Small Subset Research Articles

Peripheral nerve decompression for pain relief in the setting of previous obstetrical brachial plexus injury: a retrospective case series.

In children with previous obstetrical brachial plexus injury (OBPI), upper extremity pain is present in 45 to 66% of patients. Recent literature reports this as musculoskeletal or neuropathic in nature. The purpose of the study is to demonstrate that peripheral nerve decompression, and neurolysis may be an effective treatment for patients with upper extremity pain in the context of previous OBPI. A retrospective chart review was performed on patients undergoing peripheral nerve decompression and neurolysis after OBPI by senior author. The primary outcome assessed was pain, and secondary outcome measure was range of motion of the wrist and elbow. Outcome measures were assessed preoperatively as well as at their subsequent follow-up. Six patients were included, with a mean age of 14years old at time of decompression. Three patients underwent median nerve, two patients underwent ulnar nerve, and one patient underwent posterior interosseous nerve decompression. There was a substantial improvement in pain post-operatively, demonstrated by reduction or resolution of subjective pain in all patients and resolution of Tinel's sign. There was a modest improvement in range of motion. This study demonstrates an improvement in subjective pain and range of motion after decompression and neurolysis in small subset of OBPI patients. It generates the hypothesis that peripheral nerve compression is a source of pain that can be addressed in this population. Future research should focus on confirming this hypothesis and assessing treatment options on a larger scale.

Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation

Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. However, standard evaluation tools merely possess the capacity to provide a general overview of a system’s performance; they lack consistency and effectiveness in their use, as evidenced by most recent studies on the topic. Furthermore, traditional evaluation techniques fail to detect potentially harmful data on small subsets. Moreover, they generally lack explainable features to interpret how such minor variations could affect the system’s performance. This proposal focuses on data clustering for recommender evaluation and applies a cluster assessment technique to locate such performance issues. Our new approach, named group validation , aids in spotting critical performance variability in compact subsets of the system’s data and unravels hidden weaknesses in predictions where such unfavorable variations generally go unnoticed with typical assessment methods. Group validation for recommenders is a modular evaluation layer that complements regular evaluation and includes a new unique perspective to the evaluation process. Additionally, it allows several applications to the recommender ecosystem, such as model evolution tests, fraud/attack detection, and the capacity for hosting a hybrid model setup.

Detection of Ulcerative Colitis Lesions from Weakly Annotated Colonoscopy Videos Using Bounding Boxes

Ulcerative colitis is a chronic disease characterized by bleeding and ulcers in the colon. Disease severity assessment via colonoscopy videos is time-consuming and only focuses on the most severe lesions. Automated detection methods enable fine-grained assessment but depend on the training set quality. To suit the local clinical setup, an internal training dataset containing only rough bounding box annotations around lesions was utilized. Following previous works, we propose to use linear models in suitable color spaces to detect lesions. We introduce an efficient sampling scheme for exploring the set of linear classifiers and removing trivial models i.e., those showing zero false negative or positive ratios. Bounding boxes lead to exaggerated false detection ratios due to mislabeled pixels, especially in the corners, resulting in decreased model accuracy. Therefore, we propose to evaluate the model sensitivity on the annotation level instead of the pixel level. Our sampling strategy can eliminate up to 25% of trivial models. Despite the limited quality of annotations, the detectors achieved better performance in comparison with the state-of-the-art methods. When tested on a small subset of endoscopic images, the best models exhibit low variability. However, the inter-patient model performance was variable suggesting that appearance normalization is critical in this context.

Portfolio analysis of global tobacco control research funding at the National Cancer Institute, 2000-2019.

Research in low- and middle-income countries (LMICs), where the majority of global tobacco users reside, is critical to addressing the global tobacco epidemic. This analysis describes the global tobacco control research portfolio funded by the National Cancer Institute from fiscal years 2000 to 2019. We used the National Institutes of Health Query, View, Report database to identify extramural grants relevant to global tobacco control research. Abstracts were analyzed to describe grant characteristics, including topic areas, tobacco products, countries, and regions of focus. Bibliometric and co-authorship network analyses were performed for publications associated with relevant grants. Of the 93 relevant grants with foreign (non-US) involvement, the majority (83.9%) supported research in upper and lower middle-income countries. The majority of grants (86.0%) focused on cigarettes, with a small subset of grants addressing smokeless tobacco, waterpipe use, or other non-cigarette products. Most grants focused on at least one of the six tobacco control policy measures in the World Health Organization MPOWER package; almost half (48.4%) focused on monitoring tobacco use and around one-third (32.3%) focused on offering tobacco cessation treatment, while other MPOWER measures received less attention in the research portfolio. While most of these grants, and the funding initiatives that supported them, emphasized research in low- and middle-income countries (LMICs), only 3 of 93 grants were awarded directly to LMIC-based institutions. There is a critical need for research to develop and test strategies to adapt, implement, and scale up evidence-based interventions across diverse LMIC settings. This study identified gaps in research activity that should be addressed to strengthen global tobacco control research capacity.

