Dynamic Ligand Discrimination in the Notch Signaling Pathway.

The Notch signaling pathway comprises multiple ligands that are used in distinct biological contexts. In principle, different ligands could activate distinct target programs in signal-receiving cells, but it is unclear how such ligand discrimination could occur. Here, we show that cells use dynamics to discriminate signaling by the ligands Dll1 and Dll4 through the Notch1 receptor. Quantitative single-cell imaging revealed that Dll1 activates Notch1 in discrete, frequency-modulated pulses that specifically upregulate the Notch target gene Hes1. By contrast, Dll4 activates Notch1 in a sustained, amplitude-modulated manner that predominantly upregulates Hey1 and HeyL. Ectopic expression of Dll1 or Dll4 in chick neural crest produced opposite effects on myogenic differentiation, showing that ligand discrimination can occur in vivo. Finally, analysis of chimeric ligands suggests that ligand-receptor clustering underlies dynamic encoding of ligand identity. The ability of the pathway to utilize ligands as distinct communication channels has implications for diverse Notch-dependent processes.

Combinatorial Signal Perception in the BMP Pathway.

The bone morphogenetic protein (BMP) signaling pathway comprises multiple ligands and receptors that interact promiscuously with one another and typically appear in combinations. This feature is often explained in terms of redundancy and regulatory flexibility, but it has remained unclear what signal-processing capabilities it provides. Here, we show that the BMP pathway processes multi-ligand inputs using a specific repertoire of computations, including ratiometric sensing, balance detection, and imbalance detection. These computations operate on the relative levels of different ligands and can arise directly from competitive receptor-ligand interactions. Furthermore, cells can select different computations to perform on the same ligand combination through expression of alternative sets of receptor variants. These results provide a direct signal-processing role for promiscuous receptor-ligand interactions and establish operational principles for quantitatively controlling cells with BMP ligands. Similar principles could apply to other promiscuous signaling pathways.

Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection.

Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features-such as cancer cell fraction (CCF), multiplicity, and error rate-would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1-46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72-173, p = 1.73e-12; Cohort I: OR 2.24, 95% CI 1.44-3.52, p = 1.66e-04; and Cohort N: OR 1.78, 95% CI 1.14-2.79, p = 7.86e-03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14-2.11, p = 3.85e-03; Cohort I: OR 1.78, 95% CI 1.23-2.56, p = 1.34e-03; and Cohort N: OR 1.94, 95% CI 1.63-2.31, p = 2.83e-14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e-07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.

Antibacterial efficacy of mycobacteriophages against virulent Mycobacterium tuberculosis.

Tuberculosis (TB) remains a major global health concern, with drug-resistant strains posing a significant challenge to effective treatment. Bacteriophage (phage) therapy has emerged as a potential alternative to combat antibiotic resistance. In this study, we investigated the efficacy of widely used mycobacteriophages (D29, TM4, DS6A) against Mycobacterium tuberculosis (M. tuberculosis) under pathophysiological conditions associated with TB, such as low pH and hypoxia. We found that even at low multiplicity of infection (MOI), mycobacteriophages effectively infected M. tuberculosis, got rapidly amplified, and lysed M. tuberculosis, demonstrating their potential as therapeutic agents. Furthermore, we observed a novel phage tolerance mechanism with bacteria forming aggregates after several days of phage treatment. These aggregates were enriched with biofilm components and metabolically active bacteria. However, no phage tolerance was observed upon treatment with the three-phage mixture, highlighting the dynamic interplay between phages and bacteria and emphasizing the importance of phage cocktails. We also observed that phages were effective in lysing bacteria even under low pH and low oxygen concentrations as well as antibiotic-resistant bacteria. Our results provide key insights into phage infection of slow-growing bacteria and suggest that mycobacteriophages can effectively eliminate M. tuberculosis in complex pathophysiological environments like hypoxia and acidic pH. These results can aid in developing targeted phage-based therapies to combat antibiotic-resistant mycobacterial infections.

Plasmodium falciparum genetic diversity and multiplicity of infection based on msp-1, msp-2, glurp and microsatellite genetic markers in sub-Saharan Africa: a systematic review and meta-analysis.

