Multiple Sets Research Articles

The influence of atmospheric turbulence-induced optical intensity scintillation on the accuracy of satellite-to-ground laser one-way timing

This paper elucidates the mechanism by which atmospheric turbulence leads to errors in satellite-to-ground laser one-way timing, investigates the influence of optical intensity scintillation on the time delay jitter of laser signal arrival in single-satellite-to-ground transmission link. Furthermore, the impact patterns and the magnitude of atmospheric turbulence-induced one-way timing errors and positioning errors across various gradients of observation zenith angles in the GPS four-satellite-to-ground transmission link is also investigated. Besides, the study also explores the impact patterns and magnitude ranges of the effects of optical intensity scintillation on the four-satellite-to-ground time transfer system under different observed zenith angles with similar geometric dilution of precision (GDOP) conditions. The results show that smaller receiver zenith angles result in reduced errors caused by atmospheric turbulence-induced optical intensity scintillation, leading to higher accuracy in four-satellite positioning. When the GDOP factors range from 6.5 to 7.5, the timing errors for multiple sets of satellites consistently exhibit a normal distribution. Specifically, the four-satellite-to-ground laser one-way time transfer deviation averages around 2 ns, with jitter remaining within 1 ns.

Volume assessment comparing femoral vein and inferior vena cava among chest pain patients presenting to the emergency department

BackgroundInferior vena cava (IVC) diameter measurement using ultrasound for volume status assessment has shown satisfactory results and is being adopted in Emergency and critical care settings. IVC diameter can vary depending on the cardiac function, respiratory efforts, intraabdominal pressure, and mechanical ventilation. Due to these factors, IVC measurement cannot be considered a stand-alone technique appropriate for every patient. The femoral vein (FV), a more superficial vein than IVC, can be considered an alternative method for assessing fluid responsiveness in patients presenting to the Emergency department. It is easily accessible and can be used in scenarios where IVC cannot be visualized or reliable.MethodsThis was a single-center diagnostic study where 85 patients who presented to the ED with chest pain were enrolled prospectively. IVC and femoral vein collapsibility indices, stroke volume, and cardiac output are measured using an ultrasound machine. The measurements were repeated after a passive leg-raising test. These values were compared with each other to assess an intra-class correlation between IVC and femoral vein collapsibility indices. We have also evaluated the relationship between the collapsibility indices of both veins and cardiac output.Discussion & limitationsOur findings show an insufficient correlation between IVC and FV collapsibility indices. However, both vein diameters significantly increased after passive leg raising (PLR), indicating a response to fluid challenge. Post-PLR reduced IVC, and FV collapsibility index (CI) suggests intravascular volume expansion after a fluid challenge, also reflected in the hemodynamic parameters. Our study was conducted only in a subset of relatively stable patients. The applicability of the study in different subsets of patients presenting to ED is still questionable.ConclusionWe conclude that femoral vein indices may not be an accurate alternative for volume assessment in the chosen cohort of patients. IVC and FV metrics do not correlate and may not be accurate for volume responsiveness. We may need to explore the utility of FV and its indices in a larger population in multiple settings for a better understanding of its role in volume assessment and responsiveness.Trial registration(EC/NEW/INST/2021/1707). Registered 03 January 2023.

Analyzing and predicting short-term substance use behaviors of persons who use drugs in the great plains of the U.S.

Substance use induces large economic and societal costs in the U.S. Understanding the change in substance use behaviors of persons who use drugs (PWUDs) over time, therefore, is important in order to inform healthcare providers, policymakers, and other stakeholders toward more efficient allocation of limited resources to at-risk PWUDs. This study examines the short-term (within a year) behavioral changes in substance use of PWUDs at the population and individual levels. 237 PWUDs in the Great Plains of the U.S. were recruited by our team. The sample provides us longitudinal survey data regarding their individual attributes, including drug use behaviors, at two separate time periods spanning 4-12 months. At the population level, we analyze our data quantitatively for 18 illicit drugs; then, at the individual level, we build interpretable machine learning logistic regression and decision tree models for identifying relevant attributes to predict, for a given PWUD, (i) which drug(s) they would likely use and (ii) which drug(s) they would likely increase usage within the next 12 months. All predictive models were evaluated by computing the (averaged) Area under the Receiver Operating Characteristic curve (AUROC) and Area under the Precision-Recall curve (AUPR) on multiple distinct sets of hold-out sample. At the population level, the extent of usage change and the number of drugs exhibiting usage changes follow power-law distributions. At the individual level, AUROC's of the models for the top-4 prevalent drugs (marijuana, methamphetamines, amphetamines, and cocaine) range 0.756-0.829 (+2.88-7.66% improvement with respect to baseline models using only current usage of the respective drugs as input) for (i) and 0.670-0.765 (+4.34-18.0%) for (ii). The corresponding AUPR's of the said models range 0.729-0.947 (+2.49-13.6%) for (i) and 0.348-0.618 (+26.9-87.6%) for (ii). The observed qualitative changes in short-term substance usage and the trained predictive models for (i) and (ii) can potentially inform human decision-making toward efficient allocation of appropriate resources to PWUDs at highest risk.

