) and rates of clinical, nonvertebral, and hip fractures were lower (differences not significant). Similar BMD and fracture outcomes were observed with PSW-MI and PSM sensitivity analyses. The sequence of Romo/DMAb resulted in greater BMD gains and higher probability of achieving T-scores > -2.5, significantly reduced new vertebral fracture incidence, and numerically lowered the incidence (not significant) of clinical, nonvertebral, and hip fractures versus DMAb only through 24months.

The impact of microfinance on entrepreneurship and welfare among women borrowers in rural Pakistan

This study investigates the effects of microfinance on entrepreneurial activity and household welfare among women borrowers in rural Pakistan. Using survey data from 463 clients of multiple microfinance institutions and propensity score matching methods, we find that women who invest their loans in microenterprises experience significantly higher income, clothing expenditures, poverty scores, and income diversification compared to those who use loans for other purposes. However, we do not observe increases in health and education spending or reductions in child labour among entrepreneurial borrowers. The results highlight the potential for microfinance to stimulate women’s entrepreneurship and improve economic conditions in rural Pakistani households, but also underscore the limitations in promoting human capital investment. We discuss implications for enhancing microfinance interventions to support holistic welfare improvements for women entrepreneurs in Pakistan and beyond.

Government Subsidies and IPO Firms’ Short-Term Excess Returns: Evidence from China

AbstractAs an important tool for local governments to intervene in the listing process in their jurisdiction, the implementation effect of government subsidy has been quite controversial. This study examines the pricing mechanism of Initial Public Offerings (IPO) within China’s macro institutional environment. The period from April 2012 to December 2015 was selected as the sample observation period, with companies listed on Growth Enterprise Market (GEM) as the study objects. By focusing on IPO firms’ short-term excess returns, the multiple linear regression model and propensity score matching method (PSM) were applied to evaluate the actual impact of local government intervention through financial subsidies during the listing process. The results show that government subsidies improve IPO firm’s short-term excess returns. This promotion effect is more significant in state-owned enterprises rather than in private enterprises. Moreover, the implicit window guidance weakens the promotion effect of government subsidies on the short-term excess returns. Further, the more government subsidies the IPO firms receive, the worse their long-term performance will be. The findings demonstrate that local governments employ financial subsidies to expedite the listing process within their jurisdiction, thereby facilitating IPO companies in generating short-term excessive returns while simultaneously impeding the normal production and operation. The conclusion enhances theoretical understanding regarding high underpricing, while also offering investors a novel approach to interpreting government subsidy information and assessing IPO firm’s earnings sustainability.

Learnable Graph Matching: A Practical Paradigm for Data Association.

Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet. For point cloud registration, we also achieve competitive results.

Efektivitas Penggunaan Essay Pada Evaluasi Pembelajaran Matematika Kelas IV Di MIN 2 Kota Palangka Raya

In the midst of the dynamics of Indonesian education, learning evaluation plays a very important role. Evaluations not only measure student achievement, but also identify effective teaching methods. The success of teaching is not only seen from the learning outcomes achieved by students, but also in terms of the process. To determine the success of learning objectives, an educator needs to carry out learning evaluations. In conducting evaluations, teachers are given the freedom to determine their own form of evaluation used. There are many types of evaluation tools in learning such as multiple choice, essays, matching and many more. The aim of this evaluation is to make it easier for a teacher to carry out a task or achieve a goal effectively and efficiently. This research aims to determine the effectiveness of using essays as a learning evaluation. This research uses descriptive qualitative research. The technique used in data collection was an interview with one of the 4th grade teachers at MIN 2 Palangka Raya City. The research results show that the use of essays in mathematics subjects in grade 4 is more effective than using other forms of evaluation. Because, students can be invited to think more critically in working on the questions given, so that students can solve them well.

