Incomplete Coverage Research Articles

Identifying vehicle types from trajectory data based on spatial-semantic information

ABSTRACT Obtaining information about various vehicle types traveling on road networks is crucial for estimating traffic loads on roads, evaluating the adequacy of road design standards, and providing personalized navigation guidance. Traditional intrusive and non-intrusive methods for vehicle-type identification often encounter challenges such as high maintenance costs, incomplete coverage of all roads, and technical limitations in adverse weather conditions. In recent years, vehicle GNSS trajectory data accumulation has provided a continuous, dynamic, wide-coverage, and cost-effective data resource for identifying vehicle types. However, existing trajectory-based methods still have some drawbacks in micro-trip segmentation and limitation of movement features. Hence, this paper proposes a novel approach for identifying vehicle types by leveraging spatial-semantic information from trajectory data. This proposed method initially detects staying points from trajectory data, then utilizes DBSCAN clustering on these detected staying points to adaptively segment original vehicle trajectories into various micro-trips in a steady-moving pattern. Subsequently, several statistical indicators related to velocity and acceleration are calculated as movement features for each micro-trip. Additionally, each vehicle trajectory’s driving-road hierarchy and staying-place information are quantified as the geo-semantic features. Finally, the calculated movement and geo-semantic features are utilized for two classification tasks using three typical classification models. Experimental results demonstrate that the proposed method achieves reliable performance in identifying various vehicle types by incorporating adaptive micro-trip segmentation and multiple spatial-semantic features, particularly outperforming fixed-size trip segmentation and using only movement features. Furthermore, it is observed that the classification accuracy of coaches, trucks, and semis is consistently higher than that for large, medium, and small passenger cars, indicating that the vehicle purposes may be more distinguishable than vehicle loads.

Ensuring safe and effective pharmacotherapy: The role of “community pharmacology” in attaining “health for all” from the Indian perspective

ABSTRACT The novel approach of “Community Pharmacology” integrates pharmacological principles with community health to achieve the “Health for all” goal through safe and efficient health care. Pharmacovigilance, medication errors (ME), irrational prescriptions, and antimicrobial resistance in the community could be the key areas. Though life expectancy and other health indicators have improved in India, the disparity between rural and urban quality healthcare access should be addressed. Despite India’s enormous progress in vaccine manufacture, supply, and mass immunization through the Universal Immunization Programme (UIP) and Mission Indradhanush, issues remain in incomplete coverage and vaccine hesitancy. Other key challenges include high health expenditures, shortage of trained healthcare professionals, and lack of patient safety and irrational pharmacotherapy in remote areas. Community Pharmacology offers a multifaceted solution in community healthcare delivery through a well-designed integrated network equipped with skills in practical implications of pharmacology aimed at better medication supply, storage and dispensing, adherence to formularies according to the community needs, preparing Essential Medicine List and implementation of rational prescribing. Antimicrobial stewardship (AMS), adverse drug reaction (ADR) monitoring, and evidence-based usage of different drug formulations or drug delivery systems must be implemented beyond the tertiary level, involving both public and private stakeholders. Integrating Community Pharmacology into public health is crucial for fulfilling the global health initiatives to reach the Sustainable Development Goals (SDGs) as well as attaining India’s ambitious overall development targets.

Spatial multi-attribute decision making: An axiomatic incomplete preference information coverage model

In addressing the spatial multi-attribute decision making with conflicting incomplete preference information, this study presents an axiomatic preference information coverage model, which can determine the group’s collective stance without distorting individual’s original spatial preferences. First, with a Preference Information (PI) set to encapsulate the incomplete spatial preference information, we extend existing additive spatial value function with a stochastic weight space to assign utilities to alternatives. Dominance relations among pairwise alternatives based on expected utility maximization inform the alternatives priority sequences. Within this context, a novel preference information measure is introduced, serving as the basis for defining the coverage degree associated with alternatives priority sequences relative to the PI. In alignment with the axiomatic foundations governing coverage degree and the principle of maximizing this metric, a dominant rule is formulated to dictate the hierarchy among alternatives priority sequences. The model’s applicability is demonstrated via a numerical example, followed by simulation analyses that probe the influence of different PIs on coverage degree.