Successful Models of Virtual Experiential Education Initiatives in Global Health for International Students.

Background: Experiential learning activities help students prepare for their future careers by providing opportunities for hands‑on practice experiencing real‑world scenarios. Innovations in technology can facilitate experiential learning and cross‑cultural connections for large groups of students in multiple global settings through a virtual platform. However, designing these opportunities with diverse groups of students for a virtual environment can be challenging. Objective: The purpose of this paper is to highlight three examples of innovative virtual experiential learning initiatives that were developed and implemented by the Global Health Program of the Association of Pacific Rim Universities (APRU), a non‑profit network of 60+ leading research universities in the Asia‑Pacific. Findings: We have leveraged the expertise of our wide network to enhance student learning through the purposeful design of virtual educational experiences centered around pedagogical approaches that emphasize active learning, self‑reflection, and knowledge exchange with people from other cultures and disciplines. The annual global health joint virtual courses, the annual APRU Virtual Global Health Case Competition, and the APRU Mini Certificate foster meaningful engagement with other students and experts in the field, expanding the lens of students to foster an increased awareness and appreciation of the diversity of perspectives represented in an international network such as APRU. These allow students to practice real‑world application of knowledge gained in a traditional didactic classroom setting. Conclusions: The benefits of virtual experiential learning to students greatly outweigh the challenges in the design and implementation of such programs. While relatively short‑term, these virtual initiatives have had a demonstrable impact on student participants. Such programs can enhance student learning and provide cost‑effective ways to allow large international cohorts to experience global experiential learning.

Association of commingled human skeletal remains by their elemental profile using handheld laser-induced breakdown spectroscopy

When multiple sets of skeletonized human remains exist in the same context, they can become commingled due to multifactorial circumstances that affect the postmortem environment. Numerous techniques exist for reassociating commingled skeletal remains (e.g., visual pair-matching, and osteometric pair-matching); however, the scale of commingled skeletal assemblages influences the effectiveness of these approaches. Laser-Induced Breakdown Spectroscopy (LIBS) has recently been proposed as a rapid technique for obtaining elemental signatures from skeletal elements of multiple individuals and then reassociating those elements with their correct individual. This study evaluates the potential of portable LIBS as a useful tool for reassociating commingled human skeletal remains in forensic contexts. In this study six skeletons drawn from the donated skeletal collection at the Florida Institute for Forensic Anthropology and Applied Science, at the University of South Florida, were used to assess whether LIBS data could be used to reassociate multiple skeletal elements from the same individual. LIBS data were collected at 206 anatomical locations from 28 individual bones across each skeleton in the sample. LIBS data were reassigned to their individual with an accuracy of 91 % using quadratic discriminant analysis of dimensionally reduced data (via principal component analysis). The study demonstrates that portable LIBS has potential for reassociating commingled human skeletal remains from forensic contexts.

Enhancing Care Coordination in Oncology and Nononcology Thoracic Surgery Care Pathways Through a Digital Health Solution: Mixed Methods Study.