BACKGROUND: In sub-Saharan Africa (SSA), Plasmodium falciparum causes most of the malaria cases. Despite its crucial roles in disease severity and drug resistance, comprehensive data on Plasmodium falciparum genetic diversity and multiplicity of infection (MOI) are sparse in SSA. This study summarizes available information on genetic diversity and MOI, focusing on key markers (msp-1, msp-2, glurp, and microsatellites). The systematic review aimed to evaluate their influence on malaria transmission dynamics and offer insights for enhancing malaria control measures in SSA. METHODS: The review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Two reviewers conducted article screening, assessed the risk of bias (RoB), and performed data abstraction. Meta-analysis was performed using the random-effects model in STATA version 17. RESULTS: The review included 52 articles: 39 cross-sectional studies and 13 Randomized Controlled Trial (RCT)/cohort studies, involving 11,640 genotyped parasite isolates from 23 SSA countries. The overall pooled mean expected heterozygosity was 0.65 (95% CI: 0.51-0.78). Regionally, values varied: East (0.58), Central (0.84), Southern (0.74), and West Africa (0.69). Overall pooled allele frequencies of msp-1 alleles K1, MAD20, and RO33 were 61%, 44%, and 40%, respectively, while msp-2 I/C 3D7 and FC27 alleles were 61% and 55%. Central Africa reported higher frequencies (K1: 74%, MAD20: 51%, RO33: 48%) than East Africa (K1: 46%, MAD20: 42%, RO33: 31%). For msp-2, East Africa had 60% and 55% for I/C 3D7 and FC27 alleles, while West Africa had 62% and 50%, respectively. The pooled allele frequency for glurp was 66%. The overall pooled mean MOI was 2.09 (95% CI: 1.88-2.30), with regional variations: East (2.05), Central (2.37), Southern (2.16), and West Africa (1.96). The overall prevalence of polyclonal Plasmodium falciparum infections was 63% (95% CI: 56-70), with regional prevalences as follows: East (62%), West (61%), Central (65%), and South Africa (71%). CONCLUSION: The study shows substantial regional variation in Plasmodium falciparum parasite genetic diversity and MOI in SSA. These findings suggest a need for malaria control strategies and surveillance efforts considering regional-specific factors underlying Plasmodium falciparum infection.

A unified Bayesian framework for bias adjustment in multiple comparisons from clinical trials.

In clinical trials, multiple comparisons arising from various treatments/doses, subgroups, or endpoints are common. Typically, trial teams focus on the comparison showing the largest observed treatment effect, often involving a specific treatment pair and endpoint within a subgroup. These findings frequently lead to follow-up pivotal studies, many of which do not confirm the initial positive results. Selection bias occurs when the most promising treatment, subgroup, or endpoint is chosen for further development, potentially skewing subsequent investigations. Such bias can be defined as the deviation in the observed treatment effects from the underlying truth. In this article, we propose a general and unified Bayesian framework to address selection bias in clinical trials with multiple comparisons. Our approach does not require a priori specification of a parametric distribution for the prior, offering a more flexible and generalized solution. The proposed method facilitates a more accurate interpretation of clinical trial results by adjusting for such selection bias. Through simulation studies, we compared several methods and demonstrated their superior performance over the normal shrinkage estimator. We recommended the use of Bayesian Model Averaging estimator averaging over Gaussian Mixture Models as the prior distribution based on its performance and flexibility. We applied the method to a multicenter, randomized, double-blind, placebo-controlled study investigating the cardiovascular effects of dulaglutide.

Addressing researcher degrees of freedom through minP adjustment.

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers' analytical choices, an issue also referred to as "researcher degrees of freedom". Combined with selective reporting of the smallest p-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the "minP" adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative p a O 2 on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error-and thus the risk of publishing false positive results that may not be replicable.

Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.

Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.

Derivation of Bose's Entropy Spectral Density from the Multiplicity of Energy Eigenvalues.