Investigation and application of data balancing and combined discriminant model in rock burst severity prediction

In the development of intelligent rock burst prediction models, issues such as incomplete data coverage and data imbalance are frequently encountered. These issues may lead to risks of overfitting in predictive models, poor generalization capabilities, and increased bias, which in turn may result in misjudgments and unpredictable losses. To accurately predict rock burst disasters and mitigate or eliminate related threats, this paper proposes a composite prediction model that integrates Density-Based Nonlinear Resampling (DBNR)-Tomek Link data balancing algorithms with Bayesian Optimization (BO)-Multilayer Perceptron (MLP)-Random Forest (RF). Initially, this study collected and organized a total of 301 recorded rock burst disaster field observation data, covering various tectonic plates, engineering types, rock origins, rock textures, and rock burst types. Subsequently, from a data analysis perspective, we employed the PCA-SSA-K-means unsupervised clustering algorithm to delve into the underlying information contained within the data, thereby validating the rationality of categorizing rock bursts into four grades. Then, using the L2 norm to optimize the dimensionality of the indicators and supplementing with indicator importance ranking and hypothesis testing, we selected the maximum tangential stress of the surrounding rock, the ratio of the maximum tangential stress of the surrounding rock to the uniaxial compressive strength of the rock (stress coefficient), and the elastic energy index as the criteria for rock burst intensity grading. Following that, the DBNR-Tomek Link sampling method was applied to balance the sample data, optimizing the data sample ratio and ultimately expanding the sample size to 396, improving the proportion of data samples from 2:3:4:1 to 1:1:2:1, thereby enhancing the model’s generalization performance. Ultimately, a BO-MLP-RF composite prediction model was constructed based on Bayesian Optimization (BO), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms, with the Bayesian Optimization method ensuring that the model fits the training data well and generalizes to the test data. The results of tenfold cross-validation demonstrated that the model’s accuracy is consistently around 92.5%, combining the training results of rock burst models with imbalanced datasets, proving that the MLP model, adept at modeling nonlinear data, and the RF model, skilled in modeling large-scale data, serve as basic classifiers. This demonstrates that the application of data balancing and combined discriminative model schemes have enhanced the model’s predictive performance and stability. The model is capable of providing high-accuracy, high-efficiency early warning monitoring services for rock burst phenomena in rock engineering, thereby ensuring engineering safety.

Weakly supervised chest X-ray abnormality localization with non-linear modulation and foreground control

Chest X-ray is widely used to diagnose lung diseases. Due to the demand for accelerating analysis and interpretation to reduce the workload of radiologists, there has been a growing interest in building automated systems of chest X-ray abnormality localization. However, fully supervised methods usually require well-trained radiologists to annotate bounding boxes manually, which is labor-intensive and time-consuming. As a result, weakly supervised chest X-ray abnormality localization is gaining increasing attention because it only requires image-level annotations. Existing weakly supervised object localization (WSOL) techniques, which typically utilize class activation maps, often result in incomplete coverage and fragmentation of the objects and rely on class-specific classification accuracy. In this study, we propose a novel WSOL framework for chest X-ray abnormality localization that uses VMamba as the backbone and integrates three practical components to improve localization accuracy. First, we propose a non-linear modulation module to refine Foreground Prediction Maps (FPM) by expanding the foreground activation region and enhancing its continuity. Second, we design an FPM fusion module to strengthen the foreground and suppress the background, thereby improving their separability in chest X-ray images. Third, we craft a novel foreground control loss that regulates the feature maps to refine the background and foreground activation for better foreground identification. The proposed method is evaluated on two commonly used chest X-ray datasets, the NIH chest X-ray dataset and the RSNA dataset, and demonstrates superior performance over six state-of-the-art WSOL methods. In addition, the robustness and applicability of the proposed method are evaluated using three additional datasets with varying modalities and image quality.