Health-system fragmentation in Quebec significantly impacts care coordination, leading to interruptions in patients' care pathways and adverse effects on their health. Coordinating interfacility service corridors is complex and requires collaboration between multiple health care providers (HCPs) and care settings. Effective care coordination is essential to ensure optimal patient management at transition points. This study aims to improve oncology and nononcology thoracic surgery care pathways by enhancing care coordination during interfacility transfers through a digital health solution. A multicenter implementation study was conducted across 2 health regions and 2 health care facilities in Quebec. We conducted 27 semistructured interviews with HCPs and managers to better understand the care pathways. Participatory design workshops were held with future users and key stakeholders at an early stage of the technology's design to validate the prototype's functionalities and workflows. A web survey was sent to all end users (N=13) to assess their experience with the platform. All participants (100%) either "agreed" or "strongly agreed" that the platform provided significant benefits. It enhanced interestablishment coordination (4/13, 31% agreed and 9/13, 69% strongly agreed) and continuity of care and services (8/13, 62% agreed and 5/13, 38% strongly agreed), and it contributed to better management and patient intake (10/13, 77% agreed and 3/13, 23% strongly agreed) and process fluidity (3/13, 77% agreed and 3/13, 23% strongly agreed). Surgeons from the McGill University Health Centre confirmed that the platform facilitated and secured information transmission (2/5, 40% agreed and 3/5, 60% strongly agreed) and kept track of oncology patient referrals, follow-up needs, and cases where surgery is unnecessary (2/5, 40% agreed and 3/5, 60% strongly agreed). Nursing staff from the Centre intégré de santé et de services sociaux de l'Outaouais and McGill University Health Centre reported high satisfaction with the platform's support during preoperative visit, surgery, and discharge processes. All participants perceived the platform as intuitive and easy to use. Additionally, participants valued its efficiency in providing rapid access to patient data, which reduces task time and ensures document security, thereby improving care coordination across facilities. The project's success has convinced the HCPs and senior management at both health care facilities to pursue long-term use of the Akinox digital health platform. This pilot project represents a significant advancement in thoracic surgery care pathways and the coordination of interfacility health care service corridors. The project provides care pathways that are adaptable to other surgical specialties. It also paves the way for improving care in cancer and other health care networks while highlighting the key role of nurse navigators in patient care management. The project underscores the value of strategic leadership and stakeholders' collaboration to improve care coordination and operational efficiency by demonstrating technology's essential role in patient care pathways.

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Consolidating the evidence on the effectiveness of strategies to promote vegetable intake in priority settings: An overview of systematic reviews.

Effective interventions to increase vegetable intake are urgently needed. This systematic rapid review aimed to summarise the effectiveness of interventions targeting increased vegetable intakes across diverse settings. The review was guided by the conduct of rapid reviews from the Cochrane Handbook. The literature was searched in February 2024 across PubMed, Web of Science and Cochrane Central for systematic review articles published since 2014. The Risk of Bias In Systematic Reviews tool was used and characteristics of reviews synthesised narratively with intervention effectiveness results were summarised. A total of 20 systematic reviews met the eligibility criteria. Most targeted school-based settings (n = 7) or community/home-based settings (n = 4). Early childhood education and workplaces had one review each, with none in retail, secondary or tertiary education, food service, food relief or aged care. The mean change in vegetable consumption was +0.12 serves per day, with increases of up to +0.42 serves reported (range -0.09 to +0.42). By setting, the largest increases were reported for interventions in school settings (+0.42 serves/day), followed by home (+0.38 serves/day). Almost half the studies reporting effect sizes suggested no effect on intake (46%), 41% suggested a small effect and 13% suggested a medium effect. Greater effect sizes were achieved in interventions implemented across multiple settings. Review findings indicate that the average increase in vegetable intake following interventions is about one-eighth of a serve but up to almost half a serve in some settings. An increase of this magnitude could have a substantial population impact, particularly in population groups with persistently low intakes.

A composite scaling network of EfficientNet for improving spatial domain identification performance

Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.

SingletSeeker: an unsupervised clustering approach for automated singlet discrimination in cytometry.