Abstract B068: FOXM1 mediates the crosstalk between tumor microenvironment and ovarian cancer stem cells as revealed by patient-derived organotypic models

Abstract The unfavorable prognosis of high-grade serous ovarian cancer, HGSOC, may be driven by a small subset of ovarian cancer stem cells (OCSC), which are endowed with tumor-initiating ability and are intrinsically resistant to cytotoxic treatments. These characteristics are sustained by signals coming from the tumor microenvironment (TME). As a common site of HGSOC presentation is the omentum, we partially recapitulated the architecture of human omentum in vitro using 3D co-cultures of primary human omental fibroblasts and mesothelial cells. Patient-derived HGSOC cells were then integrated in these organotypic models, either as bulk cancer cells or as OCSC from the same patients. After co-culture in this model, malignant cells were re-isolated by FACS sorting and subjected to whole-transcriptome RNA sequencing, comparing them to control cells that have not been in contact with the in vitro TME. Our data revealed transcriptional reprogramming induced by the in vitro TME in HGSOC cells. We found a number of pathways differentially regulated in the bulk population of OC cells vs OCSC, shedding light on the impact of the TME on OC stemness. Moreover, we identified a panel of transcription factors (TFs) specifically altered by the in vitro TME in OCSC. These TF-regulated gene networks represent OCSC vulnerabilities and, hence, potential targets in relapsing tumors. Among these, we focused on FOXM1, a TF with pleiotropic functions in cancer cells, whose activity is altered in 84% of ovarian cancer patients. The FOXM1 pathway was strongly induced by the in vitro TME specifically in OCSC. Mechanistically, the contact with the in vitro TME triggered a FAK/YAP axis which, in turn, accounted for the induction of FOXM1 and its downstream target genes. Moreover, the pharmacological inhibition of FOXM1 significantly impaired OCSC fitness, supporting, on one hand, the key role of TME in orchestrating OC stemness and suggesting, on the other, a new approach for the eradication of OCSC from the omental niche. Overall, our patient-derived organotypic platform offers the opportunity to unravel the impact of the TME on OCSC at the molecular level in experimental models with clinical relevance and can be exploited to design innovative HGSOC-eradicating treatments. Citation Format: Chiara Battistini, Hilary A. Kenny, Valentina Nieddu, Valentina Melocchi, Alessandra Decio, Beatrice Malacrida, Alessia Gatto, Fabrizio Bianchi, Raffaella Giavazzi, Nicoletta Colombo, Frances R. Balkwill, Ernst Lengyel, Ugo Cavallaro. FOXM1 mediates the crosstalk between tumor microenvironment and ovarian cancer stem cells as revealed by patient-derived organotypic models [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B068.

Abstract A035: Genome-wide CRISPRi screening reveals determinants of platinum sensitivity and resistance in high-grade serous ovarian cancer

Abstract A035: Genome-wide CRISPRi screening reveals determinants of platinum sensitivity and resistance in high-grade serous ovarian cancer