Detecting Damage to Cultural Heritage during Armed Conflict Using the Case of Ukraine

Armed conflict not only causes civilian casualties, but also damages or destroys civilian infrastructure, including cultural heritage sites. Despite the common occurrence of damage to heritage sites during conflict, monitoring is usually done on a case-by-case basis once conflict ends, which leads to delayed and incomplete coverage. The lack of systematic, large-scale, and near real-time monitoring limits research and response efforts intended to help communities recover from conflict. To address this gap, we advance a methodology to systematically identify potential damage to heritage sites during active armed conflict. We use NASA’s Fire Information for Resource Management System (FIRMS) data and information on the location of heritage sites obtained from OpenStreetMap to determine which heritage sites likely suffered damage and when. We describe our methodology and showcase its accuracy in detecting damage to cultural heritage sites. We compare FIRMS predictions to “ground truth” estimates generated from AI-processed moderate resolution satellite imagery of building damage in Donetsk Oblast, Ukraine. We conclude with a discussion of the uses of this methodology to monitor damage to civilian spaces during armed conflict.

Comparative Economic Analysis of Bifacial Roof-top PV Systems

This study addresses the existing literature’s incomplete coverage of detailed LCOE (Levelised Cost of Electricity) and economic evaluation for bifacial photovoltaic (PV) systems. LCOE is a crucial metric in assessing the economic viability of energy generation. This paper introduces a novel model that captures the relationship between LCOE and the innovative technology of bifacial PV. It also analyzes the LCOE and energy payback period for an 8 kW bifacial PV plant, which is installed on the rooftop of the IIT Kharagpur campus. The LCOE derived from the real-time PV plant stands at 3.46 INR/kWh, while PVSyst predicts it as 3.37 INR/kWh. The corresponding payback period for the actual bifacial plant is approximately five years and five months, compared to five years and three months projected by PVSyst. It explicitly compares the economic feasibility of two rooftop PV systems: traditional monofacial panels and bifacial panels for scaled-up systems (1 MW). The analysis encompasses both systems’ initial costs, energy output, and payback periods. The findings reveal that the bifacial PV system outperforms the monofacial system in electricity generation and offers a shorter payback period. Consequently, the study demonstrates that the bifacial PV system represents a more economically advantageous choice for rooftop solar installations.

Suboptimal Vaccination Coverage and Serological Screening in Western Australian Children With Inflammatory Bowel Disease Receiving Immunosuppressive Therapy: An Opportunity for Improvement.

Background Patients with inflammatory bowel disease (IBD) face an increased likelihood of severe illnesses, including those caused by vaccine-preventable diseases. Consequently, the purpose of this study was to evaluate both vaccination rates and serological screening in children with IBD in Western Australia, focusing on compliance with routine and additional vaccines, and pre-treatment screening for infections before starting immunosuppressive (IS) treatment. Method The study was conducted at Perth Children's Hospital (PCH) from June 2021 to February 2022, focusing on children aged 0-18 with confirmed IBD diagnoses. Demographic and medical data were collected and matched with immunization records from the Australian Immunisation Register (AIR) to audit compliance with routine childhood vaccinations and additional vaccines (23-valent pneumococcal, human papillomavirus (HPV), and annual influenza). Data from medical records were analyzed for compliance with serologic testing (QuantiFERON TB, Hep B and C, Varicella, and Epstein-Barr virus (EBV)) before initiating IS therapy, which included immunomodulators, biologics, or small molecules. Results Of the 243 patients, 120 (52%) were diagnosed with Crohn's disease and 106 (43%) with ulcerative colitis. A total of 181 patients (74.5%) were treated with immunomodulators, while 62 (26%) received biologic therapies. Incomplete routine vaccination coverage was identified in 71 (29.2%) patients, with no notable differences observed between the IS and non-IS groups (p=0.3). Specific vaccines with incomplete coverage included HPV in 49 (24%) patients, Varicella in 39 (16%) patients, and diphtheria-tetanus-pertussis (DTP in 16 (6.5%) patients. Pre-treatment serological screening was also suboptimal, with the lowest testing rate for EBV at 32 (13.2) patients and the highest for Varicella at 181 (74.6%) patients. Conclusion The results emphasized the importance of targeted interventions to enhance vaccination and screening practices, enhancing disease management, and reducing the possibility of preventable infections in the vulnerable populace.

Toward Commercial-Scale Perovskite Solar Cells: The Role of ALD-SnO2 Buffer Layers in Performance and Stability.