Flow cytometry is a high-throughput, high-dimensional technique that generates large sets of single-cell data. Prior to analyzing this data, it is common to exclude any events that contain two or more cells, multiplets, to ensure downstream analysis and quantification is of single-cell events, singlets, only. The process of singlet discrimination is critical yet fundamentally subjective and time-consuming; it is performed manually by the user, where the proper exclusion of multiplets depends on the user's expertise and often varies from experiment to experiment. To address this problem, we have developed an algorithm to automatically discriminate singlets from other unwanted events such as multiplets and debris. Using parameters derived from imaging, the algorithm first identifies high-density clusters of events using a density-based clustering algorithm, and then classifies the clusters based on their properties. Multiplets are discarded in the first step, while singlets are distinguished from debris in the second step. The algorithm can use different strategies on imaging feature selection-based user's preferences and imaging features available. In addition, the relative importance of singlets precision vs. sensitivity can be further tweaked via a density coefficient adjustment. Twenty-two datasets from various sites and of various cell types acquired on the BD FACSDiscover™ S8 Cell Sorter with CellView™ Image Technology were used to develop and validate the algorithm across multiple imaging feature sets. A consistent singlets precision >97% with a solid >88% sensitivity has been demonstrated with a LightLoss feature set and the default density coefficient. This work yields a high-precision, high-sensitivity algorithm capable of objective and automated singlet discrimination across multiple cell types using various imaging-derived parameters. A free FlowJo™ Software plugin implementation is available for simple and reproducible singlet discrimination for use at the beginning of any user's workflow.

Behavioral health treatment delivery by social workers in accountable care organizations

ABSTRACT Nearly half of Traditional Medicare beneficiaries receive their care through an Accountable Care Organization (ACO), in which provider groups are subject to healthcare cost and quality benchmarks for a defined patient population. Although the skills of the social work profession align with the goals of these contracts (coordination of service needs across multiple health and social care settings), there is little information on social worker inclusion as behavioral health providers in ACOs. We developed and administered a national survey of organizations (n = 227) with Medicare and Medicaid ACO contracts to provide an estimate of the percentage of ACOs that reported social worker-delivered behavioral health treatment. Approximately half of the respondents reported that social workers delivered mental health treatment, while a third reported that social workers delivered substance use treatment. Organizations that included specialty mental health treatment facilities were more likely to report social worker-delivered mental health and substance use treatment. Organizations that included rural healthcare facilities were less likely to report social worker-delivered substance use treatment. By describing the prevalence and predictors of social worker-delivered behavioral health treatment in ACOs, this study contributes foundational estimates for future research on the role of this important workforce in ACOs.

Small Town Population Forecasting and Planning Method Based on Genetic Algorithm and BP Neural Network

It is very important to accurately predict the population pattern in the framework of spatial planning in the township development track. In this paper, the basic principle and application field of population forecasting method of urban spatial planning are deeply studied, and the applicability of BP neural network method of genetic evolution to predict population size is described. The study initially used genetic algorithms to refine the initial weights and structure of BP neural networks to improve their proficiency and generalization ability in the interpretation of demographic data. The empirical results show that the method produces superior predictive performance on multiple township demographic data sets, especially when trying to cope with complex population dynamics. In addition, when benchmarked against traditional forecasting models, the technology showed significant enhancements in the accuracy, stability, and adaptability of predictive models. These results suggest that combining GA-driven evolution with BP neural networks provides a more robust and precise tool for population prediction.

A Tensor Space for Multi-View and Multitask Learning Based on Einstein and Hadamard Products: A Case Study on Vehicle Traffic Surveillance Systems

Since multi-view learning leverages complementary information from multiple feature sets to improve model performance, a tensor-based data fusion layer for neural networks, called Multi-View Data Tensor Fusion (MV-DTF), is used. It fuses M feature spaces X1,⋯,XM, referred to as views, in a new latent tensor space, S, of order P and dimension J1×⋯×JP, defined in the space of affine mappings composed of a multilinear map T:X1×⋯×XM→S—represented as the Einstein product between a (P+M)-order tensor A anda rank-one tensor, X=x(1)⊗⋯⊗x(M), where x(m)∈Xm is the m-th view—and a translation. Unfortunately, as the number of views increases, the number of parameters that determine the MV-DTF layer grows exponentially, and consequently, so does its computational complexity. To address this issue, we enforce low-rank constraints on certain subtensors of tensor A using canonical polyadic decomposition, from which M other tensors U(1),⋯,U(M), called here Hadamard factor tensors, are obtained. We found that the Einstein product A⊛MX can be approximated using a sum of R Hadamard products of M Einstein products encoded as U(m)⊛1x(m), where R is related to the decomposition rank of subtensors of A. For this relationship, the lower the rank values, the more computationally efficient the approximation. To the best of our knowledge, this relationship has not previously been reported in the literature. As a case study, we present a multitask model of vehicle traffic surveillance for occlusion detection and vehicle-size classification tasks, with a low-rank MV-DTF layer, achieving up to 92.81% and 95.10% in the normalized weighted Matthews correlation coefficient metric in individual tasks, representing a significant 6% and 7% improvement compared to the single-task single-view models.