The modern textbook analysis of the thermal state of photons inside a three-dimensional reflective cavity is based on the three quantum numbers that characterize photon's energy eigenvalues coming out when the boundary conditions are imposed. The crucial passage from the quantum numbers to the continuous frequency is operated by introducing a three-dimensional continuous version of the three discrete quantum numbers, which leads to the energy spectral density and to the entropy spectral density. This standard analysis obscures the role of the multiplicity of energy eigenvalues associated to the same eigenfrequency. In this paper we review the past derivations of Bose's entropy spectral density and present a new analysis of energy spectral density and entropy spectral density based on the multiplicity of energy eigenvalues. Our analysis explicitly defines the eigenfrequency distribution of energy and entropy and uses it as a starting point for the passage from the discrete eigenfrequencies to the continuous frequency.

Transcriptomic Analysis of Mycobacterial Infected Macrophages Reveals a High MOI Specific Type I IFN Signaling.

Macrophage (MΦ) infection models are important tools for studying host-mycobacterial interactions. Although the multiplicity of infection (MOI) is an important experimental variable, the selection of MOI in mycobacterial infection experiments is largely empirical, without reference to solid experimental data. To provide relevant data, we used RNA-seq to analyze the gene expression profiles of MΦs 4 or 24 h after infection with Mycobacterium marinum (M. m) at MOIs ranging from 0.1 to 50. Analysis of differentially expressed genes (DEGs) showed that different MOIs are linked to distinct transcriptomic changes and only 10% of DEGs were shared by MΦ infected at all MOIs. KEGG pathway enrichment analysis revealed that type I interferon (IFN)-related pathways were inoculant dose-dependent and enriched only at high MOIs, whereas TNF pathways were inoculant dose-independent and enriched at all MOIs. Protein-protein interaction (PPI) network alignment showed that different MOIs had distinct key node genes. By fluorescence-activated cell sorting and follow-up RT-PCR analysis, we could separate infected MΦs from uninfected MΦs and found phagocytosis of mycobacteria to be the determinant factor for type I IFN production. The distinct transcriptional regulation of RAW264.7 MΦ genes at different MOIs was also seen with Mycobacterium tuberculosis (M.tb) infections and primary MΦ infection models. In summary, transcriptional profiling of mycobacterial infected MΦs revealed that different MOIs activate distinct immune pathways and the type I IFN pathway is activated only at high MOIs. This study should provide guidance for selecting the MOI most appropriate for different research questions.

Bayesian multivariate probability of success using historical data with type I error rate control.

In clinical trials, it is common to have multiple clinical outcomes (e.g., coprimary endpoints or a primary and multiple secondary endpoints). It is often desirable to establish efficacy in at least one of multiple clinical outcomes, which leads to a multiplicity problem. In the frequentist paradigm, the most popular methods to correct for multiplicity are typically conservative. Moreover, despite guidance from regulators, it is difficult to determine the sample size of a future study with multiple clinical outcomes. In this article, we introduce a Bayesian methodology for multiple testing that asymptotically guarantees type I error control. Using a seemingly unrelated regression model, correlations between outcomes are specifically modeled, which enables inference on the joint posterior distribution of the treatment effects. Simulation results suggest that the proposed Bayesian approach is more powerful than the method of Holm (1979), which is commonly utilized in practice as a more powerful alternative to the ubiquitous Bonferroni correction. We further develop multivariate probability of success, a Bayesian method to robustly determine sample size in the presence of multiple outcomes.

Morphological, biological and genomic characterization of lytic phages against Streptococcus agalactiae causing streptococcosis in tilapia.