Enhancing the Performance and Accuracy in Real-Time Football and Player Detection Using Upgraded YOLOv5 Architecture

The study presents a significantly improved version of the YOLOv5 real-time object detection model for football player recognition. The proposed technique includes feature-tuning and hyper-parameter optimization methods that have been carefully selected to enhance both speed and accuracy, resulting in a superior real-time performance of the YOLOv5 architecture. Furthermore, the YOLOv5 model incorporates a SimSPPF module that enables multi-scale feature extraction with less computational power, making it a highly efficient and effective solution. We selected the GhostNet module to reduce complexity and the Slim scale detection layer for precise bounding box prediction. Our tests, conducted with recordings of multiple football matches, demonstrate that our model accurately detects both the football and players even in complex scenarios with occlusions and dynamic illumination. The suggested method outperforms the original YOLOv5n model in terms of precision, recall, and mean average precision at 0.5 IoU. It is also more computationally efficient. This method has potential applications in live broadcasting, player monitoring, and sports analytics. The upgraded YOLOv5 model demonstrates superior accuracy and efficiency compared to previous methods that rely on traditional image processing techniques or two-stage detectors. This makes it highly suitable for practical, real-world deployments.

Timing of magnetic resonance imaging in pregnancy for outcome prediction in congenital diaphragmatic hernia.

To evaluate the impact of the timing of MRI on the prediction of survival and morbidity in patients with CDH, and whether serial measurements have a beneficial value. This retrospective cohort study was conducted in two perinatal centers, in Germany and Italy. It included 354 patients with isolated CDH having at least one fetal MRI. The severity was assessed with the observed-to-expected total fetal lung volume (o/e TFLV) measured by two experienced double-blinded operators. The cohort was divided into three groups according to the gestational age (GA) at which the MRI was performed (< 27, 27-32, and > 32weeks' gestation [WG]). The accuracy for the prediction of survival at discharge and morbidity was analyzed with receiver operating characteristic (ROC) curves. Multiple logistic regression analyses and propensity score matching examined the population for balance. The effect of repeated MRI was evaluated in ninety-seven cases. There were no significant differences in the prediction of survival when the o/e TFLV was measured before 27, between 27 and 32, and after 32 WG (area under the curve [AUC]: 0.77, 0.79, and 0.77, respectively). After adjustment for confounding factors, it was seen, that GA at MRI was not associated with survival at discharge, but the risk of mortality was higher with an intrathoracic liver position (adjusted odds ratio [aOR]: 0.30, 95% confidence interval [95%CI] 0.12-0.78), lower GA at birth (aOR 1.48, 95%CI 1.24-1.78) and lower o/e TFLV (aOR 1.13, 95%CI 1.06-1.20). ROC curves showed comparable prediction accuracy for the different timepoints in pregnancy for pulmonary hypertension, the need of extracorporeal membrane oxygenation, and feeding aids. Serial measurements revealed no difference in change rate of the o/e TFLV according to survival. The timing of MRI does not affect the prediction of survival rate or morbidity as the o/e TFLV does not change during pregnancy. Clinicians could choose any gestational age starting mid second trimester for the assessment of severity and counseling.

Automated vs. manual coding of neuroimaging reports via natural language processing, using the international classification of diseases, tenth revision