Multi-output discrete grey model tailored for electricity consumption forecast

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the Bayesian Optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

Lightweight Research on Wireless Charging System for Unmanned Aerial Vehicles Based on High Efficiency

Abstract: Unmanned aerial vehicles (UAVs) are characterized by safety, flexibility, and economy, and are widely used in many fields. At present, due to the limitation of UAV loads and the bottleneck of battery technology, UAVs can't carry out missions for a long time. The wireless charging system for UAVs has the advantages of safety, flexibility, automation, and environmental adaptability, so it has gained wide attention. Electromagnetic induction wireless energy transmission system is a system that transmits electrical energy through a magnetic field, and in electromagnetic induction wireless energy transmission system, it is necessary to reach the goals of lightweight, high efficiency, anti-skewing, and modularization. In order to achieve the goals of high efficiency and anti-deviation, this paper adopts three sets of series-connected rounded rectangular coils as the transmitting stage to improve the charging efficiency and anti-deviation ability; in order to achieve the goal of lightweight, this paper reduces the weight by changing the material of the magnetic core, and using the annulus core amorphous alloy core; in order to achieve the goal of modularity, this paper designs to carry multiple sets of receiving coils, which can be switched on and off according to the demand in order to satisfy the different power requirements. Finally, a bilateral LCC compensation topology network is established to control the input and output currents to achieve the design goal. The method proposed in this paper helps to realize the automatic charging of UAV and improve the endurance of UAV, so as to enhance the working hours and inspection range of UAV, and make UAV capable of doing more work.

Two-step inertial accelerated algorithms for solving split feasibility problem with multiple output sets

In this paper, we present and study two new two-step inertial accelerated algorithms for finding an approximate solution of split feasibility problems with multiple output sets. Our methods are extensions of CQ algorithms previously studied in the literature. In contrast to the related iterative methods for solving SFP, our methods incorporate a two-step inertial technique that speeds up the convergence rate of the generated sequences to the unique solution of SFP studied in this work. Furthermore, we present weak and strong convergence results where the strong convergence result of our method is obtained without the on-line rule, a feature absent in related algorithms in the literature. Finally, we demonstrate the applicability and performance of our algorithms through numerical experiments. Our numerical results reveal that our methods perform better than existing related methods in the literature.

Direct and Vicarious Experiences of COVID-19-Related Racism Across Racial and Ethnic Groups in the UnitedStates.

Due to stigmatization associated with the COVID-19 pandemic, certain groups were believed to be the cause of COVID-19 and thus experienced COVID-19-related racism through direct interpersonal and vicarious experiences. This study used quantitative and qualitative responses to examine whether the prevalence of experiencing these types of racism varied across racial and ethnic groups. This cross-sectional study included 5,480 participants in the REACH-US (Race-Related Experiences Associated with COVID-19 and Health in the United States) study, which is a nationally representative survey administered to 5,500 U.S. adults from January26, 2021-March 3, 2021. COVID-19-related racism was measured using singleitems about whether participants: 1.) experienced racism because they were thought to belong to a group more likely to get COVID-19 (direct); 2.) witnessed racism against others who were thought to belong to a group more likely to get COVID-19 (vicarious). Logistic regression examined differences in experiencing COVID-19-related racism across racial and ethnic groups, adjusting for sociodemographic characteristics. Themes emerged from open-ended descriptions of racism experiences coded in a thematic analysis and were reported across racial and ethnic groups. Overall, 6.4% and 15.9% of adults experienced direct and vicarious COVID-19-related racism, respectively. All racial and ethnic groups (except Hispanic/Latino English Language Preference) were significantly more likely than White adults to experience direct (AORs: 2.06-4.92) and vicarious (AORs: 1.63-3.02) COVID-19-related racism. Racial and ethnic differences were observed across thematic domains of type of mistreatment and settings where racism occurred. Direct and vicarious COVID-19-related racism were more prevalent among marginalized racial and ethnic groups, comprised various types of mistreatment, and occurred across multiple settings, thus highlighting the need for integrated efforts to reduce and prevent racism.

Negotiating care in organizational borderlands: a grounded theory of inter-organizational collaboration in coordination of care

BackgroundAlthough coordination of care and integrated care models aim to enhance patient satisfaction and perceived care quality, evidence regarding their practical implementation remains scarce. Understanding the nuances of collaboration across care providers to achieve effective coordination of care is imperative for seamless care integration. The aim of this study was to construct a grounded theory of how inter-organizational collaboration is performed to support coordination of care for patients with complex care needs.MethodsA qualitative design with a constructivist grounded theory approach was applied. In total, 86 participants with diverse backgrounds were recruited across multiple care settings, including hospitals, ambulance services, primary care centers, municipal home healthcare and home care services. The grounded theory was developed iteratively, based on a combination of observations and interviews, and using constant comparative analysis.ResultsCoordination of care, a complex process that occurs across interconnected healthcare organizations, is manifested as “Negotiating care in organizational borderlands.” Care coordination evolves through a spectrum of inter-organizational collaboration, ranging from “Dividing care by disease-specific expertise” to “Establishing paths for collaboration” and ultimately “Co-constructing a comprehensive whole.” These categories highlight the challenges of coordinating care across both professional and organizational boundaries. In the multifaceted healthcare landscape, effective care coordination occurs when healthcare professionals actively bridge the divides, leveraging their collective expertise. Importantly, organizational boundaries may serve a purpose and should not be dissolved to facilitate effective care coordination.ConclusionsThe key to effective care coordination lies in robust inter-organizational collaboration. Even when patients receive integrated care, healthcare professionals may have fragmented roles. This research emphasizes the importance of clearly defined lines of accountability, reinforcing mutual responsibility and facilitating bridging of professional and organizational boundaries. Healthcare professionals and policymakers can use these insights to effectively utilize inter-organizational collaboration in supporting care coordination for patients with complex care needs.

Low tristetraprolin expression activates phenotypic plasticity and primes transition to lethal prostate cancer in mice.

Phenotypic plasticity is a hallmark of cancer and increasingly realized as a mechanism of resistance to androgen receptor (AR)-targeted therapy. Now that many prostate cancer (PCa) patients are treated upfront with AR-targeted agents, it's critical to identify actionable mechanisms that drive phenotypic plasticity, to prevent the emergence of resistance. We showed that loss of tristetraprolin (TTP, gene ZFP36) increased NF-κB activation, and was associated with higher rates of aggressive disease and early recurrence in primary PCa. We also examined the clinical and biological impact of ZFP36 loss with co-loss of PTEN, a known driver of PCa. Analysis of multiple independent primary PCa cohorts demonstrated that PTEN and ZFP36 co-loss was associated with increased recurrence risk. Engineering prostate-specific Zfp36 deletion in vivo, induced prostatic intraepithelial neoplasia, and, with Pten co-deletion, resulted in rapid progression to castration-resistant adenocarcinoma. Zfp36 loss altered the cell state driven by Pten loss, demonstrated by enrichment of EMT, inflammation, TNFα/NF-κB, IL6-JAK/STAT3 gene sets. Additionally, our work revealed that ZFP36 loss also induced enrichment of multiple gene sets involved in mononuclear cell migration, chemotaxis, and proliferation. Use of the NF-κB inhibitor, dimethylaminoparthenolide (DMAPT) induced marked therapeutic responses in tumors with PTEN and ZFP36 co-loss and reversed castration resistance.

Micro-DeMix: a mixture beta-multinomial model for investigating the heterogeneity of the stool microbiome compositions.

Extensive research has uncovered the critical role of the human gut microbiome in various aspects of health, including metabolism, nutrition, physiology, and immune function. Fecal microbiota is often used as a proxy for understanding the gut microbiome, but it represents an aggregate view, overlooking spatial variations across different gastrointestinal (GI) locations. Emerging studies with spatial microbiome data collected from specific GI regions offer a unique opportunity to better understand the spatial composition of the stool microbiome. We introduce Micro-DeMix, a mixture beta-multinomial model that deconvolutes the fecal microbiome at the compositional level by integrating stool samples with spatial microbiome data. Micro-DeMix facilitates the comparison of microbial compositions across different GI regions within the stool microbiome through a hypothesis-testing framework. We demonstrate the effectiveness and efficiency of Micro-DeMix using multiple simulated data sets and the Inflammatory Bowel Disease (IBD) data from the NIH Integrative Human Microbiome Project. The R package is available at https://github.com/liuruoqian/MicroDemix. Supplementary data are available at Bioinformatics online.