Streptococcus agalactiae, a highly invasive pathogen causing streptococcosis, is a major disease imparting devastating effect in the aquaculture, worldwide. As bacteriophage therapy is getting more attention recently, as an alternative viable biocontrol agent to antibiotic and vaccine, this study aimed to isolate and purify obligately lytic bacteriophages and study its morphological, genetic and biological characteristics. Host range analysis of the four bacteriophages isolated in this study, such as Phage- 12 P, 15 F, 16 E and 20D exhibited 100% infectivity to S. agalactiae serotype Ia, a predominant serotype infecting fish. Morphotype of the phages was revealed by HR-TEM and found that the phage 20D belong to the family Myoviridae and the phages 12 P, 15 F, 16 E belonged to the family Siphoviridae with typical head and tail structure. Lytic potential of the phages were ascertained by multiplicity of infection and one step lytic curve and it is found that the phages exhibit high burst size at an MOI of 0.01. Random amplified polymorphic DNA revealed the genetic diversity of these four phages with distinct banding pattern. The phages were found to be lytic with the absence of genes coding for integrase, transposase and recombinase on PCR based screening. Phages exhibited stability and viability at various physic-chemical parameters such as temperature ranging from 4 to 45 °C, pH of 4-12 and salinity ranging from 0 to 6%. Thus the present study revealed that S. agalactiae specific phages such as Phage- 12 P, 15 F, 16 E and 20D are highly stable and potential to eliminate the S. agalactiae serotype Ia infecting fish. After the complete characterization of the phages by whole genome sequencing and exploring the defense function against S. agalactiae infection in vivo, it may be applied as a therapeutic agent against S. agalactiae infection in aquaculture.

Generation and Characterization of a Tetraradical Embedded in a Curved Cyclic Paraphenylene Unit.

Unique spin-spin (magnetic) interactions, ring-size effects on ground-state spin multiplicity, and in-plane aromaticity has been found in localized 1,3-diradicals embedded in curved benzene structures such as cycloparaphenylene (CPP). In this study, we characterized the magnetic interactions in a tetraradical consisting of two localized 1,3-diradical units connected by p-quaterphenyl within a curved CPP skeleton by electron paramagnetic resonance (EPR) spectroscopy and quantum chemical calculations. Persistent triplet species with zero-field splitting parameters similar to those of a triplet 1,3-diphenylcyclopentane-1,3-diyl diradical were observed by continuous wave (CW) or pulsed X-band EPR measurements. The quintet state derived from the ferromagnetic interaction between the two triplet diradical moieties was not detected at 20 K under glassy matrix conditions. At the B3LYP/6-31G(d) level of theory, the singlet state was lower in energy than the triplet and quintet states. These findings will aid in the development of open-shell species for material science application.

Oncological impact of cystoscopic findings in non-muscle-invasive bladder cancer: a meta-analysis.

OBJECTIVE: To assess the association between cystoscopic findings and oncological outcomes in patients with non-muscle-invasive bladder cancer (NMIBC) given that the oncological impact of quantity and quality assessment of tumours with cystoscopy has not been well verified. METHODS: Multiple databases were queried in May 2022 for studies investigating the association of oncological outcomes, such as recurrence-free (RFS), progression-free (PFS), and cancer-specific survival (CSS), with cystoscopic findings, including multiplicity, size, and gross appearance of tumours in patients with NMIBC. RESULTS: Overall, 73 studies comprising 28 139 patients were eligible for the meta-analysis. Tumour multiplicity was associated with worse RFS (pooled hazard ratio [HR] 1.61, 95% confidence interval [CI] 1.48-1.74) and PFS (pooled HR 1.44, 95% CI 1.18-1.76) in NMIBC patients (including both Ta and T1). Tumour size (≥3 cm) was associated with worse RFS (pooled HR 1.97, 95% CI 1.69-2.30) and PFS (pooled HR 1.81, 95% CI 1.52-2.15) in NMIBC patients. In patients with T1 bladder cancer (BCa), tumour multiplicity and size (≥3 cm) were also associated with worse RFS, PFS and CSS. By contrast, among patients treated with bacillus Calmette-Guérin (BCG), tumour multiplicity was not associated with worse RFS, and tumour size (≥3 cm) was not associated with worse PFS. Sessile tumours were associated with worse RFS (pooled HR 2.14, 95% CI 1.52-3.01) and PFS (pooled HR 2.17, 95% CI 1.42-3.32) compared to pedunculated tumours. Compared to papillary tumours, solid tumours were associated with worse RFS (pooled HR 1.84, 95% CI 1.25-2.72) and PFS (pooled HR 3.06, 95% CI 2.31-4.07) in NMIBC patients, and CSS in T1 BCa patients (pooled HR 2.32, 95% CI 1.63-3.30). CONCLUSIONS: Cystoscopic findings, including tumour multiplicity, size, and gross appearance, strongly predict oncological outcomes in NMIBC patients. Cystoscopic visual features can help in the decision-making process regarding the timeliness and extent of tumour resection as well as future management such as intravesical therapy.