ObjectiveNatural language processing (NLP) can generate diagnoses codes from imaging reports. Meanwhile, the International Classification of Diseases (ICD-10) codes are the United States' standard for billing/coding, which enable tracking disease burden and outcomes. This cross-sectional study aimed to test feasibility of an NLP algorithm's performance and comparison to radiologists' and physicians' manual coding. MethodsThree neuroradiologists and one non-radiologist physician reviewers manually coded a randomly-selected pool of 200 craniospinal CT and MRI reports from a pool of >10,000. The NLP algorithm (Radnosis, VEEV, Inc., Minneapolis, MN) subdivided each report's Impression into “phrases”, with multiple ICD-10 matches for each phrase. Only viewing the Impression, the physician reviewers selected the single best ICD-10 code for each phrase. Codes selected by the physicians and algorithm were compared for agreement. ResultsThe algorithm extracted the reports' Impressions into 645 phrases, each having ranked ICD-10 matches. Regarding the reviewers' selected codes, pairwise agreement was unreliable (Krippendorff α = 0.39-0.63). Using unanimous reviewer agreement as “ground truth”, the algorithm's sensitivity/specificity/F2 for top 5 codes was 0.88/0.80/0.83, and for the single best code was 0.67/0.82/0.67. The engine tabulated “pertinent negatives” as negative codes for stated findings (e.g. “no intracranial hemorrhage”). The engine's matching was more specific for shorter than full-length ICD-10 codes (p = 0.00582x10−3). ConclusionsManual coding by physician reviewers has significant variability and is time-consuming, while the NLP algorithm's top 5 diagnosis codes are relatively accurate. This preliminary work demonstrates the feasibility and potential for generating codes with reliability and consistency. Future works may include correlating diagnosis codes with clinical encounter codes to evaluate imaging's impact on, and relevance to care.

Does Ecotourism in Nature Reserves Have an Impact on Farmers’ Income? Counterfactual Estimates Based on Propensity Score Matching

Farmers’ participation in ecological tourism management in nature reserves is an important way to increase income. Based on 921 pieces of household survey data from 44 villages in six nature reserves in Liaoning Province, this paper uses multiple linear regression (OLS) and propensity score matching (PSM) to explore the impact of ecotourism on rural household income. The research results show that (1) a total of 90 rural households participated in ecotourism management, accounting for 9.78% of the total, and 831 rural households did not participate in ecotourism management, accounting for 90.22% of the total. The participation rate of farmers around the nature reserves was not high; (2) the participation in ecotourism management of farmers around the nature reserve has a positive and significant impact on the per capita annual net income of their households; and (3) multiple linear regression analysis will overestimate the income effect of ecotourism. This article provides inspiration for the government to propose relevant policies to encourage farmers to participate in ecotourism.

Effect of visual impairment on subjective cognitive decline in older adults: a cross-sectional study in China

ObjectivePeople with visual impairment have more functional limitations associated with subjective cognitive decline (SCD), and those with SCD are extremely susceptible to transitioning to irreversible cognitive impairment. This study aimed...

Influence of the combination of SGLT2 inhibitors and GLP-1 receptor agonists on eGFR decline in type 2 diabetes: post-hoc analysis of RECAP study.

Accumulating evidence has demonstrated that both SGLT2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP1Ra) have protective effects in patients with diabetic kidney disease. Combination therapy with SGLT2i and GLP1Ra is commonly used in patients with type 2 diabetes (T2D). We previously reported that in combination therapy of SGLT2i and GLP1Ra, the effect on the renal composite outcome did not differ according to the preceding drug. However, it remains unclear how the initiation of combination therapy is associated with the renal function depending on the preceding drug. In this post hoc analysis, we analyzed a total of 643 T2D patients (GLP1Ra-preceding group, n = 331; SGLT2i-preceding group, n = 312) and investigated the differences in annual eGFR decline. Multiple imputation and propensity score matching were performed to compare the annual eGFR decline. The reduction in annual eGFR decline in the SGLT2i-preceding group (pre: -3.5 ± 9.4mL/min/1.73m2/year, post: -0.4 ± 6.3mL/min/1.73m2/year, p < 0.001), was significantly smaller after the initiation of GLP1Ra, whereas the GLP1Ra-preceding group tended to slow the eGFR decline but not to a statistically significant extent (pre: -2.0 ± 10.9mL/min/1.73m2/year, post: -1.8 ± 5.4mL/min/1.73m2/year, p = 0.83) after the initiation of SGLT2i. After the addition of GLP1Ra to SGLT2i-treated patients, slower annual eGFR decline was observed. Our data raise the possibility that the renal benefits-especially annual eGFR decline-of combination therapy with SGLT2i and GLP1Ra may be affected by the preceding drug.

SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection

Current 3D object detection methods for indoor scenes mainly follow the voting-and-grouping strategy to generate proposals. However, most methods utilize instance-agnostic groupings, such as ball query, leading to inconsistent semantic information and inaccurate regression of the proposals. To this end, we propose a novel superpoint grouping network for indoor anchor-free one-stage 3D object detection. Specifically, we first adopt an unsupervised manner to partition raw point clouds into superpoints, areas with semantic consistency and spatial similarity. Then, we design a geometry-aware voting module that adapts to the centerness in anchor-free detection by constraining the spatial relationship between superpoints and object centers. Next, we present a superpoint-based grouping module to explore the consistent representation within proposals. This module includes a superpoint attention layer to learn feature interaction between neighboring superpoints, and a superpoint-voxel fusion layer to propagate the superpoint-level information to the voxel level. Finally, we employ effective multiple matching to capitalize on the dynamic receptive fields of proposals based on superpoints during the training. Experimental results demonstrate our method achieves state-of-the-art performance on ScanNet V2, SUN RGB-D, and S3DIS datasets in the indoor one-stage 3D object detection. Source code is available at https://github.com/zyrant/SPGroup3D.

The detrimental effect of biopsy preceding resection in surgically accessible glioblastoma: results from the national cancer database.

Aggressive resection in surgically-accessible glioblastoma (GBM) correlates with improved survival over less extensive resections. However, the clinical impact of performing a biopsy before definitive resection have not been previously evaluated. We analyzed 17,334 GBM patients from the NCDB from 2010-2014. We categorized them into: "upfront resection" and "biopsy followed by resection". The outcomes of interes included OS, 30-day readmission/mortality, 90-day mortality, and length of hospital stay (LOS). The Kaplan-Meier methods and accelerated failure time (AFT) models were applied for survival analysis. Multivariable binary logistic regression were performed to compare differences among groups. Multiple imputation and propensity score matching (PSM) were conducted for validation. "Upfront resection" had superior OS over "biopsy followed by resection" (median OS:12.4 versus 11.1months, log-rank p = 0.001). Similarly, multivariable AFT models favored "upfront resection" (time ratio[TR]:0.83, 95%CI: 0.75-0.93, p = 0.001). Patients undergoing "upfront gross-total resection (GTR)" had higher OS over "upfront subtotal resection (STR)", "GTR following STR", and "GTR or STR following initial biopsy" (14.4 vs. 10.3, 13.5, 13.3, and 9.1months;TR: 1.00 [Ref.], 0.75, 0.82, 0.88, and 0.67). Recent years of diagnosis, higher income, facilities located in Southern regions, and treatment at academic facilities were significantly associated with the higher likelihood of undergoing upfront resection. Multivariable regression showed a decreased 30 and 90-day mortality for patients undergoing "upfront resection", 73% and 44%, respectively (p < 0.001). Pre-operative biopsies for surgically accessible GBM are associated with worse survival despite subsequent resection compared to patients undergoing upfront resection.

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.

DOA Estimation of Broadband Sources Using Dimension-Reduced Matrix Filter with Deep Nulling in a Strong Interference Environment

To enhance the performance of the adaptive matrix filter with nulling, a dimension-reduced matrix filter with deep nulling (DR-MFDN) is proposed in this paper and used for direction of arrival (DOA) estimation of broadband sources in a strong interference environment. Consider the change of noise after filtering, the gaussian matrix is used for prewhitening to prevent the noise from converting into colored noise without affecting the response characteristics of the beam-space. Finally, the multiple measurement vector orthogonal matching pursuit (MOMP) algorithm is used for DOA estimation of weak targets after filtering, and the simulation results show a better DOA estimation performance compared to the OMP and MUSIC algorithms with short snapshots.