Hybrid organic-inorganic perovskite solar cells (PSCs) have shown significant potential in photovoltaic applications due to their superior optoelectronic properties. However, the conventional electron transport layer (ETL) of C60 in PSCs poses challenges such as incomplete coverage and metal diffusion, leading to reduced performance and stability. This work explores the efficacy of atomic layer deposition (ALD) of SnO2 as an interlayer between C60 and electrode to enhance the performance and stability of devices. Devices with varying SnO2 thicknesses were fabricated, revealing that a 15 nm ALD-SnO2 layer optimally improved the power conversion efficiency (PCE) to 23.85%, compared to the 22.86% achieved with a BCP layer. Moreover, the SnO2-based devices exhibited superior open-circuit voltage (VOC), short-circuit current density (JSC), and fill factor (FF). Modules (30 × 30 cm) with ALD-SnO2 demonstrated notable enhancements in efficiency and uniformity, suggesting the potential for scalable commercial applications. Photoluminescence (PL) and electrochemical impedance spectroscopy (EIS) analyses confirmed the improved charge extraction and reduced recombination with the SnO2 buffer layer. This research indicates that ALD-SnO2 is a promising interlayer candidate for PSCs, providing a pathway toward higher efficiency and stability in perovskite solar technology.

Saturation profiling of drug-resistant genetic variants using prime editing.

Methods to characterize the functional effects of genetic variants of uncertain significance (VUSs) have been limited by incomplete coverage of the mutational space. In clinical oncology, drug resistance arising from VUSs can prevent optimal treatment. Here we introduce PEER-seq, a high-throughput method based on prime editing that can evaluate the functional effects of single-nucleotide variants (SNVs). PEER-seq introduces both intended SNVs and synonymous marker mutations using prime editing and deep sequences the endogenous target regions to identify the introduced SNVs. We generate and functionally evaluate 2,476 SNVs in the epidermal growth factor receptor gene (EGFR), including 99% of all possible variants in the canonical tyrosine kinase domain. We determined resistance profiles of 95% of all possible EGFR protein variants encoded in the whole tyrosine kinase domain against the common tyrosine kinase inhibitors afatinib, osimertinib and osimertinib in the presence of the co-occurring substitution T790M, in PC-9 cells. Our study has the potential to substantially improve the precision of therapeutic choices in clinical settings.

Morphological remodeling of the repaired sigmoid sinus bone wall in patients with pulsatile tinnitus after successful surgical reconstruction: an ultra-high-resolution CT study.

Sigmoid sinus wall reconstruction (SSWR) is an effective treatment for pulsatile tinnitus (PT). However, follow-up postoperative imaging manifestations have not been extensively reported. To evaluate the morphological changes in patients with PT after successful SSWR using ultra-high-resolution computed tomography (U-HRCT). Data were retrospectively analyzed from 10 patients with PT who underwent successful SSWR primarily with autologous bone powder. U-HRCT scans were performed within 3 days of surgery and repeated 6 months later. The integrity, relative density, extent, and shape of the repaired wall were analyzed. The chi-square test was used to compare the categorical variables and the Phi (φ) coefficient was used to represent the magnitude of the correlation. Among the 10 patients with PT, 1 (10%) achieved complete coverage of the defect with the residual bone, 8 (80%) had partial coverage, and 1 (10%) showed complete separation. A gap between the repaired wall and residual bone in the initial U-HRCT was linked to incomplete defect coverage in the subsequent U-HRCT scan (P < 0.001, φ = 0.903). The repaired wall shrank from the periphery to the center and the density increased. The repaired wall compressed into the sigmoid sinus retracts over time, reshaping into a naturally curved sigmoid sinus sulcus. Morphological remodeling is a typical characteristic of the repaired sigmoid sinus wall in patients with PT. Short-term incomplete repair may imply incomplete coverage of the defect in the future, but this is not correlated with recurrence.

Management of genomic surveillance for respiratory viral infections circulating in the territory of the city of Moscow

Introduction. Outbreaks of respiratory viral infections have an economic impact on the healthcare system and pose a threat to public well-being. Monitoring for the incidence of acute respiratory viral infections in a metropolitan area, especially during the COVID-19 pandemic, was in dire need of an adequate tool to identify mutations and variants that evade vaccine immunity and initiate an increase in incidence. Genomic surveillance was required for a timely response. Purpose of the study. Management of the Moscow Genomic Center for genomic surveillance for acute respiratory viral infections and COVID-19, with an assessment of the results during seasonal outbreaks in the capital. Materials and methods. With the support of the Moscow City Health Department, management and logistics measures were developed and implemented, the NGS testing program in patient samples on a planned basis and during the period of increased incidence of acute respiratory viral infections and COVID-19, technical procedures for high-throughput sequencing were developed, the security of data exchange and biosafety of medical personnel were ensured. Results. The Moscow Genome Center was established within the structure of the Moscow City Health Department. For the period from October 2023 to April 2024, targeted NGS data (a panel of thirty two pathogens, 6528 samples were tested): SARS-COV-2 dominated the entire period, the incidence of influenza A (H3N2) increased sharply from the beginning of December and decreased sharply by the end of January. Sars-CoV-2 genetic lineage rotation: XBB lineage dominated (most represented: XBB.1.16, XBB.1.16.11) from October to the end of January, then JN lineage prevailed (most represented: JN.1, also found: JN.1.13, JN.1.18, JN.1.19, JN.1.4). Influenza A strain rotation: H3N2 (A/Massachusetts/01/2020) and H3N2 (A/Massachusetts/38/2019) dominated from December to February. Research limitations. A targeted panel of 32 respiratory viruses most common in the Moscow population was used for NGS. Incomplete coverage of the entire viriome is a conditional limitation. Conclusion. Representative, high-quality and continuous genetic surveillance has been managed in the capital. The Moscow Genome Center has successfully implemented the NGS Testing Program and support for the Russian Genomic Resource of Microorganisms VGARus.

Fast Building Instance Proxy Reconstruction for Large Urban Scenes.

Digitalization of large-scale urban scenes (in particular buildings) has been a long-standing open problem, which attributes to the challenges in data acquisition, such as incomplete scene coverage, lack of semantics, low efficiency, and low reliability in path planning. In this paper, we address these challenges in urban building reconstruction from aerial images, and we propose an effective workflow and a few novel algorithms for efficient 3D building instance proxy reconstruction for large urban scenes. Specifically, we propose a novel learning-based approach to instance segmentation of urban buildings from aerial images followed by a voting-based algorithm to fuse the multi-view instance information to a sparse point cloud (reconstructed using a standard Structure from Motion pipeline). Our method enables effective instance segmentation of the building instances from the point cloud. We also introduce a layer-based surface reconstruction method dedicated to the 3D reconstruction of building proxies from extremely sparse point clouds. Extensive experiments on both synthetic and real-world aerial images of large urban scenes have demonstrated the effectiveness of our approach. The generated scene proxy models can already provide a promising 3D surface representation of the buildings in large urban scenes, and when applied to aerial path planning, the instance-enhanced building proxy models can significantly improve data completeness and accuracy, yielding highly detailed 3D building models.

GPR Image Retrieval and Localization Based on Spatial Prior Constraints

Abstract Localizing Ground Penetrating Radar (LGPR) offers the distinct advantage of being unaffected by weather and light conditions changes. As a novel auxiliary driving localizing system, LGPR enhances the robustness of the auto-drive system. Traditional ground penetrating radar localizing technologies rely heavily on high-quality map data, exhibiting optimal performance in environments with complete, high-precision maps. However, deviations from the mapped trajectory can compromise localization accuracy. We introduce a novel approach: a ground penetrating radar image retrieval and localization method based on spatial prior constraints of array antennas to address this challenge. This method aims to mitigate mismatches in ground penetrating radar navigation and localization encountered under incomplete map coverage. We performed a comparative analysis with the LGPR baseline and then validated them experimentally on our dataset. The results show that the proposed method achieved significant enhancement in accuracy. In particular, the superiority of the proposed method is demonstrated in low overlap GPR image matching and localisation.

Role of Ti3AlC2 MAX phase in regulating biodegradation and improving electrical properties of calcium silicate ceramic for bone repair applications

Calcium silicate ceramic is a promising bioceramic for various biomedical applications, but its high biodegradation rate and low strength restrict its clinical utility. As a result, the study devised an innovative solution to address these issues by utilizing the titanium aluminum carbide phase, potentially for the first time in biological applications, in conjugation with hydroxyapatite. Then, using powder metallurgy technology, they added these phases to calcium silicate to create nanocomposites. After soaking in simulated body fluid for ten days, the produced nanocomposites were assessed for bioactivity and biodegradability using scanning electron microscopy, inductively coupled plasma-atomic emission spectroscopy, and weight loss assays. Their electrical and dielectric properties were also measured before and after soaking in the simulated body fluid solution. Furthermore, the tribo-mechanical properties of all sintered samples were measured. Interestingly, adding 40% hydroxyapatite nanoparticles to calcium silicate reduced the porosity from 12 to 6%. However, adding five vol% of the titanium aluminum carbide phase to the same sample increased the porosity to 8%. Importantly, these recorded percentages of porosity were comparable to those of compact bone porosity, which range from 5 to 13%. The addition of hydroxyapatite and titanium aluminum carbide phase significantly improved the rapid biodegradation of calcium silicate, albeit with a slight decrease in its bioactive properties, as evidenced by the incomplete surface coverage of the samples with the hydroxyapatite layer in the scanning electron microscopy images. The electrical properties of the nanocomposites were better with the addition of hydroxyapatite and titanium aluminum carbide phase, which helped the bone heal faster. The addition of a titanium aluminum carbide phase significantly improved the mechanical properties of the resulting nanocomposites. For example, the calculated values for compressive strength of all examined samples were 131, 115, 105, 147, and 135 MPa. Based on the results, the prepared samples can be used in orthopaedic and dental applications.

An atlas of genetic effects on the monocyte methylome across European and African populations.

Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis- regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Mechanism of Action and Pharmacokinetics of Approved Bispecific Antibodies.

Bispecific antibodies represent a significant advancement in therapeutic antibody engineering, offering the ability to simultaneously target two distinct antigens. This dual-targeting capability enhances therapeutic efficacy, especially in complex diseases, such as cancer and autoimmune disorders, where drug resistance and incomplete target coverage are prevalent challenges. Bispecific antibodies facilitate immune cell engagement and disrupt multiple signaling pathways, providing a more comprehensive treatment approach than traditional monoclonal antibodies. However, the intricate structure of bispecific antibodies introduces unique pharmacokinetic challenges, including issues related to their absorption, distribution, metabolism, and excretion, which can significantly affect their efficacy and safety. This review provides an in-depth analysis of the structural design, mechanisms of action, and pharmacokinetics of the currently approved bispecific antibodies. It also highlights the engineering innovations that have been implemented to overcome these challenges, such as Fc modifications and advanced dimerization techniques, which enhance the stability and half-life of bispecific antibodies. Significant progress has been made in bispecific antibody technology; however, further research is necessary to broaden their clinical applications, enhance their safety profiles, and optimize their incorporation into combination therapies. Continuous advancements in this field are expected to enable bispecific antibodies to provide more precise and effective therapeutic strategies for a range of complex diseases, ultimately improving patient outcomes and advancing precision medicine.

Improved protein interaction models predict differences in complexes between human cell lines.

The interactions of proteins to form complexes play a crucial role in cell function. Data on protein-protein or pairwise interactions (PPI) typically come from a combination of sample separation and mass spectrometry. Since 2010, several extensive, high-throughput mass spectrometry-based experimental studies have dramatically expanded public repositories for PPI data and, by extension, our knowledge of protein complexes. Unfortunately, challenges of limited overlap between experiments, modality-oriented biases, and prohibitive costs of experimental reproducibility continue to limit coverage of the human protein assembly map, both underscoring the need for and spurring the development of relevant computational approaches. Here, we present a new method for predicting the strength of protein interactions. It addresses two important issues that have limited past PPI prediction approaches: incomplete feature sets and incomplete proteome coverage. For a given collection of protein pairs, we fused data from heterogeneous sources into a feature matrix and identified the minimal set of feature partitions for which a non-empty set of protein pairs had complete values. For each such feature partition, we trained a classifier to predict PPI probabilities. We then calculated an overall prediction for a given protein pair by weighting the probabilities from all models that applied to that pair. Our approach accurately identified known and highly probable PPI, far exceeding the performance of current approaches and providing more complete proteome coverage. We then used the predicted probabilities to assemble complexes using previously-described graph-based tools and clustering algorithms and again obtained improved results. Lastly, we used features for three human cell lines to predict PPI and complex scores and identified complexes predicted to differ between those cell lines.

Survival and Health Care Burden of Children With Retinoblastoma in Europe

Studies on the epidemiology of retinoblastoma (RB) could lead to improvement in management. To estimate the incidence and survival of RB in European children and the occurrence of second primary tumors (other than RB) in these patients. This cohort study used population-based data from 81 cancer registries in 31 European countries adhering to the European Cancer Registries (EUROCARE-6) project. Data collection took place between January 2000 and December 2013. European children aged 0 to 14 years diagnosed with RB were included. Data were analyzed from May to November 2023. Diagnosis of RB with International Classification of Diseases for Oncology, Third Edition (ICD-O-3), morphology coded 9510-9514 (retinoblastoma) and malignant behavior (fifth digit of morphology code, 3). Annual incidence (per million children aged 0-14 years), 5-year survival (%), and the standardized incidence ratio (SIR) of subsequent malignant neoplasms. The study included 3262 patients (mean [SD] age, 1.27 [1.63] years; 1706 [52%] male and 1556 [48%] female) from 81 registries. Of these, 3098 patients were considered in trend analysis after excluding registries with incomplete time coverage: 940 in 2000 to 2003, 703 in 2004 to 2006, 744 in 2007 to 2009, and 856 in 2010 to 2013. The estimated overall European incidence rate was 4.0 (95% CI, 3.9-4.1). Rates among countries varied from less than 2 million to greater than 6 million per year. No time trend of incidence was observed in any area. The overall European 5-year survival was 97.8% (95% CI, 95.5-98.9; 3180 cases). Five-year survival was lower in Estonia and Bulgaria (<80%) and 100% in several countries. Twenty-five subsequent malignant neoplasms were recorded during follow-up (up to 14 years), with an SIR of 8.2 and with cases occurring at mean ages between 1.3 and 8.9 years across different sites. An increased risk was found for hematological tumors (SIR, 5) and bone and soft tissue sarcomas (SIR, 29). This study showed RB incidence remained stable at 4.0 per 1 000 000 European children aged 0 to 14 years from 2000 to 2013, but estimates varied among countries and differences in survival across countries persist. These data might be used to monitor RB management and occurrences of second tumors. The findings suggest future registry studies should aim to collect standardized RB stage at diagnosis and treatment to interpret disparities and potentially improve surveillance.

Whole genome sequencing approaches for taxonomic profiling and evaluation of wastewater quality

Tracking metagenomic abundance in wastewater is undoubtedly a powerful tool to detect emerging variants and improve community health. However, there are a few factors that limit environmental water-based genomic monitoring: sampling variability, incomplete coverage, genetic fragmentation, degradation, data analysis and interpretation. The decreasing costs of high-throughput sequencing and high-end supercomputers have increased the use and accuracy of genomic data for microbial detection and monitoring in wastewater samples within any given region. To better understand the microbial dynamics and to determine the target sequencing throughput required to establish taxa that may pose as bio-indicators of an epidemiological outbreak, wastewater samples were collected from distinct locations within the Emirate of Abu Dhabi, United Arab Emirates using appropriate sampling methods. A reference database of ∼27,000 known species was developed and used for further analysis. The results showed that 15 % of data in each sample matched any of ∼27,000 known bacterial, viral, fungal, or protozoan species. Despite the high fraction of unclassified data (85 %), more than 2000 species from >800 genera across >30 phyla were detected in each sample. Both 5 Gb and 10 Gb of sequenced data detected the top ∼2000 species with highest abundance. Doubling the target sequencing throughput (i.e., 10 Gb vs 5 Gb) detected ∼500 additional low-abundance species per sample however it did not affect the overall sample composition or translate into higher per-sample species diversity captured. There was a marginal increase in the number of species detected in each sample beyond 0.20 Gb of classified data. Overall, the results indicate that sequencing to a 3 Gb throughput detects nearly 95 % of all species in the samples.