Plasmodium falciparum population structure inferred by msp1 amplicon sequencing of parasites collected from febrile patients in Kenya.

BACKGROUND: Multiplicity of infection (MOI) is an important measure of Plasmodium falciparum diversity, usually derived from the highly polymorphic genes, such as msp1, msp2 and glurp as well as microsatellites. Conventional methods of deriving MOI lack fine resolution needed to discriminate minor clones. This study used amplicon sequencing (AmpliSeq) of P. falciparum msp1 ï»¿(Pfmsp1) to measure spatial and temporal genetic diversity of P. falciparum. METHODS: 264 P. falciparum positive blood samples collected from areas of differing malaria endemicities between 2010 and 2019 were used. Pfmsp1 gene was amplified and amplicon libraries sequenced on Illumina MiSeq. Sequences were aligned against a reference sequence (NC_004330.2) and clustered to detect fragment length polymorphism and amino acid variations. RESULTS: Children < 5 years had higher parasitaemia (median = 23.5 ± 5 SD, p = 0.03) than the > 5-14 (= 25.3 ± 5 SD), and those > 15 (= 25.1 ± 6 SD). Of the alleles detected, 553 (54.5%) were K1, 250 (24.7%) MAD20 and 211 (20.8%) RO33 that grouped into 19 K1 allelic families (108-270 bp), 14 MAD20 (108-216 bp) and one RO33 (153 bp). AmpliSeq revealed nucleotide polymorphisms in alleles that had similar sizes, thus increasing the K1 to 104, 58 for MAD20 and 14 for RO33. By AmpliSeq, the mean MOI was 4.8 (± 0.78, 95% CI) for the malaria endemic Lake Victoria region, 4.4 (± 1.03, 95% CI) for the epidemic prone Kisii Highland and 3.4 (± 0.62, 95% CI) for the seasonal malaria Semi-Arid region. MOI decreased with age: 4.5 (± 0.76, 95% CI) for children < 5 years, compared to 3.9 (± 0.70, 95% CI) for ages 5 to 14 and 2.7 (± 0.90, 95% CI) for those > 15. Females' MOI (4.2 ± 0.66, 95% CI) was not different from males 4.0 (± 0.61, 95% CI). In all regions, the number of alleles were high in the 2014-2015 period, more so in the Lake Victoria and the seasonal transmission arid regions. CONCLUSION: These findings highlight the added advantages of AmpliSeq in haplotype discrimination and the associated improvement in unravelling complexity of P. falciparum population structure.

The diversity of Plasmodium falciparum isolates from asymptomatic and symptomatic school-age children in Kinshasa Province, Democratic Republic of Congo.

BACKGROUND: Understanding Plasmodium falciparum population diversity and transmission dynamics provides information on the intensity of malaria transmission, which is needed for assessing malaria control interventions. This study aimed to determine P. falciparum allelic diversity and multiplicity of infection (MOI) among asymptomatic and symptomatic school-age children in Kinshasa Province, Democratic Republic of Congo (DRC). METHODS: A total of 438 DNA samples (248 asymptomatic and 190 symptomatic) were characterized by nested PCR and genotyping the polymorphic regions of pfmsp1 block 2 and pfmsp2 block 3. RESULTS: Nine allele types were observed in pfmsp1 block2. The K1-type allele was predominant with 78% (229/293) prevalence, followed by the MAD20-type allele (52%, 152/293) and RO33-type allele (44%, 129/293). Twelve alleles were detected in pfmsp2, and the 3D7-type allele was the most frequent with 84% (256/304) prevalence, followed by the FC27-type allele (66%, 201/304). Polyclonal infections were detected in 63% (95% CI 56, 69) of the samples, and the MOI (SD) was 1.99 (0.97) in P. falciparum single-species infections. MOIs significantly increased in P. falciparum isolates from symptomatic parasite carriers compared with asymptomatic carriers (2.24 versus 1.69, adjusted b: 0.36, (95% CI 0.01, 0.72), p = 0.046) and parasitaemia > 10,000 parasites/µL compared to parasitaemia < 5000 parasites/µL (2.68 versus 1.63, adjusted b: 0.89, (95% CI 0.46, 1.25), p < 0.001). CONCLUSION: This survey showed low allelic diversity and MOI of P. falciparum, which reflects a moderate intensity of malaria transmission in the study areas. MOIs were more likely to be common in symptomatic infections and increased with the parasitaemia level. Further studies in different transmission zones are needed to understand the epidemiology and parasite complexity in the DRC.