The COVID-19 quandemic

BackgroundThe terms syndemic and infodemic have both been applied to the COVID-19 pandemic, and emphasize concurrent socio-cultural dynamics that are distinct from the epidemiological outbreak itself. We argue that the COVID-19 pandemic has exposed yet another important socio-political dynamic that can best be captured by the concept of a quandemic – a portmanteau of “quantification” and “pandemic”.Main textThe use of quantifiable metrics in policymaking and evaluation has increased throughout the last decades, and is driven by a synergetic relationship between increases in supply and advances in demand for data. In most regards this is a welcome development. However, a quandemic, refers to a situation where a small subset of quantifiable metrics dominate policymaking and the public debate, at the expense of more nuanced and multi-disciplinary discourse. We therefore pose that a quandemic reduces a complex pandemic to a few metrics that present an overly simplified picture. During COVID-19, these metrics were different iterations of case numbers, deaths, hospitalizations, diagnostic tests, bed occupancy rates, the R-number and vaccination coverage. These limited metrics came to constitute the internationally recognized benchmarks for effective pandemic management.Based on experience from the Nordic region, we propose four distinct dynamics that characterize a quandemic: 1) A limited number of metrics tend to dominate both political, expert, and public spheres and exhibit a great deal of rigidity over time. 2) These few metrics crowd-out other forms of evidence relevant to pandemic response. 3) The metrics tend to favour certain outcomes of pandemic management, such as reducing hospitalization rates, while not capturing potential adverse effects such as social isolation and loneliness. 4) Finally, the metrics are easily standardized across countries, and give rise to competitive dynamics based on international comparisons and benchmarking.ConclusionA quandemic is not inevitable. While metrics are an indispensable part of evidence-informed policymaking, being attentive to quandemic dynamics also means identifying relevant evidence that might not be captured by these few but dominant metrics. Pandemic responses need to account for and consider multilayered vulnerabilities and risks, including socioeconomic inequities and comorbidities.

A Data and DFT-Driven Framework for Predicting the Microstructure of Submonolayer Inverse Metal Oxide on Metal Catalysts.

Metal oxides on metal (inverse) catalysts can selectively drive many important reactions. However, understanding the active site under experimentally relevant conditions is lacking. Herein, we introduce a computational framework for predicting atomic models of stable inverse catalysts and demonstrate it for WOx on Pt(553) and a Pt79 nanoparticle at variable WOx coverages. An evolutionary algorithm identifies a small (5%) subset of promising atomic configurations on which DFT simulations are performed. We predict a maximum coverage of ∼50% WOx on Pt(553), consisting of small clusters (tetramers and pentamers), which preferentially reside on the terrace, with their oxygen atoms interacting with the Pt step sites. Consistently, WOx does not lie on curved and undercoordinated metal sites of Pt nanoparticles. The oxide clusters prefer a partially reduced oxidation state. Theoretical EXAFS spectra for select configurations provide insights into interpreting experimental spectra of inverse catalysts. The framework applies to other catalysts.

Analysis of the impact of corrective actions for stochastic project networks

In project management, a project plan is constructed that assigns a planned start time to each project activity. Based on this plan, the total planned project duration and cost can be determined. However, during project execution, deviations from the plan are inevitable due to uncertainty and variability. When these deviations endanger the timely completion of projects, the project manager should take corrective actions to get the project back on track. In this study, corrective actions are modelled as modifications of the original activity duration distributions (i.e., reduced mean and/or standard deviation) to account for the uncertain nature of their impact. Further, an analytical procedure is developed to rank activities according to their expected impact on the project duration distribution when they are controlled by a corrective action. This activity ranking is used to determine the number of actions that should be taken and to select the set of activities that will be controlled. A computational experiment on a large set of project networks with varying network complexity and network structures has been conducted. These experiments have shown that taking actions on a relatively small subset of activities, rather than on the entire set of project activities, proves more efficient, when the subset of activities is carefully selected. More precisely, the efficiency of the corrective actions process depends on both the number of actions and the activity selection criterion (activity ranking).

Accretion-induced flickering variability among symbiotic stars from space photometry with NASA TESS

Context. Symbiotic binaries exhibit a wide range of photometric variability across different timescales. These changes can be attributed to factors such as orbital motion, intrinsic variability of the individual components, or interactions between the two stars. In the range from minutes to hours, a variability induced by accretion processes that is likely to originate from the accretion disks has been detected and subsequently denoted as flickering. This variability could mimic solar-like oscillations exhibited by luminous red giants. Aims. We aim to investigate whether it is possible to utilize the precise observations of the NASA TESS mission to detect flickering in symbiotic stars, despite the fact that such studies are usually performed at shorter wavelengths than those of TESS observations. Additionally, our goal is to develop a quantitative method for the detection of accretion-induced flickering that does not rely solely on a subjective assessment of the light curves. Methods. We obtained the light curves of known symbiotic stars and a comprehensive control sample of assumed single red giants from the TESS full-frame images. To ensure consistency, all the data were processed using the same methodology, which involves filtering out the background, systematic, and long-term trends. From the processed light curves and their power spectral densities, we measured the amplitudes of the variability and other relevant parameters. Results. We introduce a method that enables a differentiation between flickering sources and stars that do not exhibit this type of variability. We detected flickering-like variability in 20 symbiotic stars utilizing TESS data, of which 13 had not previously been identified as flickering sources. Moreover, the TESS observations facilitate the detection of related variations occurring over timescales of a few days, as well as changes in the flickering behavior across multiple sectors. Conclusions. The flickering is now likely to be detected in a total of 35 known symbiotic stars. While this represents only a small subset of all symbiotic binaries, when focusing solely on accreting-only symbiotic stars where the detection of flickering is presumably more straightforward, the fraction could reach as high as ∼80%. This result suggests that accretion disks may indeed be prevalent in these binaries.

Deep Curious Feature Selection: A Recurrent, Intrinsic-Reward Reinforcement Learning Approach to Feature Selection

Feature selection (FS) is an important step in the process of building machine learning-based models. The goal of the feature selection step is to find a small subset of features that will provide good prediction results by removing noisy, irrelevant, or repetitive features. Commonly used Wrapper methods use a machine learning model as a black box and its performance as the goal function for evaluating different features' subsets and selecting the best one. To avoid examining all possible subsets (an NP-hard problem), search algorithms are used to find the subsets to be examined, in a heuristic way. As exhaustive search methods are computationally complicated, most methods use simple and greedy search methods that yield only locally optimal results and are not sensitive to possible features interactions, which means that a feature may be chosen at the expense of two others that are more informative together. We analyze the problem of searching the features subset space with reference to two dimensions, namely, memory of past selected features subset and future selected features. We propose a new wrapper feature selection method based on the deep artificial curiosity framework, which implements intrinsic reward reinforcement learning, with long short-term memory unit (LSTM). This novel algorithm integrates these two elements of memory and future step. We show that our method, called the Deep Curious Feature Selection (DCFS) algorithm, deals with feature interactions, and provides a feature subset which improves the accuracy of learning models on artificial and real datasets.

Latissimus Dorsi Tendon Rupture Repair: A Surgical Technique Video Case Report and Cadaveric Anatomic Demonstration

Isolated latissimus dorsi tendon rupture is an exceedingly rare injury with a paucity of literature available to help guide management. The anatomy of the posterior aspect of the axilla and chest wall is complex. Nonoperative treatment has been described as an acceptable form of management for these injuries. A small subset of case reports and case series also show success with latissimus dorsi repair. We show a one-incision technique for repair of a humeral-sided avulsion of the latissimus dorsi tendon.

Assessing Riverbank Change Caused by Sand Mining and Waste Disposal Using Web-Based Volunteered Geographic Information

River water is one of the most important natural resources for economic development and environmental sustainability. However, river water systems are vulnerable in some of the densely populated regions across the globe. Intense sand mining and waste disposal can lead to river changes, loose foundations, and biodiversity loss. This study aims to develop a web-based geographic information system (GIS) platform to monitor river channel changes and their impacts on river environments due to sand mining and waste burial. The system integrates open-source software, Windows Server 2012, a web server, and PostgreSQL with PostGIS plugins for efficient mapping and storage of geospatial data and volunteered reporting of location events. Interferometric methods, including SNAP2STAMPS Automated Algorithm, persistent scatterer interferometry (PSI), small baseline subset (SBAS), and Snap software, were used to analyze spatial changes of subsidence from Sentinel-1 satellite data from 2021 to 2023 in the Gadar River in Oshnavieh, Iran. The results showed that the maximum subsidence at the riverbank was −10.1 cm due to sand mining, and the maximum uplift was 8.2 cm due to waste landfilling. The average subsidence was reported to be −5.1 cm. The results emphasize spatial analysis, showcasing material mining’s impact on subsidence trends and underscoring the significance of public participation in monitoring river health. Three parameters—completeness, correctness, and quality—were used to validate the system. Validation results showed completeness, correctness, and quality rates of 94.15%, 92.48%, and 86.63%, respectively.

Free flap jaw reconstruction with dental implantation: A single-institution experience.

We sought to review our institution's experience with dental implant placement in free flap jaw reconstruction to determine factors impacting restoration of dental occlusion. Exactly 48 patients underwent free flap jaw reconstruction with or without dental restoration from 2017 to 2022. Primary outcome was achievement of restored dental occlusion after jaw free flap reconstruction. A total of 48 patients with a mean age of 59.8 ± 16.4 years underwent jaw reconstruction from 2017 to 2022. Ten patients (20.8%) received osteointegrated dental implants. Two patients received a temporary dental prosthesis, 12 ± 4 months after initial reconstruction. Three patients received a final prosthesis, with a mean time to final prosthesis of 17.7 ± 12.4 months. Five patients did not receive any prosthesis despite placement of implants. A minority of patients received dental implant placement with free flap jaw reconstruction and only a small subset of these received a definitive dental prosthesis.

Molecular Profiling and the Impact of Treatment on Outcomes in Adenoid Cystic Carcinoma Type I and II.

Adenoid cystic carcinoma (ACC) is an uncommon salivary gland cancer with no approved therapies available to treat advanced, incurable disease. Recent molecular profiling efforts have identified two important subtypes: the more aggressive ACC-I is characterized by Notch pathway alterations and MYC amplification whereas ACC-II demonstrates a more indolent phenotype and TP63 overexpression. This retrospective observational cohort study involved de-identified samples from 438 patients with ACC with tumor samples sent for commercially-available molecular profiling (Caris Life Sciences). Next-generation whole-exome and whole-transcriptomic sequencing was performed on primary and metastatic samples. Immunostaining for PD-L1 and RNA deconvolution (quanTIseq) was used to explore the tumor immune microenvironment (TME). Real-world clinical and survival outcome metrics were extracted from insurance claims data. MYC expression was 1.61-fold higher (39.8 vs. 24.7; P < 0.0001) among NOTCH1-mutant ACC-I tumors, whereas MYB/L1 fusion rates were similar among ACC-I/II. The median B-cell fraction in the TME was higher among ACC-II (7.1% vs. 5.8%; P < 0.01), although infiltrating T cells subsets were low among either ACC subgroup (both <1%). When pooling systemic treatment categories, ACC-I patients had worse outcomes with available therapies (HR, 3.06; 95% confidence interval, 1.65-5.68; P < 0.01), with no significant difference in overall survival between ACC-I/II based on chemotherapy or VEGFR tyrosine kinase inhibitor exposure in smaller subsets. We confirmed the previously reported associations with MYC and TP63 in the prognostically relevant subgroups of ACC-I and -II, respectively, and report immunologic differences among these subtypes. Survival outcomes are comparatively worse in ACC-I regardless of treatment type.

Prediction of airport runway settlement using an integrated SBAS-InSAR and BP-EnKF approach

The prediction of surface settlement occupies a crucial role in achieving effective catastrophe prevention and mitigation, as well as facilitating the maintenance of airport runways. Given the challenges associated with the manual collection of settlement data and the suboptimal timeliness of the Back Propagation (BP) technique in neural networks, this study proposes an integrated prediction method that combines the Ensemble Kalman Filter (EnKF) with BP. This study focuses on the processing of Synthetic Aperture Radar (SAR) pictures obtained from the ascending orbit of the Sentinel-1A satellite. The Small Baseline Subset Interferometric synthetic aperture radar (SBAS-InSAR) technology is employed to derive the settlement time series on the runway of Kangding Airport. Moreover, three sites with high coherence within the primary settlement region are employed to assess the dependability of the model after the expansion of the data by cubic spline interpolation. The findings of the study indicate that both the BP-EnKF and BP models exhibit favorable outcomes in predicting airport runway settlement. However, following the alteration of data caused by external environmental influences, the BP-EnKF model has superior adaptability to variations in data. It has been shown that the BP-EnKF model exhibits a prediction accuracy that surpasses the BP model by 9.25%.

Efficiently improving the Wi-Fi-based human activity recognition, using auditory features, autoencoders, and fine-tuning

Human activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a challenge for achieving high accuracy levels with machine learning techniques. In this study, multiple deep learning models for HAR were employed to achieve acceptable accuracy levels with much less training data than other methods. A pretrained encoder trained from a Multi-Input Multi-Output Autoencoder (MIMO AE) on Mel Frequency Cepstral Coefficients (MFCC) from a small subset of data samples was used for feature extraction. Then, fine-tuning was applied by adding the encoder as a fixed layer in the classifier, which was trained on a small fraction of the remaining data. The evaluation results (K-fold cross-validation and K = 5) showed that using only 30% of the training and validation data (equivalent to 24% of the total data), the accuracy was improved by 17.7% compared to the case where the encoder was not used (with an accuracy of 79.3% for the designed classifier, and an accuracy of 90.3% for the classifier with the fixed encoder). While by considering more calculational cost, achieving higher accuracy using the pretrained encoder as a trainable layer is possible (up to 2.4% improvement), this small gap demonstrated the effectiveness and efficiency of the proposed method for HAR using Wi-Fi signals.

Bi-specific autoantigen-T cell engagers as targeted immunotherapy for autoreactive B cell depletion in autoimmune diseases.

In autoimmune diseases, autoreactive B cells comprise only the 0.1-0.5% of total circulating B cells. However, current first-line treatments rely on non-specific and general suppression of the immune system, exposing patients to severe side effects. For this reason, identification of targeted therapies for autoimmune diseases is an unmet clinical need. Here, we designed a novel class of immunotherapeutic molecules, Bi-specific AutoAntigen-T cell Engagers (BiAATEs), as a potential approach for targeting the small subset of autoreactive B cells. To test this approach, we focused on a prototype autoimmune disease of the kidney, membranous nephropathy (MN), in which phospholipase A2 receptor (PLA2R) serves as primary nephritogenic antigen. Specifically, we developed a BiAATE consisting of the immunodominant Cysteine-Rich (CysR) domain of PLA2R and the single-chain variable fragment (scFv) of an antibody against the T cell antigen CD3, connected by a small flexible linker. BiAATE creates an immunological synapse between autoreactive B cells bearing an CysR-specific surface Ig+ and T cells. Ex vivo, the BiAATE successfully induced T cell-dependent depletion of PLA2R-specific B cells isolated form MN patients, sparing normal B cells. Systemic administration of BiAATE to mice transgenic for human CD3 reduced anti-PLA2R antibody levels following active immunization with PLA2R. Should this approach be confirmed for other autoimmune diseases, BiAATEs could represent a promising off-the-shelf therapy for precision medicine in virtually all antibody-mediated autoimmune diseases for which the pathogenic autoantigen is known, leading to a paradigm shift in the treatment of these diseases.

Estimating urban land subsidence with satellite data using a spatially multiscale geographically weighted regression approach

Land subsidence has attracted widespread attention because of its potential hazards to sustainable urban development. The traditional methods for estimating land subsidence disregard the crucial aspect of altitude by analyzing spatial data solely on the 2D plane (longitude and latitude geographic coordinates). This research incorporates the altitude feature into the construction of the spatial matrix to develop a new method named spatially multiscale geographically weighted regression (SM-GWR) for a better estimation of land subsidence, enabling to determine the optimal bandwidth at 3D spatial scales. By adopting a 3D perspective, the new algorithm accurately fits settlement points based on spatial distances, addressing previous limitations of projecting points on a two-dimensional surface and ignoring height differences, thereby providing a more realistic depiction of spatial relationships. The satellite data is first used to monitor land subsidence through the small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). Then, the SM-GWR model is developed to construct the 3D spatial weight matrix and update estimate parameters through back-fitting calibration. The proposed method's feasibility is confirmed through examples of land subsidence in China and Singapore. The case study results demonstrate that: (1) The SM-GWR model shows significant improvement in fitting land subsidence compared to the traditional method (R2 value of 0.908 and 0.934 in predicting annual subsidence and cumulative subsidence, respectively); (2) The contribution of the influencing factors is highly related to the nature properties and urban development of the city. The novelty of this study lies in the development of an SM-GWR model combined with satellite monitoring to achieve high-precision estimation of land subsidence, considering the 3D structure of spatial geographic data.

Using name overlap analysis to understand medication name search safety.

To examine the degree of left-to-right character overlap in medication names as they appear in real-world computer systems. We programmed a computer to create and automatically analyze left-to-right character overlap in names appearing on 20,020 lists of real-world medication names. The lists varied in length from 100 to 500 medication names and were created by randomly drawing names from a pool of 2,249 medication names extracted from an operating medication use system database. Overall maximum left-to-right character overlap varied in lists of 100 to 500 medication names from 4 to 29 characters (mode of 14 characters). For a small subset of names for high-alert medications that must never be administered in error, overall maximum left-to-right character overlap varied from 3 to 10 characters (mode of 6 characters). Further, for users searching for medications by name in computer systems, the keystrokes that do the most work to disambiguate medication names on a list are always the initial few keystrokes. Medication name left-to-right character overlap on lists of names searched ranges widely. Instead of requiring all users to type a set number of characters when searching for medications by name, search safety can potentially be improved by upgrading computer systems to dynamically respond to each keystroke entered. Using incremental dynamic search, searchers would often be able to type fewer than 5 characters to isolate a single medication by name but would sometimes have to type many more than 5 characters to do so.