A Bayesian approach to study design and analysis with type I error rate control for response variables of mixed types.

There has been increased interest in the design and analysis of studies consisting of multiple response variables of mixed types. For example, in clinical trials, it is desirable to establish efficacy for a treatment effect in primary and secondary outcomes. In this article, we develop Bayesian approaches for hypothesis testing and study planning for data consisting of multiple response variables of mixed types with covariates. We assume that the responses are correlated via a Gaussian copula, and that the model for each response is, marginally, a generalized linear model (GLM). Taking a fully Bayesian approach, the proposed method enables inference based on the joint posterior distribution of the parameters. Under some mild conditions, we show that the joint distribution of the posterior probabilities under any Bayesian analysis converges to a Gaussian copula distribution as the sample size tends to infinity. Using this result, we develop an approach to control the type I error rate under multiple testing. Simulation results indicate that the method is more powerful than conducting marginal regression models and correcting for multiplicity using the Bonferroni-Holm Method. We also develop a Bayesian approach to sample size determination in the presence of response variables of mixed types, extending the concept of probability of success (POS) to multiple response variables of mixed types.

Conceptualising the value of simulation modelling for public engagement with policy: a critical literature review.

As we face complex and dynamically changing public health and environmental challenges, simulation modelling has come to occupy an increasingly central role in public engagements with policy. Shifts are occurring not only in terms of wider public understandings of modelling, but also in how the value of modelling is conceptualised within scientific modelling communities. We undertook a critical literature review to synthesise the underlying epistemic, theoretical and methodological assumptions about the role and value of simulation modelling within the literature across a range of fields (e.g., health, social science and environmental management) that engage with participatory modelling approaches. We identified four cross-cutting narrative conceptualisations of the value of modelling across different research traditions: (1) models simulate and help solve complex problems; (2) models as tools for community engagement; (3) models as tools for consensus building; (4) models as volatile technologies that generate social effects. Exploring how these ideas of 'value' overlap and what they offer one another has implications for how participatory simulation modelling approaches are designed, evaluated and communicated to diverse audiences. Deeper appreciation of the conditions under which simulation modelling can catalyse multiple social effects is recommended.

Type-I-error rate inflation in mixed models for repeated measures caused by ambiguous or incomplete model specifications.

Pre-specification of the primary analysis model is a pre-requisite to control the family-wise type-I-error rate (T1E) at the intended level in confirmatory clinical trials. However, mixed models for repeated measures (MMRM) have been shown to be poorly specified in study protocols. The magnitude of a resulting T1E rate inflation is still unknown. This investigation aims to quantify the magnitude of the T1E rate inflation depending on the type and number of unspecified model items as well as different trial characteristics. We simulated a randomized, double-blind, parallel group, phase III clinical trial under the assumption that there is no treatment effect at any time point. The simulated data was analysed using different clusters, each including several MMRMs that are compatible with the imprecise pre-specification of the MMRM. T1E rates for each cluster were estimated. A significant T1E rate inflation could be shown for ambiguous model specifications with a maximum T1E rate of 7.6% [7.1%; 8.1%]. The results show that the magnitude of the T1E rate inflation depends on the type and number of unspecified model items as well as the sample size and allocation ratio. The imprecise specification of nuisance parameters may not lead to a significant T1E rate inflation. However, the results of this simulation study rather underestimate the true T1E rate inflation. In conclusion, imprecise MMRM specifications may lead to a substantial inflation of the T1E rate and can damage the ability to generate confirmatory evidence in pivotal clinical trials.

A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition.

Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets.